teleren/devto huggingface dataset:
🚀Cara Mudah Ganti Akun Github di Windows
Tags: indonesia, git, github, tips
Published: 2024-05-21 01:35:56

Mungkin diantara kita masih banyak yang kesulitan untuk mengganti akun github di perangkatnya, apalagi jika bekerja dengan repository yang bersifat private, tentu hanya akun Github yang diberikan izin yang dapat mengakses repository tersebut.

Indikasi paling umum untuk masalah ini adalah error dengan tulisan

```
remote: Repository not found.
```


![Contoh error Repository not found](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/3bdiuqtfnq8095pq0lfe.png)

## Referensi

Dari beberapa sumber dan juga pengalaman pribadi saya, cara paling mudah untuk mengatasi masalah tersebut adalah dengan menghapus credential Github di perangkat Windows kita.


![Hapus Credential Github di Windows](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/qxvjwdcrahorgcwl9r58.png)

Ketika menghapus sebuah credential dan saat berinteraksi dengan remote repository, maka credential di Windows akan meminta kita untuk login. Inilah cara mudah yang saya maksud.

![Login Ulang setelah Menghapus Credential](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/1nxenbposhky9inlweld.png)

Setelah menghapus credential Github, coba deh berinteraksi dengan remote repository, misal git pull atau git push, maka secara otomatis kita akan diminta untuk login ulang.

![Berinteraksi dengan Github](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/tbec6ku0ol0agmut9n8a.png)

Masalah selesai.

Sekian catatan singkat saya kali ini, semoga bermanfaat.

NB: pembaca yang baik akan meninggalkan jejak :)
Using Panda in Python Programming Language
Tags: webdev, python
Published: 2024-05-21 01:06:17

If you are a data enthusiast, you must have heard about the powerful library in Python called Pandas. Pandas is a versatile tool that is widely used by data scientists, analysts, and programmers for data manipulation, analysis, and visualization. In this article, we will take a closer look at how you can harness the power of Pandas in your Python programming projects.

One of the key features of Pandas is its data structures - the Series and DataFrame. These data structures make it easy to work with tabular data, allowing you to perform various operations such as filtering, sorting, grouping, and merging datasets. With Pandas, you can easily load data from various sources like CSV files, Excel spreadsheets, SQL databases, and even web APIs.

Pandas also provides a wide range of functions for data cleaning and preparation. You can handle missing values, duplicate records, and outliers with ease using Pandas functions like dropna(), fillna(), drop_duplicates(), and clip(). Additionally, Pandas allows you to perform data transformations, such as reshaping data, aggregating values, and creating new columns based on existing ones.

When it comes to data analysis, Pandas offers a plethora of statistical functions for descriptive analysis, such as mean, median, standard deviation, and correlation. You can also visualize your data using Pandas integration with popular visualization libraries like Matplotlib and Seaborn.

One of the reasons why Pandas is so popular among Python programmers is its simplicity and ease of use. The syntax is intuitive and easy to understand, making it accessible for both beginners and experienced programmers. With just a few lines of code, you can perform complex data operations and gain valuable insights from your datasets.

In conclusion, Pandas is a powerful tool that every Python programmer should have in their toolkit. Whether you are working on data analysis projects, machine learning models, or simply exploring datasets, Pandas can help you streamline your workflow and make your life easier. So why wait? Start using Pandas in your Python programming projects today and unlock the full potential of your data.
Is it Easy to Write an Ebook in 2024?
Tags: ebook, ebookwriting, writeanebook, ebookwriter
Published: 2024-05-21 01:00:22

Writing an ebook in 2024 has become increasingly accessible due to advances in technology and the availability of comprehensive [ebook writing services](https://bookwritinggenie.com/ebook-writing-service/). However, while the process is more manageable than ever, achieving success still requires careful planning, quality content, and effective marketing.

The initial phase of ebook writing involves developing a compelling idea and structuring your content to engage readers. Utilizing an ebook writing service can significantly ease this process. These services offer professional writers who can help shape your ideas into a coherent, well-structured manuscript. They ensure that your content is not only informative but also engaging, catering to your target audience's needs.

