Will 2026 truly be the real year of Linux gaming on the desktop? Perhaps! And now even GOG are hiring towards building up their Linux support for GOG Galaxy.
We already knew that GOG were going to be looking more seriously at Linux, as covered here previously on GamingOnLinux. However, they’re going a step further as noted in their job listing on how “Linux is the next major frontier”.
Well I will not share a screencast where a local LLM helps with code completion on a private project. You talk like you’re a proficient developer, you can try that on your own. And where is the fallacy?
We’ve got studies that show AI makes you feel more productive while you’re actually less productive. And all you’re offering is a feeling you feel. Get high on your own supply if you want, but don’t drag down good companies with your evangelism.
Don’t act so stupid, dude. You know what post you’re in, or at least I hope you do.
If you want to claim that AI can magically do something that not even AI companies themselves can prove, then prove it. Ed Zitron has been begging AI evangelists like you to prove it for at least a year now. Otherwise, I call bullshit on your evangelism.
Hey I’m against corporate AI too, but when anyone can create a very basic ML program that runs locally with public domain data, eventually something both useful and ethical will emerge. It’s good to be skeptical, but you don’t have to be an AI bro to see that some specific tools might meet or exceed your standards.
I don’t like image or video generators, but the core tech is really useful for frame interpolation, a usecase that is not inherently controversial and badly needs improvement.
Sorry to not-x-it’s-y, but it’s not about forcing the big tool into your workflow, it’s about finding the 1001 little tools that work every time and collecting them. Or, wait for these tools to be consolidated.
If I seem naive, It’s cause I believe in reclaiming as much from tainted technology as possible.
Considering the GOG announced their AI usage to the world with an AI-generated image, and the technology currently cannot be remotely useful without being extremely unethical, I do not share your optimism.
There’s plenty of real technology that can be reclaimed right now, though! From textile machines to lithium ion battery technology, the world is your oyster.
None of what you brought up as a positive are things an LLM does. Most of those things existed before the modern transformer-based LLMs were even a thing.
LLM-s are glorified text prediction engines and nothing about their nature makes them excel at formal languages. It doesn’t know any rules. It doesn’t have any internal logic. For example if the training data consistently exhibits the same flawed piece of code then an LLM will spit out the same flawed piece of code, because that’s the most likely continuation of its current “train of thought”. You would have to fine-tune the model around all those flaws and then hope some combination of a prompt won’t lead the model back into that flawed data.
I’ve used LLMs to generate SQL, which according to you is something they should excel at, and I’ve had to fix literal syntax errors that would prevent the statement from executing. A regular SQL linter would instantly pick up that the SQL is wrong but an LLM can’t pick up those errors because an LLM does not understand the syntax.
I’ve seen humans generate code with syntax errors, try to run it, then fix it. I’ve seen llms do the same stuff - it does that faster than the human though
But that extra time is then wasted because humans still have to review the code an LLM generates and fix all the other logical errors it makes because at best an LLM does exactly what you tell them to do. I’ve worked with a developer who did exactly what the ticket says and nothing more and it was a pain in the ass because their code always needed double checking that their narrow focus on a very specific problem didn’t break the domain as a whole. I don’t think you’re gaining any productivity with LLMs, you’re only shifting the work from writing code to reviewing code and I’ve yet to meet a developer who enjoys reviewing code more than writing code, which means code will receive less attention and thus becomes more prone to bugs.
So you agree those models have already been made, and running them no longer require 50 exawatts of power, right?
Not sure why you decide to change the context to training the models instead of running it like the other guy was claiming.
(As if you genuinely believe those are the ones GOG is using.)
I thought the context was changed to general use of LLM as a tool for programmers, not specifically about GOG? Can’t even double check it now because the mod removed the comment for some reason.
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Doomposting about AI inevitability is only beneficial to AI companies… If your claim is even true. And if it is, we should shame everybody else.
removed by mod
Citation needed.
You’re on a post about Linux, an OS that’s grown in popularity thanks to Microsoft ruining Windows with the “true aids” you’re promoting here.
