alt Hacker NewsSolid points. I will add a few points.
1) A lot of the time i spent deciding on interfaces (methods, classes, etc.) for humans. E.g. should this be two methods or one, should this method be in this class or moved to utility. Those problems went away. 2) What about performant code? This can be prompted away and when the measurements in your performance tests do not go down, then you can step in. 3) Sad to say but the AI has always been better than me at code-reviews. Maybe this is just me and if so I own that, but to the articles point, it might be harder to fix now. 4) "vibe-coded projects devolve over time into an unmaintainable mess". Preventing and managing this mess is the new skill sets we need to develop as software engineers. 5) Another skill-set we will need to master is how to maintain and grow our coding skills. Some ideas are: a) every once in a while implement a feature yourself. b) no AI Tuesdays! c) Have the AI quiz you on the code base. d) Have the AI develop HTML docs about how the code works.
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1) Those problems did not go away. They only "went away" if you assume no one will ever need to read code again. And if you believe that, you clearly aren't reading the code already. Which I guess is why you think naming and architecture problems went away. AI is the absolute worst at architecture decisions.
2) not even sure what you mean by this
3) probably shouldn't admit that. It implies the reviewer has a less than average understanding of the code they're reviewing.
4) preventing and managing vibe code devolving into a pile of slop requires programmers not use AI. 80% accuracy repeated in more and more layers === more and more failures. In other words, the skill required is exactly the skill of being a good programmer without AI.
5) e) no AI all the time or only use AI as search. You're almost there with a and b. With c, it just doesn't understand well enough to "quiz you". With d, how are you going to know if the docs are correct if you aren't reading the slop?
I've found it helpful to make an explicit delineation in the codebase for which interfaces I'm going to agonize over and refine and which I'm going to ignore.
One set goes into haxe files and I use reflaxe macros to write tiny compilers that generate docs, clients, servers, cli's, test cases, serializers and deserializers, etc in whatever language is appropriate. That leaves gaps, which the AI can then fill in.
So I iterate on the haxe stuff. If the AI is struggling to "draw the rest of the owl," I change the source of truth until it has enough guidance re: type related errors, failing tests, and documentation. As requirements change, these things change.
As for the rest of the owl, it's disposable. Every few months I'll delete it and have an AI rewrite it from scratch (now with a smarter model and better docs and API specs and tests to guide it). This keeps the cruft from accumulating while preserving human contact with the code.