Once the manuscript is complete, the focus shifts to [ebook publishing services](https://bookwritinggenie.com/book-publishing-company/). These services handle the technical aspects of formatting your ebook to meet the standards of various platforms, such as Amazon Kindle, Apple Books, and Google Play Books. They also assist with obtaining an ISBN, designing a professional cover, and ensuring your ebook is visually appealing and error-free.

However, writing and publishing an ebook is only half the battle. Effective ebook marketing is crucial for reaching your intended audience and achieving sales. Ebook marketing involves a strategic approach that includes SEO optimization, social media promotion, email marketing, and leveraging online advertising. Professional [ebook marketing services](https://bookwritinggenie.com/book-marketing-services/) can help create a tailored marketing plan that maximizes visibility and drives downloads.

Despite the ease provided by these services, the success of your ebook ultimately hinges on the quality of its content and the effectiveness of your marketing efforts. The competition in the ebook market is fierce, making it essential to invest time and resources into both the writing and promotion stages.

In conclusion, while writing an ebook in 2024 is easier than in the past, it still requires a strategic approach. By utilizing ebook writing services, ebook publishing services, and effective ebook marketing, authors can navigate the process more efficiently and increase their chances of success in the competitive digital publishing landscape.
１３章4
Published: 2024-05-21 00:44:51

Javaプログラムは2つのクラス、`Trash`と`CacheTrash`を定義しています。これらのクラスはリスト管理の基本的な操作を実装しており、`CacheTrash`は`Trash`を拡張しています。以下にコードにコメントを追加して説明します。

```java
import java.util.ArrayList;
import java.util.List;

// Trashクラスは文字列のリストを管理する基本クラス
public class Trash {
    // 文字列のリスト
    List<String> list = new ArrayList<>();

    // コンストラクタ：新しい文字列をリストに追加する
    protected Trash(String val) {
        add(val);
    }

    // 文字列がリストに存在するかどうかを常にtrueで返す
    public boolean test(String val) {
        return true;
    }

    // 文字列をリストから削除する
    public void delete(String val) {
        list.remove(val);
    }

    // 文字列をリストに追加する
    public final void add(String val) {
        list.add(val);
    }
}

// CacheTrashクラスはTrashクラスを拡張し、削除された文字列を一時的に保存する
public class CacheTrash extends Trash {
    // 削除された文字列を一時的に保存する変数
    private String tmp = null;

    // コンストラクタ：親クラスのコンストラクタをnullで呼び出す
    public CacheTrash() {
        super(null);
    }

    // 文字列がリストに存在するかどうかをチェックする
    public boolean test(String val) {
        return list.contains(val);
    }

    // 文字列を削除し、一時変数に保存する
    public void delete(String val) {
        this.tmp = val;
    }

    // 一時変数に保存された文字列を親クラスのdeleteメソッドでリストから削除する
    public void flush() {
        super.delete(tmp);
    }
}
```

### クラス `Trash`
- **`list`**: 文字列のリストを保持する。
- **`Trash(String val)`**: コンストラクタで与えられた文字列をリストに追加する。
- **`test(String val)`**: 文字列が存在するかをチェックするメソッド（常に`true`を返す）。
- **`delete(String val)`**: 文字列をリストから削除する。
- **`add(String val)`**: 文字列をリストに追加する。

### クラス `CacheTrash`
- **`tmp`**: 一時的に削除された文字列を保持するための変数。
- **`CacheTrash()`**: コンストラクタで親クラスのコンストラクタを`null`で呼び出す。
- **`test(String val)`**: 文字列がリストに存在するかをチェックする（`list.contains(val)`を使用）。
- **`delete(String val)`**: 文字列を一時変数に保存する（リストからは削除しない）。
- **`flush()`**: 一時変数に保存された文字列を親クラスの`delete`メソッドでリストから削除する。

このプログラムは基本的なリスト操作を行うクラスと、それを拡張して一時的に削除された文字列を保持するクラスを示しています。
A new, but old way of backend development
Tags: mvc, microservices, java, csharp
Published: 2024-05-21 00:33:51

Recently, I got exposed to creating backend APIs and services using Java, more specifically, **Springboot**. This was something I didn't think I would enjoy, as it felt somewhat archaic in a way. Part of this was probably because of the stigma that Java has in my region, where it's considered old and redundant similar to C#.

