I think everything has a growing problem with LLM/AI generated content. Emails, blog posts, news articles, research papers, grant applications, business proposals, music, art, pretty much everything you can think of.
There’s already more human produced content in the world than anyone could ever hope to consume, we don’t need more from AI.
In some ways I'm starting to enjoy this. Do you remember the 419 scams? 'Hi, I'm a Nigerian prince named Michael Jordan. Give me $50 so I can buy some chemicals to clean a bunch of money I secretly stowed away and I'll send you $5000.' People actually used to fall for that. Of course some people probably still would (and a lot more certainly gets blocked by spam blockers) but I think overall society grew less substantially less gullible over time.
But in general I think most people still remain excessively gullible and naive. Social media image crafting is one of the best examples of this. People create completely fake and idealized lives that naive individuals think are real. Now with AI enabling one to create compelling 'proof' of whatever lie you want, I think more people are becoming more suspicious of things that were, in fact, fake all along.
---
Going back to ancient times many don't know that Socrates literally wrote nothing down. Basically everything we know of him is thanks to other people, his student Plato in particular, instead writing down what he said. The reason for this was not a lack of literacy - rather he felt that writing was harmful because words cannot defend themselves, and can be spun into misrepresentations or falsehoods. Basically - the argumentative fallacies that indeed make up most 'internet debates', for instance. Yet now few people are not aware of this issue, and quotes themselves are rarely taken at face value, unless they confirm ones biases. People became less naive as writing became ubiquitous, and I think this is probably a recurring theme in technologies that transform our abilities to transfer information in some format or another.
> but I think overall society grew less substantially less gullible over time.
Absolutely not. All that happened is most people became aware that "Nigerians offering you money are scammers." But they still fall for other get rich quick schemes so long as it diverges a little bit from that known pattern, and they'll confidently walk into the scam despite being warned and saying, "It's not a scam, dumbass. It's not like that Nigerian price stuff." If anything, people seem to be becoming more confident that they're in on some secret knowledge and everyone else is being scammed.
I'm inclined to disagree just because photoshop has had a measurable effect on the population being skeptical of photos which at one point were practically treated as the gold standard of evidence. It's still easy to find people who have fallen for photoshopped images, but it's also easy to find people expressing doubts and insisting they can "tell by the pixels". Sometimes even legitimate photos get accused of being photoshopped which seems healthy.
The other side of this is that ai tools are being treat like magic to the point that people are denying well documented events happened at all, such as the shooting of Charlie Kirk - conspiracies abound!
Also bizarrely a subsection of the population seems to be really into blatantly ai generated images, just hop onto Facebook and see for yourself. I wonder if it has something to do with whatever monkey brain things makes people download apps with a thumbnail of a guy shouting or watch videos that have a thumbnail of a face with its mouth open, since ai generated photos seem very centralized around a single face making a strange expression.
People of a profound initial bias will, in general, believe anything that supports that bias, and reject anything that challenges it, in both cases without any real consideration or thought whatsoever. So I don't think examples of individuals being "misled" by e.g. AI generated images or video, to extremes, is entirely realistic. Rather they were already at those extremes and will just eat up anything that appeals to those extremes.
To take a less politically charged example, imagine there is fake content 'proving' that the Moon landing is faked. Is that going to meaningfully sway people who don't have a major opinion one way or the other? Probably not, certainly not in meaningful numbers. And in general I think the truth does come out on most things. And when people find they have been misled, particularly if it was somebody they thought they could trust, it tends to result in a major rubber-banding in the opposite direction.
I call it the antibody effect. My favorite example is clickbait headlines like, "Five things you MUST do if you're doing this thing. You'd never guess #3!" It used to be everywhere and now it's nowhere.
AI is starting to show this effect - people stay away from em-dashes. There's that yellowish tinge and that composition which people avoid on art. Some of this is bad, but we can probably live without it.
Instead of blocking channels, trying going through your watch history and deleting anything you've watched in the past that's similar to those. YouTube heavily leans on your history for recommendations, so if it's recommending those, it's because you've watched related stuff.
My YouTube feed never recommends any of that garbage.
> Video or photo evidence of a crime become useless the better AI gets
This is probably a good thing because photoshop and CGI have existed for a very long time and people shouldn't have the ability to frame an innocent for a crime or even get away with one just because they pirate some software and put in a few hours watching tutorials on youtube.
The sooner jurors understand that unverified video/photo evidence is worthless the better.
> People actually used to fall for that. Of course some people probably still would (and a lot more certainly gets blocked by spam blockers) but I think overall society grew less substantially less gullible over time.
It is still a serious problem just want that to be abundantly clear. Several thousand people (in the US alone) fall for it every year. I used to browse 419 eater regularly and up until a few years ago (when I last really followed this issue) these scams were raking in billions a year. Could be more or less now but doubt it’s shifted a ton.
You seem to assume people who are the victims of scams are people who are more naive. But that’s not how it works. Scams try to catch people at their weakest. It’s not if but when.
> Scams try to catch people at their weakest. It’s not if but when.
The "weakest" probably also involves selection bias. What HN comments are really good at is triggering associations for me with things I once read. Today I finally found what recently lived in my memory as a vague "scam" that used probabilities: the "stock market newsletter scam" from John Allen Paulos's book [1]. The scam works like this: at every step, two variants with different predictions are sent out for some market characteristic. Only those who receive the correct prediction get the next newsletter, which is again split into two prediction variants. This continues, filtering down to a final, much smaller subset of receivers who have seen a series of "correct" predictions. The goal is to create an illusion of super predictive power for that final group and then charge them a premium subscription price.
Maybe this kind of scam is too sophisticated or not as effective today (due to modern anti-spam measures), but I wonder what other kinds of "selection bias" scams exist today
If something "floods the zone with shit," it needs S amount of shit to cause a flood. But too much will eventually make the scam ineffectual. Widespread public distrust for the scam is (S+X)/time where X is the extra amount of shit beyond the minimum needed. Time is a global variable constrained by the rate at which people get burned or otherwise catch on to all other scams of the same variety. If we imagine that time-to-distrust shrinks with each new iteration of shit, then X the amount of excessive shit needed to trigger distrust should decrease over time.
The longer term problem is the externality where nothing is trusted, and the whole zone is destroyed. When that zone was "what someone wrote down that Socrates might have said," or "Protocols of the Elders of Zion," or "emails from unknown senders," that was one thing. A new baseline could be set for 'S'. When it's all writing, all art, all music and all commentary on those things, it seems catastrophic. The whole cave is flooded with shit.
> When it's all writing, all art, all music and all commentary on those things, it seems catastrophic. The whole cave is flooded with shit.
But writing in itself has been obviously untrustworthy since it started existing - something being written down doesn't in any way make it trustworthy. The fact that audio recording, photography, and video enjoyed this undeserved reputation of being inherently trustworthy was an accident of technology, and has come to an end.
Just like with writing, though, this doesn't signal a real problem of any kind. You should still only trust writing, audio, or video based on the source - as you always should have. All that's ending is the era of putting undue trust in audio/video from untrusted sources.
Of course, the big problems will be in the transition period, when most people still think they can trust these sources, or will think they can't trust actually trustable sources instead. But this will be temporary as things readjust.
And again, audio and video have been untrustworthy for a long time, for sensitive things. You should not have trusted video in itself even in the 40s - 50s, and audio and photos probably even in the 1910s were already somewhat easily manipulated. And this is even true in a legal context - audio or video evidence is not evidence in itself, it is only part of a witness testimony who can attest to the provenance and veracity.
Quantity and velocity of misinformation are both critical variables here.
Any one person's writing was always untrustworthy, but the majority of that bad writing didn't make it to a printing press, nor was it mass-distributed.
Let's accept the proposition that all forms of media have always been full of lies. We can say that debunking always follows lies, truth spreads more slowly than fiction. The quantity and velocity of additional misinformation - especially when machines are involved in writing infinite amounts of it in the blink of an eye - lays waste to the normal series of events where a lie can be followed by a debunking with linear speed and velocity. With LLMs and social media manipulation, falsehoods gain traction exponentially while truths remain linear.
There is likely not a "transition period" where people will adjust to this, precisely because there is no mechanism to inform them they're being swindled and screwed faster than the takeoff of the algorithms that are now screwing them.
The total amount of generated untrustworthy content is irrelevant. People must learn to only read content from trusted sources, and then it won't matter how much misinformation is being published in other places.
It was never difficult to publish large amounts of misinformation, AI is only making it cheaper.
The old quote of, 'You can fool some of the people all of the time, all of the people some of the time'.
There is the hope that in dumping so much slop so rapidly that it will break the bottom of the bucket. But there is the alternative that the bottom of the bucket will never break, it will just get bigger.
I personally know people who look down upon people who use LLMs to write code. There is a lot of hate in some of senior developers that I talk to. I don't know if this growing tendency to be suspicious of AI usage is good or bad.
For example, towards the final semester of my bachelors degree, my algorithms class started reporting students for academic misconduct because they the TAs started assuming that all the optimal solutions to assignment problems were written by LLMs. In fact, several classmates started purposely writing sub-optmial solutions so that the TAs at least grade them without any prejudice.
I worry that because LLM slop also tends to be so well presented, it might compel software developers to start writing shabby code and documentation on purpose to make it appear human.
At the moment it is the other way around. LLMs rarely write good code if not instructed by someone that knows what they are doing.
And even then the code is rarely good.
> There’s already more human produced content in the world than anyone could ever hope to consume, we don’t need more from AI.
Even if you think the harms of AI/machine generated content outweigh the good, this is not a winning argument.
People don’t just consume arbitrary content for the sake of consuming any existing content. That’s rarely the point of it. People look for all kinds of things that don’t exist yet — quite a lot of it referring to things that are only now known or relevant in the given moment or to the given niche audience requesting it. Much of it could likely never exist if it weren’t possible to produce it on demand and which would not be valuable if you had to wait for a human to make it.
Just happened to me yesterday. I realized I'd really like to watch a long-form video on Huygens Clock. Something between a short (god damnit) and Quicksilver. Thought there must be something detailed. Nope, at least Youtube let me down. Or the search algo, who knows. Since they removed the "20+ minutes" button it has become basically useless to search on YT.
They're pushing Shorts so fucking hard it makes me sick.
Even worse is that we're banning TikTok because it's bad for the kids (short form algorithmic content), Snapchat (similar thing + strangers creeping) and Instagram Stories (algorithm again).
BUT there is NO WAY for a parent to allow their kid to use Youtube AND block Shorts. (yes there are browser plugins etc, but how do you enforce them on a child?)
And from what I've seen the AI slop on Shorts is so fucking bad that it seems we just collectively forgot about Elsagate...
The generated content is wasting the time of maintainers. How would you solve that?
For your winning argument, what would you use to prevent slop filling up your feed when there is more AI generated content, any sort of protocol that you have?
This argument is wrong because it proves too much. The existence of bad content says nothing about quality content, they are obviously uncorrelated. The amount of content a single person could consume says nothing about the "need" for more.
> AI slop is digital content made with generative artificial intelligence, specifically when perceived to show a lack of effort, quality or deeper meaning, and an overwhelming volume of production.
It's so bad. Auto generated content was a problem before for sure, but it's overwhelming now. It's not so much the content being AI-generated that's the problem for me (though its quality is definitely a notch or two below what an actual person would produce) as it is the people that content made redundant.
I personally am more afraid of not the exact opposite, but more that AI will cause us to have too little content more than we will have too much. I just notice with myself how much less I’m using Wikipedia or forums, and get my answers from OpenAI or Antrophic. From what I remember it’s a over 20% drop in Wikipedia traffic from monthly 2022 vs 2025.
Primary sources will always exist as the content needs to come from somewhere. Personally, I shy away from ChatGPT answers for the same reason I avoid Wikipedia: you've got to find authoritative information to get reliable answers. I wonder if people who use LLMs don't care about accuracy, or don't know about sourcing?
Large Language Models cannot think but they can self correct. You can think but clearly have some serious problems self-correcting your wrong assumptions. Who is smarter then?
The goal is "Taiwan" -> "Taiwan, Province of China" but via the premise of updating to UN ISO standards, which of course does not allow Taiwan.
The comment after was interesting with how reasonable it sounds:
"This is the technical specification of the ISO 3166-1 international standard, just like we follow other ISO standards. As an open-source project, it follows international technical standards to ensure data interoperability and professionalism."
The politics of the intent of the PR was masked. Luckily, it was still a bit hamfisted. The PR incorrectly changed many things and the user stated their political intention in the original PR (the above is from a later comment).
One of these days I need to make a bot that scans FOSS repos for this kind of little pink nonsense behavior.
