This project provides an AI-powered system to automatically generate beginner-friendly tutorials from existing codebases, targeting developers and students who need to understand new code quickly. It leverages a lightweight LLM framework to analyze GitHub repositories, identify core abstractions, and produce clear explanations with visualizations.

How It Works

The system utilizes a 100-line LLM framework, Pocket Flow, to crawl GitHub repositories. It analyzes the codebase structure, identifies key abstractions and their interactions, and then transforms this complex code into easily digestible tutorial content. The approach aims to simplify code comprehension by automating the process of explaining code logic and structure.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Set up LLM credentials in utils/call_llm.py (e.g., Gemini Pro 2.5 API key).
• Run tutorial generation: python main.py --repo <github_repo_url> --include " .py" " .js"
• Supports local directories via --dir.
• Requires Python and an LLM API key.
• Official YouTube Development Tutorial and Substack Post Tutorial are available.

Highlighted Details

• Reached Hacker News front page with over 800 upvotes.
• Demonstrates AI-generated tutorials for popular GitHub repositories like AutoGen, CrewAI, and LangGraph.
• Supports custom LLM models and multiple output languages (e.g., Chinese).
• Offers options to include/exclude specific file types and set maximum file sizes for analysis.

Maintenance & Community

The project is a tutorial for Pocket Flow, a 100-line LLM framework. Further community and maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not mentioned.

Limitations & Caveats

The project is presented as a tutorial, and its robustness for production use or handling extremely large/complex codebases is not detailed. LLM API key setup is a manual step required for functionality.