I’m just curious, what would need to happen for you to change your opinion about this? Are you basically of the opinion that it’s not good enough today, never will be good enough in the future, and we should just wind back the clock 3 years and pretend these tools don’t exist?
It feels to me like a lot of this is dogma. If the code is broken or needs more testing, that can be solved. But it’s orthogonal: the LLM can be used to implement the unit testing and fuzz testing that would beat this library into shape, if it’s not already there. It’s not about adding a human touch, it’s about pursuing completeness. And that’s true for all new projects going from zero to one, you have to ask yourself whether the author drove it to completeness or not. That’s always been true.
You want people to hedge their projects with disclaimers that it probably sucks and isn’t production worthy. You want them to fess up to the fact that they cheated, or something. But they’re giving it away for free! You can just not use it if you don’t want to! They owe you nothing, not even a note in the readme. And you don’t deserve more or less hacker points depending on whether you used a tool to generate the code or whether you wrote it by hand, because hacker points don’t exist, because the value of all of this is (and always will be) subjective.
To the extent that the modern tools and models can’t oneshot anything, they’re going to keep improving. And it doesn’t seem to me like there’s any identifiable binary event on the horizon that would make you change your mind about this. You’re just against LLMs, and that’s the way it is, and there’s nothing that anyone can do to change your mind?
I mean this in the nicest way possible: the world is just going to move on without you.
>I’m just curious, what would need to happen for you to change your opinion about this?
Imagine a machine that can calculate using logic circuits and one that uses a lookup table.
LLMs right now is the latter (please don't take literally, It is just an example). You can argue that the look up table is so huge that it works most of the time.
But I (and probably the parent commenter) need it to be the former. And that answers your question.
So it does not matter how huge the lookup table will grow in the future so that it will work more often, it is still a lookup table.
So people are divided into two groups right now. One group that goes by appearance, and one that goes by what the thing actually is fundamentally, despite the appearances.
There never was a cohesive generic open source community. There are no meaningful group norms. This was and always will be a fiction.
I’m tempted to just start putting co-authored-by: Claude in every commit I make, even the ones that I write by hand, just to intentionally alienate people like you.
The best guardrails are linters, autoformatters, type checkers, static analyzers, fuzzers, pre-commit rules, unit tests and coverage requirements, microbenchmarks, etc. If you genuinely care about open source code quality, you should be investing in improving these tools and deploying them in the projects you rely on. If the LLMs are truly writing bad or broken code, it will show up here clearly.
But if you can’t rephrase your criticism of a patch in terms of things flagged by tools like those, and you’re not claiming there’s something architecturally wrong with the way it was designed, you don’t have a criticism at all. You’re just whining.
I tried to control LLM output quality by different means, including fuzzing. Had several cases when LLM "cheated" on that too. So, I have my own shades and grades of being sure the code is not BS.
But once you told it to stop cheating, did it eventually figure it out? I mean, correctly implementing fuzzer support for a project is entirely within the wheelhouse of current models. It’s not rocket science.
You’ve gotta read the code. It doesn’t matter how it got there but if you don’t fully understand it (which implies reading it) don’t get mad when you try to push slop on people. It’s the equivalent of asking an LLM to write an email for somebody else to read that you didn’t read yourself. It’s basic human trust - of course people get annoyed with you. You’re untrustworthy.
Sorry but these are just not accurate as blanket statements anymore, given how good the models have gotten.
As other similar projects have pointed out, if you have a good test suite and a way for the model to validate its correctness, you can get very good results. And you can continue to iterate, optimize, code review, etc.
ii am trying to not take issue with this comment because im aware of the huge stigma around ai generated code.
i needed this project so i made it for my use case and had to build on top of it. the only way to ensure quality is to read it all line by line.
if you give me code that you yourself have not reviewed i will not review it for you.
I’m just curious, what would need to happen for you to change your opinion about this? Are you basically of the opinion that it’s not good enough today, never will be good enough in the future, and we should just wind back the clock 3 years and pretend these tools don’t exist?
