Quixotic, unworkable, pointless. It’s fundamentally impossible (at least without a level of surveillance that would obviously be unavceptable) to prove the “artisanal hand-crafted human code” label.
> contributors should "fully understand" their submissions and would be accountable for the contributions, "including vouching for the technical merit, security, license compliance, and utility of their submissions".
This is in the right direction.
I think the missing link is around formalizing the reputation system; this exists for senior contributors but the on-ramp for new contributors is currently not working.
Perhaps bots should ruthlessly triage in-vouched submissions until the actor has proven a good-faith ability to deliver meaningful results. (Or the principal has staked / donated real money to the foundation to prove they are serious.)
I think the real problem here is the flood of low-effort slop, not AI tooling itself. In the hands of a responsible contributor LLMs are already providing big wins to many. (See antirez’s posts for example, if you are skeptical.)
Difficulty of enforcing is a detail. Since the rule exists, it can be used when detection is done. And importantly it means that ignoring the rule means you’re intentionally defrauding the project.
They can spin up LLM-backed contributors faster than you can ban them.
IIRC Mitchell Hashimoto recently proposed some system of attestations for OSS contributors. It’s non-obvious how you’d scale this.
It is. You haven't argued it at all, right here. You just asserted it as if it were self-evident, talked about your feelings, then demanded policy.
Your only job here was to convince people to align with you, and you didn't bother. It makes me suspect that you haven't really solidified the argument in your own mind.
The right way might be to fight AI slop with AI enforced guard rails. It's not actually that hard to implement technically. You can literally just put some markdown skills in your repository and periodically have an AI bot apply the review PR skill to incoming PRs to pre-screen them and automatically close PRs that obviously fall short of well documented criteria, and label & prioritize remaining ones.
Open claw might be a bridge too far for some at this point but it's maybe a glimpse of a future where we have AI bots filtering, screening and funneling inbound information. If you are a bit handy, you can just unleash codex or code claude on your issue tracker and pull requests right now. Worth experimenting with at a small scale.
Criteria could include doing a quick code review. Is the code appropriate and minimal? Does it meet all the documented criteria? Does it fix something that is worth fixing? Does it need further review? AIs can do all sorts of things with PRs ranging from commenting to closing the PR, prioritizing it, commenting on them, or flagging it for escalation to the right person in the team. By the time a real person chooses to spend time on a PR it should have already passed a lot of quality gates and be in a decent enough shape.
A second innovation here could be doing more with reputation. Github users build up reputation by virtue of all the contributions they make over time to all sorts of projects. Also, git allows people to sign their commits. That allows AI gate keepers to sort incoming contributions by reputation. Maybe be lenient towards known repeat contributors. Give the benefit of the doubt to new contributors but scrutinize them more and be very strict on everybody else by default. In a meritocracy, you build reputation by consistently doing good work. Dumping a lot of AI slop on somebody's desk would be the opposite.
Just some thoughts. I have a few smaller github projects but I'm so far not burdened by a lot of low quality PRs.
So leaving that aside, it just seems to be the revulsion that programmers feel towards a lot of LLM slop and the aggravation of getting a lot of slop submissions? Something that seems to be universal in the FOSS social environment, but also seems to be indicative of a boundary issue for me:
The fact that machines have started to write reasonable code doesn't mean that you don't have any responsibility to read or review it before you hand it to someone. You could always write shit code and submit it without debugging it or refactoring it sanely, etc. Projects have always had to deal with this, and I suspect they've dealt with this through limiting the people they talk to to their friends, putting arbitrary barriers in front of people who want to contribute, and just being bitchy. While they were doing this, non-corporate FOSS was stagnating and dying because 1) no one would put up with that without being paid, and/or 2) money could buy your way past barriers and bitchiness.
Projects need to groom contributors, not simply pre-filter contributions by identity in order to cut down on their workload. There has to be an onboarding process, and that onboarding process has to include banning and condemning people that give you unreviewed slop, and spreading their names and accounts to other projects that could be targeted. Zero tolerance for people who send you something to read that they didn't bother to read. If somebody is getting AI to work for them, then trust grows in that person, and their contributions should be valued.
I think the AI part is a distraction. AI is better for Debian that almost anyone else, because Debian is copyleft and avoids the problems that copyleft poses for other software. The problem is that people working within Free Software need some sort of structured social/code interaction where there are reputations to be gained and lost that aren't isolated to single interactions over pull requests, or trying to figure out how and where to submit patches. Where all of the information is in one place about how to contribute, and also about who is contributing.
Priority needs to be placed on making all of this stuff clear. Debian is a massive enough project, basically all-encompassing, where it could actually set up something like this for itself and the rest of FOSS could attach itself later. Why doesn't Debian have a "github" that mirrors all of the software it distributes? Aren't they the perfect place? One of the only good, functional examples of online government?
edit: There's no reason that Debian shouldn't be giving attribution to every online FOSS project that could possibly be run on Linux (it will be run on Debian, and hopefully distributed through apt-get.) Maybe a Debian contributor slash FOSS-in-general social network is the way to do that? Isn't debian.org almost that already?
Seriously how is lwn.net even still so popular with such an atrocious unreadable ugly website. Well yes I get the irony of asking that on HN (I use an extension to make it better).
It is over. Everyone is using AI (sure, you are the exception. For now).
A lot of low quality AI contributions arrive using free tiers of these AI models, the output of which is pretty crap. On the other hand, if you max out the model configs, i.e. get "the best money can buy", then those models are actually quite useful and powerful.
OSS should not miss out on the power LLMs can unleash. Talking about the maxed out versions of the newest models only, i.e. stuff like Claude 4.5+ and Gemini 3, so developments of the last 5 months.
But at the same time, maintainers should not have to review code written by a low quality model (and the high quality models, for now, are all closed, although I heard good things about Minmax 2.5 but I haven't tried it).
Given how hard it is to tell which model made a specific output, without doing an actual review, I think it would make most sense to have a rule restricting AI access to trusted contributors only, i.e. maintainers as a start, and maybe some trusted group of contributors where you know that they use the expensive but useful models, and not the cheap but crap models.
Both can look like the same exact type of AI-generated code. But one is a broken useless piece of shit and the other actually does what it claims to do.
The problem is just how hard it is to differentiate the two at a glance.
This is one of those areas where you might have been right.. 4-6 months ago. But if you're paying attention, the floor has moved up substantially.
For the work I do, last year the models would occasionally produce code with bugs, linter errors, etc, now the frontier models produce mostly flawless code that I don't need to review. I'll still write tests, or prompt test scenarios for it but most of the testing is functional.
If the exponential curve continues I think everyone needs to prepare for a step function change. Debian may even cease to be relevant because AI will write something better in a couple of hours.
The problem is having an unwritten rule is sometimes worse than a written one, even if it "works".
Whenever I you tried to develop using guardrails with LLMs, I found out that they are much better at ,,cheating'' than a human: getting around the guardrails by creating the ugliest hacks around them.
Hence why banning AI contributions is meaningless, you literally only punish 'good' actors.
I could guess at arguments but the ones that come to mind are pretty weak. For copyrightability, if half the lines in a FLOSS project are public domain, the license will still be effective. For infringement when training, that's not really the user's problem. For LLMs being proprietary, that doesn't infect the output, also many LLMs are not proprietary. For danger, there's not a lot of that specifically in the code-making context, and I don't see how danger makes something anti-FLOSS either.
At least they didn't win.
