Turn Codebases into Easy Tutorials with AI: A Beginner's Guide

Ever feel lost staring at a new codebase? What if you could instantly understand complex projects? This guide shows you how to use AI to analyze GitHub repositories and create beginner-friendly tutorials. This method demystifies code and makes learning new technologies easier than ever.

AI-Powered Codebase Analysis: Learn Code Faster

This tutorial leverages Pocket Flow, a 100-line LLM framework, to:

• Crawl GitHub repositories and build a comprehensive knowledge base.
• Identify core abstractions and their interactions within the codebase.
• Transform complex code into clear, visualized tutorials tailored for beginners.

Jumpstart your learning with a new approach to codebase understanding.

Real-World Examples: AI-Generated Tutorials in Action

See the power of AI in action! These tutorials were fully generated by AI, providing valuable insights into popular GitHub repositories:

• AutoGen Core: Build AI teams that collaborate like coworkers.
• FastAPI: Create APIs quickly with automatically generated documentation.
• NumPy Core: Master the engine behind data science in Python.

Witness how AI-generated tutorials are transforming the way developers learn and contribute.

Getting Started: Your Path to AI-Powered Code Understanding

Ready to unlock the secrets of any codebase? Follow these steps to get started:

1. Clone the repository: Get the code you need to build this tool.
2. Install dependencies: pip install -r requirements.txt will set up your environment by installing necessary libraries.
3. Configure your LLM: In utils/call_llm.py, set up access to your preferred LLM (like Gemini Pro 2.5).

Don't let complex code intimidate you—start building your AI-powered codebase tutorial generator today!

Generate Your First Tutorial: Command Line Options

Use the main.py script to analyze repositories or local directories with ease. Here are a few command examples:

• Analyze a GitHub repository:

  python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000
  

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• Analyze a local directory:

  python main.py --dir /path/to/your/codebase --include "*.py" --exclude "*test*"
  

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• Create a tutorial in another language:

  python main.py --repo https://github.com/username/repo --language "Chinese"
  

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Customize your analysis using these flags:

• --repo or --dir: Specify the repository URL or directory path.
• -n, --name: Project name (optional, derived from URL/directory).
• -i, --include: Files to include (e.g., ".py", ".js").
• -e, --exclude: Files to exclude (e.g., "tests/", "docs/").

The application will crawl, analyze, and generate the tutorial output, saving it in the specified directory (default: ./output).

Development Secrets: Agentic Coding and Pocket Flow

This project was built using the cutting-edge Agentic Coding paradigm, where humans design and AI agents code. Pocket Flow, a compact 100-line LLM framework, empowers AI agents to build complex solutions efficiently. Explore these tools to become a faster, more effective developer.