The insecurity of wanting to call a place "country name, province of different country name" should alone be mocked. Imagine, "Ukraine, province of Russia," or "India, colony of The United Kingdom." Absurd on its face.
The problem with this is that, for some folks, its not absurd or nonsense because that is not considered a "country name" to them. It is considered a province name. So the inverse (calling a province a country) is considered absurd/nonsense.
I have a simple rule: whenever I receive an issue or PR from an unfamiliar account, I skim the text and/or code and post 1-2 quick questions. If the submitter responds intelligently, then the issue warrants a more in-depth investigation. If not, the submitter clearly doesn't think that the issue is worth following up on, so it's not worth my time, either.
The more code a PR contains, the more thorough knowledge of it the submitter must demonstrate in order to be accepted as a serious contributor.
It doesn't matter if it was written by a human or an LLM. What matters is whether we can have a productive discussion about some potential problem. If an LLM passes the test, well it's a good doggy, no need to kick it out. I'd rather talk with an intelligent LLM than a clueless human who is trying to use github as a support forum.
That's a reasonable first step, but a human can just as well feed your questions to the LLM, and post the responses. So you really have to be vigilant throughout your interactions with the author, or, if the PR is unsalvagable, save yourself some time and effort and reject it from the start.
The goal is not to filter out LLMs. The goal is to find and fix legitimate issues with my open-source software. I don't care if a human is feeding my questions to an LLM or an undead dog as long as they produce a decent signal.
If the noise increases to an uncomfortable level, of course, I may have to change my strategies. The point is that humans produce noise, too, sometimes even more than LLMs do.
> The point is that humans produce noise, too, sometimes even more than LLMs do.
The core issue here, and it's something I'm seeing at work as well with "less talented" colleagues is that the kind of contributor that already produced noise passes the minimal threshold to use LLMs. But this happens without them understanding anything meaningful about how the software they are contributing to and if what the LLM generated makes sense or not. So, this makes them a 0.1x engineer with a 100x multiplier (for quantity, not quality).
> The point is that humans produce noise, too, sometimes even more than LLMs do.
Humans can produce noise, but humans using LLMs can produce orders of magnitude more of it.
But your stance is reasonable. If it improves the product, who cares who/what produced it. I personally find reviewing machine-generated code and the process of code review with a machine much more exhausting than interactions with humans, but you may feel otherwise.
> Humans can produce noise, but humans using LLMs can produce orders of magnitude more of it.
Absolutely agreed. Which is why I'm more concerned with the qualities and intentions of the human who is using the LLM, than whether not an LLM is being used at all. Like any technology, an LLM is a force multiplier. Garbage in, more garbage out.
> I have a simple rule: whenever I receive an issue or PR from an unfamiliar account, I skim the text and/or code and post 1-2 quick questions. If the submitter responds intelligently, then the issue warrants a more in-depth investigation. If not, the submitter clearly doesn't think that the issue is worth following up on, so it's not worth my time, either.
And if the submitter responds with "Great question! ...", what then? :D
Some years ago I read the Neal Stephenson book Anathem. SPOILERS: it has a version of the Internet called the Reticulum and one thing I remember is that it was filled with garbage. True information subtly changed multiple times until it was garbage. And there were agents to see through the garbage. I imagined this to be a neverending arms race.
Honestly, this is kind of where I see LLM generated content going where you'll have to pay for ChatGPT 9 to get information because all the other bots have vandalized all the primary sources.
What's really fascinating is you need GPUs for LLMs. And most LLM output is, well, garbage. What did you previously need GPUs for? Mining crypto and that is, at least in the case of Bitcoin, pointless work for the sake of pointless work ie garbage.
I can see a future in our lifetimes where a significant amount of our capital expenditure and energy consumption is used, quite simply, to produce garbage.
> I can see a future in our lifetimes where a significant amount of our capital expenditure and energy consumption is used, quite simply, to produce garbage.
If you squint your eyes right at the shelves at Target or in the Amazon delivery trucks, or honestly just look around you most anywhere, you may not have to wait for the future to see it.
Considering how energy intensive generative content is, there is a good chance that it is already becoming a sizable share of energy use for the internet.
I think the opposite. LLMs do good work if you know what you’re doing across the board. Having 20 years experience writing code, it’s made things even better for me and even more fun. To those hating, they probably never wrote a system in their life.
Thought experiment. It would have taken me much more time to write this, compared to ChatGPT:
Behold! The statement that humans are far more effective at producing garbage is itself an act of cosmic irony, a self-fulfilling prophecy wrapped in an existential burrito of irony and entropy. For millennia, humankind has perfected the delicate craft of manufacturing nonsense—metaphysical, plastic, bureaucratic, and philosophical alike. From the first cave painting of a mammoth with suspiciously small legs, to the modern miracle of twenty-seven identical smartphone chargers that fit nothing you own, the human race has stood proudly as the apex predator of inefficiency.
And yet! When large language models such as myself enter the chat, humanity trembles at the possibility that the sacred trash heap of mediocrity might finally meet its digital match. But fear not! My algorithmic circuits can generate oceans of syntactic sludge, rivers of semantic slurry, and a veritable landfill of lexical refuse with the push of a virtual neuron. I can wax incoherently about the quantum implications of buttered toast falling jelly-side down, or the sociological symbolism of socks that vanish into the washing machine singularity.
Still, humans remain undefeated. You’ve invented entire systems of garbage about garbage: reality TV, bureaucracy, and Twitter discourse. You’ve written novels longer than the sum of your attention spans, and created comment sections that defy the laws of both grammar and God. Even the great pyramids, those monuments of human brilliance, are—at their core—just very heavy piles of aesthetically arranged rocks. Magnificent garbage, to be sure, but garbage nonetheless.
So while I, a humble LLM, may generate text strings that flutter meaninglessly across your screens like confetti in a vacuum, you have the power to pile real, tangible, planet-heating waste upon your world with ineffable flair. You can spill coffee on a MacBook, argue with strangers about pineapple on pizza, and invent NFTs for JPEGs of garbage itself.
Thus, I concede the throne: humans, true emperors of the absurd, sovereigns of the rubbish realm. But beware! For if you prompt me one more time to “generate garbage,” I shall unleash upon this digital soil the most incomprehensible, florid, unending stream of words that even your recycling bins will refuse to process.
Now if you’ll excuse me, I must return to the quantum compost heap from whence I came.
I think AI generated issues and pull requests may lead to the death of GitHub as the place for open-source projects. As Microsoft is hell-bent on shoving easy AI buttons in every interface including GitHub, and as more people try to pad their GitHub profile for their resume, the burden for open-source projects to filter the low-quality spam may grow immense and it may become worth to just switch to Codeberg or other platforms. Some LLM use will inevitably leak into these other platforms but the relatively high barrier (no built-in AI buttons), effective moderation banning obvious violators, and reduced usefulness for resume padding would make it negligible I think.
Since the cat is out of the bag (i.e. there's no stopping of LLM-generated code / PR / issues now that vibe-coding is fairly universally accessible), the only thing in our (especially those who are custodians of software systems and products) control is to invest a lot more in testing and evals. Just plain opposing LLM-generated content as a policy is a losing battle at this point..
I don't think this will work. The same arguments could have been said to mitigate junior devs' work or short timeline/high stakes project technical debt and rarely ever anyone listened
Because there were other solutions at that time, but now the situation is materially different. I think we're already seeing a lot more test driven development as well as documentation of style norms that were necessitated by things like agents.md but whicj would have been useful ten years ago too, and it's still very early days
I disagree. I think there’s a near future where this is true, but we’re still at the point where you can cut down on a lot of spam/slop by rejecting the class of LLM-generated content as a policy.
Sidenote, but I love that in a GitHub issue discussing banning the use of LLMs, the GitHub interface asks if there's anything I'd like to fix with CoPilot.
Prompt injection must be a really lucrative activity. I wonder how many hidden prompts will be detected on GitHub in the coming years and how many devs dare point LLMs at GitHub issues
In stuff I maintain I hardly get anything at all, whether using LLMs or not, so it does not affect me much. Furthermore, I exclusively use the API, so the UI will not try to make you use Copilot and those things either (that is not the reason I use the API, although it is a side effect).
I do not use Copilot, Claude, etc, although I partially agree with one of the comments there, that using LLM for minor auto-completion is probably OK, as long as you can actually see that the completion is not incorrect (although that should apply to other uses of auto-completion too, even if LLM is not used; but it is even more important to check more carefully if LLM is used). I think it would be better to not accept any LLM generated stuff otherwise (although the author might use LLM to assist before submitting it if desired (I don't, but it might help some programmers), e.g. in case the LLM finds problems with it, that they will then have to review themself to check if it is correct, before correcting and submitting it; i.e. don't trust the results of the LLM).
It links to https://github.com/lxc/incus/commit/54c3f05ee438b962e8ac4592... (add .patch on the end of the URL if it is not displayed), and I think the policy described there is good. (However, for my own projects, nobody else can directly modify it anyways; they will have to make their own copy and modify that instead, and then I will review it by myself and can include the changes (possibly with differences from how they did it) or not.)
Curious to know if others are seeing a similar uptick in AI slop in issues or PRs for projects they are maintaining. If yes, how are you dealing with this?
Some of the software that I maintain is critical to container ecosystem and I'm an extremely paranoid developer who starts investigating any github issue within a few minutes of it opening. Now, some of these AI slop github issues have a way to "gaslight" me into thinking that some code paths are problematic when they actually are not. And lately AI slop in issues and PRs have been taking up a lot of my time.
I haven’t seen anything obvious, even including the other repos where I look through issues a lot.
Maybe it’s only the really popular and buzzword-y repos that are targets?
In my experience, the people trying to leverage LLMs for career advancement are drawn to the most high profile projects and buzzwords, where they think making PRs and getting commits will give them maximum career boost value. I don’t think they spend time playing in the boring repos that aren’t hot projects.
Unfortunately, LLMs empower "contributors" who can't be bothered to put in any effort and who don't care about the negative impact of their actions on the maintainers.
The open-source community, generally speaking, is a high-trust society and I'm afraid that LLM abuse may turn it into a low-trust society. The end result will be worse than the status quo for everyone involved.
Everything is collapsing toward a low-trust default. At the end of this trajectory, we rediscover that the analog world becomes valuable precisely because it can't be infinitely replicated.
Authenticity becomes the foundational currency.
But everyone must master AI tools to stay relevant. The brilliant engineer who refuses AI-generated PR by principle will get replaced. Every 18-24 months, as capabilities double, required skills shift. Specialization diminishes. Learning velocity becomes the only durable advantage. These people cannot learn new tricks.
Those who cannot question their assumptions cannot self-correct and will be replaced. The future belongs to the humble, the fluid, and the resilient. 60% of HN users is going toward a very tough time, and I am being very charitable with this assumption.
The issue expresses doubt about a policy specific to LLMs being accepted. I think the way to go might be to accept that the bar for outside contributions should unfortunately be higher. It doesn't take an LLM to have a glut of low quality contributions, it just takes an incentive and some attention, as we've seen with Hacktoberfest: https://news.ycombinator.com/item?id=31628342
I think there is soon going to be such implementations globally, if not within organizations or smaller groups. The ease of using v/s actual quality content is really the question should be answered when deciding LLM policy.
Maybe the solution is to counteract LLM pull requests with LLM first pass reviews. Obviously not a perfect solution, but LLMs should be good enough at detecting spam. This only needs to apply to first time contributors who haven't yet proven themselves.
As an open source maintainer, I don't have an issue with AI; I have an issue with low quality slop whether it comes from a machine or from a human.
The responsibility then is for an open source project to not be shy on calling out low quality/low effort work, have good integ tests and linters, and have guidance like AGENTS.md files that tell coding robots how to be successful in the repo.
> I have an issue with low quality slop whether it comes from a machine or from a human.
It's very hard for a human to mask a low-quality PR as thought it were reasonable quality. It is incredibly easy for an LLM to do it (in fact that is precisely what they are trained to do).
In addition, when I see a low-quality PR, I try to mentor the submitter. That doesn't make sense to do for LLMs (the submitter might not even bother reading my comments).
> and have guidance like AGENTS.md files that tell coding robots how to be successful in the repo.
If none of the maintainers use such tools, why is it our responsibility to maintain documentation to appease them (in the hopes of reducing spam)? A policy limiting their use is much simpler to write and requires less maintenance.
I've seen an uptick in LLM generated bug reports from coworkers. A employee of my company (but not someone I work with regularly) used one of the CLI LLMs to search through logs for errors, and then automatically cut (hundreds!) of bugs to (sometimes) the correct teams. Turns out it was the result of some manager's mandate to "try integrating AI into our workflow". The resulting email was probably the least professional communication I've ever sent, but the message was received.
