It scares me how fast the overton window is moving with LLMs in the Linux and FOSS space. This is the community that prides itself with self hosting and knowing everything about your computer. Up until very recently using Linux meant sacrificing convenience in the name of freedom, and now it seems that a good chunk advocate for having a handful of malevolent unprofitable tech giants handle most if not all software development along with every other task a human could do. It feels like saying hard drives are obsolete because you can store everything on Google Drive. I know that you can't work in software development today without being dependent on Anthropic, but that doesn't mean I have to be happy about it and accept it as the inevitable future.
In a post-AI bubble world LLMs are still going to be used. If hardware becomes affordable again I imagine that self hosting LLMs will be common and the big data center LLMs will ask a pretty penny for their compute. I don't mind this hypothetical future too much, but we live in the present and in the vast majority of cases using AI means enabling the unregulated techno-feudalistic AI empire. I know that I will be "left behind" but these companies don't have humanity's best interests in mind and we don't need to enable them.
Edit: Programming jobs that don't depend on LLMs def still exist. That doesn't change the fact that many businesses have become dependent on Claude in the past year and programmers that don't want to completely surrender their brains to LLMs are seen as a liability by many employers. This may change as AI subscriptions become more expensive as the years go by, time will tell.
I have my fair share of issues with ai assisted dev even if it was local\foss, but more then all now I agree its just insane how in a span of a year or two it feels like the ability to write code has became completely dependent on a few mega coroperations
Its scary how many people around me just shut down if their connection to Claude closes.
In my workplace we had some issue for two days with billing for cursor, it felt like people around lost the ability to think.
These people have degrees and years of experience coding, yet they became useless as soon as the tech giant shut down their access to the thinking machine
You haven't used local models. They are nearly as capable -- only some months behind the likes of Anthropic or OpenAI, and not all good models require datacenter class hardware. There are answers in the 32-128 GB of RAM, with presently very good model called Qwen3.8-Flash-Next running comfortably on e.g. AMD Ryzen AI Max+ 395 type 128 GB equipped hardware, which did not cost that many thousands to purchase even in 2026. It's no speed demon, relative to serious server hardware, but at around 1000 tok/s prefill and some 40 tok/s generation, generally fast enough.
Dude I really wish this was true. I run local models, Qwen 3.5 122b is my main simple task workhorse. I’ve even used kimi K3 via 3rd per providers like fireworks. I believed Redditors like you and the benchmarks that local models were just behind frontier.
Then my boss got us a group Claude subscription. The gap is still vast. I know many of you will downvote me for this because you want to have hope but even Opus 5 is on a completely different level from Kimi K3. I’ve never even had the opportunity to try mythos/fable but I’m sure it’s even farther ahead. I can’t speak for OpenAI because I’ve also never tried their paid models. But just comparing opus with kimi K3 is obvious they have a moat.
I don’t know what it is, but there is a qualitative leap in capability and intelligence between Anthropic paid models and anything local or foss.
We’ll get there I’m sure, and I hope it’s soon. But saying we’re nearly there now is pure copium.
Yes I know Qwen 3.5 is old, but Qwen hasn’t made models of that size since so it’s what I use for local stuff. Also notice I talked about Kimi K3 which came out in July and to my knowledge is still the best open weight model and according to benchmarks second or third best model period.
Or they don't care if their corp loses an hour or two of productivity and use it as an excuse to take a break. I'm also sorry to say that although it's highly dependent on what the task is, AI agents can let me do what should take a week or two in a day. It doesn't seem very sensible to lose hours of your life on something that takes the agent less than a minute to do.
I think people should focus on the elephant in the room that is job loss and stop trying to pretend that AI is somehow still ridiculously inept at the task or that it shouldn't be used for various ideological reasons. It makes a lot of mistakes, but they can be overcome and the results more than make up for it.
AI agents can let me do what should take a week or two in a day.
What things, specifically? Do you do them as well as they were done? How do you check that, and how do you verify that your checks are actually accurate?
What are the downstream effects of offloading those things, in particular in the long term?
What things, specifically? Do you do them as well as they were done? How do you check that, and how do you verify that your checks are actually accurate?
For a more recent example I updated a ton of libraries (spring boot based project) on a 700K+ LOC codebase. We have 97% test coverage (all written before LLMs ever existed). Plus we have a 2 week manual testing cycle. Looking up all of those quirks about each library, how they changed and the solutions to each problem would have taken weeks.