Whatever MS bakes into Windows is not what I listed above. Spin up a local LLM trained on your code base and try using it.
No thanks AI bro.
I don’t buy your evidence-free praise of AI. And I don’t buy your No True Scotsman fallacy.
Well I will not share a screencast where a local LLM helps with code completion on a private project. You talk like you’re a proficient developer, you can try that on your own. And where is the fallacy?
We’ve got studies that show AI makes you feel more productive while you’re actually less productive. And all you’re offering is a feeling you feel. Get high on your own supply if you want, but don’t drag down good companies with your evangelism.
What‘s the good company in this context?
Don’t act so stupid, dude. You know what post you’re in, or at least I hope you do.
If you want to claim that AI can magically do something that not even AI companies themselves can prove, then prove it. Ed Zitron has been begging AI evangelists like you to prove it for at least a year now. Otherwise, I call bullshit on your evangelism.
I’m curious about these studies. Do you have a citation?
Probably referencing this: https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
Hey I’m against corporate AI too, but when anyone can create a very basic ML program that runs locally with public domain data, eventually something both useful and ethical will emerge. It’s good to be skeptical, but you don’t have to be an AI bro to see that some specific tools might meet or exceed your standards.
I don’t like image or video generators, but the core tech is really useful for frame interpolation, a usecase that is not inherently controversial and badly needs improvement.
Sorry to not-x-it’s-y, but it’s not about forcing the big tool into your workflow, it’s about finding the 1001 little tools that work every time and collecting them. Or, wait for these tools to be consolidated.
If I seem naive, It’s cause I believe in reclaiming as much from tainted technology as possible.
Considering the GOG announced their AI usage to the world with an AI-generated image, and the technology currently cannot be remotely useful without being extremely unethical, I do not share your optimism.
There’s plenty of real technology that can be reclaimed right now, though! From textile machines to lithium ion battery technology, the world is your oyster.
None of what you brought up as a positive are things an LLM does. Most of those things existed before the modern transformer-based LLMs were even a thing.
LLM-s are glorified text prediction engines and nothing about their nature makes them excel at formal languages. It doesn’t know any rules. It doesn’t have any internal logic. For example if the training data consistently exhibits the same flawed piece of code then an LLM will spit out the same flawed piece of code, because that’s the most likely continuation of its current “train of thought”. You would have to fine-tune the model around all those flaws and then hope some combination of a prompt won’t lead the model back into that flawed data.
I’ve used LLMs to generate SQL, which according to you is something they should excel at, and I’ve had to fix literal syntax errors that would prevent the statement from executing. A regular SQL linter would instantly pick up that the SQL is wrong but an LLM can’t pick up those errors because an LLM does not understand the syntax.
I’ve seen humans generate code with syntax errors, try to run it, then fix it. I’ve seen llms do the same stuff - it does that faster than the human though
But that extra time is then wasted because humans still have to review the code an LLM generates and fix all the other logical errors it makes because at best an LLM does exactly what you tell them to do. I’ve worked with a developer who did exactly what the ticket says and nothing more and it was a pain in the ass because their code always needed double checking that their narrow focus on a very specific problem didn’t break the domain as a whole. I don’t think you’re gaining any productivity with LLMs, you’re only shifting the work from writing code to reviewing code and I’ve yet to meet a developer who enjoys reviewing code more than writing code, which means code will receive less attention and thus becomes more prone to bugs.
removed by mod
I wasn’t talking about existing tools.
We had all of those things before AI and they worked just fine and didn’t require 50 Exowatts of electricity to run.
Neither does a locally run LLM model.
Hey Steven, how do you think they make those models?
(As if you genuinely believe those are the ones GOG is using.)
So you agree those models have already been made, and running them no longer require 50 exawatts of power, right? Not sure why you decide to change the context to training the models instead of running it like the other guy was claiming.
I thought the context was changed to general use of LLM as a tool for programmers, not specifically about GOG? Can’t even double check it now because the mod removed the comment for some reason.