Show HN: I built an AI that turns GitHub codebases into easy tutorials

This 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 more suited for a slightly technical PM moreso than a software engineer. This can probably be mitigated by refining the prompt.

The prompt would also maybe be better if it encouraged variety in diagrams. For somethings, a flow chart would fit better than a sequence diagram (e.g., a durable state machine workflow written using AWS Step Functions).

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 good answers about its purpose and implementation from a completely cold read is very impressive.

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.

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.