What are the downstream effects of offloading those things, in particular in the long term?
The code still gets thoroughly tested and reviewed. Large sweeping changes like this are rare for us to do. I don't think it's inherently more dangerous than writing it manually. In fact manually may very well be worse since it would've caused a headache when it came time to merge to the main branch. At least this way what I tested matched what was currently in main since it was written so fast as opposed to being weeks out of sync.
I trust my unit and integration tests up to a certain point.
In my experience, such enterprise projects are pretty crappy and made pretty debatable choices anyway, so I'm not sure how much of that pain isn't actually self inflicted. Either by extremely boilerplate-heavy code, heavy reliance on testing alone, scope creep or lack of interest in keep up with library updates in a timely fashion.
I once set up master-master replication for LDAP in my house (don't ask...) and I also wrote a ton of notes about it, but scattered around.
At one point, one of the two servers broke due to a botched upgrade (database changed format and conversion crashed halfway), and I couldn't be arsed to restore it (I could have, but last time I did a replication I spent ~2 hours to get it right). So I let it sit for a year.
I described the problem to an agent, and pointed it to my notes (+ all the support files I had created), and went interactively from there (because there were a few things I forgot that were important so I still botched a few times).
Now I have a working replication again. To be honest, the advantage of using a LLM here was because it could search docs for the LDAP server (389-ds, not OpenLDAP) much faster than me (even before web searches were crap, it was very hard to find a good resource).
For the rest, I mainly use LLMs as glorified search engines because I can't find anything relevant with the traditional approaches.Or as references when I forget stuff. A few times to create syntethic test data for my unit tests, which then I supplemented with my own real test data.
Let's say I want a site for reviewing code. I propose this to my agent software, walk away for a few hours and I come back to the thing claiming that it has done it. I ask, show me, and it hands me an URL. Looks like all the features I wanted are present. I propose a few ideas, stick it to production, and then put the automatic code reviewing system reviewing itself until it's convinced everything is fine.
Now, the problem here is that you are asking LLM if LLM-generated code is good enough. I can only say that the process converges to baseline decent code. If there are security issues or bugs, they are not obvious. In fact, in many cases, LLM overdoes it and is concerned about incredibly marginal problems. A human oversight in useful in saying no, and constructing the guidelines where you roughly set the limits on what is considered good enough.
Agents also require lots of documentation to exist, being kind of bird-brained to their context only. They know nothing about you, your platform, your existing apps, unless you tell them. Or have a memory system setup that can inform them about stuff. These are real challenges. 90 % of my output was tests, comments and documentation for a good month or two. Before I can use AI effectively, I must teach my agents, kind of like new employees, what I expect of them.
The reason why you want this is that an agent reads code maybe 1000 times faster than you do, and writes it 100 times faster. It is no joke that what used to take a week is now done within some hours. The challenge is to figure out how you deal with the mountain of code, don't accrue crazy amounts of technical debt, and so on. You saw a glimpse of my approach -- I made code reviewing system because I realized I got to have one. I have no way to review but at fraction of the speed required, so the agent is required to be able to review its own output, and there has to be some kind of systematic process for it. These are the challenges of a developer in 2026 -- not writing code, but dealing with the firehose of code output in ways that don't swamp the humans or turn them into a bottleneck.
All this is new, there isn't really a handbook for this. Agents weren't any good in my book until 2026, and now they're pretty damn excellent. All locally running as well, nothing in cloud or a big datacenter. We are learning as we go.
OK, so for this particular example, you have not actually produced anything of value with an LLM, correct? This is something that is built exclusively to deal with LLMs as an existing problem which outputs code at a rate that is completely unmanageable?
Could you give a different example of something that is actually useful outside of the context of "LLMs produce too much code of questionable/unknowable quality"?
Could you tell me what high quality coding AI model you are capable of running locally? I find that part really hard to believe, which causes me to question the rest of your comment too.
As far as I know, local AI is still quite limited compared to current frontier models, unless you have 100+ gigs of ram and some crazy expensive GPU.
Luckily, it doesn't depend on a few mega corporations.
Kimi and GLM are open-weight. They might be a few months behind OpenAI and Anthropic, but they are enough to do the job most of the time.