It feels to me like a lot of this is dogma. If the code is broken or needs more testing, that can be solved. But it’s orthogonal: the LLM can be used to implement the unit testing and fuzz testing that would beat this library into shape, if it’s not already there. It’s not about adding a human touch, it’s about pursuing completeness. And that’s true for all new projects going from zero to one, you have to ask yourself whether the author drove it to completeness or not. That’s always been true.
You want people to hedge their projects with disclaimers that it probably sucks and isn’t production worthy. You want them to fess up to the fact that they cheated, or something. But they’re giving it away for free! You can just not use it if you don’t want to! They owe you nothing, not even a note in the readme. And you don’t deserve more or less hacker points depending on whether you used a tool to generate the code or whether you wrote it by hand, because hacker points don’t exist, because the value of all of this is (and always will be) subjective.
To the extent that the modern tools and models can’t oneshot anything, they’re going to keep improving. And it doesn’t seem to me like there’s any identifiable binary event on the horizon that would make you change your mind about this. You’re just against LLMs, and that’s the way it is, and there’s nothing that anyone can do to change your mind?
I mean this in the nicest way possible: the world is just going to move on without you.
>I’m just curious, what would need to happen for you to change your opinion about this?
Imagine a machine that can calculate using logic circuits and one that uses a lookup table.
LLMs right now is the latter (please don't take literally, It is just an example). You can argue that the look up table is so huge that it works most of the time.
But I (and probably the parent commenter) need it to be the former. And that answers your question.
So it does not matter how huge the lookup table will grow in the future so that it will work more often, it is still a lookup table.
So people are divided into two groups right now. One group that goes by appearance, and one that goes by what the thing actually is fundamentally, despite the appearances.
But logic circuits are look up tables.
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This might be true, but we can continue to try and require the communities we have been part of for years to act a certain way regarding disclosures.
If the community majority changes it mind then so be it. But the fight will continue for quite some time until that is decided.
There never was a cohesive generic open source community. There are no meaningful group norms. This was and always will be a fiction.
I’m tempted to just start putting co-authored-by: Claude in every commit I make, even the ones that I write by hand, just to intentionally alienate people like you.
The best guardrails are linters, autoformatters, type checkers, static analyzers, fuzzers, pre-commit rules, unit tests and coverage requirements, microbenchmarks, etc. If you genuinely care about open source code quality, you should be investing in improving these tools and deploying them in the projects you rely on. If the LLMs are truly writing bad or broken code, it will show up here clearly.
But if you can’t rephrase your criticism of a patch in terms of things flagged by tools like those, and you’re not claiming there’s something architecturally wrong with the way it was designed, you don’t have a criticism at all. You’re just whining.
3 replies →
I see this as the same argument as saying GMO label not needed, no need to mention artificial flavours in food, etc.
I mean this in the nicest way possible: the world is just going to insist that AI generated output is marked clearly as AI produced output.
Not sure whether giving a LICENSE even makes sense.
I tried to control LLM output quality by different means, including fuzzing. Had several cases when LLM "cheated" on that too. So, I have my own shades and grades of being sure the code is not BS.
Well, that’s obviously bad.
But once you told it to stop cheating, did it eventually figure it out? I mean, correctly implementing fuzzer support for a project is entirely within the wheelhouse of current models. It’s not rocket science.
You’ve gotta read the code. It doesn’t matter how it got there but if you don’t fully understand it (which implies reading it) don’t get mad when you try to push slop on people. It’s the equivalent of asking an LLM to write an email for somebody else to read that you didn’t read yourself. It’s basic human trust - of course people get annoyed with you. You’re untrustworthy.
Pack it in everyone, the “OK for prod use” guy has spoken.
That ship has sailed, man…
No it has not - if it had, there'd be no need to shout down folk who disagree.
Not everyone buys into the inevitabilism. Why should I read code "author" didn't bother to write?
Sorry but these are just not accurate as blanket statements anymore, given how good the models have gotten.
As other similar projects have pointed out, if you have a good test suite and a way for the model to validate its correctness, you can get very good results. And you can continue to iterate, optimize, code review, etc.