Just take a look at the pull requests / issues opened of a repository that’s popular with LLM agents, to understand how well that works.

If there’s one take away it’s that these agents need more, not less, oversight. I don’t agree at all with the “just remove a few tools and you can remove the human from the loop” approach. It just reduces the blast radius in case the agent gets it wrong, not the fact that it gets it wrong.

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iot_devs 14 hours ago | parent [-]

Yeah, but my projects are personal and not popular.

I crafted the AI loop to do exactly what I would be doing by manually.

Out of 10 PRs, 6 to 7 gets merged. The other simply get closed.

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stingraycharles 14 hours ago | parent [-]

Yeah my experience is that this works for a short time and then after a few weeks your codebase is a complete disaster.

	▲	codebje 13 hours ago \| parent [-]
		I could believe a ~66% success rate on asking an agent to run a linter and make PRs addressing issues found, that sounds about right: very tightly bounded problem, a sensible solution is often offered by the tool, and verification of success is binary. Structural changes, in which attention to the small details of a task is directly at odds with the need to consider less overt factors like cohesion and coherence, are where an agent will turn your code base into a dog's breakfast. The vibe coded software I have for my own use only is like that. Giant hundred-line functions, poor separation of concerns, easy for a change to have unintended behaviour somewhere else. It's probably a step up from the spreadsheet I was using before it, but not by enough to justify current RAM prices.