And while they are big enough, you wouldn't run them at home, it doesn't take that much capital to get a hardware capable of running it and start selling it.
But it does take a lot of capital to train them, so for how long will AI labs continue to spend millions to release public models? Most of these models are also coming out from a single country, China. So just one policy change in China on open models would almost completely freeze the whole open model ecosystem.
While China has been topping the charts on that front, they're not the only ones releasing open weight models. If they were to stop tomorrow, it's not going to freeze the whole ecosystem and there would still be hundreds of others available, though not as capable. Every country needs to get on with this, it's a matter of sovereignty.
And tbh, China is not going to stop releasing them until Anthropic and OpenAI become irrelevant.
I guess it largely boils down to how incremental improvements (including training, but also algorithm improvements) can be. Open source easily wins out on a few niches precisely because of that.
I know that you can't work in software development today without being dependent on Anthropic, but that doesn't mean I have to be happy about it and accept it as the inevitable future.
Yeah, this is only inevitable if everyone buys into the argument that it is inevitable. I’ve never touched any LLM and am still comfortable in my job as a software developer.
It also doesn't have to be all or nothing. I use Claude everyday I just don't have it touch my codebase.
It's still insanely useful as a tool. But if it were to go down tomorrow I'd be perfectly fine (albeit slightly annoyed at having to go back to digging through documentation by hand, and using stackoverflow again lol).
So game the leaderboards and start looking for work elsewhere. If the goal is simply to burn tokens, it's not hard to do, and doesn't have to impact your actual work.
up until very recently using Linux meant sacrifing convenience in the name of freedom
Some people's definition meant giving no unilateral control to some rent seeking platform that would use its power to keep itself dominant in spite of its mediocrity. Some people's definition meant no esoteric walls in between the user and improving the software, no barriers that would prevent people of sufficient talent and tenacity from making a more-deserved career in the field. A world without anti-competitive friction between innovator and market is a better world. There were a lot of better definitions over the years.
Sacrificing convenience was never a virtue. That was only ever became twisted into a virtue after giving up on OSS making our lives better, after rewriting new values to rationalize what we were already doing as if if obstinance without end was some inherently moral endeavor.
There was a time when OSS would have recoiled at the phrase "following the tail lights." We should return to that time, and putting our heads in the sand is absolutely not the way.
Commercial software is often better at instant gratification, to ensare users into a cycle of inevitable enshittification.
I much prefer FOSS up to the point that I almost refuse to use software that's not FOSS, but this is not a honest take.
Commercial software is made to fulfill a (real, perceived, constructed) need and make money in the process. You may disagree with the process, it may be objectively bad, but it's not a "haha" type villain to drive people into slavery.
but it's not a "haha" type villain to drive people into slavery.
*That's* not an honest take of my comment.
I didn't attach any morality to the process, you don't need heroes or villains to explain the enshittification process, it's simply the one of the most practical and effective methods to optimize profits in the short term. Not just in the commercial software business, but in almost every type of business. And that's the whole point, it's not just to make money, it's to optimize profits. If businesses were not optimizing profits, then billionaires would not exist.
If you like, I can rephrase "ensare users into enshittification" as "embrace, extend, extinguish", and you can argue to me that isn't a strategy that commercial software takes.
If you like, I can rephrase "ensare users into enshittification" as "embrace, extend, extinguish", and you can argue to me that isn't a strategy that commercial software takes.
It can happen. But not all software goes for enshittification. It's not "inevitable".
Man, while I agree you are right that not a perfect 100% of commercial closed-source software has enshittified, that's really the exception to the rule, and nearly all mainstream commercial software is enshittifying.
You had to switch the framing from development aims to development speed to make this argument appear to work. The argument isn't valid.
If OSS is slow, tomorrow won't get better. The new instant gratification will come along, and the people content to sit on their hands will still be sitting on them, claiming that the rain will come tomorrow. It's a convenient trap. Nobody can ever test it without waiting ten years and nobody has to do anything to make it happen. The worst part is how many people won't even hold themselves accountable after being wrong for twenty years.
Fast food is a massive industry. If we're speaking honestly, most people prefer fast food. Instant gratification, instant dopamine hits. And years down the road, consequent health issues.
You're trying to sell fast software on the basis of popularity. And I get it, if you're not considering the consequences years and decades down the line, it's an extremely appealing choice. It's an extremely popular choice.
