Thanks for reading it! I didn't use an LLM, lol.

On documentation, I agree with you, and have gone done the same road. I actually built out a little chat app which acts as a wrapper around the codex app which does exactly this. Unfortunately, the UI sucks pretty bad, and I never find myself using it.

I actually asked codex if it could find the chat where I created this in my logs. It turns out, I used the web interface and asked it to make a spec. Here's the link to the chat. Sorry the way I described wasn't really what happened at all! lol. https://chatgpt.com/share/69b77eae-8314-8005-99f0-db0f7d11b7...

As it happens, I actually speak-to-texted my whole prompt. And then gippity glazed me saying "This is a very good idea". And then it wrote a very, very detailed spec. As an aside, I kind of have a conspiracy theory that they deploy "okay" and "very very good" models. And they give you the good model based on if they think it will help sway public opinion. So it wrote a pretty slick piece of software and now here I am promoting the LLM. Oof da!

I didn't really mention - spec first programming is a great thing to do with LLMs. But you can go way too far with it, also. If you let the LLM run wild with the spec it will totally lose track of your project goals. The spec it created here ended up being, I think, a very good spec.

I think "code readability" is really not a solved problem, either pre or post LLM. I'm a big fan of "Code as Data" static analysis tools. I actually think that the ideal situation is less of "here is the prompt history" and something closer to Don Knuth's Literate Programming. I don't actually want to read somebody fighting context drift for an hour. I want polished text which explains in detail both what the code does and why it is structured that way. I don't know how to make the LLMs do literate programming, but now that I think about it, I've never actually tried! Hmmm....