@bekkaboo@girlcock.club Linux itself is heavily using AI models. Therefore, every Linux distribution moving to recent kernel versions is heavily built on top of using AI models. Creating a Linux distribution not heavily using it would require a hard fork of the Linux kernel and many other projects.
It's unclear what would be accomplished by banning AI for a tiny portion of the code while continuing to use Linux, AOSP, Chromium and hundreds of other projects heavily using it. We'd still be benefiting from it.
GrapheneOS is currently defending its use of AI coding tools on Mastodon against complaints by various accounts claiming to be users.
We do not understand where you’re coming from or why you’re so incredibly angry with us. It’s not justified and does not make sense.
I haven’t dug into the code, but blanket hate for AI in code is extremely frustrating. There’s a difference between vibe coding and the hundred other ways to “use AI” on a project.
I understand this but, you have to consider the ecological impact of it, the thousands or more people whose work was stealed to train AI, the constant shoving of AI into everything and the problems that have arise from it, the campain AI companies did and do telling people they are going to lose their jobs and constant fearmongering, the constant blatantly lying of “AGI achieved”, the layoffs by senile CEOs, the debt that is causing because stupid people in power gonna be stupid, etc. There are plenty of reasons to hate AI and what it is doing to our world and people lives in general. Sure there are positives but the negatives clearly outweighs the positives.
There’s a difference between vibe coding and the hundred other ways to “use AI” on a project.
I am not talking about the Linux kernel or the Rust project - they have stabdards way above average commercial projects - but generally “using ai” in practice means to use it for code generation. Then, programmers are expected to review the output but are not given extra time for it. This creates an extremely slippery slope leading to mostly unsupervised code creation. Also because reading and understanding buggy third party code means more effort than writing new code.
Then there is the idea to get rid of the bugs by “debugging”, but debugging is frankly not a software engineering strategy, because you can always just recognize a fraction of the bugs that your process created in the first place. Debugging is also more expensive tha writing good code.
Then, what makes the process between product managers and software developers very often a bit friction-loaded is that good developers always want to have clear specifications what they are supposed to build.
LLM code generation largely replaces clear specifications with plausible assumptions. That appears to make the process fast, but the result is often wrong. At times obviously wrong, and at times subtly and catastrophically wrong.
Ssshh…Most of those having a knee-jerk “AI BAD! YOU BAD!” reaction don’t have any clue WTF an IDE is, how code completion works, nor the fact that by most metrics 95%+ of code now has “AI” in the chain…and has had that for YEARS.
Here’s a crazy idea…how about instead of “AI BAD! ME HATE AI” how about some nuance? Assume ALL software in 2026 has had AI assistance, and review it on its merits.
As for the devs at GrapheneOS directly…why were you (the general you) trusting them before? Humans can be plenty dicey in ways that affect code all on their own.
Supposedly wonderful “human artisanal code” has plenty of fuckery.
These are supply chain attacks and in the case of xz utils, the attacker had gone to extreme lenghts to hide the attack from a well-meaning, good-hearthed but overworked and burnt out solo maintainer.
To compare this to bugs that people unwittingly introduce in normal human-written code is not sincere.
That’s the thing about “pure human slop”: it doesn’t need to be malicious to be catastrophic.
The second link is particularly salient - kills the “supply chain attacks are special” argument because it is precisely about “unwitting bugs in normal human-written code just happen”
Okay, but they still happen with orders of magnitude less frequency than bugs in AI code. Consider that the time between when rsync first adopted LLM-generated code and users en masse reporting rsync internal protocol errors during a backup was on the order of months.
I don’t recall that one…but in fairness…AI generates a metric shit ton more code than humans. We’d have to normalize the results.
Interestingly, looking it up now, someone DID normalize for that very case. Bug rate per commit for the AI-assisted versions landed within normal historical range. A pre-AI release had more regressions. The 3.4.3 regressions were primarily from the CVE security patches, not the AI work. Zero CVEs from the Claude-assisted commits.
I’ve been a programmer for two thirds of my life up to this point. I don’t see how the machine that generates code that must be assumed to be broken, and statements about said code that must be assumed to be wrong, until both have been manually verified, because the “hallucination” issue is inherent to the design of LLMs and cannot be solved, is supposed to make my life easier. Especially since a well known problem in computer science is that writing code is more fun than reading it.
If you’re using AI for code generation, I hope you like reading code.
If you’re using AI to learn a repo, I hope you’re prepared to fact check everything it tells you.
If you’re using AI for code review before merging a PR, fucking yikes. What made you think the sycophant machine that once told users a soggy cereal bar was a great business opportunity and cannot even transcribe McDonald’s orders correctly was suitable for that purpose?
