🎯 D4Vinci/Scrapling landed on trending. Worth a proper look.

🔗 https://github.com/D4Vinci/Scrapling
📝 🕷️ An adaptive Web Scraping framework that handles everything from a single request to a full-scale crawl!
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The Scrapling framework is designed for effortless web scraping, handling everything from single requests to full-scale crawls. Its key features include adaptive parsing, which learns from website changes, and fetchers that bypass anti-bot systems like Cloudflare Turnstile. The framework also includes a spider framework for scaling up to concurrent, multi-session crawls with pause/resume and automatic proxy rotation.

To use Scrapling, you can start with a simple Fetcher or StealthyFetcher to fetch a website, and then use css selectors to scrape data. For larger crawls, you can define a Spider class and use the start method to begin the crawl.

Some technical highlights of Scrapling include its use of real-time stats and streaming for fast crawls, as well as its support for proxy rotation and anti-bot bypass. The framework is designed for web scrapers and regular users alike, with a simple and intuitive API.

Scrapling is suitable for a wide range of users, from beginner web scrapers to experienced developers. Whether you're looking to scrape a single website or build a large-scale web crawling application, Scrapling has the tools and features you need.

Here's an example of how to use Scrapling:

from scrapling.fetchers import StealthyFetcher
StealthyFetcher.adaptive = True
p = StealthyFetcher.fetch('https://example.com', headless=True, network_idle=True)
products = p.css('.product', auto_save=True)

In short, Scrapling is the ultimate web scraping framework - scrape the web, effortlessly.

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🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into pbakaus/impeccable — fresh off the trending list.

🔗 https://github.com/pbakaus/impeccable
📝 The design language that makes your AI harness better at design.
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The Impeccable project is a curated design skill that elevates your frontend design game with 7 domain-specific references and 23 commands. It helps you avoid common design pitfalls by including 27 deterministic anti-pattern rules. The impeccable skill can be used through a CLI installer, or by downloading from the website, and is compatible with a range of tools such as Cursor, Claude Code, and Codex CLI.

Key features include polish, audit, critique, and distill commands, as well as a standalone CLI for detecting anti-patterns. The project is designed for developers, designers, and anyone looking to improve their design skills.

To get started, simply run

npx impeccable skills install

and access the commands through /impeccable. With Impeccable, you can design like you mean it.

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🧠 Channel: https://t.me/GithubRe

🎯 p-e-w/heretic landed on trending. Worth a proper look.

🔗 https://github.com/p-e-w/heretic
📝 Fully automatic censorship removal for language models
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Heretic is a tool that automatically removes censorship from transformer-based language models. It uses a combination of directional ablation and a TPE-based parameter optimizer powered by Optuna. The approach enables Heretic to work completely automatically, finding high-quality abliteration parameters to produce a decensored model that retains the original model's intelligence.

Key Features:
- Supports most dense models, including multimodal models and MoE architectures
- Automatically decensors models without requiring configuration or understanding of transformer internals
- Provides features for research into model interpretability, including generating plots of residual vectors and printing residual geometry details

Usage:
To use Heretic, prepare a Python 3.10+ environment with PyTorch 2.2+ installed, then run pip install -U heretic-llm and heretic [model_name] to decensor a model.

Technical Highlights:
- Combines advanced directional ablation with TPE-based parameter optimization
- Co-minimizes the number of refusals and KL divergence from the original model
- Supports model quantization with bitsandbytes to reduce VRAM requirements

Audience:
- Researchers and developers working with language models
- Users looking to decensor language models without requiring extensive technical knowledge

Takeaway:
With Heretic, you can automatically decensor language models and unlock their full potential, all without needing to be an expert in transformer internals.

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🧠 Channel: https://t.me/GithubRe

⚡ EveryInc/compound-engineering-plugin is making waves. Here's the full picture.