With the advent of LLMs, AI-autocomplete, and agent-based development workflows, my ability to deliver reliable, high-quality code is restored and (arguably) better. Personally, I love the "hallucinations" as they help me fine-tune my prompts, base instructions, and reinforce intentionality; e.g. is that >really< the right solution/suggestion to accept? It's like peer programming without a battle of ego.
When analyzing problems, I think you have to look at both upsides and downsides. Folks have done well to debate the many, many downsides of AI and this tends to dominate the conversation. Probably thats a good thing.
But, on the flip side, I personally advocate hard for AI from the point-of-view on accessibility. I know (more-or-less) exactly what output I'm aiming for and control that obsessively, but it's AI and my voice at the helm instead of my fingertips.
I also think it incorrect to look at it from a perspective of "does the good outweigh the bad?". Relevant, yes, but utilitarian arguments often lead to counter-intuitive results and end up amplifying the problems they seek to solve.
I'd MUCH rather see a holistic embrace and integration of these tools into our ecosystems. Telling people "no AI!" (even if very well defined on what that means) is toothless against people with little regard for making the world (or just one specific repo) a better place.
That doesn't address the controversy because you are a reasonable person assuming that other people using AI are reasonable like you, and know how to use AI correctly.
The rumors we hear have to do with projects inundated with more pull requests that they can review, the pull requests are obviously low quality, and the contributors' motives are selfish. IE, the PRs are to get credit for their Github profile. In this case, the pull requests aren't opened with the same good faith that you're putting into your work.
In general, a good policy towards AI submission really has to primarily address the "good faith" issue; and then explain how much tolerance the project has for vibecoding.
No AI needed. Spam on the internet is a great example of the amount of unreasonable people on the internet. And for this I'll define unreasonable as "committing an action they would not want committed back at them".
AI here is the final nail in the coffin that many sysadmins have been dealing with for decades. And that is that unreasonable actors are a type of asymmetric warfare on the internet, specifically the global internet, because with some of these actors you have zero recourse. AI moved this from moderately drowning in crap to being crushed under an ocean of it.
Going to be interesting to see how human systems deal with this.
"Open source" does not mean "open contribution", i.e. just because the software is open source does not imply that your contribution (or in particular a not-high-effort contribution) is welcome.
A well-known application that is open source in the strictest sense, but not open contribution is SQLite.
There's a way to handle this: put an automatic AI review of every PR from new contributors. Fight fire with fire.
(Actually, this was the solution for spam even before LLMs. See "A plan for SPAM" by Paul Graham. Basically, if you have a cheap but accurate filter (specially, a filter you can train for your own patterns), it should be enabled as a first line of defense. Anything the filter doesn't catch and the user had to manually mark as spam should become data to improve the filter)
Moreover, if the review detects LLM-generated content but the user didn't disclose it, maybe there should be consequences
And the problem is filtering them out. That is real work that can be draining and demoralizing as unreasonable persons usually have their sad story why they are the way they are, but you cannot do therapy or coaching for random strangers while trying to get a project going.
So if people contribute good things, engage with them. If they contribute slob (AI generated or not) - you say no to them.
People who are doing those harmful things with AI aren’t going to stop because of a policy. They are just going to lie and not admit their submissions are AI generated.
At that point, you will still have to review the code and reject it if it is bad quality, just like you had to without an AI policy. The policy doesn’t make it any easier to filter out the bad faith AI submissions.
In fact, if we DO develop an efficient way to weed out the bad faith PRs that lie about using AI, then why do we need the policy at all? Just use that same system to weed out the bad submissions, and just skip the policy completely.
This is the technique I've picked up and got the most from over the past few months. I don't give it hard, high-level problems and then review a giant set of changes to figure it out. I give it the technical solution I was already going to implement anyway, and then have it generate the code I otherwise would have written.
It cuts back dramatically on the review fatigue because I already know exactly what I'm expecting to see, so my reviews are primarily focused on the deviations from that.
I also use LLM assistance, and I love it because it helps my ADHD brain get stuff done, but I definitely miss stuff that I wouldn’t miss by myself. It’s usually fairly simple mistakes to fix later but I still miss them initially.
I’ve been having luck with LLM reviewers though.
This is how I've found myself to be productive with the tools, or since productivity is hard to measure, at least it's still a fun way to work. I do not need to type everything but I want a very exact outcome nonetheless.
My simple solution: I use Whisper to transcribe my text, and feed the output to an LLM for cleanup (custom prompt). It's fantastic. Way better than stuff like Dragon. Now I get frustrated with transcribing using Google's default mechanism on Android - so inaccurate!
But the ability to take notes, dictate emails, etc using Whisper + LLM is invaluable. I likely would refuse to work for a company that won't let me put IP into an LLM.
Similarly, I take a lot of notes on paper, and would have to type them up. Tedious and painful. I switched to reading my notes aloud and use the above system to transcribe. Still painful. I recently realized Gemini will do a great job just reading my notes. So now I simply convert my notes to a photo and send to Gemini.
I categorize all my expenses. I have receipts from grocery stores where I highlight items into categories. You can imagine it's painful to enter that into a financial SW. I'm going to play with getting Gemini to look at the photo of the receipt and categorize and add up the categories for me.
All of these are cool applications on their own, but when you realize they're also improving your health ... clear win.
FWIW, I have a pet project for a family recipe book. I normalize all recipes to a steps/instructions/ingredients JSON object. A webapp lets me snap photos of my old recipes and AI reliably yields perfectly structured objects back. The only thing I've had to fix is odd punctuation. For production, use is low, so `gemini-2.5-flash` works great and the low rate limits are fine. For development the `gemma-3-27b-it` model has MUCH higher limits and still does suprisingly well.
I'd bet you can pull this off and be very happy with the result.
Honestly, you can probably build what I built entirely with Gemini or Claude, probably with a nice frontend to boot.
I understand that your use case is different, so AI may help handicapped people. Nothing wrong with that.
The problem is that the term AI encompasses many things, and a lot of AI led to quality decay. There is a reason why Microsoft is now called Microslop. Personally I'd much prefer for AI to go away. It won't go away, of course, but I still would like to see it gone, even if I agree that the use case you described is objectively useful and better for you (and others who are handicapped).
> I also think it incorrect to look at it from a perspective of "does the good outweigh the bad?". Relevant, yes, but utilitarian arguments often lead to counter-intuitive results and end up amplifying the problems they seek to solve.
That is the same for every technology though. You always have a trade-off. So I don't think the question is incorrect at all - it applies the same just as it is for any other technology, too. I also disagree that utilitarian arguments by their intrinsic nature lead to counter-intuitive results. Which result would be counter-intuitive when you analyse a technology for its pros and cons?
Because young people repeat things they see on social media?
The accessibility angle is really important here. What we need is a way to stop people who make contributions they don't understand and/or can not vouch they are the author for (the license question is very murky still, and no what the US supreme court said doesn't matter here in EU). This is difficult though.
I agree with you that there's a huge distinction between code that a person understands as thoroughly as if they wrote it, and vibecoded stuff that no person actually understands. but actually doing something practical with that distinction is a difficult problem to solve.
No, it's not that simple. AI generated code isn't owned by anyone, it can't be copyrighted, so it cannot be licensed.