If you’re telling yourself it’s okay because there’s a human in the loop, please read some studies about decision fatigue and consider that increasing the number of critical decisions a person must make per day, while giving them a suggested answer that’s right 90% of the time and catastrophically wrong the remaining 10%, and the only way to tell the difference is to read in depth, might not have the desired effect. Especially if the human in question is then put under time pressure.
And all that is before I go on a five paragraph rant about the ethical transgressions of every major AI company.
The fact that it isn’t perfect is the grey. And sure, it has a lot of problems but not only is it still useful anyways, but those problems are being ironed out. They probably won’t disappear completely but the tech is already miles further than what we had 2 years ago.
To make you understand what I mean by black and white, you have a user that put “AI is evil” in big bold letters in the comments. The hate is almost cult like for part of the population, and imo, it’s mostly because the media tells them to be mad.
Most of it depends on the task in any case. I don’t work with massive code bases and it doesn’t take long reading through a few scripts, running it and checking the output. The people I know that do work with huge code bases are using it as well, but I can’t really talk for them either. They don’t hate it tho.
And the fact how it is used in practice, actually, in reality, is the very dark grey that some people call “black” for better clarity. The fact that you can use a large butchers knife for practical things does not mean that entering a primary school with one is the right thing. What are the actual intentions for grabbing that tool?
The person who put “AI is evil” in big block letters is objectively correct. See homes getting claimed under eminent domain to build power lines for datacenters, electricity prices for consumers rising as service quality gets worse, and water usage to the tune of millions of gallons per datacenter per day. See companies buying rare books in bulk, cutting them apart so they can be scanned faster, and not publishing the scans, literally destroying culture so they can have one more scrap of training data guaranteed to be free of slop that their competitors don’t have. See companies operating web scrapers that deliberately evade every block that website owners try to use to stop them from reading their sites, and then scraping so many different pages so frequently that it’s indistinguishable from a DDoS attack, hosting bills skyrocket, servers crash, indie webmasters pay the price for corporate greed. See xAI operating natural gas turbines on the property of their datacenter for extra electricity in flagrant violation of the Clean Air Act, spewing toxic smoke into the air and generating powerful, inaudible infrasound vibrations that have been linked to everything from nausea to irritability to hearing loss to seizures. See Anthropic treating Minority Report as an instruction manual. See how ChatGPT use affects test scores. See AI psychosis. See chatbot-induced suicides. See deskilling. See how little AI companies care.
These are all largely either “the government is evil” or “corpos are evil”. FOSS models, while currently worse, prove that AI can be done… well, at least more ethically. I’m not gonna sit here and claim that no FOSS model has contributed to these issues.
Yeah. Even if you run the model entirely on your own 100% solar-powered hardware, there’s still the problem of training data, and there’s still the problem of the myriad adverse effects on the user.
Sorry, I don’t bootlick the copyright machine. Anyone with half a brain can see it’s transformative. I don’t get why collage is fine but AI isn’t, it’s just copyright mega corps and data brokers that smell blood in the water. All the data isn’t even owned by the artist, you are fighting for universal records, not your local guitarist.
For the climate aspect, it’s mostly grid issues and corruption. It’s laughable to blame AI when we have president pedo prostrating himself in front of oil companies and destroying renewable projects. We need regulations and all that on where they can be placed and how they get powered, but in the end, it’s just software. I could use AI 8 hours a day for my whole life and I still wouldn’t be close to the damage one ticket to Europe does. It’s a scape goat being served by a corrupt pro oil lobby.
Don’t get me wrong, I know what datacenters entail with the amount of people they serve, but if you look at it at an individual level, it’s a drop in the bucket, or it would be if they weren’t using plane turbines. We need regulations to curb the stupidity, but it’s doesn’t make it all inherently bad.
Climate wise, it’s only an issue because of a corrupt administration, it’s not a death blow to it. It’s at the heart of it overblown by a reasonable measure imo.
I strongly remember a CompSci professor ~2005 being disgusted with auto-generated Java GUI. And yes it was clunky and more memory intensive than it could have been, but also, I really don’t care about how GridView is defined most of the time.
Bad code is bad code, touched by AI or not. For working code, a volunteer group spending resources to determine if it might have been touched by AI is at the very least an incredibly frustrating standard to try to meet. And very likely impossible to verify in any meaningful way.
I haven’t dug into the code, but blanket hate for AI in code is extremely frustrating. There’s a difference between vibe coding and the hundred other ways to “use AI” on a project.
I understand this but, you have to consider the ecological impact of it, the thousands or more people whose work was stealed to train AI, the constant shoving of AI into everything and the problems that have arise from it, the campain AI companies did and do telling people they are going to lose their jobs and constant fearmongering, the constant blatantly lying of “AGI achieved”, the layoffs by senile CEOs, the debt that is causing because stupid people in power gonna be stupid, etc. There are plenty of reasons to hate AI and what it is doing to our world and people lives in general. Sure there are positives but the negatives clearly outweighs the positives.