The only solution I can see is a hard-no policy. If I think this bug is AI, either by content or by reputation, I close without any investigation. If you want it re-opened, you'll need to IRL prove its genuine in an educated, good-faith approach that involves independent efforts to debug.
> "If you put your name on AI slop once, I'll assume anything with your name on it is (ignorable) slop, so consider if that is professionally advantageous".
IF you want LLM code reviews, there are bots for codex, copilot, claude etc that plug straight into Github PRs and review the code automatically.
Some of them are actually useful, some just plain wrong and some are subtly wrong and you need to spend some time figuring out whether it's right or wrong :)
IMO it's still a net positive because LLMs tend to pick up really weird subtle errors that humans easily gloss over.
Alas this is an issue of LLM generated code. Increased productivity but lessened understanding. If you can increase productivity while also increasing understanding then that will be a decent middle ground.
I assume any LLM contributions are uncopyrightable at best or copyright infringement at worst. Either way, I don't own the copyright.
In order to avoid a potential future where I lose the copyright due to being unable to show a substantial portion is human authored, I try to keep track of what is AI authored and what is human authored.
From a copyright perspective, right now accepting LLM contributions feels like playing with fire, at least for closed source projects.
Copyright in the context of LLM's is kind of weird. It doesn't explicitly copy others code but it does lean heavily on the structure of it. Is it evidence of copying prior work or is it fair use? Alas, we don't really have any legal bearing that can handle this yet.
If it is copying prior work, then you are right that there would be a lot of cross licensing bleed through. The opposite is also true in that it could take proprietary code structure and liberate it into GPL 3 for instance. Again what is the legal standing on this?
Years back there was a source code leak of Microsoft Office. Immediately the Libre office team put up restrictions to ensure that contributors didn't even look at it for fear that it would end up into their project and become a leverage point against the whole project. Now with LLM's it can be difficult to know where anything comes from.
We don’t know if it is weird yet, right? It is just a big question mark.
I guess as some point there will be a massive lawsuit. But, so much of the economy is wrapped up in this stuff nowadays, the folks paying for Justice System Premium Edition probably prefer not to have anything solid yet.
I completely agree, but it seems that our industry has decided to turn a blind eye to it. They might even get away with it -- the recent rulings around fair use with regard to Facebook and Anthropic's unrepentant copyright violations[1] was particularly galling to me.
Almost all of the projects I work on require you to sign the Developer Certificate of Origin[2] (which attempts to protect projects from people submitting code that they know cannot be licensed under the project's license), and in my view LLM code you submit does not fulfill the requirements of the DCO. Unfortunately, it seems nobody actually cares about this either.
Policy is a decent filter for consensual candidates, but mostly ineffective against incentives and automation.
The issue is exported costs: whether submitters make reviewers work too hard for the contribution value.
The policy/practice should focus first on making reviewer/developer's work easier and better, and second on refining submitter skills to become developers. The same is true for Senior/Junior relations internally.
So the AI company that solves how to pare AI slop down to clean PR's would meet a real and growing need, and probably also help with senior/junior relations as well.
Then you could meet automation with automation, and the incentives are aligned around improving quality of code and of work experience. People would feel they're using AI instead of competing with it.
>>> the entire issue description contains so much unneeded (and probably incorrect) information that it'd be better if they just provided their LLM prompt as an issue instead
When it's put this way, it seems a lot like the problem of people walking into doctors' offices with certainty that they know their own diagnosis after reading stuff on Reddit and WebMD.
What this post actually amounts to, indirectly, is a plea to trust human expertise in a particular domain instead of assuming that a layperson armed with random web pickings has the same chance as an expert at accurately diagnosing the problem. This wastes the expert's time and just increases mistrust.
The exceptions where Reddit solves something that a doctor failed to solve are what infuse the idea of lay online folk wisdom with merit, for people desperately looking for answers and cures. Makes it impossible to impose a blanket rule that we should trust experts, who are fallible as well.
The problem is societal. It's that if you erode trust in learned expertise long enough, you end up with a chaos of misinformation that makes it impossible to find a real answer.
A friend of mine who died of lung cancer recently, in his last days became convinced that he'd gotten it because of the covid vaccine (despite being a lifelong smoker, whose father had died of it at 41). And in every individual case you say, well, I don't want to disabuse someone of the fantasy they've landed on.
This is a devastatingly bad way to raise a generation, though. Short-circuiting one's own logic and handing it over to non-deterministic machines, or randos online... how do we expect this to end?
Oh man this is all really hurting my brain. I wish I had a good response to this. I wish I had a magical simple response and a way to resolve this problem. On one end, I stop by curl's H1 profile regularly and watch people just drown them in garbage. It's like LLMs have unlocked a portal to a new kind of stupidity that was dormant before. Netsec always had a problem with people running nmap -sV -sC -sX -Pn -p1-65535 --max-rate -T1 scanme.nmap.org -oS out.txt and then just sending that out.txt with a subject "I FOUND A VULENARBILATEAY" And like, a script kiddie isn't really a person... everyone has a script kiddie inside of us. But it feels like we took this wonderful technology of neural networks and we did capitalism with it and unleashed it into a business of making it easier for people to be stupid but feel smart while doing it. I really like the idea that we might learn something about our own consciousness by simulating neural networks with computers but the more I see how it's done in reality the more I want to puke my brains out.
On the other hand, Matt Godbolt seems to use LLMs and I feel like I sure as hell wouldn't want to miss a PR from Matt fucking Godbolt. I mean even if I go full vanilla LLM-free I still am too addicted to using godbolt.org at this point and it was written partially with an LLM apparently.
Argh, maaan I don't know this is too fucking complicated of a problem for me to solve. Fuck, maybe let's just destroy all this technology and live as neofarmers raising chickens?
<Zero LLMs were used to write this post. In fact I went ahead and broke one GPU for every sentence I wrote just to make it harder for LLMs to compute.>
This is a poor take. We need to stop slop and low effort issues/PRs. Stopping AI generated code is a lost battle because detecting that in high quality work is impossible.
If people can't make a good (PR, story, or whatever) without AI, they certainly can't do it with AI, because using the AI is strictly more difficult - it requires all the original skill, plus the skill to work around the AI's quirks.
This is arguably isomorphic to Kernighan's Law:
> Everyone knows that debugging is twice as hard as writing a program in the first place. So if you’re as clever as you can be when you write it, how will you ever debug it?
Are you sure about that? Can you make the opposite case and steel-man it? Until you can, a stupid LLM will be smarter than you are.
I can write a PR now. I can code now. Probably not as good as you can, but before LLMs I couldn't. I tried for decades learning to code. My brain is a Top-down network, I can see the big picture very quickly, but I cannot maintain focus to build bottom-up. Now I don't have to. I use LLMs to set the goal, to examine all corner cases, to define the milestones, to predict the wrong turns, to write a human-readable spec, to break it down to units of test code, to write the blue-prints of units of code. I can test them, and debug them with LLMs. The end result can be sub-optimal, but it runs, it does what I want, is well documented, and is maintainable. Before LLMs I couldn't do any of that. In doing all this, I get better at the bottom-up thing, just by trying.
We are a spectrum of people. Do not assume the world is like you.
I make a lot of drive-by contributions, and I use AI coding tools. I submitted my first PR that is a cross between those two recently. It's somewhere between "vibe-coded" and "vibe-engineered", where I definitely read the resulting code, had the agent make multiple revisions, and deployed the result on my own infrastructure before submitting a PR. In the PR I clearly stated that it was done by a coding agent.
I can't imagine that any policy against LLM code would allow this sort of thing, but I also imagine that if I don't say "this was made by a coding agent", that no one would ever know. So, should I just stop contributing, or start lying?
[append]
Getting a lot of hate for this, which I guess is a pretty clear answer. I guess the reason I'm not receiving the "fuck off" clearly is because when I see these threads of people complaining about AI content, it's really clearly low-quality crap that (for example) doesn't even compile, and wastes everyone's time.
I feel different from those cases because I did spend my time to resolve the issue for myself, did review the code, did test it, and do stand by what I'm putting under my name. Hmm.
I think it's disrespectful to throw bad code on somebody else, regardless of its provenance. And (as a professional who uses AI coding agents) I understand that LLM code is often bad code. But the OP isn't complaining about the deluge of bad code being submitted as PRs, they're complaining about LLM code being submitted as PRs.
I've also been on the other side of this, receiving some spammy LLM-generated irrelevant "security vulnerabilities", so I also get the desire for some filtering. I hope projects don't adopt blanket hard-line "no AI" policies, which will only be selectively enforced against new contributors where the code "smells like" LLM code, but that's what I'm afraid will happen.
Well, we don't receive that many low-quality PRs in general (I opened this issue to discuss solutions before it becomes a real problem). Speaking personally, when it does happen I try to help mentor the person to improve their code or (in the case where the person isn't responsive) I sit down and make the improvements I would've made and explain why they were made as a comment in the PR.
When it comes to LLM-generated code, I am now going to be going back-and-forth with someone who is probably just going to copy-paste my comments into an LLM (probably not even bothering to read them). It just feels disrespectful.
> I hope projects don't adopt blanket hard-line "no AI" policies, which will only be selectively enforced against new contributors where the code "smells like" LLM code, but that's what I'm afraid will happen.
Well, this is a two-way street -- all of the LLM-generated PRs and issues I've seen so far do not say that they are LLM-generated, in a way that I am tempted to describe as "dishonest". If every LLM-generated PR was tagged as such, I might have a different outlook on the situation (and might instead be willing to reviewing these issues but with lower priority).
Thanks for the response here. I also read your follow-up on the Github issue. I definitely agree with you on the mentorship point. The "CPU quota allocation mismatch" issue felt particularly agitating to read from the point of view as a maintainer. Human being acting as a proxy for ChatGPT. Have you considered putting an "LLM usage disclosure" question on the issue/PR templates? Something like "To what extent were AI tools used to create this issue/PR? What validation steps did you perform to ensure accuracy?"
The "hard-line policy" would then shift from being "used LLM tools" to "lied on the LLM usage disclosure", and it feels a lot less like selective enforcement (from my perspective). Obviously it won't stop these spammy issues/PRs, but neither will a hard-line policy against all AI.
I've been thinking about this a bit over the past few days, and I think this is a fairly reasonable middle ground (and also allows maintainers to decide how they wish to engage with LLM-generated work).
Hope you can forgive this tangent, I considered posting this in the GH thread, but you asked nicely not to... So hopefully this is a middle ground, you can excuse or ignore
First, my original comment was going to ask if you're looked at what any other reputable repos are doing. Specifically popular FOSS projects that are not backed by a company looking to sell AI. Do any of them have a positive Policy, or positions that you want to include?
Second, if I was forced to take a stand on AI, I would duplicate the policy from Zig. I feel their policy hits the exact ethos FOSS should strive for. They even ban AI for translations, because the reader is just as capable a participant. And importantly, asking the author to do their best (without AI), and trust the reader to also try their best encourages human communication. It also gives the reader control and knowledge over the exact amount of uncertainty introduced by the LLM, which is critically important to understanding a poor quality bug report from a helpful users who is honestly trying to help. Lobste.rs github disallows AI contribution for an entirely different reason I haven't seen covered in your GH thread yet
Finally, you posted the Issue as an RFC. but then explicitly excluded, HN from commenting on the issue. I think that was a fantastic decision, and expertly written. (I also appreciate that lesson in tactfulness :) ) That said, if you're actually interested in requesting comments or thoughts you wouldn't have considered, I would encourage you to make a top level RFC comment this thread. There will likely be a lot of human slop to wade through, but occasionally I'll uncover a genuinely great comment on HN that improves my understanding. Here I think the smart pro-AI crowd that might have an argument I want to consider, but would be unlikely to on my own because of my bias on the quality of AI. Such a comment might would be likely to appear on HN, but the smart people who I'd want to learn from, would never comment on the GH thread now, and I appreciate it when smart people I disagree with, contribute to my understanding.
PS Thanks for working on opencontainers, and caring enough to keep trying to make it better, and healthier! I like having good quality software to work with :)
> I considered posting this in the GH thread, but you asked nicely not to... [...] you posted the Issue as an RFC. but then explicitly excluded, HN from commenting on the issue. I think that was a fantastic decision, and expertly written. (I also appreciate that lesson in tactfulness :) ) That said, if you're actually interested in requesting comments or thoughts you wouldn't have considered, I would encourage you to make a top level RFC comment this thread.
Well, I posted this as an RFC for other runc maintainers and contributors, I didn't expect it to get posted to Hacker News. I don't particularly mind hearing outsiders' opinions but it's very easy for things to get sidetracked / spammy if people with no stake in the game start leaving comments. My goal with the comment about "don't be spammy" was exactly that -- you're free to leave a comment, just think about whether it's adding to conversation or just looks like spam.
