Does it use the docs in the repository or only the code?
By default we use both based on regex: DEFAULT_INCLUDE_PATTERNS = { " .py", " .js", " .jsx", " .ts", " .tsx", " .go", " .java", " .pyi", " .pyx", " .c", " .cc", " .cpp", " .h", " .md", " .rst", "Dockerfile", "Makefile", " .yaml", " .yml", } DEFAULT_EXCLUDE_PATTERNS = { " test ", "tests/ ", "docs/ ", "examples/ ", "v1/ ", "dist/ ", "build/ ", "experimental/ ", "deprecated/ ", "legacy/ ", ".git/ ", ".github/ ", ".next/…
Show HN: I built an AI that turns GitHub codebases into easy tutorials
101–110 of 181 posts
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#102Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#103I had not used gemini before, so spent a fair bit of time yak shaving to get access to the right APIs and set up my Google project. (I have an OpenAPI key but it wasn't clear how to use that service.) I changed it to use this line: api_key=os.getenv("GEMINI_API_KEY", "your-api_key") instead of the default project/location option. and I changed it to use a different model: model = os.getenv("GEMINI_MODEL", "gemini-2.5…
https://github.com/mooreds/prime-mvc-tutorial
https://github.com/mooreds/railsquickstart-tutorial
https://github.com/mooreds/fusionauth-jwt-tutorial/
Other than renaming the index.md file to README.md and modifying it slightly, I made no changes.
Edit: added note that there are examples in the original link.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#104from ollama import chat, ChatResponse
def call_llm(prompt, use_cache: bool = True, model="phi4") -> str: response: ChatResponse = chat( model=model, messages=[{ 'role': 'user', 'content': prompt, }] ) return response.message.content
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#105That's a game changer for a new Open source contributor's onboarding. Put in postgres or redis codebase, get a good understanding and get going to contribute.
Isn't that overly optimistic? The postgres source code is really complex, and reading a dummy tutorial isn't going to make you a database engine ninja. If a simple tutorial can, imagine what a book on the topic could do.
The burden of understanding still is with the engineers. All you would get is some (partially inaccurate at places) good overview of where to look for.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#106Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#107Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#108If you want to use Ollama to run local models, here’s a simple example: from ollama import chat, ChatResponse def call_llm(prompt, use_cache: bool = True, model="phi4") -> str: response: ChatResponse = chat( model=model, messages=[{ 'role': 'user', 'content': prompt, }] ) return response.message.content
I'd love the ability to run the LLM locally, as that would make it easier to run on non public code.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#109Yes! AI for docs is one of the usecases I’m bullish on. There is a nice feedback loop where these docs will help LLMs to understand your code too. You can write a GH action to check if your code change / release changes the docs, so they stay fresh. And run your tutorials to ensure that they remain correct.
Do you have examples of LLMs running tutorials you can share?