This matters for open source projects that care about licensing. It should also matter for proprietary code bases, as anyone can copy and distribute "their" AI generated code for any purpose, including to compete with the "owner".
we do often choose automation when possible (especially in computer realms), but there are endless examples in programing and other fields of not-so-surprising-in-retrospect failures due to how automation affects human behavior.
so it's clearly not true. what we're debating is the amount of harm, not if there is any.
Think of it like random noise in an image editor: you do own the random pixels since they're generated by the computer, but you can still use them as part of making your art - you do not lose copyright to your art because you used a random noise filter.
These people (myself included) made their work available free of charge under some very friendly conditions such as being credited or sharing work built upon theirs under the same license. Now we are being shit on because obscenely rich people think we are no longer relevant and that they can get away with it.
What happens to you if, say 2 years down the line, "AI" or AI has absorbed all your knowledge and can do all of your work instead of you better and faster? Do you imagine you'll keep paying for AI and having it work for you or can you also imagine a future where AI companies decide to cut out the middle-man (you) and take over your customers directly?
If it makes them go thru AI contributions to make sure there is no AI nonsense in them, that's already massive win.
The AI on itself is not a problem
> But, on the flip side, I personally advocate hard for AI from the point-of-view on accessibility. I know (more-or-less) exactly what output I'm aiming for and control that obsessively, but it's AI and my voice at the helm instead of my fingertips.
and you are the 1% (assuming your claims are true and not hallucinated gains, which are common in AI world too), vast majority of AI contributions are peak lazy, or at best goal-seeking with no regard of the target, consequences or quality
THAT is what people complain about. If AI was just used to shortcut the boring, augument the knowledge and produce better quality code, there would be very little arugments against AI-driven contributions. But that is not the case, the AI pundits will purposefully not check the AI output just because that would require time and knowledge and that looks bad on "how faster AI makes you" KPI
I'm much better now after tons of rehab work (no surgery, thankfully), but I don't have the stamina to type as much as I used to. I was always a heavy IDE user and a very fast coder, but I've moved platforms too many times and lost my muscle memory. A year ago I found the AI tools to be basically time-wasters, but now I can be as productive as before without incurring significant pain.
This reads almost like satire of an AI power user. Why would you like it when an LLM makes things up? Because you get to write more prompts? Wouldn't it be better if it just didn't do that?
It's like saying "I love getting stuck in traffic because I get to drive longer!"
Sorry but that one sentence really stuck out to me
Building up style guidelines for AI tools has been an eye opening experience in realizing how many stylistic choices we make that aren’t embedded in the linter, and aren’t documented anywhere else either. The resulting files have actually been a really good resource not just for the AI but for new developers on the project too.
It all depends on what your specific goal is.
I like it because I have no expectation of perfection-- out of others, myself, and especially not AI. I expect "good enough" and work upwards from there, and with (most) things, I find AI to be better than good enough.
Wish you the best.
My solution so far is to use my instructions to call out the fact that my comments are transcribed and full of errors. I also focus more on "plan + apply" flows that guide agents to search out and identify code changes before anything is edited to ensure the relevant context (and any tricky references) are clearly established in the chat context.
It's kinda like learning vim (or emacs, if you prefer). First it was all about learning shortcuts and best practices to make efficient use of the tool. Then it was about creating a good .vimrc file to further reduce the overhead of coding sessions. Then it was about distributing that .vimrc across machines (and I did a LOT of ssh-based work) for consistency. Once that was done, it became unimaginable to code any other way.
It has been even more true here: agent-based workflows are useless without significant investment in creating and maintaining good project documentation, agent instructions, and finding ways to replicate that across repos (more microservice hell! :D) for consistency. There is also some conflict, especially in corporate environments, with where this information needs to live to be properly maintained.
Best of luck!
This resonates. Recently, I've started to consider Claude as a partner. I like how he's willing to accept he's wrong when you provide evidence. It can be more pleasant than working with humans.
I think you have a good point
That's like saying I love hiring fuck ups that randomly do out of context and out of ruleset work for me when I ask them to perform tasks.
I would also argue to you that "folks" have done more well to debate the upsides of AI. It is pretty much all I ever see when I come to this website any more the last couple of years. Oh, and by coincidence, the operator/owner of the website just happens to be at the helm of ChatGPT. How convenient.
I do agree that at large, the theoretical upsides of accessibility are almost certainly completely overshadowed by obvious downsides of AI. At least, for now anyway. Accessibility is a single instance of the general argument that "of course there are major upsides to using AI", and there a good chance the future only gets brighter.
My point, essentially, is that I think this is (yet another) area in life where you can't solve the problem by saying "don't do it", and enforcing it is cost-prohibitive. Saying "no AI!" isn't going to stop PR spam. It's not going to stop slop code. What is it going to stop (see edit)? "Bad" people won't care, and "good" people (who use/depend-on AI) will contribute less.
Thus I think we need to focus on developing robust systems around integrating AI. Certainly I'd love to see people adopt responsible disclosure policies as a starting point.
--
[edit] -- To answer some of my own question, there are obvious legal concerns that frequently come up. I have my opinions, but as in many legal matters, especially around IP, the water is murky and opinions are strongly held at both extremes and all to often having to fight a legal battle at all* is immediately a loss regardless of outcome.
Sorry, the rest of your comment could have the recipe for fat free deep fried blowjobs that cure cancer and I wouldn't read past that.
I think the ugly unspoken truth whether Mozilla or Debian or someone else, is that there are going to be plausible and valuable use cases and that AI as a paradigm is going to be a hard problem the same way that presiding over, say, a justice system is a hard problem (stay with me). What I mean is it can have a legitimate purpose but be prone to abuse and it's a matter of building in institutional safeguards and winning people's trust while never fully being able to eliminate risk.
It's easy for someone to roll their eyes at the idea that there's utility but accessibility is perfect and clear-eyed use case, that makes it harder to simply default to hedonic skepticism against any and all AI applications. I actually think it could have huge implications for leveling the playing field in the browser wars for my particular pet issue.
Given the liabilities of relying on public and chat users markdown data to sell to other users without compensation raises a number of issues:
1. Copyright: LLM generated content can't be assigned copyright (USA), and thus may contaminate licensing agreements. It is likely public-domain, but also may conflict with GPL/LGPL when stolen IP bleeds through weak obfuscation. The risk has zero precedent cases so far (the Disney case slightly differs), but is likely a legal liability waiting to surface eventually.
2. Workmanship: All software is terrible, but some of it is useful. People that don't care about black-box obfuscated generated content, are also a maintenance and security liability. Seriously, folks should just retire if they can't be arsed to improve readable source tree structure.
3. Repeatability: As the models started consuming other LLM content, the behavioral vectors often also change the content output. Humans know when they don't know something, but an LLM will inject utter random nonsense every time. More importantly, the energy cost to get that error rate lower balloons exponentially.
4. Psychology: People do not think critically when something seems right 80% of the time. The LLM accuracy depends mostly on stealing content, but it stops working when there is nothing left to commit theft of service on. The web is now >53% slop and growing. Only the human user chat data is worth stealing now.
5. Manipulation: The frequency of bad bots AstroTurf forums with poisoned discourse is biasing the delusional. Some react emotionally instead of engaging the community in good faith, or shill hard for their cult of choice.
6. Sustainability: FOSS like all ecosystems is vulnerable to peer review exhaustion like the recent xz CVE fiasco. The LLM hidden hostile agent problem is currently impossible to solve, and thus cannot be trusted in hostile environments.