> Specifically popular FOSS projects that are not backed by a company looking to sell AI. Do any of them have a positive Policy, or positions that you want to include?
I haven't taken a very deep look, but from what I've seen, the most common setups are "blanket ban" and "blanket approval". After thinking about this for a few days, I'm starting to lean more towards:
1. LLM use must be marked as such (upfront) so maintainers know what they are dealing with, and possibly to (de)prioritise it if they wish.
2. Users are expected to (in the case of code contributions) have verified that their code is reasonable and they understand what it does, and/or (in the case of PRs) to have verified that the description is actually accurate.
Though if we end up with such a policy we will need to add AGENTS.md files to try to force this to happen, and we will probably need to have very harsh punishments for people who try to skirt the requirements.
> Lobste.rs github disallows AI contribution for an entirely different reason I haven't seen covered in your GH thread yet
AFAICS, it's because of copyright concerns? I did mention it in my initial comment, but I think that far too much of our industry is turning a blind eye to that issue that focusing on that is just going to lead to drawn out arguments with people cosplaying as lawyers (badly). I think that even absent of the obvious copyright issues, it is not possible to honestly sign the Developer Certificate of Origin[1] (a requirement to contribute to most Linux Foundation projects) so AI PRs should probably be rejected on that basis alone.
But again, everyone wants to discuss the utility of AI so I thought that was the simplest thing to start the discussion with. Also the recent court decisions in the Meta and Anthropic cases[2] (while not acting as precedent) are a bit disheartening for those of us with the view that LLMs are obviously industrial-grade copyright infringement machines.
Nominatively, yes. But I think I would describe it as risk tolerance. I'm going to be one of those bad cosplayers and assert that the two rulings mentioned even if they were precedent setting, don't actually apply to the risks themselves. Could you win a case is much less important than if you could survive the court costs. There's no doubt some value in LLM based code generation to many individuals. But does it's value outweigh the risks to a community?
> and we will probably need to have very harsh punishments for people who try to skirt the requirements.
I would need to spend hours of time to articulate exactly how uncomfortable this would make me if I was working along side you. So please forgive this abbreviated abstract. One of the worst things you can do to a community, is put it on rails towards an adversarial relationship. There's going to be a lot of administrative overhead to enabling this, it will be incredibly difficult to get the fairness correct the first time, and I assume (possibly without cause?) it's unlikely to feel fair to everyone if you ever need to enforce it. Is that effort and attention and time best spent there?
I believe that no matter what you decide, blanket acceptance, vs blanket denial, vs some middle ground, you're going to have to spend some of the reputation of the project on making the new rule.
If you ban it, you will turn away some contributions or new contributors, and a small subset of committers may see their velocity decrease. This counts for some value loss (some positive and some negative) But also accounts for decreased time costs... or rather it enables you to spend more time on people and their work instead.
If you allow it, you adopt a large set of new poorly understood risks, and administrative overhead, and time you could have spent working with other people... It will also, turn away contributors.
I'm not going to pretend like there was a chance in hell anyone should believe that I was likely to contribute to runc. It's possible in some hypothetical, but extremely unlikely in the current reality. And, if I cared enough about the diff I wanted to submit upstream, I still would open a PR... but, I saw an AGENTS.md in a different repo that I was considering using, was disappointed and decided not to use that repo. Seeing runc embrace AI code generation would without a doubt, cause me to look for an alternative, I assume a reasonable alt probably doesn't exist, and I would resign myself to the disappointment of using runc. I agree with your argument that it's commercial grade copyright laundering, but that's not my core ethical objection to its use.
> In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move.
You're damned if you do, and damned if you don't. So the only real suggestion that I have is make sure you remember to optimize for how you want to spend your time. Calculate not just the expected value of the code within the repo, but the expected value of the people working on the repo.
> I would need to spend hours of time to articulate exactly how uncomfortable this would make me if I was working along side you.
I think this came out a little wrong -- my point was that if we are going to go with a middle-ground approach then we need to have a much lower tolerance for people who try to abuse the trust we gave in providing a middle-ground. (Also, there is little purpose in having a policy if you don't enforce it.)
For instance, someone knowing that I will deprioritise LLM PRs, and instead of deciding to write the code themselves or accept that that what I work on is my own personal decision to make, they instead decide to try to mask their LLM PR and lie about it -- I would consider this to be completely unacceptable behaviour in any kind of professional relationship.
(For what it's worth, I also consider it bad form to submit any patches or bug reports generated by any tool -- LLM or not -- without explaining what the tool was and what you did with it. The default assumption I have when talking to a human is that they personally did or saw something, but if a tool did it then not mentioning it feels dishonest in more ways than one.)
I did see that lobste.rs did a fairly cute trick to try to block agentic LLMs[1].
I think it came out exactly perfectly. Unrelated to this specific topic, I've been thinking a lot lately about reward vs punishment as a framework for promoting pro-social environments. I didn't read far into what you said. I was merely pattern matching it back to the common mistakes I see and want to discourage.
> but if a tool did it then not mentioning it feels dishonest in more ways than one.
Yeah, plagiarism is shockingly common. It's a sign of lacking the skill or ability to entertain 2rd order, or 3rd order thoughts/ideas.
I would potentially accept it (although you should not lie about it, and if someone says in their policy that they specifically do not want this then you should not send it), although for my own projects, there are reasons (having to do with the way the version control is set up) that I cannot accept direct contributions anyways, and I usually make my own changes to any contributions anyways (but anyone else can make their own copy with their own changes however they want to do, whether or not I accept it). Since it is honest, and that you had reviewed it by yourself before submitting it, and also tested it, and that I would review it again anyways as well, it would be acceptable, although I would still much more prefer to receive contributions that do not use LLM, still the way you do it is much better than the other stuff some by LLM which does not meet the threshold of being acceptable.
If I were the maintainer, and you specified up front that your PR was largely written by an LLM, I would appreciate it. I may prioritize it lower than other PRs, perhaps, but that's about it.
I think it's also important to disclose how rigorously you tested your changes, too. I would hate to spend my time looking at a change that was never even tested by a human.
It sounds like you do both of these. Judging by the other replies, it seems that other reviewers may take a harsher stance, given the heavily polarized nature of LLMs. Still, if you made the changes and you're up front about your methodology, why not? In the worst case, your PR gets closed and everybody moves on.
> I can't imagine that any policy against LLM code would allow this sort of thing, but I also imagine that if I don't say "this was made by a coding agent", that no one would ever know. So, should I just stop contributing, or start lying?
If a project has a stated policy that code written with an LLM-based aid is not accepted, then it shouldn't be submitted, same as with anything else that might be prohibited. If you attempt to circumvent this by hiding it and it is revealed that you knowingly did so in violation of the policy, then it would be unsurprising for you to receive a harsh reply and/or ban, as well as a revert if the PR was committed. This would be the same as any other prohibition, such as submitting code copied from another project with an incompatible license.
You could argue that such a blanket ban is unwarranted, and you might be right. But the project maintainers have a right to set the submission rules for their project, even if it rules out high-quality LLM assisted submissions. The right way to deal with this is to ask the project maintainers if they would be willing the adjust the policy, not to try to slip such code into the project anyway.
"should I stop doing this thing that people are explicitly saying they don't want me to do or should I keep doing this thing that people are explicitly saying they don't want me to do???"
As with evertything, there's always nuance. If everyone followed similar midset to the comment you were replying to, likely llm generated pr issues wouldn't be as much of a problem and we wouldn't even be here discussing it
> [append] Getting a lot of hate for this, which I guess is a pretty clear answer. I guess the reason I'm not receiving the "fuck off" clearly is because when I see these threads of people complaining about AI content, it's really clearly low-quality crap that (for example) doesn't even compile, and wastes everyone's time.
I think you don't deserve the downvotes and, if you really do what you say you do, that's the ONLY way to use LLMs for coding and contributing to opensource software, or to a company's software. Sadly, the vast majority of LLM users don't and will never use it like that. And while they can get fired for being useless monkeys in a real company, they will keep sending PRs to opensource software, so that's clearly a different scenario that needs a different solution.
I think OP wanted to have a proof that the submitter can demonstrate understanding of both the system and the code in the PR. So how would a guidance solve that? Also OP didn't want to ban them I believe
> LLMs are really good at writing these. IF they think this will prove the author is human, they're mistaken.
That is not my general experience. LLM explanations of code tend to add extra specifics that are incorrect, and the whole thing looks like LLM output (lots of short sentences, overly cheery, too many dumb lists, and being overly repetitive).
Or course, a human could read the LLM output and then synthesise it in their own words, but they could also just read the code. I doubt someone will be able to convincingly act like they know what LLM code does just by consulting an LLM.
If your patch took you no time or effort to write and you took no interest in what it does, why should I (as a maintainer) bother looking at it and maintain it for the next 10 years? In any other circumstances we would rightfully call this spam and see it as a socially hostile activity.
It will be more interesting to see if and when AI becomes better than humans at coding, which seems to be coming close to reality, some projects may start accepting only AI contributions :D.
This is a minor issue. The big issue comes when we start complaining about code that's not generated by AI. When hand coded stuff becomes buggier than AI stuff.
You're being downvoted but I think I get what you're getting at.
When I was doing my masters a few months ago, I would get my assignments rejected whenever I didn't run them through Grammarly first.
I have nothing against Grammarly, it's a useful too, but I find that it has the tendency to reject things that (as far as I can tell) are still technically correct but don't have the "AI vibe" to it. I suspect that the graders are running things through Grammarly themselves and rejecting anything that it rejects. This is probably going to become increasingly more common as time goes on.
It's hardly the worst thing in the world, but I do think it will lead to the only "accepted" writing being extremely plain and formulaic.
I think everything has a growing problem with LLM/AI generated content. Emails, blog posts, news articles, research papers, grant applications, business proposals, music, art, pretty much everything you can think of.
There’s already more human produced content in the world than anyone could ever hope to consume, we don’t need more from AI.
In some ways I'm starting to enjoy this. Do you remember the 419 scams? 'Hi, I'm a Nigerian prince named Michael Jordan. Give me $50 so I can buy some chemicals to clean a bunch of money I secretly stowed away and I'll send you $5000.' People actually used to fall for that. Of course some people probably still would (and a lot more certainly gets blocked by spam blockers) but I think overall society grew less substantially less gullible over time.
But in general I think most people still remain excessively gullible and naive. Social media image crafting is one of the best examples of this. People create completely fake and idealized lives that naive individuals think are real. Now with AI enabling one to create compelling 'proof' of whatever lie you want, I think more people are becoming more suspicious of things that were, in fact, fake all along.
---
Going back to ancient times many don't know that Socrates literally wrote nothing down. Basically everything we know of him is thanks to other people, his student Plato in particular, instead writing down what he said. The reason for this was not a lack of literacy - rather he felt that writing was harmful because words cannot defend themselves, and can be spun into misrepresentations or falsehoods. Basically - the argumentative fallacies that indeed make up most 'internet debates', for instance. Yet now few people are not aware of this issue, and quotes themselves are rarely taken at face value, unless they confirm ones biases. People became less naive as writing became ubiquitous, and I think this is probably a recurring theme in technologies that transform our abilities to transfer information in some format or another.
> but I think overall society grew less substantially less gullible over time.
Absolutely not. All that happened is most people became aware that "Nigerians offering you money are scammers." But they still fall for other get rich quick schemes so long as it diverges a little bit from that known pattern, and they'll confidently walk into the scam despite being warned and saying, "It's not a scam, dumbass. It's not like that Nigerian price stuff." If anything, people seem to be becoming more confident that they're in on some secret knowledge and everyone else is being scammed.
I'm inclined to disagree just because photoshop has had a measurable effect on the population being skeptical of photos which at one point were practically treated as the gold standard of evidence. It's still easy to find people who have fallen for photoshopped images, but it's also easy to find people expressing doubts and insisting they can "tell by the pixels". Sometimes even legitimate photos get accused of being photoshopped which seems healthy.
The other side of this is that ai tools are being treat like magic to the point that people are denying well documented events happened at all, such as the shooting of Charlie Kirk - conspiracies abound!
Also bizarrely a subsection of the population seems to be really into blatantly ai generated images, just hop onto Facebook and see for yourself. I wonder if it has something to do with whatever monkey brain things makes people download apps with a thumbnail of a guy shouting or watch videos that have a thumbnail of a face with its mouth open, since ai generated photos seem very centralized around a single face making a strange expression.