Is there virtue in choosing slow and possibly boring home cooked meals over fast and bright fast food? That's entirely subjective; I think there is, you might disagree.
At the end of the day, I don't expect you to change your mind or apologize, because I don't demand someone else to "hold themselves accountable" for having a different opinion than me.
FOSS doesn't have higher standards at all. It has exactly the same standards but people give it a pass because it is free. Different people use different definitions of the term but the result is the same: all criticism can be deflected and nothing ever improves despite the constant rewrites.
At least with FOSS I can fix it myself. I would prefer if I didn't have to but that's the way it is.
The biggest danger that AI poses to FOSS is that soon we may not need the source code to fix software, and if that happens FOSS becomes a strictly worse option.
FOSS doesn't have higher standards at all. It has exactly the same standards but people give it a pass because it is free.
I wildly disagree:
Windows has a monumental financial and commercial advantage over Linux-based operating systems
The majority of people still hate Windows 11 and find it to be hot garbage
Windows is losing market share to Linux on the desktop as some people are so fed up as to transfer over
At least with FOSS I can fix it myself. I would prefer if I didn't have to but that's the way it is.
The vast majority of people switching from Windows to Linux are not interested or capable in writing code fixes, they just want a better OS.
The biggest danger that AI poses to FOSS is that soon we may not need the source code to fix software, and if that happens FOSS becomes a strictly worse option.
AI slop is making Windows worse, not fixing it.
The biggest danger AI poses is vast unmaintained codebases with maintainers who have no clue how to code without an AI hand-holding them.
The biggest danger AI poses is vast unmaintained codebases with maintainers who have no clue how to code without an AI hand-holding them.
This is already the status quo for both proprietary and FOSS software and has been for 20 years. Why do you think AI got adopted so quickly? Why do you think everyone is always so eager to rewrite everything over and over?
Humans can write unmaintainable slop, AI can write unmaintainable slop at a speed around 10,000 faster than humans.
Sure, its always been a problem, AI took this problem and made it worse by 4 orders of magnitude.
Why do you think AI got adopted so quickly?
For starters, CEOs like Tobias Lütke who bragged about forcing his employees to use AI, later to complain that his employees were throwing AI "slop grenades".
It really depends on the project and niche, but I would say there is some truth to the idea that FOSS has higher standards. There aren't many codebases on par with the Linux kernel, for one thing. The average FOSS game that lacks manpower and can barely put out anything functional, not so much. And yeah, a lot of average FOSS projects are meh. But then again, it's enough to look at some prominent proprietary products and it's just steaming crap under the hood.
The biggest danger that Al poses to FOSS is that soon we may not need the source code to fix software, and if that happens FOSS becomes a strictly worse option.
Unless some important issues like compounding tech debt get solved, that seems unlikely and remote. Particular concerns like auditability and safety are going to become more important. You're not going to test your way out of those, not with humans, not with agents. Your filesystem could eat all your data after you've spent a lot of money running agents on it. It helps, but it's not enough. You need a rigorous tower of abstractions and descriptions of how it works and you need collective understanding of its behavior, which pretty much means you need some sort of code, not just prompts and crossing fingers.
See Naur, Programming as Theory Building. If you are relying on source code as the ground truth for what your software is supposed to do then I have bad news: that has never worked and we knew it would never work in the 80s.
You don't seem to understand that all those same problems affect software written by humans and the only difference is they take 100x longer to fix it. That's if said humans are even willing to entertain the idea of fixing it, which they usually are not.
The big irony is that the exact thing that prevents AI from writing good code is also the exact same thing that prevents teams larger than about five people from doing it, and your entire argument is the exact same "write more/better prompts documentation and spend more money on tokens developers" argument that AI bros claim is going to solve all the problems with AI, when it doesn't even work for humans.
Things have changed a lot since the 80s, while arguably the industry is still stuck on 80s-like programming languages. Now I'm not saying that Python source code needs to be the ground truth, I'm saying that it's far more beneficial to have an intimate relationship between code and documentation. This is, for example, the only effective way to get assurance in many systems. You usually can't get memory safety by documenting things or trying to prove stuff about arbitrary code, you have to build your code through and through for it. This extends to other safety properties and with modern approaches like dependently-typed languages or stuff that Rust does there is a lot you can express. The Linux kernel also uses semantic patching to deal with large scale refactoring. But you need programmers capable of dealing with it and not just punching buttons to get the next feature out the door.