🔗 https://github.com/EveryInc/compound-engineering-plugin
📝 Official Compound Engineering plugin for Claude Code, Codex, Cursor, and more
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The Compound Engineering Plugin is a game-changer for software development. Its philosophy is to make each unit of engineering work easier than the last, inverting the traditional approach that accumulates technical debt. This plugin provides a set of AI skills and agents that assist in planning, review, and execution, ensuring that knowledge is codified and reusable.

Key features include:

* /ce-brainstorm for interactive Q&A to think through a feature or problem
* /ce-plan to turn feature ideas into detailed implementation plans
* /ce-code-review for multi-agent code review before merging
* /ce-compound to document learnings and make future work easier

To get started, simply install the plugin and run /ce-setup in your project. The plugin currently ships with 37 skills and 51 agents, and is available for various platforms, including Claude Code, Cursor, Codex, and more.

With the Compound Engineering Plugin, you can compound your knowledge and expertise, making each subsequent unit of work easier and more efficient. As the plugin's philosophy states, "a good brainstorm makes the plan sharper, a good plan makes execution smaller, and a good review catches the pattern, not just the bug."
The takeaway: Work smarter, not harder, with the Compound Engineering Plugin.

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🧠 Channel: https://t.me/GithubRe

🔍 Deep-diving into TauricResearch/TradingAgents — fresh off the trending list.

🔗 https://github.com/TauricResearch/TradingAgents
📝 TradingAgents: Multi-Agents LLM Financial Trading Framework
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The TradingAgents GitHub repository offers a multi-agent trading framework for simulating real-world trading firms. This framework utilizes LangGraph and supports various LLM providers, including OpenAI, Google, Anthropic, and more.

Key features include:
- A modular design for flexibility and customization
- Support for multiple LLM providers and models
- Distributed debate among agents for informed trading decisions
- Integration with various data sources for market insights

To use TradingAgents, simply clone the repository, install the required dependencies, and launch the interactive CLI.

The framework is designed for research purposes and provides a comprehensive platform for testing and evaluating trading strategies. It's perfect for data scientists, researchers, and traders looking to leverage AI in their trading decisions.

Technical highlights include:
- Multi-provider LLM support
- Customizable configuration options
- Integration with Docker for easy deployment

In summary, TradingAgents is a powerful tool for simulating and optimizing trading strategies using AI and machine learning. With its modular design, support for multiple LLM providers, and customizable configuration options, it's an ideal choice for anyone looking to revolutionize their trading approach.
One-liner takeaway: Unlock the full potential of AI-powered trading with TradingAgents.

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🧠 Channel: https://t.me/GithubRe

⚡ revfactory/harness is making waves. Here's the full picture.

🔗 https://github.com/revfactory/harness
📝 A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.
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Harness is a team-architecture factory for Claude Code, designed to simplify complex tasks by decomposing them into coordinated teams of specialized agents. With harness, you can generate agent teams and skills tailored to your domain by simply saying "build a harness for this project".

Key features include agent team design with six pre-defined architectural patterns, skill generation with progressive disclosure, orchestration for inter-agent data passing and error handling, and validation for trigger verification and testing.

To get started, you can install harness via the marketplace or by direct installation as a global skill. The plugin structure includes a plugin.json manifest, SKILL.md definition, and references for agent design patterns, orchestrator templates, and skill writing guides.

Harness is part of the Claude Code ecosystem, sitting at the L3 Meta-Factory layer, and can be used in conjunction with other plugins like Archon for deterministic runtime configurations or meta-harness for Codex runtime.

With harness, you can create custom agent teams for various domains, such as deep research, website development, or webtoon production. The output includes generated agent definition files and skills, which can be integrated and orchestrated for effective task management.

One-liner takeaway: Harness simplifies complex tasks by generating custom agent teams and skills, making it a powerful tool for Claude Code users to streamline their workflow and improve productivity.

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🧠 Channel: https://t.me/GithubRe