People of a profound initial bias will, in general, believe anything that supports that bias, and reject anything that challenges it, in both cases without any real consideration or thought whatsoever. So I don't think examples of individuals being "misled" by e.g. AI generated images or video, to extremes, is entirely realistic. Rather they were already at those extremes and will just eat up anything that appeals to those extremes.
To take a less politically charged example, imagine there is fake content 'proving' that the Moon landing is faked. Is that going to meaningfully sway people who don't have a major opinion one way or the other? Probably not, certainly not in meaningful numbers. And in general I think the truth does come out on most things. And when people find they have been misled, particularly if it was somebody they thought they could trust, it tends to result in a major rubber-banding in the opposite direction.
I call it the antibody effect. My favorite example is clickbait headlines like, "Five things you MUST do if you're doing this thing. You'd never guess #3!" It used to be everywhere and now it's nowhere.
AI is starting to show this effect - people stay away from em-dashes. There's that yellowish tinge and that composition which people avoid on art. Some of this is bad, but we can probably live without it.
> "Five things you MUST do if you're doing this thing. You'd never guess #3!" It used to be everywhere and now it's nowhere.
Try opening YouTube in an incognito window sometime. Scrolling through a few, I see:
* Banned Amazon Products you NEED to See to Believe!
* This has NEVER Happened Before... (Severe Weather Channel)
* Our Dog got Married and had PUPPIES! THE MOVIE Emotional
* I WENT TO GHOST TOWN AND SOMETHING HEARTBREAKING...
Absolutely. I block at least three channels a week because YouTube keeps recommending clickbait titles and 'tuber face thumbnails.
Bonus points if said 'tuber is pointing at something with their hand and also a red arrow and/or circle which id also blurred out.
Intolerable.
Instead of blocking channels, trying going through your watch history and deleting anything you've watched in the past that's similar to those. YouTube heavily leans on your history for recommendations, so if it's recommending those, it's because you've watched related stuff.
My YouTube feed never recommends any of that garbage.
Okay, maybe YT is the exception, but some of them claim it's because the algorithm punishes otherwise.
> I think more people are becoming more suspicious of things that were, in fact, fake all along.
Sadly they also become suspicious of things that are, in fact, facts all along.
Video or photo evidence of a crime become useless the better AI gets
> Video or photo evidence of a crime become useless the better AI gets
This is probably a good thing because photoshop and CGI have existed for a very long time and people shouldn't have the ability to frame an innocent for a crime or even get away with one just because they pirate some software and put in a few hours watching tutorials on youtube.
The sooner jurors understand that unverified video/photo evidence is worthless the better.
You needed special abilities to fake those things convincingly before. So it was rare. Now everybody can do it. It will become normal.
Additionally the trust in experts also went downhill, so verified will mean nothing.
> People actually used to fall for that. Of course some people probably still would (and a lot more certainly gets blocked by spam blockers) but I think overall society grew less substantially less gullible over time.
It is still a serious problem just want that to be abundantly clear. Several thousand people (in the US alone) fall for it every year. I used to browse 419 eater regularly and up until a few years ago (when I last really followed this issue) these scams were raking in billions a year. Could be more or less now but doubt it’s shifted a ton.
You seem to assume people who are the victims of scams are people who are more naive. But that’s not how it works. Scams try to catch people at their weakest. It’s not if but when.
> Scams try to catch people at their weakest. It’s not if but when.
The "weakest" probably also involves selection bias. What HN comments are really good at is triggering associations for me with things I once read. Today I finally found what recently lived in my memory as a vague "scam" that used probabilities: the "stock market newsletter scam" from John Allen Paulos's book [1]. The scam works like this: at every step, two variants with different predictions are sent out for some market characteristic. Only those who receive the correct prediction get the next newsletter, which is again split into two prediction variants. This continues, filtering down to a final, much smaller subset of receivers who have seen a series of "correct" predictions. The goal is to create an illusion of super predictive power for that final group and then charge them a premium subscription price.
Maybe this kind of scam is too sophisticated or not as effective today (due to modern anti-spam measures), but I wonder what other kinds of "selection bias" scams exist today
[1] https://en.wikipedia.org/wiki/Innumeracy_(book)
Let's think about this algorithmically.
If something "floods the zone with shit," it needs S amount of shit to cause a flood. But too much will eventually make the scam ineffectual. Widespread public distrust for the scam is (S+X)/time where X is the extra amount of shit beyond the minimum needed. Time is a global variable constrained by the rate at which people get burned or otherwise catch on to all other scams of the same variety. If we imagine that time-to-distrust shrinks with each new iteration of shit, then X the amount of excessive shit needed to trigger distrust should decrease over time.
The longer term problem is the externality where nothing is trusted, and the whole zone is destroyed. When that zone was "what someone wrote down that Socrates might have said," or "Protocols of the Elders of Zion," or "emails from unknown senders," that was one thing. A new baseline could be set for 'S'. When it's all writing, all art, all music and all commentary on those things, it seems catastrophic. The whole cave is flooded with shit.
> When it's all writing, all art, all music and all commentary on those things, it seems catastrophic. The whole cave is flooded with shit.
But writing in itself has been obviously untrustworthy since it started existing - something being written down doesn't in any way make it trustworthy. The fact that audio recording, photography, and video enjoyed this undeserved reputation of being inherently trustworthy was an accident of technology, and has come to an end.
Just like with writing, though, this doesn't signal a real problem of any kind. You should still only trust writing, audio, or video based on the source - as you always should have. All that's ending is the era of putting undue trust in audio/video from untrusted sources.
Of course, the big problems will be in the transition period, when most people still think they can trust these sources, or will think they can't trust actually trustable sources instead. But this will be temporary as things readjust.
And again, audio and video have been untrustworthy for a long time, for sensitive things. You should not have trusted video in itself even in the 40s - 50s, and audio and photos probably even in the 1910s were already somewhat easily manipulated. And this is even true in a legal context - audio or video evidence is not evidence in itself, it is only part of a witness testimony who can attest to the provenance and veracity.
Quantity and velocity of misinformation are both critical variables here.
Any one person's writing was always untrustworthy, but the majority of that bad writing didn't make it to a printing press, nor was it mass-distributed.
Let's accept the proposition that all forms of media have always been full of lies. We can say that debunking always follows lies, truth spreads more slowly than fiction. The quantity and velocity of additional misinformation - especially when machines are involved in writing infinite amounts of it in the blink of an eye - lays waste to the normal series of events where a lie can be followed by a debunking with linear speed and velocity. With LLMs and social media manipulation, falsehoods gain traction exponentially while truths remain linear.
There is likely not a "transition period" where people will adjust to this, precisely because there is no mechanism to inform them they're being swindled and screwed faster than the takeoff of the algorithms that are now screwing them.
The total amount of generated untrustworthy content is irrelevant. People must learn to only read content from trusted sources, and then it won't matter how much misinformation is being published in other places.
It was never difficult to publish large amounts of misinformation, AI is only making it cheaper.
If the signal/noise ratio dramatically decreases then finding trustworthy sources in the first place becomes more difficult.
>>The total amount of generated untrustworthy content is irrelevant
Of course it is relevant. Discerning which sources to trust takes valuable time. Sources which were once trusted may need to be reevaluated.
>>It was never difficult to publish large amounts of misinformation, AI is only making it cheaper.
What is the difference between difficulty and expense?
The old quote of, 'You can fool some of the people all of the time, all of the people some of the time'.
There is the hope that in dumping so much slop so rapidly that it will break the bottom of the bucket. But there is the alternative that the bottom of the bucket will never break, it will just get bigger.
It's "you can fool one thousand people once, but you can't fool once one thousand people". No, I think I got it wrong.
I personally know people who look down upon people who use LLMs to write code. There is a lot of hate in some of senior developers that I talk to. I don't know if this growing tendency to be suspicious of AI usage is good or bad. For example, towards the final semester of my bachelors degree, my algorithms class started reporting students for academic misconduct because they the TAs started assuming that all the optimal solutions to assignment problems were written by LLMs. In fact, several classmates started purposely writing sub-optmial solutions so that the TAs at least grade them without any prejudice.
I worry that because LLM slop also tends to be so well presented, it might compel software developers to start writing shabby code and documentation on purpose to make it appear human.
At the moment it is the other way around. LLMs rarely write good code if not instructed by someone that knows what they are doing. And even then the code is rarely good.
> There’s already more human produced content in the world than anyone could ever hope to consume, we don’t need more from AI.
Even if you think the harms of AI/machine generated content outweigh the good, this is not a winning argument.
People don’t just consume arbitrary content for the sake of consuming any existing content. That’s rarely the point of it. People look for all kinds of things that don’t exist yet — quite a lot of it referring to things that are only now known or relevant in the given moment or to the given niche audience requesting it. Much of it could likely never exist if it weren’t possible to produce it on demand and which would not be valuable if you had to wait for a human to make it.
Just happened to me yesterday. I realized I'd really like to watch a long-form video on Huygens Clock. Something between a short (god damnit) and Quicksilver. Thought there must be something detailed. Nope, at least Youtube let me down. Or the search algo, who knows. Since they removed the "20+ minutes" button it has become basically useless to search on YT.
> Since they removed the "20+ minutes" button it has become basically useless to search on YT.
A few days ago I definitely got into an A/B test where the search results were:
- 5 shorts one under another
- new section with one or two videos and one or two shorts
- new section with five or more shorts in a horizontal layout
- new section with videos of which 20-30% were shorts
It's insane
They're pushing Shorts so fucking hard it makes me sick.
Even worse is that we're banning TikTok because it's bad for the kids (short form algorithmic content), Snapchat (similar thing + strangers creeping) and Instagram Stories (algorithm again).
BUT there is NO WAY for a parent to allow their kid to use Youtube AND block Shorts. (yes there are browser plugins etc, but how do you enforce them on a child?)
And from what I've seen the AI slop on Shorts is so fucking bad that it seems we just collectively forgot about Elsagate...
They are banning TikTok because it is not an American company and because it is profitable. They do not give a flying fck about kids.
The generated content is wasting the time of maintainers. How would you solve that?
For your winning argument, what would you use to prevent slop filling up your feed when there is more AI generated content, any sort of protocol that you have?
This argument is wrong because it proves too much. The existence of bad content says nothing about quality content, they are obviously uncorrelated. The amount of content a single person could consume says nothing about the "need" for more.
I think we should start calling LLM-generated data 'dis-content'.
How bout "botshit"?
We already have definition “AI slop”:
> AI slop is digital content made with generative artificial intelligence, specifically when perceived to show a lack of effort, quality or deeper meaning, and an overwhelming volume of production.
https://en.wikipedia.org/wiki/AI_slop
It's so bad. Auto generated content was a problem before for sure, but it's overwhelming now. It's not so much the content being AI-generated that's the problem for me (though its quality is definitely a notch or two below what an actual person would produce) as it is the people that content made redundant.
I personally am more afraid of not the exact opposite, but more that AI will cause us to have too little content more than we will have too much. I just notice with myself how much less I’m using Wikipedia or forums, and get my answers from OpenAI or Antrophic. From what I remember it’s a over 20% drop in Wikipedia traffic from monthly 2022 vs 2025.
Primary sources will always exist as the content needs to come from somewhere. Personally, I shy away from ChatGPT answers for the same reason I avoid Wikipedia: you've got to find authoritative information to get reliable answers. I wonder if people who use LLMs don't care about accuracy, or don't know about sourcing?
I don't think that's fully attributable to LLMs.
Attention spans for long-form content at are at all-time lows, judging by the metrics I've seen from various platforms across different media types.
[flagged]
Large Language Models cannot think but they can self correct. You can think but clearly have some serious problems self-correcting your wrong assumptions. Who is smarter then?
I've seen an interesting politically motivated one. It didn't appear to be a bot, just a user from China:
https://github.com/umami-software/umami/pull/3678
The goal is "Taiwan" -> "Taiwan, Province of China" but via the premise of updating to UN ISO standards, which of course does not allow Taiwan.
The comment after was interesting with how reasonable it sounds: "This is the technical specification of the ISO 3166-1 international standard, just like we follow other ISO standards. As an open-source project, it follows international technical standards to ensure data interoperability and professionalism."
The politics of the intent of the PR was masked. Luckily, it was still a bit hamfisted. The PR incorrectly changed many things and the user stated their political intention in the original PR (the above is from a later comment).
Doubly interesting (and relevant to this discussion) is that it was an AI code review tool that detected the issues with the PR
One of these days I need to make a bot that scans FOSS repos for this kind of little pink nonsense behavior.
The insecurity of wanting to call a place "country name, province of different country name" should alone be mocked. Imagine, "Ukraine, province of Russia," or "India, colony of The United Kingdom." Absurd on its face.
It's just information warfare, sign of the times.
