Unlock Any Codebase: AI-Powered Tutorials Explained

Ever feel lost staring at a complex codebase? This guide shows you how to use AI to understand any project, turning cryptic code into clear, step-by-step tutorials. Imagine effortlessly grasping new technologies and contributing to projects faster than ever before. This method uses a powerful AI agent to analyze GitHub repositories and create beginner-friendly tutorials, explaining exactly how the code works.

Transform Confusion into Clarity: Instantly Understand Complex Code

• Stop struggling: Decode unfamiliar codebases quickly and efficiently.
• Learn by example: Explore clear visualizations and step-by-step explanations.
• Become a code whisperer: Master new technologies with AI-powered guidance.

This process leverages Pocket Flow, a streamlined LLM framework, to crawl GitHub repositories and construct a knowledge base. Then, the AI analyzes the entire codebase to pinpoint core abstractions and their interactions. The result? Complex code transformed into intuitive tutorials.

See AI in Action: Example Tutorials for Popular Projects

These tutorials are a testament to the power of AI, generated entirely by automatically scanning the GitHub repo:

• AutoGen Core: Build AI teams.
• FastAPI: Create APIs at lightning speed.
• NumPy Core: Master the heart of data science.

This AI-driven approach works across various projects, from web development to data science. You can quickly grasp the inner workings of popular libraries and frameworks, boosting your productivity and expanding your skillset.

Get Started Now: Your Path to Code Mastery

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

1. Clone the repository: Get the necessary files.
2. Install dependencies: pip install -r requirements.txt
3. Configure your LLM: Set up your API key in utils/call_llm.py.
4. Run the script: Generate your first tutorial with a single command!

Here are some examples of ways you can run the command:

• Analyze a GitHub repository: python main.py --repo https://github.com/username/repo --include " *.py " " *.js " --exclude " tests/* " --max-size 50000
• Analyze a local directory: python main.py --dir /path/to/your/codebase --include " *.py " --exclude " *test* "
• Generate a tutorial in another language python main.py --repo https://github.com/username/repo --language " Chinese "

The application will crawl the repository, analyze the codebase structure, generate tutorial content in the specified language, and save the output in the specified directory (default: ./output).

Customize Your Experience: Tailor Tutorials to Your Needs

The main.py script offers powerful customization options:

• --repo or --dir: Specify a GitHub repo URL or a local directory path.
• -n, --name: Project name (optional, derived from URL/directory if omitted).
• -t, --token: GitHub token (or set GITHUB_TOKEN environment variable).
• -o, --output: Output directory (default: ./output).
• -i, --include: Files to include (e.g., "*.py" "*.js").
• -e, --exclude: Files to exclude (e.g., "tests/" "docs/").
• -s, --max-size: Maximum file size in bytes (default: 100KB).
• --language: Language for the generated tutorial (default: "english").

You can tailor the tutorials to your specific needs, focusing on relevant files and excluding unnecessary ones.

The Future of Coding: Agentic Coding and Pocket Flow

This project was built using Agentic Coding, a development paradigm that leverages AI agents to automate the coding process. The secret weapon is Pocket Flow, a 100-line LLM framework that empowers AI to build for you. The combination allows to turn any codebase into easy tutorials with AI assistance.