I don't have the prompt, but I used codex. I probably wrote a medium sized paragraph explaining the architecture. It scaffolded out the app, and I think I prompted it twice more with some very small bugfixes. That got me to an MVP which I used to build LaTeX pipelines. Since then, I've added a few features out as I've dogfooded it.

It's a bit challenging / frustrating to get LLMs to build out a framework/library and the app that you're using the framework in at the same time. If it hits a bug in the framework, sometimes it will rewrite the app to match the bug rather than fixing the bug. It's kind of a context balancing act, and you have to have a pretty good idea of how you're looking to improve things as you dogfood. It can be done, it takes some juggling, though.

I think LLMs are good at golang, and also good at that "lightweight utility function" class of software. If you keep things skeletal, I think you can avoid a lot of the slop feeling when you get stuck in a "MOVE THE BUTTON LEFT" loop.

I also think that dogfooding is another big key. I coded up a calculator app for a dentist office which 2-3 people use about 25 times a day. Not a lot of moving parts, it's literally just a calculator. It could basically be an excel spreadsheet, except it's a lot better UX to have an app. It wouldn't have been software I'd have written myself, really, but in about 3 total hours of vibecoding, I've had two revisions.

If you can get something to a minimal functional state without a lot of effort, and you can keep your dev/release loop extremely tight, and you use it every day, then over time you can iterate into something that's useful and good.

Overall, I'm definitely faster with LLMs. I don't know if I'm that much faster. I was probably most fluent building web apps in Django, and I was pretty dang fast with that. LLMs are more about things like "How do you build tests to prevent function drift" and "How can I scaffold a feedback loop so that the LLM can debug itself".

Are you an expert in this field? I'm curious if the AI generated code here is actually good.

Eh, sorry, I may have been too quick to judge, but in the past when I have shared examples of AI-generated code to skeptics, the conversation rapidly devolves into personal attacks on my ability as an engineer, etc.