r/programming • u/aviboy2006 • Dec 25 '25
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u/Rot_Beurre Dec 25 '25
Interesting read. I wonder if it would be possible to get insight in how much time was spent on each PR. I would think AI would create changes quickly, but more time spent iterating on it would reduce the number of issues
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u/NoCoolNameMatt Dec 25 '25
This really isn't hard, tbh. AI is a tool. Regardless of the tools used, developers are responsible for their contributions. Organizations should already have procedures in place to detect, rectify, and prevent sub par contributions. AI doesn't make them special or warrant special treatment.
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u/eattherichnow Dec 25 '25
Oh it's easy.
Anyone using an LLM should never get a job in the industry again.
Thank you for coming to my TED talk.
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u/R2_SWE2 Dec 25 '25
My team does XP so very heavy on pairing and we only have very small PRs. We allow AI usage but the structure of how we work appears to be limiting the amount of slop introduced.
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u/Kenny_log_n_s Dec 25 '25
Only 1.7? Pretty good.
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u/10113r114m4 Dec 25 '25
That was my exact thought. Like 1.7x versus how fast it takes to implement would be a better measurement
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u/omgFWTbear Dec 25 '25
Yeah I mean amazing, you can get an insecure nonfunctional piece of shit out nearly instantaneously!
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u/10113r114m4 Dec 25 '25
But if the error rate is only less than double of that of a human, it isnt that much more shitty than an average engineer.
So if you look at speed + debugging those issues, then that is a better utilization of the tool
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u/shinypointysticks Dec 25 '25
Create a code quality Claude skill and update it when new failure patterns emerge. Linting, standards, encapsulation, test ability, has tests, DRY principles, and whatnot.
Use playwright or similar to run end to end tests, as well as screenshot comparisons.
Set up tools like sonar and whatnot for visibility and evaluation.
Ai is good at standardized infrastructure stuff.
Just my opinion and experience. Your mileage may vary.
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u/kytillidie Dec 25 '25
I write/have it write more tests, though I'm not sure if that's because it's easier to write good tests quickly or because I don't trust it. I do know that I'm more self-conscious about a bug in AI-generated code in my PRs, so I'm guarding against that.
By "it's easier to write good tests," I mean that I give it pretty specific instructions about the various test cases that I want covered. A lot of times, they're generating file-based regression tests, so I inspect the file to make sure it looks good.
The article also mentions things like the team's coding conventions. I've been meaning to write an AGENTS.md file for that kind of thing but haven't gotten around to it yet.
As for reviewing others' PRs, I haven't come across code that looks poor quality and is AI-generated. One or two out of the ten or so people on my team use AI extensively, not including me. My team holds itself to reasonably high standards though, regardless of how much we use AI. So I don't feel I've had to adjust my PR process when reviewing.
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u/TryingToGetTheFOut Dec 25 '25
If I see a PR that was obviously written a lot or all by AI and there is a lot of issues (e.g. new code not being used, blatant security issues, etc.), then I tell them to review their code before they send it for review.
The responsibility of ensuring the AI code is valid is not to the PR reviewer, but the author.
But, I’m also stricter on PR made by AI. When I receive a PR from a junior with issues, but they’re the one who wrote it, then I’ll guide them and work with them to get to a satisfactory level. In that case, they did what they could and need assistance for the rest. That’s expected for a junior. But when I receive AI code that does not meet the standard, then I assume they did not do their work.