However, after just browsing the guides and building a microservice with Springboot over the past week, I can confidently say that it has been a great experience, with very functional libraries included that make the lives of us developers so much easier. The guides have been nothing short of informative, instructional and easy to follow, especially coming from a beginner's perspective to Java programming.

C# users, brace yourself for a formidable, EntityFramework-esque rival.

## Pros

1.Much like EntityFramework, interacting with a SQL database has never been easier.

- Simply make use of either the **CrudRepository** or **JpaRepository** (based on your required specifications) and create your own **repository**, which serves as Entities (not to be confused with Models, which layout the schema/fields of your tables) 

- From here, you're able to add your SQL queries in order to retrieve specific information, and even join tables using the models you defined!

2.Writing to the database has also been made easy!

- Simply pass the record that you want to save to the table in the entityName.save(recordHere) method!

- If you need to do bulk inserts, use a data structure such as a ArrayList to store all of your records first, before passing it to the **saveAll()** method instead!

These are just scratching the **tip** of the surface of understanding the world of Springboot, Java programming and Microservice architecture.

I hope to share even more tips and insights like this in the upcoming weeks and months, and stay tuned for more articles & leave comments regarding these topics so we can all learn together as a community!
Introduction to Continuous Integration and Continuous Deployment (CI/CD)
Tags: webdev, javascript, beginners, programming
Published: 2024-05-21 00:31:40

# Understanding CI/CD: Continuous Integration and Continuous Deployment

## Introduction

Continuous Integration and Continuous Deployment (CI/CD) is a software development practice that has gained popularity in recent years. CI/CD is a set of automated processes that allow developers to deliver code changes more frequently and reliably. It streamlines the development, testing, and deployment processes and ensures a faster and more efficient delivery of software.

## Advantages of CI/CD

One major advantage of CI/CD is the time-saving aspect. By automating the development and deployment processes, developers are able to save time and focus on more important tasks. Additionally, CI/CD ensures the frequent delivery of code changes, which allows for faster feedback and improved collaboration among team members. Moreover, with every code change being automatically tested, it reduces the chances of errors and promotes consistent quality of the software.

## Disadvantages of CI/CD

While CI/CD offers numerous benefits, it also comes with certain disadvantages. One of the major concerns is the initial setup cost and complexity. Setting up a CI/CD pipeline requires specific tools and infrastructure, which can be costly for some organizations. Another disadvantage is the need for continuous maintenance and monitoring of the pipeline, which can be time-consuming.

## Features of CI/CD

The most crucial aspect of CI/CD is the continuous integration, where developers merge their code changes frequently to a shared repository. The code is then automatically built, tested, and reviewed. Next, the continuous delivery feature ensures that the code changes are ready for deployment, and the continuous deployment feature automatically deploys the code to the designated servers.

### Example Workflow of CI/CD

```yaml
# Sample CI/CD Pipeline using YAML Syntax
stages:
  - build
  - test
  - deploy

build_job:
  stage: build
  script:
    - echo "Building the application..."
    - build_command

test_job:
  stage: test
  script:
    - echo "Running tests..."
    - test_command

deploy_job:
  stage: deploy
  script:
    - echo "Deploying to production..."
    - deploy_command
```

This example outlines a basic CI/CD pipeline using YAML syntax, demonstrating the three critical stages: Build, Test, and Deploy. Each stage contains scripts that simulate the actions typically performed during these stages.