7. Ethics: Every LLM ruined town economic simulations, nuked humanity 94% of the time in every war game, and encouraged the delusional to kill IRL
While I am all for assistive technologies like better voice recognition, TTS, and individuals computer-user interfaces. Most will draw a line at slop code, and branch to a less chaotic source tree to work on.
I think it is hilarious some LLM proponents immediately assume everyone also has no clue how these models are implemented. =3
"A Day in the Life of an Ensh*ttificator "
Something might be required now as some people might think that just asking an LLM is "the most he can done", but it's not about using AI it's about being aware and responsible about using it.
But like the XZ attack, we kind of have to assume that advanced perissitant threats are a reality for FOSS too.
I can envisage a Sybil attack where several seemingly disaparate contributors are actually one actor building a backdoor.
Right now we have a disparity in that many contributors can use LLMs but the recieving projects aren't able to review them as effectively with LLMs.
LLM generated content often (perhaps by definition) seems acceptable to LLMs. This is the critical issue.
If we had means of effectively assessing PRs objectively that would make this moot.
I wonder if those is a whole new class of issue. Is judging a PR harder than making one? It seems so right now
Depends on the assumptions. If you assume good intent of the submitter and you spend time to explain what he should improve, why something is not good, etc, than it's a lot of effort. If you assume bad intent, you can just reject with something like "too large review from unproven user, please contribute something smaller first".
Yes, we might need to take things a bit slower, and build relations to the people you collaborate with in order to have some trust (this can also be attacked, but this was already possible).
Quality should always be the responsibility of the person submitting changes. Whether a person used LLMs should not be a large concern if someone is acting in good-faith. If they submitted bad code, having used AI is not a valid excuse.
Policies restricting AI-use might hurt good contributors while bad contributors ignore the restrictions. That said, restrictions for non-quality reasons, like copyright concerns, might still make sense.
Sure now it is easy, but in 3-10 years AI will get significantly better. It is a lot like the audio quality of an MP3 recording. It is not perfect (lossless audio is better), but for the majority of users it is "good enough".
At a certain point AI generated content, PR's, etc will be good enough for humans to accept it as "human". What happens then, when even the best checks and balances are fooled?
The trick is to define what works - set the bar high enough that you're driving away raw human contributors, annoying high value humans, or arbitrarily barring AI users out of dogma or politics or whatever. A hierarchy of maintainers, with people willing to volunteer to sift through submissions, each handing up a much reduced list to the next level, is probably where big projects will have to go.
At some point it won't matter; while it does, look for enthusiastic volunteers and make good, sensible, functional rules that get the best results.
But for software infrastructure; Kernels, operating systems, compilers, browsers, etc, it is crazy we are even considering AI at it's current ability. If we are going to do that, we need to switch to Ada/SPARK or some other type of formally verifiable system.
Maybe I'm overreacting, but all I want to do right now is escape. It horrifies me to think that one day I may be driving a car with a braking system vibe coded in C++.
Great care and attention is required for critical system components and LLMs lack both.
Not to mention the copyright risks - do we really want a piece of code that can’t be licensed or turns out to be a verbatim copy from another project to end up in the kernel or something? (No, the answer is we don’t want.).
What a banger sub-thread: https://lists.debian.org/debian-vote/2026/02/msg00020.html
Basically "let's not screw anyone"
It's a good policy
(If anything, the copyright to model-generated code cannot possibly be said to belong to the human contributor. They… didn’t write it! I’m glad to see that aspect was discussed though I’m surprised it wasn’t the main thrust.)
It’s implied because it’s illegal (infringes on the original author’s rights). Of course, LLMs aren’t people.
The days of AI hardcoding things to past tests are going to be behind us as they gain the ability to generalize not just in their knowledge but problem solving instead, especially if you know how to hit AI where it hurts.
This is where it becomes relevant: upstreaming hardware support, if someone wants to add / fix a bug and they successfully test it and it works there has to be some kind of middle ground where the PR should be justified and worth the effort of a review, but the person submitting the PR possibly has no idea about what they're talking about and just has to trust AI.
I do not have an answer to that except for limiting the size and scope of such PRs. Possibly require previous work being acknoledged (hand made) rather than AI generated to validate that you at least know what the PR is about and what it does.
LLM submissions are cheap to send and expensive to receive.
This creates a spam (slop) problem, just like in other contexts.
If a change used to take a day or two, and now requires a few minutes, then it's fair to ask for a couple hours more prompting to add the additional tangible tests to compensate for any risks of hallucinations or low quality code sneaking in
Google found that with Jules years ago at this point, same for other automated tools.
When I first saw the headline though, it sounded like someone was listening to one of my favorite Rush songs.
"""
If you choose not to decide
You still have made a choice
You can choose from phantom fears
And kindness that can kill
I will choose a path that’s clear
I will choose free will.
"""
It it really true the LLM's are non-deterministic? I thought if you used the exact input and seed with the temperature set to 0 you would get the same output. It would actually be interesting to probe the commit prompts to see how slight variants preformed.
I think they can also be differences on different hardware, and also usually temperature is set higher than zero because it produces more "useful/interesting" outputs
In my experience (albeit with non-coding questions), ChatGPT 5.2 is often quite eager to critique snippets of its own replies from previous conversations. And reasoning models can definitely find flaws in LLM-written code.
I think that's backwards, at least as far as accepting a PR. Better that all code is reviewed as if it is probably a carefully thought out Trojan horse from a dedicated enemy until proven otherwise.
The core issue is that it takes a large amount of effort to even assess this, because LLM generated code looks good superficially.
It is said that static FP languages make it hard to implement something if you don't really understand what you are implementing. Dynamically typed languages makes it easier to implement something when you don't fully understand what you are implementing.
LLMs takes this to another level when it enables one to implement something with zero understanding of what they are implementing.
The people following the policies are the most likely to use AI responsibly and not submit low-effort contributions.
I’m more interested in how we might allow people to build trust so that reviewers can positively spend time on their contributions, whilst avoiding wasting reviewers time on drive-by contributors. This seems like a hard problem.
Therefore, policies restricting AI-use on the basis of avoiding low-quality contributions are probably hurting more than they’re helping.
Actually not shrink, but just transfer it to reviewers.
Can you reliably tell that the contributor is truly the author of the patch and that they aren't working for a company that asserts copyright on that code? No, but it's probably still a good idea to have a policy that says "you can't do that", and you should be on the lookout for obvious violations.
It's the same story here. If you do nothing, you invite problems. If you do something, you won't stop every instance, but you're on stronger footing if it ever blows up.
Of course, the next question is whether AI-generated code that matches or surpasses human quality is even a problem. But right now, it's academic: most of the AI submissions received by open source projects are low quality. And if it improves, some projects might still have issues with it on legal (copyright) or ideological grounds, and that's their prerogative.
But the projects aren't drowning under PRs from reputable people. They're drowning in drive-by PRs from people with no reputation to speak of. Even if you outright ban their account, they'll just spin up a new one and try again.
Blocking AI submissions serves as a heuristic to reduce this flood of PRs, because the alternative is to ban submissions from people without reputation, and that'd be very harmful to open source.
And AI cannot be the solution here, because open source projects have no funds. Asking maintainers to fork over $200/month for "AI code reviews" just kills the project.
That's an OK view to hold, but I'll point out two things. First, it's not how the tech is usually wielded to interact with open-source software. Second, your worldview is at odds with the owners of this technology: the main reason why so much money is being poured into AI coding is that it's seen by investors as a replacement for the individual.
