Setting up a linter, formatter and having a lot of strict type checking are really helpful when using an llm to generate code, just like they are when you start receiving contributions from people who don't know or don't agree with the style rules that you implicitly follow. As are tests. Basically anything automated that helps ensure the right style and behaviour of your code becomes very useful with coding agents as they can just run whatever command you tell them to check for issues and/or fix them if possible.

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arduanika 5 days ago | parent | next [-]

I believe you that these tools help a lot, but they would not prevent ~any of the examples listed in the article (under "The smell of vibe coding").

	▲	helloplanets 5 days ago \| parent \| next [-]
		Most of those look like context issues to me. Repo map (using Tree-sitter, etc) and documentation would already do wonders. Feeding 32-64kTok of context directly into a model like Gemini Pro 2.5 is something that more people should try out in situations like this. Or even 128kTok+.
	▲	Cthulhu_ 5 days ago \| parent \| prev \| next [-]
		Probably not, but that's where code review comes in. Which can also partially be done by AI, ironically.
	▲	dkdbejwi383 5 days ago \| parent \| prev [-]
		With cursor at least it recognises linter errors and failing tests and attempts to correct its own problems, with varying levels of success

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antihero 5 days ago | parent | prev [-]

This just masks the problem.