Thanks pmxi. I read that post when it trended, and it got me thinking. I then wrote a post that tries to give a framework for deciding whether to take a gig fixing vibe code.

https://www.slater.dev/about-that-gig-fixing-vibe-code-slop/

> strongly typed languages?

Depends on training data. It's not great at rust but it can chug along in small examples. I do suspect strongly typed languages are more suited to AI if it has the opportunity to learn them properly. The movement recently has been generalization, but i personally think if we want to reach further in AI coding, we need models with language and domain specialization.

I imagine a AI agent trained to parse LLVM and feed itself static analysis output, could reach new heights of code understanding for Rust for example.

> people are reluctant to change vibe code?

I think people are reluctant to change existing code in general. Its one thing for a personal project, but for a collaborative codebase, it's a large investment of energy to parse out a unfamiliar part of the system. "The original developer made sure this works, and it passes all tests, so i shouldn't poke it too much without knowing the system as well".

I think most often people have some vibe coded stuff that kind of does what they want but they don't really understand what it all is and how it works, or any confidence it can be made into something useful, so rather than spending time cleaning up AI code they just use it to grasp the idea and write it themselves. Whether any time is saved by going through this process with the AI seems doubtful to me. Sitting down with pen and paper and thinking through things would probably be more useful.