I genuinely appreciated this comment—it made me chuckle. That said, I think there are better approaches to working with AI besides “here’s a big vague thing to work on, go write some code”. I think you have to iterate somewhat closely with the AI to write a doc describing exactly what you want the system to do and then scope out very narrow tickets and then have a separate agent do the TDD to actually produce the thing. The key insights here are (1) don’t let a code writing agent have too much scope—just a narrowly scoped ticket, (2) keep the coding agent’s context minimal, (3) don’t let the coding agent write much code without testing it. The agent should make very small changes at a time and then test that everything still works.

You will still need to QA stuff and review PRs, but I think AI done properly can genuinely make some tasks better.

> don’t let a code writing agent have too much scope—just a narrowly scoped ticket

it's interesting cuz my intuition is to give the language model writing the files as much context as possible, which means all of the previous planning thread. but I also thought you should plan with a small model and implement with a large one, and the meta seems to be plan with an expensive one and delegate code output to smaller ones. so what do I know.

> The agent should make very small changes at a time and then test that everything still works.

yeah I think if it's treated like a codegen machine it's basically just outputting code as if you're using a dsl, except the dsl is natural language and the output is meant to be edited, no `// this is generated code, do not edit` headers

> I think AI done properly can genuinely make some tasks better

thank god I dont need to write html by hand anymore, what a pita