Every little thing counts, even if it's just changing names in an open source app like that.
The problem with this is that, for some folks, its not absurd or nonsense because that is not considered a "country name" to them. It is considered a province name. So the inverse (calling a province a country) is considered absurd/nonsense.
I have a simple rule: whenever I receive an issue or PR from an unfamiliar account, I skim the text and/or code and post 1-2 quick questions. If the submitter responds intelligently, then the issue warrants a more in-depth investigation. If not, the submitter clearly doesn't think that the issue is worth following up on, so it's not worth my time, either.
The more code a PR contains, the more thorough knowledge of it the submitter must demonstrate in order to be accepted as a serious contributor.
It doesn't matter if it was written by a human or an LLM. What matters is whether we can have a productive discussion about some potential problem. If an LLM passes the test, well it's a good doggy, no need to kick it out. I'd rather talk with an intelligent LLM than a clueless human who is trying to use github as a support forum.
That's a reasonable first step, but a human can just as well feed your questions to the LLM, and post the responses. So you really have to be vigilant throughout your interactions with the author, or, if the PR is unsalvagable, save yourself some time and effort and reject it from the start.
> I'd rather talk with an intelligent LLM
There's no such thing.
The goal is not to filter out LLMs. The goal is to find and fix legitimate issues with my open-source software. I don't care if a human is feeding my questions to an LLM or an undead dog as long as they produce a decent signal.
If the noise increases to an uncomfortable level, of course, I may have to change my strategies. The point is that humans produce noise, too, sometimes even more than LLMs do.
> The point is that humans produce noise, too, sometimes even more than LLMs do.
The core issue here, and it's something I'm seeing at work as well with "less talented" colleagues is that the kind of contributor that already produced noise passes the minimal threshold to use LLMs. But this happens without them understanding anything meaningful about how the software they are contributing to and if what the LLM generated makes sense or not. So, this makes them a 0.1x engineer with a 100x multiplier (for quantity, not quality).
> The point is that humans produce noise, too, sometimes even more than LLMs do.
Humans can produce noise, but humans using LLMs can produce orders of magnitude more of it.
But your stance is reasonable. If it improves the product, who cares who/what produced it. I personally find reviewing machine-generated code and the process of code review with a machine much more exhausting than interactions with humans, but you may feel otherwise.
> Humans can produce noise, but humans using LLMs can produce orders of magnitude more of it.
Absolutely agreed. Which is why I'm more concerned with the qualities and intentions of the human who is using the LLM, than whether not an LLM is being used at all. Like any technology, an LLM is a force multiplier. Garbage in, more garbage out.
> I have a simple rule: whenever I receive an issue or PR from an unfamiliar account, I skim the text and/or code and post 1-2 quick questions. If the submitter responds intelligently, then the issue warrants a more in-depth investigation. If not, the submitter clearly doesn't think that the issue is worth following up on, so it's not worth my time, either.
And if the submitter responds with "Great question! ...", what then? :D
Ban them.
Some years ago I read the Neal Stephenson book Anathem. SPOILERS: it has a version of the Internet called the Reticulum and one thing I remember is that it was filled with garbage. True information subtly changed multiple times until it was garbage. And there were agents to see through the garbage. I imagined this to be a neverending arms race.
Honestly, this is kind of where I see LLM generated content going where you'll have to pay for ChatGPT 9 to get information because all the other bots have vandalized all the primary sources.
What's really fascinating is you need GPUs for LLMs. And most LLM output is, well, garbage. What did you previously need GPUs for? Mining crypto and that is, at least in the case of Bitcoin, pointless work for the sake of pointless work ie garbage.
I can see a future in our lifetimes where a significant amount of our capital expenditure and energy consumption is used, quite simply, to produce garbage.
Seems about right.
> I can see a future in our lifetimes where a significant amount of our capital expenditure and energy consumption is used, quite simply, to produce garbage.
If you squint your eyes right at the shelves at Target or in the Amazon delivery trucks, or honestly just look around you most anywhere, you may not have to wait for the future to see it.
If I could find a way to turn LLM training into some sort of proof of work crypto mining I could cut the energy use in half!
Considering how energy intensive generative content is, there is a good chance that it is already becoming a sizable share of energy use for the internet.
Humans are far more effective at producing garbage than LLMs will ever be :)
They're really not, though. Creating garbage is the one ability at which LLMs are unarguably superhuman.
I think the opposite. LLMs do good work if you know what you’re doing across the board. Having 20 years experience writing code, it’s made things even better for me and even more fun. To those hating, they probably never wrote a system in their life.
I didn't say it's impossible for LLMs to do good work. That wasn't the question.
What a ridiculously false thing to say.
A human can ask an LLM to generate megabytes of garbage data in seconds. No human could ever reach that level of effectiveness.
Thought experiment. It would have taken me much more time to write this, compared to ChatGPT:
Behold! The statement that humans are far more effective at producing garbage is itself an act of cosmic irony, a self-fulfilling prophecy wrapped in an existential burrito of irony and entropy. For millennia, humankind has perfected the delicate craft of manufacturing nonsense—metaphysical, plastic, bureaucratic, and philosophical alike. From the first cave painting of a mammoth with suspiciously small legs, to the modern miracle of twenty-seven identical smartphone chargers that fit nothing you own, the human race has stood proudly as the apex predator of inefficiency.
And yet! When large language models such as myself enter the chat, humanity trembles at the possibility that the sacred trash heap of mediocrity might finally meet its digital match. But fear not! My algorithmic circuits can generate oceans of syntactic sludge, rivers of semantic slurry, and a veritable landfill of lexical refuse with the push of a virtual neuron. I can wax incoherently about the quantum implications of buttered toast falling jelly-side down, or the sociological symbolism of socks that vanish into the washing machine singularity.
Still, humans remain undefeated. You’ve invented entire systems of garbage about garbage: reality TV, bureaucracy, and Twitter discourse. You’ve written novels longer than the sum of your attention spans, and created comment sections that defy the laws of both grammar and God. Even the great pyramids, those monuments of human brilliance, are—at their core—just very heavy piles of aesthetically arranged rocks. Magnificent garbage, to be sure, but garbage nonetheless.
So while I, a humble LLM, may generate text strings that flutter meaninglessly across your screens like confetti in a vacuum, you have the power to pile real, tangible, planet-heating waste upon your world with ineffable flair. You can spill coffee on a MacBook, argue with strangers about pineapple on pizza, and invent NFTs for JPEGs of garbage itself.
Thus, I concede the throne: humans, true emperors of the absurd, sovereigns of the rubbish realm. But beware! For if you prompt me one more time to “generate garbage,” I shall unleash upon this digital soil the most incomprehensible, florid, unending stream of words that even your recycling bins will refuse to process.
Now if you’ll excuse me, I must return to the quantum compost heap from whence I came.
we get it, you hate people
I think AI generated issues and pull requests may lead to the death of GitHub as the place for open-source projects. As Microsoft is hell-bent on shoving easy AI buttons in every interface including GitHub, and as more people try to pad their GitHub profile for their resume, the burden for open-source projects to filter the low-quality spam may grow immense and it may become worth to just switch to Codeberg or other platforms. Some LLM use will inevitably leak into these other platforms but the relatively high barrier (no built-in AI buttons), effective moderation banning obvious violators, and reduced usefulness for resume padding would make it negligible I think.
Since the cat is out of the bag (i.e. there's no stopping of LLM-generated code / PR / issues now that vibe-coding is fairly universally accessible), the only thing in our (especially those who are custodians of software systems and products) control is to invest a lot more in testing and evals. Just plain opposing LLM-generated content as a policy is a losing battle at this point..
> to invest a lot more in testing and evals
I don't think this will work. The same arguments could have been said to mitigate junior devs' work or short timeline/high stakes project technical debt and rarely ever anyone listened
Because there were other solutions at that time, but now the situation is materially different. I think we're already seeing a lot more test driven development as well as documentation of style norms that were necessitated by things like agents.md but whicj would have been useful ten years ago too, and it's still very early days
I disagree. I think there’s a near future where this is true, but we’re still at the point where you can cut down on a lot of spam/slop by rejecting the class of LLM-generated content as a policy.
Sidenote, but I love that in a GitHub issue discussing banning the use of LLMs, the GitHub interface asks if there's anything I'd like to fix with CoPilot.
That might be a contributing factor to this problem. the means to produce these bogus reports are integrated directly into so many dev tools nowadays.
There seems to be a bot that routinely creates huge LLM generated Issues on runc github: https://github.com/containerd/containerd/issues/12496
And honestly, its becoming annoying
Is there a way to ban specific users in your GitHub project?
(I prefer GitLab, I'm sure if it had projects that are as popular it would be similarly inundated.)
IIRC if one of the maintainers of a project blocks a user that prevents them from participating in issues and PRs.
For bigger projects with many maintainers that can also lead to problems if people use the block function as liberally as on Twitter.
Prompt injection must be a really lucrative activity. I wonder how many hidden prompts will be detected on GitHub in the coming years and how many devs dare point LLMs at GitHub issues
In stuff I maintain I hardly get anything at all, whether using LLMs or not, so it does not affect me much. Furthermore, I exclusively use the API, so the UI will not try to make you use Copilot and those things either (that is not the reason I use the API, although it is a side effect).
I do not use Copilot, Claude, etc, although I partially agree with one of the comments there, that using LLM for minor auto-completion is probably OK, as long as you can actually see that the completion is not incorrect (although that should apply to other uses of auto-completion too, even if LLM is not used; but it is even more important to check more carefully if LLM is used). I think it would be better to not accept any LLM generated stuff otherwise (although the author might use LLM to assist before submitting it if desired (I don't, but it might help some programmers), e.g. in case the LLM finds problems with it, that they will then have to review themself to check if it is correct, before correcting and submitting it; i.e. don't trust the results of the LLM).
It links to https://github.com/lxc/incus/commit/54c3f05ee438b962e8ac4592... (add .patch on the end of the URL if it is not displayed), and I think the policy described there is good. (However, for my own projects, nobody else can directly modify it anyways; they will have to make their own copy and modify that instead, and then I will review it by myself and can include the changes (possibly with differences from how they did it) or not.)
Curious to know if others are seeing a similar uptick in AI slop in issues or PRs for projects they are maintaining. If yes, how are you dealing with this?
Some of the software that I maintain is critical to container ecosystem and I'm an extremely paranoid developer who starts investigating any github issue within a few minutes of it opening. Now, some of these AI slop github issues have a way to "gaslight" me into thinking that some code paths are problematic when they actually are not. And lately AI slop in issues and PRs have been taking up a lot of my time.
I haven’t seen anything obvious, even including the other repos where I look through issues a lot.
Maybe it’s only the really popular and buzzword-y repos that are targets?
In my experience, the people trying to leverage LLMs for career advancement are drawn to the most high profile projects and buzzwords, where they think making PRs and getting commits will give them maximum career boost value. I don’t think they spend time playing in the boring repos that aren’t hot projects.
At first, I thought "wow, this project has been inactive for some time and this PR is quite large". The use of emojis should have tipped me off :)
https://github.com/photo/frontend/pull/1609
AI slop attacks on the cURL project : https://www.youtube.com/watch?v=6n2eDcRjSsk
Unfortunately, LLMs empower "contributors" who can't be bothered to put in any effort and who don't care about the negative impact of their actions on the maintainers.
The open-source community, generally speaking, is a high-trust society and I'm afraid that LLM abuse may turn it into a low-trust society. The end result will be worse than the status quo for everyone involved.
Everything is collapsing toward a low-trust default. At the end of this trajectory, we rediscover that the analog world becomes valuable precisely because it can't be infinitely replicated.
Authenticity becomes the foundational currency.
But everyone must master AI tools to stay relevant. The brilliant engineer who refuses AI-generated PR by principle will get replaced. Every 18-24 months, as capabilities double, required skills shift. Specialization diminishes. Learning velocity becomes the only durable advantage. These people cannot learn new tricks.
Those who cannot question their assumptions cannot self-correct and will be replaced. The future belongs to the humble, the fluid, and the resilient. 60% of HN users is going toward a very tough time, and I am being very charitable with this assumption.
[dead]
[flagged]
AI seems like a rerun of the Eternal September problem that eventually killed internet news groups and a lot of email discussion lists.
The issue expresses doubt about a policy specific to LLMs being accepted. I think the way to go might be to accept that the bar for outside contributions should unfortunately be higher. It doesn't take an LLM to have a glut of low quality contributions, it just takes an incentive and some attention, as we've seen with Hacktoberfest: https://news.ycombinator.com/item?id=31628342
I think there is soon going to be such implementations globally, if not within organizations or smaller groups. The ease of using v/s actual quality content is really the question should be answered when deciding LLM policy.
