This is nice and fun for getting some fast indications on an unknown codebase, but, as others said here and elsewhere, it doesn't replace human-made documentation.
Show HN: I built an AI that turns GitHub codebases into easy tutorials
141–150 of 181 posts
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#142I've said this a few times on HN: why don't we use LLMs to generate documentation? But then came the naysayers ...
Useful documentation explains why the code does what it does. Ie. why is this code there? An LLM can't magically figure out your motivation behind doing something a certain way.
Tell it the why of the API and ask it to write individual function and class docs then.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#143This is actually really cool. I just tried it out using an AI studio API key and was pretty impressed. One issue I noticed was that the output was a little too much "for dummies". Spending paragraphs to explain what an API is through restaurant analogies is a little unnecessary. And then followed up with more paragraphs on what GraphQL is. Every chapter seems to suffer from this. The generated documentation seems mor…
Answers like this are sort of what makes me wonder what most engineers are smoking when they think AI isn’t valuable. I don’t think the outright dismissal of AI is smart. (And, OP, I don’t mean to imply that you are doing that. I mean this generally.) I also suspect people who level these criticisms have never really used a frontier LLM. Feeding in a whole codebase that I’m familiar with, and hearing the LLM give goo…
> Even if the LLM never writes a line of code - this is still valuable, because helping humans understand software faster means you can help humans write software faster.
IMHO, Ai text additions are generally not valuable and I assume, until proven wrong, that Ai text provides little to no value.
I have seen so many startups fold after they made some ai product that on the surface level appeared impressive but provided no substantial value.
Now, I will be impressed by the ai that can remove code without affecting the product.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#144Earlier quoted context omitted.
Answers like this are sort of what makes me wonder what most engineers are smoking when they think AI isn’t valuable. I don’t think the outright dismissal of AI is smart. (And, OP, I don’t mean to imply that you are doing that. I mean this generally.) I also suspect people who level these criticisms have never really used a frontier LLM. Feeding in a whole codebase that I’m familiar with, and hearing the LLM give goo…
> Even if the LLM never writes a line of code - this is still valuable, because helping humans understand software faster means you can help humans write software faster. IMHO, Ai text additions are generally not valuable and I assume, until proven wrong, that Ai text provides little to no value. I have seen so many startups fold after they made some ai product that on the surface level appeared impressive but provid…
> Now, I will be impressed by the ai that can remove code without affecting the product.
Current AIs can already do this decently. With the usual caveats about possible mistakes/oversight.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#145Earlier quoted context omitted.
Many devs still think their job is to write code not build products their business needs. I use LLMs extensively and it’s helped me work better faster.
LLMs excel at some things and work very poorly at others. People working on different problems have had different experiences, sometimes opposite ends of the spectrum.
I think the people who claim 10x-100x productivity improvements are working on tasks where LLMs work really well. There is a lot of development work out there that is relatively simple CRUD and LLMs are very good at it. On the complete opposite end we have designing new algorithms/data structures or extending them in a novel way. Or implementing drivers for new hardware from incomplete specs. LLMs do not do well on these tasks or even slow down developers 10x.
So, I think the claims of improvement in productivity and regression in productivity can be true at the same time (and it's not just that people who don't find using LLMs productive are just prompting them wrong).
I think most can be gained by learning in which areas LLMs can give large productivity boosts and where it's better to avoid using them. Of course, this is a continuous process, given that LLMs are still getting better.
Personally, I am quite happy with LLMs. They cannot replace me, but they can do a chunk of the boring/repetitive work (e.g. boilerplate), so as a result I can focus on the interesting problems. As long as we don't have human-like performance (and I don't feel like we are close yet), LLMs make programming more interesting.
They are also a great learning aid. E.g., this morning I wanted to make a 3D model for something I needed, but I don't know OpenSCAD. I iteratively made the design with Claude. At some point the problem becomes too difficult for Claude, but with the code generated at that point, I have learned enough about OpenSCAD that I can fix the more difficult parts of the project. The project would have taken me a few hours (to learn the language, etc.), but now I was done in 30 minutes and learned some OpenSCAD in a pleasant way.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#146With $GEMINI_MODE=gemini-2.0-flash I also got some decent results for libraries like simonw/llm and pgcli. You can tell that because simonw writes quite heavily-documented code an the logic is pretty straightforward, it helps the model a lot! https://github.com/Florents-Tselai/Tutorial-Codebase-Knowled... https://github.com/Florents-Tselai/Tutorial-Codebase-Knowled...
How does it perform for undocumented repos?
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#147Great stuff, I may try it with a local model. I think the core logic for the final output is all in the nodes.py file, so I guess one can try and tweak the prompts, or create a template system.
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#148[dead]
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#149While the doc generator is a useful example app, the really interesting part is how you used Cursor to start a PocketFlow design doc for you, then you fine-tuned the details of the design doc to describe the PocketFlow execution graph and utilities you wanted the design of the doc-generator to follow…and then you used used Cursor to generate all the code for the doc-generator application.
This really shows off that the simple node graph, shared storage and utilities patterns you have defined in your PocketFlow framework are useful for helping the AI translate your documented design into (mostly) working code.
Impressive project!
See design doc https://github.com/The-Pocket/Tutorial-Codebase-Knowledge/bl...
Re: Show HN: I built an AI that turns GitHub codebases into easy tutorials
#150A company (mutable ai) was acquired by Google last year for essentially doing this but outputting a wiki instead of a tutorial.
I meant to write a blog post about mutable.ai but didn't get around to it before the product shut down.
I did however archive the wiki that it generated for the project I work on: https://web.archive.org/web/20240815184418/wiki.mutable.ai/g...
(The images aren't working. I believe those were auto-generated class inheritance or dependency diagrams.)
* The first paragraph is pretty good.
* The second paragraph is incorrect to call pw_rpc the "core" of Pigweed. That implies that you must always use pw_rpc and that all other modules depend on it, which is not true.
* The subsequent descriptions of modules all seemed decent, IIRC.
* The big issue is that the wiki is just a grab bag summary of different parts of the codebase. It doesn't feel coherent. And it doesn't mention the other 100+ modules that the Pigweed codebase contains.
When working on a big codebase, I imagine that tools like mutable.ai and Pocket Flow will need specific instruction on what aspects of the codebase to document.