I am not talking about the Linux kernel or the Rust project - they have stabdards way above average commercial projects - but generally “using ai” in practice means to use it for code generation. Then, programmers are expected to review the output but are not given extra time for it. This creates an extremely slippery slope leading to mostly unsupervised code creation. Also because reading and understanding buggy third party code means more effort than writing new code.
Then there is the idea to get rid of the bugs by “debugging”, but debugging is frankly not a software engineering strategy, because you can always just recognize a fraction of the bugs that your process created in the first place. Debugging is also more expensive tha writing good code.
Then, what makes the process between product managers and software developers very often a bit friction-loaded is that good developers always want to have clear specifications what they are supposed to build.
LLM code generation largely replaces clear specifications with plausible assumptions. That appears to make the process fast, but the result is often wrong. At times obviously wrong, and at times subtly and catastrophically wrong.
It’s funny because anyone seeing this as black and white clearly hasn’t ever opened an IDE.
Ssshh…Most of those having a knee-jerk “AI BAD! YOU BAD!” reaction don’t have any clue WTF an IDE is, how code completion works, nor the fact that by most metrics 95%+ of code now has “AI” in the chain…and has had that for YEARS.
https://github.blog/news-insights/research/survey-ai-wave-grows/
https://survey.stackoverflow.co/2025/ai
OMGWTFBBQ!
Here’s a crazy idea…how about instead of “AI BAD! ME HATE AI” how about some nuance? Assume ALL software in 2026 has had AI assistance, and review it on its merits.
As for the devs at GrapheneOS directly…why were you (the general you) trusting them before? Humans can be plenty dicey in ways that affect code all on their own.
https://www.reddit.com/r/PrivacyGuides/comments/13s7mv3/why_i_deleted_grapheneos_louis_rossmann/
https://factually.co/fact-checks/technology/louis-grossman-grapheneos-drama-c1f2ae
Supposedly wonderful “human artisanal code” has plenty of fuckery.
https://en.wikipedia.org/wiki/XZ_Utils_backdoor
https://thehackernews.com/2024/03/urgent-secret-backdoor-found-in-xz.html
https://simonwillison.net/2026/Mar/31/supply-chain-attack-on-axios/
https://www.hivepro.com/threat-advisory/axios-npm-supply-chain-attack-what-you-need-to-know/
These are supply chain attacks and in the case of xz utils, the attacker had gone to extreme lenghts to hide the attack from a well-meaning, good-hearthed but overworked and burnt out solo maintainer.
To compare this to bugs that people unwittingly introduce in normal human-written code is not sincere.
Very well. Here -
https://www.debian.org/security/2008/dsa-1571
https://www.finnie.org/2024/05/13/i-discovered-the-debian-openssl-bug/
That’s the thing about “pure human slop”: it doesn’t need to be malicious to be catastrophic.
The second link is particularly salient - kills the “supply chain attacks are special” argument because it is precisely about “unwitting bugs in normal human-written code just happen”
Okay, but they still happen with orders of magnitude less frequency than bugs in AI code. Consider that the time between when rsync first adopted LLM-generated code and users en masse reporting rsync internal protocol errors during a backup was on the order of months.
I don’t recall that one…but in fairness…AI generates a metric shit ton more code than humans. We’d have to normalize the results. Interestingly, looking it up now, someone DID normalize for that very case. Bug rate per commit for the AI-assisted versions landed within normal historical range. A pre-AI release had more regressions. The 3.4.3 regressions were primarily from the CVE security patches, not the AI work. Zero CVEs from the Claude-assisted commits.
EDIT: Correct URL https://alexispurslane.github.io/rsync-analysis/
I’ve been a programmer for two thirds of my life up to this point. I don’t see how the machine that generates code that must be assumed to be broken, and statements about said code that must be assumed to be wrong, until both have been manually verified, because the “hallucination” issue is inherent to the design of LLMs and cannot be solved, is supposed to make my life easier. Especially since a well known problem in computer science is that writing code is more fun than reading it.
If you’re using AI for code generation, I hope you like reading code.
If you’re using AI to learn a repo, I hope you’re prepared to fact check everything it tells you.
If you’re using AI for code review before merging a PR, fucking yikes. What made you think the sycophant machine that once told users a soggy cereal bar was a great business opportunity and cannot even transcribe McDonald’s orders correctly was suitable for that purpose?
If you’re telling yourself it’s okay because there’s a human in the loop, please read some studies about decision fatigue and consider that increasing the number of critical decisions a person must make per day, while giving them a suggested answer that’s right 90% of the time and catastrophically wrong the remaining 10%, and the only way to tell the difference is to read in depth, might not have the desired effect. Especially if the human in question is then put under time pressure.