## Conclusion

In today's fast-paced digital world, CI/CD has become an essential practice for software development companies. It offers numerous advantages such as time-saving, increased collaboration, and improved software quality. While there are certain challenges in implementing and maintaining CI/CD, the benefits far outweigh them. With CI/CD, developers can deliver high-quality software at a faster pace, ultimately benefiting both the organization and its customers.
Stay Updated with Python/FastAPI/Django: Weekly News Summary (13/05/2024–19/05/2024)
Tags: django, fastapi, flask
Published: 2024-05-21 00:30:00

Dive into the latest tech buzz with this weekly news summary, focusing on Python, FastAPI, and Django updates from May 13th to May 19th, 2024. Stay ahead in the tech game with insights curated just for you!

This summary offers a concise overview of recent advancements in the Python/FastAPI/Django framework, providing valuable insights for developers and enthusiasts alike. Explore the full post for more in-depth coverage and stay updated on the latest in Python/FastAPI/Django development.

Check out the complete article here https://poovarasu.dev/python-fastapi-django-weekly-news-summary-13-05-2024-to-19-05-2024/
What’s Next Big Thing for Artificial Intelligence? Spatial Intelligence!
Tags: llm, rag, machinelearning, genai
Published: 2024-05-21 00:24:35

It’s unfolding in front of our eyes, pay attention.

[Full Article](https://medium.com/@learn-simplified/whats-next-big-thing-for-artificial-intelligence-spatial-intelligence-311ad53516f5)

◉ What’s This Article About?
● Looking at pictures only captures one aspect of reality. To fully understand, we need to interact with our surroundings.
● Spatial intelligence helps us comprehend and navigate the 3D world by combining perception and physical action.
● This article discusses the importance of developing spatial intelligence in AI, moving beyond mere fantasy to practical application.

◉ Why Should You Care?
● Humans naturally understand and interact with the 3D world using spatial intelligence.
● Replicating this ability in machines has been challenging but is now becoming possible.
● With spatial intelligence, AI can perceive and interact with our world, leading to significant advancements in robotics and technology.

◉ But How Will We Get There?
● Recent advances in computer vision and AI are laying the groundwork for spatial intelligence.
● Algorithms now allow AI to analyze visual scenes and describe them, bridging the gap between language and visuals.

◉ AI Till Now Can See & Talk, What We Want Is to “Act”
Current AI can see and communicate but lacks the ability to interact with the 3D space. By giving AI spatial intelligence, it can understand and act within its surroundings, much like humans.

◉ How Is AI Making Progress Here?
● Researchers at Google have developed algorithms to transform 2D photos into 3D scenes.(Read full Article for other details)

◉ What Does That Give Us?
● Spatial intelligence will enable AI to act and behave in the 3D world, opening new frontiers.
● Researchers are building advanced simulation environments to help AI learn and practice.
● Early results show AI performing complex tasks, demonstrating the potential of spatial intelligence.

◉ Why It’s So Significant for the Future of AI and Us as Humans
● Spatial intelligence is transforming how machines interact with their environment and humans.
● In healthcare, AI can predict and prevent accidents, improving patient safety and reducing staff workload.
● Patients with severe paralysis could control robots with their thoughts, significantly enhancing their quality of life.

◉ Closing Thoughts
● The true potential of AI lies in developing spatial intelligence to understand and navigate the 3D world.
● This journey requires thoughtful development to ensure these technologies prioritize human needs and values.
● Spatially intelligent AI will become trusted partners, enhancing our lives and contributing to our collective well-being. The future is bright with AI that is perceptive, insightful, and spatially aware, working alongside us to create a better world.
What are the most effective strategies for scaling AI models to handle large datasets and complex problems efficiently?
Tags: webdev, javascript, beginners, programming
Published: 2024-05-21 00:17:23

Scaling AI models to handle large datasets and complex problems efficiently involves several strategies aimed at improving both the computational efficiency and the model's ability to process vast amounts of data. Here are some effective strategies:

1. Distributed Computing: Utilize distributed computing frameworks like Apache Spark or TensorFlow distributed training to distribute the workload across multiple machines or GPUs. This allows for parallel processing of data and faster model training.

2. Model Parallelism: Divide large models into smaller parts and distribute them across multiple devices or nodes for training. This enables efficient memory usage and can speed up training on GPUs or across a cluster of machines.