This is the basis of the argument - it doesn't matter if you use AI or not, but it does matter if you know what you're doing or not.
If everything the maintainer wants can (hypothetically) be one-shotted, then there is no need to accept PR's at all. Just allow forks in case of open source.
AI is predictive at a token level. I think the usefulness and power of this has been nothing short of astonishing; but this token prediction is fundamentally limiting. The difference between human _driven_ vs AI generated code is usually in design. Overly verbose and leaky abstractions, too many small abstractions that don't provide clear value, broad sweeping refactors when smaller more surgical changes would have met the immediate goals, etc. are the hallmarks of AI generated code in my experience. I don't think those will go away until there is another generational leap beyond just token prediction.
That said, I used human "driven" instead of human "written" somewhat intentionally. I think AI in even its current state will become a revolutionary productivity boosting developer aid (it already is to some degree). Not dissimilar to a other development tools like debuggers and linters, but with much broader usefulness and impact. If a human uses AI in creating a PR, is that something to worry about? If a contribution can pass review and related process checks; does it matter how much or how little AI was used in it's creation?
Personally, my answer is no. But there is a vast difference between a human using AI and an AI generated contribution being able to pass as human. I think there will be increasing degrees of the former, but the latter is improbable to impossible without another generational leap in AI research/technology (at least IMO).
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As a side note, over usage of AI to generate code _is_ a problem I am currently wrangling with. Contributors who are over relying on vibecoding are creating material overhead in code review and maintenance in my current role. It's making maintenance, which was already a long tail cost generally, an acute pain.
Crystal ball or time machine?
Past performance does not guarantee future results, of course. But acting like AI is now magically going to stagnate is also a really bold bet.
The ones that make the headlines here on HN are not subtle at all, they're probably the bottom of the barrel of AI users.
McDonalds cooks ~great~ (edit: fair enough, decent) burgers when measured objectively, but people still go to more niche burger restaurants because they want something different and made with more care.
That's not to say that an human can't use AI with intent, but then AI becomes another tool and not an autonomous code generating agent.
Wait, what? In what world are McDonalds burgers "great"? They're cheap. Maybe even a good value. But that's not the same as great.
If you believe the outputs of LLMs are derivative products of the materials the LLMs were trained on (which is a position I lean towards myself, but I also understand the viewpoint of those who disagree), then no, that's not a good thing, because it would be a license violation to accept those derived products without following the original material's license terms, such as attribution and copyleft terms. You are now party to violating the original materials' copyright by accepting AI generated code. That's ethically dubious, even if those original authors may have a hard time bringing a court case against you.
I think a lot of anti-LLM opinions just come from interacting with the lowest effort LLM slop and someone not realizing that it's really a problem with a low value person behind it.
It's why "no AI allowed" is pointless; high value contributors won't follow it because they know how to use it productively and they know there's no way for you to tell, and low value people never cared about wasting your time with low effort output, so the rule is performative.
e.g. If you tell me AI isn't allowed because it writes bad code, then you're clearly not talking to someone who uses AI to plan, specify, and implement high quality code.
I disagree that the rule is pointless, and your last point is a strawman. AI is disallowed because it’s the manner in which the would-be contributors are attempting to contribute to these projects. It’s a proxy rule.
Unfortunately for AI maximalists, code is more than just letters on the screen. There needs to be human understanding, and if you’re not a core contributor who’s proven you’re willing to stick around when shit hits the fan, a +3000 PR is a liability, not an asset.
Maybe there needs to be something like the MMORPG concept of “Dragon Kill Points (DKP)”, where you’re not entitled to loot (contribution) until you’ve proven that you give a shit.
High-value contributors follow the rules and social mores of the community they are contributing to. If they intentionally deceive others, they are not high-value.
Like its been years and years now, if all this is true, you'd think there would be more of a paradigm shift? I'm happy I guess waiting for Godot like everyone else, but the shadows are getting a little long now, people are starting to just repeat the same things over and over.
Like, I am so tired now, it's causing such messes everywhere. Can all the best things about AI be manifest soon? Is there a timeline?
Like what can I take so that I can see the brave new world just out of reach? Where can I go? If I could just even taste the mindset of the true believer for a moment, I feel like it would be a reprieve.
I know it's a cliche but it's just too perfect to answer this question.
You're right that it won't stop anyone doing harmful things with AI - all it does is codify what is and isn't considered acceptable by a project, and make it easier to justify rejections.
If a project wants to continue evaluating submissions on a case-by-base basis (and has the manpower to do it without the support of a policy) then that's entirely their choice, of course.
They're also a statement of organizational's support for people who reject slop PRs and help when the AI using author generates a smear blog post against the reviewer like we've seen before.
It's not a rumor, it's a pattern.
Which it might. And needs to be judged on a case-by-case basis, under current copyright law.
> Seems like an obvious solution
I'm not sure how you would rank quality of submissions for grading contributors like this. Just because a project accepted your PR doesnt make it high quality, the best we can hope for is that it was better than no accepting it?
For AI generated code if previous PRs aren't loaded into context then there's no lasting benefit from the time taken to review and it's blank slate each time. I think ultimately it can be solved with workflow changes (i.e. AI written code should be attributed to the AI in VCS, the full trace and manual edits should be visible for review, all human input prompts to the AI should be browsable during review without having scroll 10k lines of AI reasoning.)
Some of the best burgers I've ever had came from fast food.
I love burgers and have had many all over the US; I wouldn't turn down a McD's burger.
Off the internet. Maybe it's just time we all face the public internet is dead.
Maybe a trusted private internet, though that comes with it's own risks and tradeoffs.
Maybe we start doing PRs over mailed USB keys. Anyone with enough interest will do it, but it will cut out the bots. We're back to a 90's sneakernet. Any internet presence may become a read only site telling others how to reach you offline.
The information superhighway died a long time ago. 4chan enlightened me on the power of intelligent stupidity. The machinations of a few smart people could embolden countless stupid people to cause nearly unlimited damage. Social media gathering up the smart and dumb alike allowed bullshit asymmetry to explode onto the scene and burned out anyone with a modicum of intelligence.
But AI marketing would not like to promote it, may because it is less dramatic and does not involve a paradigm shift or something...
Without that policy it feels rude to ask, and rude to ignore in case they didn't use AI.
For example, someone might have done a lot of investigation to find the root cause of an issue, followed by getting Claude Code to implement the fix, which they then tested. That has a good chance of being a good contribution.
I think tackling this from the trust side is likely to be a better solution. One approach would be to only allow new contributors to make small patches. Once those are accepted, then allow them to make larger contributions. That would help with the real problem, which is higher volumes of low-effort contributions overwhelming maintainers.
The ability to create spam instantly, fitted perfectly to any situation, and doing that 24/7, everywhere, is very different from before. Before, spam was annoying but generally different enough to tell apart. It was also (in general) never too much as to make an entire platform useless.
With AI, the entire internet IS spam. No matter what you google or look at, there's a very high chance it's AI spam. The internet is super duper extra dead.
I get unreasonably angry when I read this statement, or similar ones.
If you mean "portions of the web I go to or my email inbox", you may be right.
But for the rest of us that hang out in one or multiple private spaces, sometimes with connections between them, the internet is better connected and easier to find people, groups, information, and interests than ever before.
AI also generates spam though, so this is a much bigger problem than merely "unreasonable" people alone.
Now, with that said I don't think we're very far from automated agents causing problems all on their own.