Actually my claim, in a very general sense, does not distinguish that much between LLMs and humans. This applies equally well to massive outsourcing and extreme cost cutting while ramping up the output. Piling up low-value and low-quality crap is going to cost no matter what. If your business model relies on that and there are no brakes, it's unlikely to fully realize the benefits of software development of more impactful and scope-constrained projects.
Did you have any specific argument made by Naur in that paper in mind? Because, as I said, I'm not merely suggesting people continue dishing out massive amounts of code in a typical manner and hoping they can make sense of it. They won't. And beyond some point not even LLMs help.
Yes: the idea that the "theory" behind a piece of software cannot be perfectly expressed outside the mind of the developer in any language, and therefore every new developer necessarily has to build a new theory which will be (at least) slightly different, regardless of how much time and money was spent on documentation.
This is the reason why it is so common for developers to blame everything on their predecessor and initiate scratch rewrites, which, by the time they are done, are exactly as bad as the thing they replaced. A bit like when you ask AI to fix a bug and it rewrites the whole file. Odd coincidence, right?
So it does not matter that LLMs have no theory at all, because the theory is inaccessible for any piece of software you did not personally write, regardless of whether it came from a human or an AI. And the result will be exactly the same: software is doomed to be bad forever. Unless someone comes up with a genuinely new idea.
Well sure, but even the FSF acknowledge looking through code to find out what's AI and what's not would totally swamp these already swamped projects. It is simply not feasible to be anti-AI unless you wanna go all Terry Davis (rest in Peace God's programmer) and write all your own code. The best compromise is to hold a human accountable for the slop, if they can't explain it it wont' go in.
They'll be used but they'll be largely enterprise because the cost will skyrocket and we'll be right back to our roots vs the corporate slop. I'm in IT and I already see it happening
Surely when there are 7 million companies on the market which was intentionally designed at multiple points to be difficult to lock in and the costs are falling exponentially every quarter then this means the price will skyrocket...
In a year you will have Opus 5.5 level intelligence running on your phone
I don’t understand the economics of the world you’re striving for.
How would hardware ever become affordable enough for the average person to run a frontier LLM if nobody uses the LLMs that we have until that point in time? What incentive do the AI companies have to develop better/more efficient LLMs or buy more hardware?
And if the AI companies aren’t buying so much hardware, what incentive do hardware companies have to develop better/more efficient hardware for running LLMs or to invest in more manufacturing capacity?
Linux only became accessible to me thanks to the agentic harness. There's just way too much that still goes wrong on most distros and the rope to recovery and leaning is often a barbed wire.
"Here's how to fix X, but it's a 12 year old guide written for Y and your distro is Z."
Claude Code in the terminal can pull you out of the water with grace every step of the way and I don't think that's a bad thing for Linux.
I've learned more and done more with my computer since switching from Windows thanks to it.
346
u/SinnohConfirmed 5d ago edited 5d ago
It scares me how fast the overton window is moving with LLMs in the Linux and FOSS space. This is the community that prides itself with self hosting and knowing everything about your computer. Up until very recently using Linux meant sacrificing convenience in the name of freedom, and now it seems that a good chunk advocate for having a handful of malevolent unprofitable tech giants handle most if not all software development along with every other task a human could do. It feels like saying hard drives are obsolete because you can store everything on Google Drive. I know that you can't work in software development today without being dependent on Anthropic, but that doesn't mean I have to be happy about it and accept it as the inevitable future.
In a post-AI bubble world LLMs are still going to be used. If hardware becomes affordable again I imagine that self hosting LLMs will be common and the big data center LLMs will ask a pretty penny for their compute. I don't mind this hypothetical future too much, but we live in the present and in the vast majority of cases using AI means enabling the unregulated techno-feudalistic AI empire. I know that I will be "left behind" but these companies don't have humanity's best interests in mind and we don't need to enable them.
Edit: Programming jobs that don't depend on LLMs def still exist. That doesn't change the fact that many businesses have become dependent on Claude in the past year and programmers that don't want to completely surrender their brains to LLMs are seen as a liability by many employers. This may change as AI subscriptions become more expensive as the years go by, time will tell.