3. Data Parallelism: Distribute copies of the model across different devices or nodes and train each copy on a different subset of the data. This allows for parallel processing of data and can significantly reduce training time for large datasets.

4. Mini-Batch Processing: Instead of processing the entire dataset at once, use mini-batches of data during training. This reduces memory requirements and enables more efficient gradient computations, especially when combined with distributed computing.

5. Model Optimization Techniques: Employ model optimization techniques such as pruning, quantization, and distillation to reduce the size of the model and improve its efficiency without sacrificing performance.

6. Transfer Learning: Start with a pre-trained model and fine-tune it on the target dataset. Transfer learning leverages knowledge learned from previous tasks or domains, reducing the amount of training data required and speeding up convergence.

7. Efficient Data Processing: Use data preprocessing techniques to reduce the size of the dataset or extract relevant features. This can include techniques like dimensionality reduction, feature selection, and data compression.

8. Hardware Acceleration: Take advantage of hardware accelerators like GPUs, TPUs, or specialized AI chips (e.g., NVIDIA's Tensor Cores, Google's TPU) to speed up computation for deep learning tasks.

9. Model Compression: Compress large models by removing redundant parameters or using techniques like knowledge distillation to transfer knowledge from a larger model to a smaller one, reducing memory and computational requirements.

10. Asynchronous Training: Implement asynchronous training algorithms that allow different workers to update the model parameters independently, reducing communication overhead and improving scalability.
How can we mitigate bias in AI algorithms and ensure fairness and equity in decision-making processes?
Tags: webdev, javascript, beginners, programming
Published: 2024-05-21 00:15:30

Mitigating bias in AI algorithms and ensuring fairness and equity in decision-making processes is a multifaceted task that involves various strategies and approaches. Here are some key steps:

1. Diverse and Representative Data: Bias often stems from biased training data. Ensuring that the data used to train AI systems is diverse and representative of the population it serves is crucial. This might involve collecting data from a variety of sources and ensuring that it adequately represents all relevant demographics and perspectives.

2. Bias Detection and Evaluation: Implement mechanisms to detect biases in both the training data and the AI algorithms themselves. Techniques such as statistical analysis, fairness metrics, and human-in-the-loop evaluation can help identify and quantify biases.

3. Algorithmic Fairness: Incorporate fairness considerations directly into the design and development of AI algorithms. This involves designing algorithms that explicitly optimize for fairness and equity, such as by using fairness-aware machine learning techniques or adjusting decision boundaries to account for disparate impact.

4. Transparency and Explainability: Make AI algorithms transparent and explainable to enable scrutiny and accountability. Users should be able to understand how decisions are made and why certain outcomes are produced, which can help identify and address biases.

4. Diverse and Inclusive Teams: Build diverse and inclusive teams involved in the development and deployment of AI systems. A diverse team can bring a variety of perspectives and experiences to the table, helping to uncover and mitigate biases that might otherwise go unnoticed.

5. Regular Audits and Monitoring: Implement regular audits and monitoring processes to continuously assess AI systems for bias and fairness. This includes monitoring system performance across different demographic groups and re-evaluating algorithms as new data becomes available.

6. Ethical Guidelines and Regulations: Develop and adhere to ethical guidelines and regulations governing the use of AI, particularly in sensitive domains such as healthcare, criminal justice, and finance. These guidelines can provide a framework for ensuring fairness and equity in AI decision-making processes.

7. Bias Mitigation Techniques: Implement specific techniques to mitigate bias in AI algorithms, such as data preprocessing methods (e.g., reweighing, data augmentation), algorithmic adjustments (e.g., regularization, counterfactual fairness), and post-processing techniques (e.g., bias correction, calibration).

By combining these approaches, we can work towards mitigating bias in AI algorithms and promoting fairness and equity in decision-making processes. However, it's important to recognize that bias mitigation is an ongoing challenge that requires continuous effort and vigilance.
Rahasia gacor hari ini
Published: 2024-05-20 23:50:11

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