Maybe the solution is to counteract LLM pull requests with LLM first pass reviews. Obviously not a perfect solution, but LLMs should be good enough at detecting spam. This only needs to apply to first time contributors who haven't yet proven themselves.
As an open source maintainer, I don't have an issue with AI; I have an issue with low quality slop whether it comes from a machine or from a human.
The responsibility then is for an open source project to not be shy on calling out low quality/low effort work, have good integ tests and linters, and have guidance like AGENTS.md files that tell coding robots how to be successful in the repo.
> I have an issue with low quality slop whether it comes from a machine or from a human.
It's very hard for a human to mask a low-quality PR as thought it were reasonable quality. It is incredibly easy for an LLM to do it (in fact that is precisely what they are trained to do).
In addition, when I see a low-quality PR, I try to mentor the submitter. That doesn't make sense to do for LLMs (the submitter might not even bother reading my comments).
> and have guidance like AGENTS.md files that tell coding robots how to be successful in the repo.
If none of the maintainers use such tools, why is it our responsibility to maintain documentation to appease them (in the hopes of reducing spam)? A policy limiting their use is much simpler to write and requires less maintenance.
What good does having integration tests and linters do when triaging issues? When trying to figure out if a bug report is hallucinated or not?
I've seen an uptick in LLM generated bug reports from coworkers. A employee of my company (but not someone I work with regularly) used one of the CLI LLMs to search through logs for errors, and then automatically cut (hundreds!) of bugs to (sometimes) the correct teams. Turns out it was the result of some manager's mandate to "try integrating AI into our workflow". The resulting email was probably the least professional communication I've ever sent, but the message was received.
The only solution I can see is a hard-no policy. If I think this bug is AI, either by content or by reputation, I close without any investigation. If you want it re-opened, you'll need to IRL prove its genuine in an educated, good-faith approach that involves independent efforts to debug.
> "If you put your name on AI slop once, I'll assume anything with your name on it is (ignorable) slop, so consider if that is professionally advantageous".
That's just stupid.
IF you want LLM code reviews, there are bots for codex, copilot, claude etc that plug straight into Github PRs and review the code automatically.
Some of them are actually useful, some just plain wrong and some are subtly wrong and you need to spend some time figuring out whether it's right or wrong :)
IMO it's still a net positive because LLMs tend to pick up really weird subtle errors that humans easily gloss over.
Alas this is an issue of LLM generated code. Increased productivity but lessened understanding. If you can increase productivity while also increasing understanding then that will be a decent middle ground.
All comes down to accountability.
A much bigger problem is that when an AI/LLM coughs up code, you have absolutely no idea what the copyright or license is.
I assume any LLM contributions are uncopyrightable at best or copyright infringement at worst. Either way, I don't own the copyright.
In order to avoid a potential future where I lose the copyright due to being unable to show a substantial portion is human authored, I try to keep track of what is AI authored and what is human authored.
From a copyright perspective, right now accepting LLM contributions feels like playing with fire, at least for closed source projects.
Copyright in the context of LLM's is kind of weird. It doesn't explicitly copy others code but it does lean heavily on the structure of it. Is it evidence of copying prior work or is it fair use? Alas, we don't really have any legal bearing that can handle this yet.
If it is copying prior work, then you are right that there would be a lot of cross licensing bleed through. The opposite is also true in that it could take proprietary code structure and liberate it into GPL 3 for instance. Again what is the legal standing on this?
Years back there was a source code leak of Microsoft Office. Immediately the Libre office team put up restrictions to ensure that contributors didn't even look at it for fear that it would end up into their project and become a leverage point against the whole project. Now with LLM's it can be difficult to know where anything comes from.
We don’t know if it is weird yet, right? It is just a big question mark.
I guess as some point there will be a massive lawsuit. But, so much of the economy is wrapped up in this stuff nowadays, the folks paying for Justice System Premium Edition probably prefer not to have anything solid yet.
I completely agree, but it seems that our industry has decided to turn a blind eye to it. They might even get away with it -- the recent rulings around fair use with regard to Facebook and Anthropic's unrepentant copyright violations[1] was particularly galling to me.
Almost all of the projects I work on require you to sign the Developer Certificate of Origin[2] (which attempts to protect projects from people submitting code that they know cannot be licensed under the project's license), and in my view LLM code you submit does not fulfill the requirements of the DCO. Unfortunately, it seems nobody actually cares about this either.
[1]: https://www.debevoise.com/insights/publications/2025/06/anth... [2]: https://developercertificate.org/
copyrighting software to begin with was always laughable.
Policy is a decent filter for consensual candidates, but mostly ineffective against incentives and automation.
The issue is exported costs: whether submitters make reviewers work too hard for the contribution value.
The policy/practice should focus first on making reviewer/developer's work easier and better, and second on refining submitter skills to become developers. The same is true for Senior/Junior relations internally.
So the AI company that solves how to pare AI slop down to clean PR's would meet a real and growing need, and probably also help with senior/junior relations as well.
Then you could meet automation with automation, and the incentives are aligned around improving quality of code and of work experience. People would feel they're using AI instead of competing with it.
>>> the entire issue description contains so much unneeded (and probably incorrect) information that it'd be better if they just provided their LLM prompt as an issue instead
When it's put this way, it seems a lot like the problem of people walking into doctors' offices with certainty that they know their own diagnosis after reading stuff on Reddit and WebMD.
What this post actually amounts to, indirectly, is a plea to trust human expertise in a particular domain instead of assuming that a layperson armed with random web pickings has the same chance as an expert at accurately diagnosing the problem. This wastes the expert's time and just increases mistrust.
The exceptions where Reddit solves something that a doctor failed to solve are what infuse the idea of lay online folk wisdom with merit, for people desperately looking for answers and cures. Makes it impossible to impose a blanket rule that we should trust experts, who are fallible as well.
The problem is societal. It's that if you erode trust in learned expertise long enough, you end up with a chaos of misinformation that makes it impossible to find a real answer.
A friend of mine who died of lung cancer recently, in his last days became convinced that he'd gotten it because of the covid vaccine (despite being a lifelong smoker, whose father had died of it at 41). And in every individual case you say, well, I don't want to disabuse someone of the fantasy they've landed on.
This is a devastatingly bad way to raise a generation, though. Short-circuiting one's own logic and handing it over to non-deterministic machines, or randos online... how do we expect this to end?
Oh man this is all really hurting my brain. I wish I had a good response to this. I wish I had a magical simple response and a way to resolve this problem. On one end, I stop by curl's H1 profile regularly and watch people just drown them in garbage. It's like LLMs have unlocked a portal to a new kind of stupidity that was dormant before. Netsec always had a problem with people running nmap -sV -sC -sX -Pn -p1-65535 --max-rate -T1 scanme.nmap.org -oS out.txt and then just sending that out.txt with a subject "I FOUND A VULENARBILATEAY" And like, a script kiddie isn't really a person... everyone has a script kiddie inside of us. But it feels like we took this wonderful technology of neural networks and we did capitalism with it and unleashed it into a business of making it easier for people to be stupid but feel smart while doing it. I really like the idea that we might learn something about our own consciousness by simulating neural networks with computers but the more I see how it's done in reality the more I want to puke my brains out.
On the other hand, Matt Godbolt seems to use LLMs and I feel like I sure as hell wouldn't want to miss a PR from Matt fucking Godbolt. I mean even if I go full vanilla LLM-free I still am too addicted to using godbolt.org at this point and it was written partially with an LLM apparently.
Argh, maaan I don't know this is too fucking complicated of a problem for me to solve. Fuck, maybe let's just destroy all this technology and live as neofarmers raising chickens?
<Zero LLMs were used to write this post. In fact I went ahead and broke one GPU for every sentence I wrote just to make it harder for LLMs to compute.>
This is a poor take. We need to stop slop and low effort issues/PRs. Stopping AI generated code is a lost battle because detecting that in high quality work is impossible.
If people can't make a good (PR, story, or whatever) without AI, they certainly can't do it with AI, because using the AI is strictly more difficult - it requires all the original skill, plus the skill to work around the AI's quirks.
This is arguably isomorphic to Kernighan's Law:
> Everyone knows that debugging is twice as hard as writing a program in the first place. So if you’re as clever as you can be when you write it, how will you ever debug it?
Are you sure about that? Can you make the opposite case and steel-man it? Until you can, a stupid LLM will be smarter than you are.
I can write a PR now. I can code now. Probably not as good as you can, but before LLMs I couldn't. I tried for decades learning to code. My brain is a Top-down network, I can see the big picture very quickly, but I cannot maintain focus to build bottom-up. Now I don't have to. I use LLMs to set the goal, to examine all corner cases, to define the milestones, to predict the wrong turns, to write a human-readable spec, to break it down to units of test code, to write the blue-prints of units of code. I can test them, and debug them with LLMs. The end result can be sub-optimal, but it runs, it does what I want, is well documented, and is maintainable. Before LLMs I couldn't do any of that. In doing all this, I get better at the bottom-up thing, just by trying.
We are a spectrum of people. Do not assume the world is like you.
It seems either your LLM is 10x better than anything I've ever used or your standards are 10x lower.
[dead]
[dead]
I make a lot of drive-by contributions, and I use AI coding tools. I submitted my first PR that is a cross between those two recently. It's somewhere between "vibe-coded" and "vibe-engineered", where I definitely read the resulting code, had the agent make multiple revisions, and deployed the result on my own infrastructure before submitting a PR. In the PR I clearly stated that it was done by a coding agent.
I can't imagine that any policy against LLM code would allow this sort of thing, but I also imagine that if I don't say "this was made by a coding agent", that no one would ever know. So, should I just stop contributing, or start lying?
[append] Getting a lot of hate for this, which I guess is a pretty clear answer. I guess the reason I'm not receiving the "fuck off" clearly is because when I see these threads of people complaining about AI content, it's really clearly low-quality crap that (for example) doesn't even compile, and wastes everyone's time.
I feel different from those cases because I did spend my time to resolve the issue for myself, did review the code, did test it, and do stand by what I'm putting under my name. Hmm.
I think it’s disrespectful of others to throw generated code their way. They become responsible for it and often donate their time.
I think it's disrespectful to throw bad code on somebody else, regardless of its provenance. And (as a professional who uses AI coding agents) I understand that LLM code is often bad code. But the OP isn't complaining about the deluge of bad code being submitted as PRs, they're complaining about LLM code being submitted as PRs.
I've also been on the other side of this, receiving some spammy LLM-generated irrelevant "security vulnerabilities", so I also get the desire for some filtering. I hope projects don't adopt blanket hard-line "no AI" policies, which will only be selectively enforced against new contributors where the code "smells like" LLM code, but that's what I'm afraid will happen.
(OP here.)
Well, we don't receive that many low-quality PRs in general (I opened this issue to discuss solutions before it becomes a real problem). Speaking personally, when it does happen I try to help mentor the person to improve their code or (in the case where the person isn't responsive) I sit down and make the improvements I would've made and explain why they were made as a comment in the PR.
When it comes to LLM-generated code, I am now going to be going back-and-forth with someone who is probably just going to copy-paste my comments into an LLM (probably not even bothering to read them). It just feels disrespectful.
> I hope projects don't adopt blanket hard-line "no AI" policies, which will only be selectively enforced against new contributors where the code "smells like" LLM code, but that's what I'm afraid will happen.
Well, this is a two-way street -- all of the LLM-generated PRs and issues I've seen so far do not say that they are LLM-generated, in a way that I am tempted to describe as "dishonest". If every LLM-generated PR was tagged as such, I might have a different outlook on the situation (and might instead be willing to reviewing these issues but with lower priority).
Thanks for the response here. I also read your follow-up on the Github issue. I definitely agree with you on the mentorship point. The "CPU quota allocation mismatch" issue felt particularly agitating to read from the point of view as a maintainer. Human being acting as a proxy for ChatGPT. Have you considered putting an "LLM usage disclosure" question on the issue/PR templates? Something like "To what extent were AI tools used to create this issue/PR? What validation steps did you perform to ensure accuracy?"
The "hard-line policy" would then shift from being "used LLM tools" to "lied on the LLM usage disclosure", and it feels a lot less like selective enforcement (from my perspective). Obviously it won't stop these spammy issues/PRs, but neither will a hard-line policy against all AI.
I've been thinking about this a bit over the past few days, and I think this is a fairly reasonable middle ground (and also allows maintainers to decide how they wish to engage with LLM-generated work).
Hope you can forgive this tangent, I considered posting this in the GH thread, but you asked nicely not to... So hopefully this is a middle ground, you can excuse or ignore
First, my original comment was going to ask if you're looked at what any other reputable repos are doing. Specifically popular FOSS projects that are not backed by a company looking to sell AI. Do any of them have a positive Policy, or positions that you want to include?