And all that is before I go on a five paragraph rant about the ethical transgressions of every major AI company.
It’s pretty black and white for me.
The fact that it isn’t perfect is the grey. And sure, it has a lot of problems but not only is it still useful anyways, but those problems are being ironed out. They probably won’t disappear completely but the tech is already miles further than what we had 2 years ago.
To make you understand what I mean by black and white, you have a user that put “AI is evil” in big bold letters in the comments. The hate is almost cult like for part of the population, and imo, it’s mostly because the media tells them to be mad.
Most of it depends on the task in any case. I don’t work with massive code bases and it doesn’t take long reading through a few scripts, running it and checking the output. The people I know that do work with huge code bases are using it as well, but I can’t really talk for them either. They don’t hate it tho.
And the fact how it is used in practice, actually, in reality, is the very dark grey that some people call “black” for better clarity. The fact that you can use a large butchers knife for practical things does not mean that entering a primary school with one is the right thing. What are the actual intentions for grabbing that tool?
The person who put “AI is evil” in big block letters is objectively correct. See homes getting claimed under eminent domain to build power lines for datacenters, electricity prices for consumers rising as service quality gets worse, and water usage to the tune of millions of gallons per datacenter per day. See companies buying rare books in bulk, cutting them apart so they can be scanned faster, and not publishing the scans, literally destroying culture so they can have one more scrap of training data guaranteed to be free of slop that their competitors don’t have. See companies operating web scrapers that deliberately evade every block that website owners try to use to stop them from reading their sites, and then scraping so many different pages so frequently that it’s indistinguishable from a DDoS attack, hosting bills skyrocket, servers crash, indie webmasters pay the price for corporate greed. See xAI operating natural gas turbines on the property of their datacenter for extra electricity in flagrant violation of the Clean Air Act, spewing toxic smoke into the air and generating powerful, inaudible infrasound vibrations that have been linked to everything from nausea to irritability to hearing loss to seizures. See Anthropic treating Minority Report as an instruction manual. See how ChatGPT use affects test scores. See AI psychosis. See chatbot-induced suicides. See deskilling. See how little AI companies care.
These are all largely either “the government is evil” or “corpos are evil”. FOSS models, while currently worse, prove that AI can be done… well, at least more ethically. I’m not gonna sit here and claim that no FOSS model has contributed to these issues.
Yeah. Even if you run the model entirely on your own 100% solar-powered hardware, there’s still the problem of training data, and there’s still the problem of the myriad adverse effects on the user.
I agree, it’s just not reasonable to assert that a technology powered entirely by IP theft and climate change acceleration must be inherently evil.
Sorry, I don’t bootlick the copyright machine. Anyone with half a brain can see it’s transformative. I don’t get why collage is fine but AI isn’t, it’s just copyright mega corps and data brokers that smell blood in the water. All the data isn’t even owned by the artist, you are fighting for universal records, not your local guitarist.
For the climate aspect, it’s mostly grid issues and corruption. It’s laughable to blame AI when we have president pedo prostrating himself in front of oil companies and destroying renewable projects. We need regulations and all that on where they can be placed and how they get powered, but in the end, it’s just software. I could use AI 8 hours a day for my whole life and I still wouldn’t be close to the damage one ticket to Europe does. It’s a scape goat being served by a corrupt pro oil lobby.
Don’t get me wrong, I know what datacenters entail with the amount of people they serve, but if you look at it at an individual level, it’s a drop in the bucket, or it would be if they weren’t using plane turbines. We need regulations to curb the stupidity, but it’s doesn’t make it all inherently bad.
Lol, “I know this is a legitimate issue, but if I creatively reframe it enough I can pretend it’s not”.
I don’t think the copyright thing is an issue.
Climate wise, it’s only an issue because of a corrupt administration, it’s not a death blow to it. It’s at the heart of it overblown by a reasonable measure imo.
I love how I say “IP” and you assert “copyright”.
This in spades.
To add; now there’s an issue in the generated code that’s been pushed. What to do? Read the code? Have new code generated? Abandon the project?
Abandon the project
Look at the comments here. Do you think the haters here care?
Noooo All AI is Baaad!!1!
/s
It’s tiresome.
Usually, if it’s noticeable that AI was used on code, the code is flawed or, at least, has copyright concerns.
Agreed.
I strongly remember a CompSci professor ~2005 being disgusted with auto-generated Java GUI. And yes it was clunky and more memory intensive than it could have been, but also, I really don’t care about how GridView is defined most of the time.
Bad code is bad code, touched by AI or not. For working code, a volunteer group spending resources to determine if it might have been touched by AI is at the very least an incredibly frustrating standard to try to meet. And very likely impossible to verify in any meaningful way.
Shh, you’re using logic on social media.
If the haters only realized that literally every software company has been using it since at least last fall, we’d have a helluva lot more Amish.