At least a bunch of lawyers already got hit when their court filings cited hallucinated cases. If this trend continues, I'll not be surprised when some end up disbarred.
so far*
I sincerely doubt that, because it still can't even generate a few hundred line script that runs on the first try. I would know, I just tried yesterday. The first attempt was using hallucinated APIs and while I did get it to work eventually, I don't think it can one shot a complex application if it can't one shot a simple script.
IMO, AI has already stagnated and isn't significantly better than it was 3 years ago. I don't see how it's supposed to get better still when the improvement has already stopped.
I routinely generate applications for my personal use using OpenCode + Claude Sonnet/Opus.
Yesterday I generated an app for my son to learn multiplication tables using spaced repetition algorithm and score keeping. It took me like 5 minutes.
Of course if you use ChatGPT it will not work but there is no way Claude Code/Open Code with any modern model isn't able to generate a one hundred line script on the first try.
Eh?
Ever hear the saying the first 90% of a problem is 90% of the work, the last 10% of the program is also 90% of the work.
AI/LLMs have improved massively in that context. That's not even including the other model types such as visual/motion-visual/audio which are to the point that telling their output from reality is a chore.
And one shotting a simple script simply doesn't mean much without context. I have it dump relatively complex powershell scripts often enough and it's helped me a lot with being able to explain scripting actions to other humans where before I'd make assumptions about the other users knowledge where it was not warranted.
In reality it's Logarithmic. Maybe with the occasional jolt. You'd think with Moores "law" that we'd know better by now that explosive growth isn't forever. Or at least that we're bound to physics as a cap to hit.
In that case a lot of proprietary software is in breach of copyleft licences. Its probably by far the commonest breach.
> You are now party to violating the original materials' copyright by accepting AI generated code. That's ethically dubious
That is arguable. Is it always ethically dubious to breach a law? If not, which is it ethically dubious to breach this law in this particular way?
Sure, but this doesn't really seem relevant to the conversation. Someone else violating software license terms doesn't justify me (or Debian, in the case of TFA) doing so.
> Is it always ethically dubious to breach a law?
I'm not really concerned with the law, here. I think it is ethically dubious to use someone else's work without compensating them in the manner they declared. Copyright law happens to be the method we've used for a couple hundred years to standardize the discussion about that compensation, and sometimes enforce it. Breaching the law doesn't really enter into the conversation, except as a way our society agrees to hold everyone to a minimum ethical standard.
OK, that is reasonable. I do not think copyright is a good mechanism though, and I think the need to compensate depends on multiple factors depending on what you use a work for and under what circumstances.
For the same model, you can just have it generate dad jokes or use it in a tool like OpenCode or Cursor or Zed or Cline or … and make it program complex things.
If I use Claude Sonnet on duck.ai I will have hard time generating something interesting. The same model in OpenCode does all my programming work.
I’m constantly amazed at the amount of scope I can now one-shot with Claude Code. It can crank out multi command cli apps with almost zero hand holding beyond telling what to generate… you know, the hard part. And then we’ll back and forth to refine the working thing it built.
If I had been a bit more intelligent at how I was learning, and focused on improving reading skill instead of simply wanting to play whatever music I liked, I would have probably never had problems. Rhythm games in general boil down to training your brain to read moving patterns and convert them to scheduled movements in your body (but mostly your hands). Once you get to an intermediate level you get these "flow states" where you realize you were basically unconscious for most of the song, not a single thought, just internally observing your body's reactions to the screen. Almost a type of meditation.
It is a bit insulting, but I get that these issues are important and people feel like the stakes are sky-high: job loss, misallocation of resources, enshitification, increased social stratification, abrogation of personal responsibility, runaway corporate irresponsibility, amplification of bad actors, and just maybe that `p(doom)` is way higher than AI-optimists are willing to consider. Especially as AI makes advances into warfare, justice, and surveillance.
Even if you think AI is great, it's easy to acknowledge that all it may take is zealotry and the rot within politics to turn it into a disaster. You're absolutely right to identify that there are some eerie similarities to the "gun's don't kill people, people kill people" line of thinking.
There IS a lot to grapple with. However, I disagree with these conclusions (so far) and especially that AI is a unique danger to humanity. I also disagree that AI in any form is our salvation and going to elevate humanity to unfathomable heights (or anything close to that).
But, to bring it back to this specific topic, I think OSS projects stand to benefit (increasingly so as improvements continue) from AI and should avoid taking hardline stances against it.
The main thing that put me off on the comment was the outright dismissal of other opinions. That's rarely a recipe for a productive conversation.
>However, I disagree with these conclusions (so far) and especially that AI is a unique danger to humanity. I
I don't think it's unique. It's simply a catalyst. In good times with a system that looks out for its people, AI could do great things and accelerate productivity. It could even create jobs. None of that is out of reach, in theory.
But part of understanding the negative sentiment is understanding that we aren't in that high trust society with systems working for the citizen. So any bouts of productivity will only be used to accelerate that distrust. Looking at the marketing of AI these past few years confirms this. So why would anyone trust it this time?
Rampant layoffs, vague hand waves of "UBI will help" despite no structures in place for that, more than a dozen high profile kerfuffles that can only be described as a grift that made millions anyway, and persistent lobbying to try and make it illegal to regulate AI. These aren't the actions of people who have the best interests of the public masses in mind. It's modern day robber barons.
>I think OSS projects stand to benefit (increasingly so as improvements continue) from AI and should avoid taking hardline stances against it.
I don't have a hard line stance on how organizations handle AI. But from my end I hear that Ai has mostly lead to being a stressor on contributors trying to weed out the flood of low quality submissions. Ai or not (again, Ai is a catalyst. Not the root cause), that's a problem for what's ultimately a volunteer position that requires highly specialized skills.
If the choice comes between banning Ai submissions, restricting submissions altogether with a different system, or burning out talent trying to review all this slop: I don't think most orgs will choose the latter.
This isn't necessarily true; I've seen some projects absorb a PR of roughly that size, and after the smoke tests and other standard development stuff, the original PR author basically disappeared.
It added a feature he wanted, he tested and coded it, and got it in.
This anecdotal argument is a dead end. The nuance is clear: not all software is the same, and not all edits to software are the same.
Your argument has nothing to do with AI and more to do with PR size and 'fire and forget' feature merges. That's what the commenter your responding to is pointing out.
And in the context of high-value contributors that GP was mentioning, they are never going to land a +3000 PR because they know there is going to be a human reviewer on the other side.
So what metric are you going to try to use to prove yourself?
You're literally saying that the upsides of hallucinanigenic gifts are worth the downside of collapsing society. I'd say that that is downplaying and misrepreting the issue. You even go so far to say
>Telling people "no AI!" (even if very well defined on what that means) is toothless against people with little regard for making the world (or just one specific repo) a better place.
These aren't balanced arguments taking both sides into considerations. It's a decision that your mindset is the only right one and anyone else is a opposing progress.
At least in the US, society has been well on it's way to collapse before the LLM came out. "Fake news" is a great example of this.
>It's a decision that your mindset is the only right one and anyone else is a opposing progress.
So pretty much every religious group that's ever existed for any amount of time. Fundamentalism is totally unproblematic, right?
IMO you can blame this on ML and the ability to microtarget[1] constituencies with propaganda that's been optimized, workshopped, focus grouped, etc to death.
Proto-AI got us there, LLMs are an accelerator in the same direction.
