alt Hacker NewsI don't quite see the benefit of this, personally.
Whoever is submitting the code is still responsible for it, why would the reviewer care if you wrote it with your fingers or if an LLM wrote (parts of) it? The quality+understanding bar shouldn't change just because "oh idk claude wrote this part". You don't get extra leeway just because you saved your own time writing the code - that fact doesn't benefit me/the project in any way.
Likewise, leaving AI attribution in will probably have the opposite effect as well, where a perfectly good few lines of code gets rejected because some reviewer saw it was claude and assumed it was slop. Neither of these cases seems helpful to anyone (obviously its not like AI can't write a single useable line of code).
The code is either good or it isn't, and you either understand it or you don't. Whether you or claude wrote it is immaterial.
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As a reviewer, I do care. Sure, people should be reviewing Claude-generated code, but they aren't scrutinizing it.
Claude-generated code is sufficient—it works, it's decent quality—but it still isn't the same as human written code. It's just minor things, like redundant comments that waste context down the road, tests that don't test what they claim to test, or React components that reimplement everything from scratch because Claude isn't aware of existing component libraries' documentation.
But more importantly, I expect humans to be able to stand by their code, and at times defend against my review. But today's agents continue to sycophantically treat review comments like prompts. I once jokingly commented on a line using a \u escape sequence to encode an em dash, how LLMs would do anything to sneak them in, and the LLM proceeded to replace all — with --. Plus, agents do not benefit from general coding advice in reviews.
Ultimately, at least with today's Claude, I would change my review style for a human vs an agent.
Knowing if an AI contributed is good data. The human is still responsible for the content of the PR.
While code is good or not, evaluating it is a bit of a subjective exercise. We like to think we are infallible code evaluating machines. But the truth is, we make mistakes. And we also shortcut. So knowing who made the commit, and if they used AI can help us evaluate the code more effectively.
It’s not about who wrote it, but about who is submitting it. The LLM co-author indicates that the agent submitted it, which is a contraindication of there being a human taking responsibility for it.
That being said, it also matters who wrote it, because it’s more likely for LLMs to write code that looks like quality code but is wrong, than the same is for humans.
Whoever is submitting the code is still responsible for it, why would the reviewer care if you wrote it with your fingers or if an LLM wrote (parts of) it?
The problem is that submitters often do not feel responsible for it anymore. They will just feed review comments back to the LLM and let the LLM answer and make fixes.
This is disrespectful of the maintainers' time. If the submitter is just vibe/slop coding without any effort on their part, it's less work to do it myself directly using an LLM than having to instruct someone else's LLM through GitHub PR comments.
In this case it's better to just submit an issue and let me just implement it myself (with or without an LLM).
If the PR has a _co-authored by <LLM>_ signal, then I don't have to spend time giving detailed feedback under the assumption that I am helping another human.
> Whoever is submitting the code is still responsible for it, why would the reviewer care if you wrote it with your fingers or if an LLM wrote (parts of) it?
Maybe one day we can say that, but currently, it matters a lot to a lot of people for many reasons.
You're quite right that the quality of the code is all that matters in a PR. My point is more historical.
AI is a very new tool, and as such the quality of the code it produces depends both on the quality of the tool, and how you've wielded it.
I want to be able to track how well I've been using the tool, to see what techniques produce better results, to see if I'm getting better. There's a lot more to AI coding than just the prompts, as we're quickly discovering.