Second, if I was forced to take a stand on AI, I would duplicate the policy from Zig. I feel their policy hits the exact ethos FOSS should strive for. They even ban AI for translations, because the reader is just as capable a participant. And importantly, asking the author to do their best (without AI), and trust the reader to also try their best encourages human communication. It also gives the reader control and knowledge over the exact amount of uncertainty introduced by the LLM, which is critically important to understanding a poor quality bug report from a helpful users who is honestly trying to help. Lobste.rs github disallows AI contribution for an entirely different reason I haven't seen covered in your GH thread yet
Finally, you posted the Issue as an RFC. but then explicitly excluded, HN from commenting on the issue. I think that was a fantastic decision, and expertly written. (I also appreciate that lesson in tactfulness :) ) That said, if you're actually interested in requesting comments or thoughts you wouldn't have considered, I would encourage you to make a top level RFC comment this thread. There will likely be a lot of human slop to wade through, but occasionally I'll uncover a genuinely great comment on HN that improves my understanding. Here I think the smart pro-AI crowd that might have an argument I want to consider, but would be unlikely to on my own because of my bias on the quality of AI. Such a comment might would be likely to appear on HN, but the smart people who I'd want to learn from, would never comment on the GH thread now, and I appreciate it when smart people I disagree with, contribute to my understanding.
PS Thanks for working on opencontainers, and caring enough to keep trying to make it better, and healthier! I like having good quality software to work with :)
> I considered posting this in the GH thread, but you asked nicely not to... [...] you posted the Issue as an RFC. but then explicitly excluded, HN from commenting on the issue. I think that was a fantastic decision, and expertly written. (I also appreciate that lesson in tactfulness :) ) That said, if you're actually interested in requesting comments or thoughts you wouldn't have considered, I would encourage you to make a top level RFC comment this thread.
Well, I posted this as an RFC for other runc maintainers and contributors, I didn't expect it to get posted to Hacker News. I don't particularly mind hearing outsiders' opinions but it's very easy for things to get sidetracked / spammy if people with no stake in the game start leaving comments. My goal with the comment about "don't be spammy" was exactly that -- you're free to leave a comment, just think about whether it's adding to conversation or just looks like spam.
> Specifically popular FOSS projects that are not backed by a company looking to sell AI. Do any of them have a positive Policy, or positions that you want to include?
I haven't taken a very deep look, but from what I've seen, the most common setups are "blanket ban" and "blanket approval". After thinking about this for a few days, I'm starting to lean more towards:
Though if we end up with such a policy we will need to add AGENTS.md files to try to force this to happen, and we will probably need to have very harsh punishments for people who try to skirt the requirements.> Lobste.rs github disallows AI contribution for an entirely different reason I haven't seen covered in your GH thread yet
AFAICS, it's because of copyright concerns? I did mention it in my initial comment, but I think that far too much of our industry is turning a blind eye to that issue that focusing on that is just going to lead to drawn out arguments with people cosplaying as lawyers (badly). I think that even absent of the obvious copyright issues, it is not possible to honestly sign the Developer Certificate of Origin[1] (a requirement to contribute to most Linux Foundation projects) so AI PRs should probably be rejected on that basis alone.
But again, everyone wants to discuss the utility of AI so I thought that was the simplest thing to start the discussion with. Also the recent court decisions in the Meta and Anthropic cases[2] (while not acting as precedent) are a bit disheartening for those of us with the view that LLMs are obviously industrial-grade copyright infringement machines.
[1]: https://developercertificate.org/ [2]: https://observer.com/2025/06/meta-anthropic-fair-use-wins-ai...
> it's because of copyright concerns?
Nominatively, yes. But I think I would describe it as risk tolerance. I'm going to be one of those bad cosplayers and assert that the two rulings mentioned even if they were precedent setting, don't actually apply to the risks themselves. Could you win a case is much less important than if you could survive the court costs. There's no doubt some value in LLM based code generation to many individuals. But does it's value outweigh the risks to a community?
> and we will probably need to have very harsh punishments for people who try to skirt the requirements.
I would need to spend hours of time to articulate exactly how uncomfortable this would make me if I was working along side you. So please forgive this abbreviated abstract. One of the worst things you can do to a community, is put it on rails towards an adversarial relationship. There's going to be a lot of administrative overhead to enabling this, it will be incredibly difficult to get the fairness correct the first time, and I assume (possibly without cause?) it's unlikely to feel fair to everyone if you ever need to enforce it. Is that effort and attention and time best spent there?
I believe that no matter what you decide, blanket acceptance, vs blanket denial, vs some middle ground, you're going to have to spend some of the reputation of the project on making the new rule.
If you ban it, you will turn away some contributions or new contributors, and a small subset of committers may see their velocity decrease. This counts for some value loss (some positive and some negative) But also accounts for decreased time costs... or rather it enables you to spend more time on people and their work instead.
If you allow it, you adopt a large set of new poorly understood risks, and administrative overhead, and time you could have spent working with other people... It will also, turn away contributors.
I'm not going to pretend like there was a chance in hell anyone should believe that I was likely to contribute to runc. It's possible in some hypothetical, but extremely unlikely in the current reality. And, if I cared enough about the diff I wanted to submit upstream, I still would open a PR... but, I saw an AGENTS.md in a different repo that I was considering using, was disappointed and decided not to use that repo. Seeing runc embrace AI code generation would without a doubt, cause me to look for an alternative, I assume a reasonable alt probably doesn't exist, and I would resign myself to the disappointment of using runc. I agree with your argument that it's commercial grade copyright laundering, but that's not my core ethical objection to its use.
> In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move.
You're damned if you do, and damned if you don't. So the only real suggestion that I have is make sure you remember to optimize for how you want to spend your time. Calculate not just the expected value of the code within the repo, but the expected value of the people working on the repo.
> I would need to spend hours of time to articulate exactly how uncomfortable this would make me if I was working along side you.
I think this came out a little wrong -- my point was that if we are going to go with a middle-ground approach then we need to have a much lower tolerance for people who try to abuse the trust we gave in providing a middle-ground. (Also, there is little purpose in having a policy if you don't enforce it.)
For instance, someone knowing that I will deprioritise LLM PRs, and instead of deciding to write the code themselves or accept that that what I work on is my own personal decision to make, they instead decide to try to mask their LLM PR and lie about it -- I would consider this to be completely unacceptable behaviour in any kind of professional relationship.
(For what it's worth, I also consider it bad form to submit any patches or bug reports generated by any tool -- LLM or not -- without explaining what the tool was and what you did with it. The default assumption I have when talking to a human is that they personally did or saw something, but if a tool did it then not mentioning it feels dishonest in more ways than one.)
I did see that lobste.rs did a fairly cute trick to try to block agentic LLMs[1].
[1]: https://github.com/lobsters/lobsters/pull/1733
> I think this came out a little wrong
I think it came out exactly perfectly. Unrelated to this specific topic, I've been thinking a lot lately about reward vs punishment as a framework for promoting pro-social environments. I didn't read far into what you said. I was merely pattern matching it back to the common mistakes I see and want to discourage.
> but if a tool did it then not mentioning it feels dishonest in more ways than one.
Yeah, plagiarism is shockingly common. It's a sign of lacking the skill or ability to entertain 2rd order, or 3rd order thoughts/ideas.
I would potentially accept it (although you should not lie about it, and if someone says in their policy that they specifically do not want this then you should not send it), although for my own projects, there are reasons (having to do with the way the version control is set up) that I cannot accept direct contributions anyways, and I usually make my own changes to any contributions anyways (but anyone else can make their own copy with their own changes however they want to do, whether or not I accept it). Since it is honest, and that you had reviewed it by yourself before submitting it, and also tested it, and that I would review it again anyways as well, it would be acceptable, although I would still much more prefer to receive contributions that do not use LLM, still the way you do it is much better than the other stuff some by LLM which does not meet the threshold of being acceptable.
If I were the maintainer, and you specified up front that your PR was largely written by an LLM, I would appreciate it. I may prioritize it lower than other PRs, perhaps, but that's about it.
I think it's also important to disclose how rigorously you tested your changes, too. I would hate to spend my time looking at a change that was never even tested by a human.
It sounds like you do both of these. Judging by the other replies, it seems that other reviewers may take a harsher stance, given the heavily polarized nature of LLMs. Still, if you made the changes and you're up front about your methodology, why not? In the worst case, your PR gets closed and everybody moves on.
They don't want your contribution, so don't disrespect them by trying to make it.
> I can't imagine that any policy against LLM code would allow this sort of thing, but I also imagine that if I don't say "this was made by a coding agent", that no one would ever know. So, should I just stop contributing, or start lying?
If a project has a stated policy that code written with an LLM-based aid is not accepted, then it shouldn't be submitted, same as with anything else that might be prohibited. If you attempt to circumvent this by hiding it and it is revealed that you knowingly did so in violation of the policy, then it would be unsurprising for you to receive a harsh reply and/or ban, as well as a revert if the PR was committed. This would be the same as any other prohibition, such as submitting code copied from another project with an incompatible license.
You could argue that such a blanket ban is unwarranted, and you might be right. But the project maintainers have a right to set the submission rules for their project, even if it rules out high-quality LLM assisted submissions. The right way to deal with this is to ask the project maintainers if they would be willing the adjust the policy, not to try to slip such code into the project anyway.
"should I stop doing this thing that people are explicitly saying they don't want me to do or should I keep doing this thing that people are explicitly saying they don't want me to do???"
As with evertything, there's always nuance. If everyone followed similar midset to the comment you were replying to, likely llm generated pr issues wouldn't be as much of a problem and we wouldn't even be here discussing it
> [append] Getting a lot of hate for this, which I guess is a pretty clear answer. I guess the reason I'm not receiving the "fuck off" clearly is because when I see these threads of people complaining about AI content, it's really clearly low-quality crap that (for example) doesn't even compile, and wastes everyone's time.
I think you don't deserve the downvotes and, if you really do what you say you do, that's the ONLY way to use LLMs for coding and contributing to opensource software, or to a company's software. Sadly, the vast majority of LLM users don't and will never use it like that. And while they can get fired for being useless monkeys in a real company, they will keep sending PRs to opensource software, so that's clearly a different scenario that needs a different solution.
[dead]
Wouldn't you provide LLM friendly guidance material in your repo instead of spending effort on a pointless endeavor to ban them ?
I think OP wanted to have a proof that the submitter can demonstrate understanding of both the system and the code in the PR. So how would a guidance solve that? Also OP didn't want to ban them I believe
> I think OP wanted to have a proof that the submitter can demonstrate understanding of both the system and the code in the PR.
LLMs are really good at writing these. IF they think this will prove the author is human, they're mistaken.
> LLMs are really good at writing these. IF they think this will prove the author is human, they're mistaken.
That is not my general experience. LLM explanations of code tend to add extra specifics that are incorrect, and the whole thing looks like LLM output (lots of short sentences, overly cheery, too many dumb lists, and being overly repetitive).
Or course, a human could read the LLM output and then synthesise it in their own words, but they could also just read the code. I doubt someone will be able to convincingly act like they know what LLM code does just by consulting an LLM.
If your patch took you no time or effort to write and you took no interest in what it does, why should I (as a maintainer) bother looking at it and maintain it for the next 10 years? In any other circumstances we would rightfully call this spam and see it as a socially hostile activity.
If you plan to accept LLM contributions then a lot more effort will have to be spent on reviewing them.
It will be more interesting to see if and when AI becomes better than humans at coding, which seems to be coming close to reality, some projects may start accepting only AI contributions :D.
This is a minor issue. The big issue comes when we start complaining about code that's not generated by AI. When hand coded stuff becomes buggier than AI stuff.
You're being downvoted but I think I get what you're getting at.
When I was doing my masters a few months ago, I would get my assignments rejected whenever I didn't run them through Grammarly first.
I have nothing against Grammarly, it's a useful too, but I find that it has the tendency to reject things that (as far as I can tell) are still technically correct but don't have the "AI vibe" to it. I suspect that the graders are running things through Grammarly themselves and rejecting anything that it rejects. This is probably going to become increasingly more common as time goes on.
It's hardly the worst thing in the world, but I do think it will lead to the only "accepted" writing being extremely plain and formulaic.
what do you mean your assignments were rejected? like rejected for being AI generated?
No, they weren’t AI generated. They have people judging grammar, and I think they will run it through grammarly to automate it.
I wrote the stuff myself.
yeah yeah I got that I'm just confused what justification your graders gave for rejection. I've never heard of a paper rejected on account of grammar