But as modern society is, it is simply accelerating the low trust factors of it and collapsing jobs (even if it can't do them yet), because that's what was already happening. But hey, assets also accelerated up. For now.
>So pretty much every religious group that's ever existed for any amount of time. Fundamentalism is totally unproblematic, right?
Religion is a very interesting factor. I have many thoughts on it, but for now I'll just say that a good 95% of religious devouts utterly fail at following what their relevant scriptures say to do. We can extrapolate the meaning of that in so many ways from there.
No, literally, he didn't.
I hope you see how this quickly will advance from a project being about accomplishing some goal, to a project becoming about humans showing they are the ones writing code. Much like we see in religions where people don't give money to the poor to benefit the poor, but show they give money to the poor to benefit themselves. Hence the game playing will continue and the underlying problem will never be addressed.
Instead of progressing to a system resilient to the fact that you can't know how code was written, you've created a rule that, because it's unenforceable and deniable, must retreat to moralization about what someone does in private.
That might make you feel good, but it won't work.
We need to rethink some UX design and processes here, not pretend low quality people are going to follow your "no low quality pls i'm serious >:(" rules. Rather, design the processes against low quality.
Also, we're in a new world where code-change PRs are trivial, and the hard part isn't writing code anymore but generating the spec. Maybe we don't even allow PRs anymore except for trusted contributors, everyone else can only create an issue and help refine a plan there which the code impl is derived?
You know, even before LLMs, it would have been pretty cool if we had a better process around deliberating and collaborating around a plan before the implementation step of any non-trivial code change. Changing code in a PR with no link to discussion around what the impl should actually look like always did feel like the cart before the horse.
The latter is where you get all known contributors from! So if you close off unknown contributors the project will eventually stagnate and die.
And for the major projects where there was a flood of PRs, it was fairly easy to identify if someone knew what they were talking about by looking at their language; Correct use of jargon, especially domain-specific jargon.
The broader reason why "unknown contributor" PRs were held in high regard is that, outside of some specific incidents (thank you, DigitalOcean and your stupid tshirts), the odds were pretty good of a drive by PR coming from someone who identified a problem in your software by using it. Those are incredibly valuable PRs, especially as the work of diagnosing the problem generally also identifies the solution.
It's very hard to design a UX that impedes clueless fools spamming PRs but not the occasional random person finding sincere issues and having the time to identify (and fix them) but not permanent project contribution.
> and the hard part isn't writing code anymore but generating the spec
My POV: This is a bunch of crap and always has been.
Any sufficiently detailed specification is code. And the cost of writing such a specification is the cost of writing code. Every time "low code" has been tried, it doesn't work for this very reason.
e.g. The work of a ticket "Create a product category for 'Lime'" consists not of adding a database entry and typing in the word 'Lime', it consists of the human work of calling your client and asking whether it should go under Fruit or Cement.
Contrary to popular belief, I believe open-source projects are poised for a significant windfall. If a project lacks the ability to transform a good problem into an advantage, that’s their prerogative, but it shouldn’t be the norm (I hope not).
I like to think of it as a stranger/volunteer using their tokens to give the project maintainers a choice. Imagine each issue having 3-4 PRs - it’s a brave new world that demands new solutions!
From a place of scarcity - to now picking from multiple PRs, not a bad problem to have. This is turning into a signal vs noise optimization problem. Could it be solved with agents specially fine-tuned for this use-case? Maybe. Why not?
Also many projects on GitHub do have discussions (typically in a long Issues thread) before someone opens a PR.
Hmmm, no? That's actually very common in open source. Maybe "banning" isn't the right word, but lots of projects don't accept random drive-by submissions and never have. Debian is a perfect example, you are very unlikely to get a nontrivial patch or package into Debian unless you have some kind of interaction or rapport with a package maintainer, or commit to the process of building trust to become a maintainer yourself.
I have seen high profile GitHub projects that summarily close PRs if you didn't raise the bug/feature as an issue or join their discord first.
> you are very unlikely to get a nontrivial patch or package into Debian unless you have some kind of interaction or rapport with a package maintainer
I did mean the "trivial" patches as well, as often it's a lot of these small little fixes to single issues that improve software quality overall.
But yes, it's true that it's not uncommon for projects to refuse outside PRs.
This already causes massive amounts of friction and contributes (heh) heavily to what makes Open Source such a pain in the ass to use.
Conversely, many popular "good" open source libraries rely extensively on this inflow of small contributions to become comprehensively good.
And so it's a tradeoff. Forcing all open source into refusing drive-by PRs will have costs. What makes sense for major security-sensitive projects with large resources doesn't make sense for others.
It's not that we won't have open source at all. It's that it'll just be worse and encourage further fragmentation. e.g. One doesn't build a good .ZIP library by carefully reading the specification, you get it by collecting a million little examples of weird zip files in the wild breaking your code.
1. You layout policy stating that all code, especially AI code has to be written to a high quality level and have been reviewed for issues prior to submission.
2. Given that even the fastest AI models do a great job of code reviews, you setup an agent using Codex-Spark or Sonnnet, etc to scan submissions for a few different dimensions (maintainability, security, etc).
3. If a submission comes through that fails review, that's a strong indication that the submitter hasn't put even the lowest effort into reviewing their own code. Especially since most AI models will flag similar issues. Knock their trust score down and supply feedback.
3a. If the submitter never acts on the feedback - close the submission and knock the trust score down even more.
3b. If the submitter acts on the feedback - boost trust score slightly. We now have a self-reinforcing loop that pushes thoughtful submitters to screen their own code. (Or ai models to iterate and improve their own code)
4. Submission passes and trust score of submitter meets some minimal threshold. Queued for human review pending prioritization.
I haven't put much thought into this but it seems like you could design a system such that "clout chasing" or "bot submissions" would be forced to either deliver something useful or give up _and_ lose enough trust score that you can safely shadowban them.
In terms of your plan though, you're just building a generative adversarial network here. Automated review is relatively easy to "attack".
Yet human contributors don't put up with having to game an arbitrary score system. StackOverflow imploded in no small part because of it.
The way to get around this without getting all the LLM influencer bros in an uproar is to come up with a system that allows open source libraries to evaluate the risk of a PR (including the author’s ability to explain wtf the code does) without referencing AI because apparently it’s an easily-triggered community.
I think that's the only shot at progress since it can address the general problem instead of trying to special-case unenforceable rules that you hope the lowest quality people follow.
For example, a 3000+ line PR with no communication beforehand is already a low quality PR before AI. And it's one of the most annoying contributions to deal with since you have to basically tell them "sorry but all that work you did isn't acceptable". Yet they probably did all of it in earnest.
Presumably you already have a policy where you accept random PRs for small tweaks like doc fixes, but you don't want unsolicited PRs that make substantial changes. So a rule against AI doesn't change anything there.
And if you saw an uptick in large unsolicited PRs, then surely the solution is to update the process like disallow PRs that don't link to an issue.
"But I ain't likely to write you no poem, if you follow me. Your AI, it just might. But it ain't no way human.”Human society exists because we value humans, full stop. The easiest way to "solve" all of humanity's problems is to simply say that humans aren't valuable. Sometimes it feels like we're conceding a ridiculous amount of ground on that basic principle every year - one more human value gone because it "doesn't matter", so hey, we've obviously made progress!
It seems that gun control—though imperfect—in regions that have implemented it has had a good bit of success and the legitimate/non-harmful capabilities lost seem worth it to me in trade for the gains. (Reasonable people can disagree here!)
