semvec/pypi-packages huggingface dataset:
web-block 1.0.2
A simple Python module to block websites.
Requires Python: >=3.9

# web_block

A simple Python module to block websites. \
It configures the hosts file (`/etc/hosts` on macOS or Linux and `C:\Windows\System32\drivers\etc\hosts` on Windows) to redirect a website passed in as an argument to localhost (`127.0.0.1`), essentially blocking it.

PyPI here: https://pypi.org/project/web-block

## Usage:
### Blocking websites
```py
import web_block
web_block.block("example.com example.org") # Use spaces to separate websites.
```
It's as simple as that.
### Unblocking websites
```py
import web_block
web_block.unblock("example.com example.org") # You can also use `https://www.example.com/abcd.html` and it will automatically get the domain for you.
```
evenet 0.1.6
Training and inference entry points for the EveNet event processing model.
Requires Python: >=3.12

# EveNet 🌌

[![Open App](https://img.shields.io/badge/Open-Doc-blue?style=for-the-badge)](https://uw-epe-ml.github.io/EveNet_Public/)


EveNet is a pretrained, multi-task foundation model for event-level collider physics. 
It comes with a scalable Ray + PyTorch Lightning training pipeline, SLURM-ready multi-GPU infrastructure, 
and modular YAML configuration, 
so researchers can quickly fine-tune it on their own datasets and extend it to new physics analyses.

---

![](docs/network_summary.png)

---

## 🎯 Pretrained Weights

Start directly from pretrained EveNet checkpoints for fine-tuning or inference:

👉 HuggingFace: [Avencast/EveNet](https://huggingface.co/Avencast/EveNet/tree/main)

---

## 📚 Documentation

Explore the full documentation site for setup options, configuration references, and tutorials:

[uw-epe-ml.github.io/EveNet_Public](https://uw-epe-ml.github.io/EveNet_Public/)

---

## 🤝 Contributing

Improvements are welcome! File an issue or open a pull request for bug fixes, new physics processes, or documentation tweaks.
When you add new components or datasets, update the relevant markdown guides so future users can follow along easily.
livedict 2.0.2
TTL-based key-value store with sandboxed hooks and (optionally) persistence backends (SQLite/Memory/Redis).
Requires Python: >=3.8

# LiveDict (Release v1.0.4)

**TTL-based, (optionally) Persistent Python Dictionary with Hook Callbacks**

LiveDict is a secure, extensible, and ephemeral key-value store designed for applications that need in-memory caching with optional persistence and encryption.

## Highlights
* AES-GCM encryption (via `cryptography`) with a deterministic fallback for test environments.
* TTL expiry driven by a heap-based monitor thread for efficient scheduling.
* Optional persistence backends:
  - SQLite (file-backed DB)
  - File-backed object store (per-bucket files)
  - Redis (thin wrapper; requires `redis` package)
* Hook callbacks (`on_access`, `on_expire`) executed safely (sandboxing recommended).
* Pydantic-backed configuration models for clarity and validation.
* Bucket policies and limits to control memory usage and namespaces.

---

# LiveDict (Release v2.0.0)

LiveDict is a small utility library providing in-memory dictionary-like structures with
synchronous and asynchronous variants, a sandbox module, and flexible storage backends.

**Important changes in v2**
- Cryptography removed: the package no longer performs encryption. Users must provide their own cryptographic handling.
- Sandbox is active: behaves as implemented in `livedict/modules/sandbox.py`.

## Sandbox usage

Review `livedict/modules/sandbox.py` for details. The sandbox module is included and active;
exercise caution and review its behavior before running untrusted code.

---

## License
MIT License. © 2025 LiveDict. All rights reserved.

[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/hemanshu03/LiveDict/blob/main/LICENSE)

## Installation

Minimum supported Python: 3.8+

Install with:
```bash
pip install livedict
```

## Demonstration

Visit [testfile.py](https://github.com/hemanshu03/LiveDict/blob/main/testfile.py) as a demonstration on how to use LiveDict.

---

## Contributing

- Generate documentation using your preferred tools (pdoc, Sphinx with napoleon for Google-style).
- This release uses Google-style docstrings across the codebase.

---

## About License:
  - SPDX-License-Identifier: MIT
  - Copyright (c) 2025 Hemanshu

---

## If you want to credit LiveDict in your docs/acknowledgements, this short line works:
LiveDict - TTL-based Python key-value store by [hemanshu03](https://github.com/hemanshu03). Find LiveDict on GitHub @[LiveDict](https://github.com/hemanshu03/LiveDict)

---

## If you find LiveDict useful and want to support continued development:
Consider sponsoring the project: [LiveDict](https://github.com/hemanshu03/LiveDict)

===
diffseek 0.1.2
diffseek - A tool for comparing long strings across devices via binary search

# diffseek

A tool for verifying and correcting manually transcribed strings across two devices using binary search.

## Use Cases

- Airgapped systems
- Low-privilege systems without email/clipboard access
- Any scenario requiring manual string transcription between devices (e.g., copying login links, tokens, or keys)

## How It Works

1. Run `diffseek` on both devices
2. Enter the reference string on device 1 (input is interactive to avoid shell history)
3. Enter your transcription on device 2
4. Compare hash-derived values (phrases, colors) displayed on each device
5. Binary search narrows down differences:
   - Matching hashes indicate error-free regions
   - Non-matching hashes indicate potential errors
   - The string is reprinted with color coding: orange (possible errors), red (definite errors), white (verified correct)

The tool uses breadth-first search when both strings have matching target lengths (to find all differences efficiently), and depth-first search when lengths differ (to find the first difference quickly).

## Usage

```bash
diffseek
# or
./diffseek.py
# or
python3 diffseek.py
```

## Disclosure

This tool was implemented by Claude Code from a specification I provided. I fixed minor bugs and verified it works for my use case. It is primarily AI-written code.