Whereas it seems to me that if we accept the proposition that the vast majority of code in the future is going to be written by AI (and I do), these valuable projects that are taking hard-line stances against it are going to find themselves either having to retreat from that position or facing insurmountable difficulties in staying relevant while holding to their stance.
It is the conservative position: it will be easier to walk back the policy and start accepting AI produced code some time down the road when its benefits are clearer than it will be to excise AI produced code from years prior if there's a technical or social reason to do that.
Even if the promise of AI is fulfilled and projects that don't use it are comparatively smaller, that doesn't mean there's no value in that, in the same way that people still make furniture in wood with traditional methods today even if a company can make the same widget cheaper in an almost fully automated way.
Why would the AI-fans even care if others who decide not to use it fall behind? Wouldn't they get to point and laugh and enjoy the benefits of "keeping up"? Their fervor should be looked at with suspicion.
There are many others like me who share this expectation, and, while we certainly may be wrong, it's not because of some sinister plan to make the prophecy come true. (There are certainly some who do have sinister/profit-seeking motives, of course!)
This is even true despite the fact that there are bad actors only a few minutes drive away in many cases (Chicago->Indiana border, for example).
(as an aside - this reminds me of the trend of Object Oriented Ontology that specifically /tried/ to imbue agency onto large-scale phenomena that were difficult to understand discretely. I remember "global warming" being one of those things - and I can see now how this philosophy would have done more to obscure the dominion of experts wrt that topic)
But post Sandy Hook, it's clear which side prevailed in this argument.
Those in favor of gun control aren't trying to lower human responsibility, they're trying to place stricter limits on the guns than the status quo. Those against gun control are trying to loosen limits on the guns.
Here this person is proposing making individual responsibility stricter compared than what it is today. And they're not arguing for loosening limits on the tech either.
Isn't that practically the opposite of your analogy?
Your assumption that all rules must be about enforcement is incorrect. Your assumption that only that which can be measured matters is incorrect. I don't know where this belief system comes from, but it strikes me as profoundly toxic.
By this logic, we obviously shouldn't ban drinking and driving - there's no way to test every driver every time, and presumably those most skilled at drunk driving would be undetectable, so it's really just religious moralism.
"Good drivers don't drink and drive even if they think they can get away with it" is just a no-true-scotsman argument, and thus we should actually encourage people to drink and drive so that they get better at it. Nobody should ever have any standards that can't be automatically enforced by a linter, after all.
And look: https://news.ycombinator.com/item?id=47340079
Unenforceable rules might just be the backbone of society, if you think about it.
This is very interesting, because there must be a line here that AI is crossing, and the line is not clearly determined yet.
Is linting code crossing the line?
Is re-factoring code with automated tools like bicycle repair man crossing the line ?
Is AI doing a code review and suggesting the code crossing the line ?
Is writing code with a specific prompt and sample code crossing the line?
Is producing a high level spec and let the AI design details and code the whole thing crossing the line ?
So, where exactly is this line ?
The next interesting question is how this could even be enforced. It's going to be hard to prove AI use when using strictly local models. Maybe they could embed some watermark like thing, but I am not sure this can't be circumvented.
Would really like to see some legal opinions on this ( unlikely to happen :)
The best I found is here: https://copyrightlately.com/thaler-is-dead-ai-copyright-ques...
Here's what the US Copyright Office says: https://newsroom.loc.gov/news/copyright-office-releases-part...
I guess we will have to wait for cases to be brought and resolved at the courts. Not a great recipe to be the leader in AI, it must be said.
An updated copyright bill from legislature, or even positive regulatory action from the executive branch would speed things up and give much planning certainty to actors here in the US.
The rest of the world won't be waiting though -- maybe Europe, but Europe sadly doesn't really matter that much anymore :(
There is no way to reliably identify code as AI-generated, unless it is explicitly labelled so. Good code produced by AI is not different from the good code produced by software engineer, so copyright is the last thing I would be worried about. Especially given the fact that reviewing all pull requests is substantial curation work on the side of maintainers: even if submitted code is not copyrightable, the final product is.
Maybe they can be subpoenaed, maybe they can sell the data to parties who care like legal teams, maybe they can make it service anyone can plug a GitHub repo into, etc.
Translation: AI generated code is in the public domain in the US (until and unless something changes).
You can freely incorporate public domain code into any other codebase. You can relicense it as you see fit. Public domain material is not viral the way the GPL is.
Furthermore, if you make changes to public domain code the derivative product is subject to copyright.
See also Red Hat IP lawyer's opinion on trying to license the chardet "rewrite": https://github.com/chardet/chardet/issues/334#issuecomment-4...
> in many circumstances these outputs will be copyrightable in whole or in part—where AI is used as a tool, and where a human has been able to determine the expressive elements they contain. Prompts alone, however, at this stage are unlikely to satisfy those requirements
I'll just note that "unlikely" doesn't mean "not possible".
It's an interesting question but it's perplexing to me that an IP lawyer of all people would be suggesting to remove a license that might or might not apply in practice. Removing it could have legal consequences if it turns out to be copyrightable. Whereas leaving the notice there wouldn't be expected to have any consequences in the event that it turns out not to apply. Particularly the notice for a permissive OSI license.
To begin with the project is MIT licensed which amounts to public domain with extra steps. So it's the same thing at the end of the day regardless of how the law turns out.
On top of that, even if we suppose the LLM output to be public domain as soon as you later make modifications to the file the license in the header would come into force. It's functionally no different than copy-pasting a piece of public domain code and then editing it a bit.
[0] https://www.copyright.gov/ai/Copyright-and-Artificial-Intell...
The extreme sides (proponents, opponents) are clear, opposites, and fight each other. More nuanced takes get buried as droplets in a bucket. Likely a goal.
> Human society exists because we value humans, full stop.
Call me cynic, but I do not believe every human being agrees with this sentiment. From HR acting as if humans are resources, to human beings being dehumanized as workers, civilians, cannon fodder, and... well, the product. Every time human rights are violated, and we do not stand up to it, we lose.
I have a very simple question as human right: the right for a human being to know the other side is a human being yes or no, and if not: to speak gratis (no additional fee allowed) to a human being instead. Futhermore, ML must always cite the used sources, and ML programmer is responsible for mistake. This would increase insurance costs so much, that LLM's in public would die, but SLM's could thrive.
Eh, human society exists because it is an emergent behavior of the evolutionary advantage afforded at the time of adoption by the human species. There is on iron rule stating that it must continue into the future, or even that it can exist into the future.
More so, the value of a human has wildly fluctuated over history and culture. The village chief, nobles, the king were all high value humans. The villagers would be middle to low value, and others may be considered no value.
The industrial age began to change this some as value started to move from the merchant class to the villager class as many high production jobs needed less and less training to complete. With industrialization businesses running machines and production lines needed as many people as they could get. Still human rights were hard fought in places like America where labor wars broke out.
In the modern US we've setup a dangerous set of idealism that will most likely end in disaster because they are in conflict with general human values. That is the "pull yourself up by your bootstraps", "Any collective action is communism and communism will turn you into a pillar of salt if you dare look at it", and "greed is good". Couple that with TV media and social media owned by rich billionaires you're not going to see much serious opposition to these ideals.
But if/as labor loses it's values, so will the humans that performed that labor. After decades of optimizing human society for maximal capital extraction, values are dead, and the ever present thought police owned by the rich will make sure you don't cause too much trouble by resurrecting them.