I’m talking about the LTT staff.
Proof of this is that for ten years, they published zero videos specifically about Windows shortcomings, but they did I believe two separate Linux “challenges”, where the main conductor was Windows users complaining about Linux. And while I agree that some of the complaints were legitimate, many others, like UX differences compared to Windows, definitely are not.
To be fair, LTT has had a very long history of shitting on Linux, while giving Windows a free pass.
Happens all the time. Also, nerds tend to overestimate the amount of resources, like time or money, someone would put on something they care about.
Right here in Lemmy I had this interaction where someone argued that if one were to lose their photos because Google had an oopsie, it’s kind of their fault because they didn’t have a backup plan.
If it succeeds, then great, people will have a faster, safer Bun.
If it fails, then great, one less JS runtime.
It’s a win-win!
The problem I’ve got is that you all have a god of the gaps, the conversation I was having 3 years ago was different to 2 years ago was different to 1 year ago
And I guess the problem I have with you, is that you seem to think that you can get results with 16GB, competitive with models that run on a Blackwell 6000 with 96GB, while ignoring the fact that the vast majority of the people in the world are running GPUs with 4 to 8 GB of VRAM, if they even have access to GPUs, at all.
That’s the gap. Most people don’t have the kind of money you think they do, and even those who do have some money, they will never achieve the same results as with cloud models, because if there’s a state of the art optimization that makes models 10 times smaller, cloud models will become 10 times bigger with that advantage. It’s pretty simple.
2026’s average gaming PC is massive amounts of memory and compute apparently
Any model that can run on 16GB or less, is not going to be any close in real world tasks, to any other cloud based model. It just cannot be. There are people out there running Qwen on the Mac Studio with 96GB, and it falls short of cloud based models in both performance and speed.
lol there are plenty of open source models in the top 100 with multiple SOTA models released in the last few months alone
The top 100 of what, exactly? Many blended benchmark results are notoriously biased, and LLMs “cheat” on benchmarks on every single opportunity, so it is still hard to tell, outside of real world tasks and speed, which models are actually better than others.
But regardless, the main point of the gap is resources. Even if the average gaming computer was really enough to run meaningful models, the vast majority of the world wouldn’t have access to it, even more so in this day and age, where a single RAM stick couldn’t be bought with a whole monthly salary in most parts of the world.
For which you still need massive amounts of memory and compute to run reliably. That, and the fact that chatbots and agents nowadays rely on all sorts of proprietary customizations, outside of the realm of LLMs, to perform certain tasks.
The gap will take decades to close, if it ever does.
I don’t like GitHub, but this looks like they had someone using an authorized SSH key, but the git client was configured to post some unknown email address. Happens all the time.
Would be funny if they only find out once they have migrated off GitHub though.
Not at all. I’m genuinely interested in the use cases for this type of library.
Is that a thing? I mean, running ClamAV in NodeJS? First time I’ve seen it. Is there any project out there that you know could use your library? Would love to see it.
What’s the goal of this project? What does it try to achieve? Genuinely curious.
We aren’t comparing humans to code.
Except for the bit where LLM behaviors aren’t deterministic, but those of most compilers in most situations are.
And before anyone says that LLVM in version X produced wildly different assembly from version Y, it is not remotely comparable to what LLMs do, not even close.
I would need a citation for that “2x-5x faster” with the same quality, because that hasn’t been my experience at all. Most of my colleagues treat LLMs as “better Google”, and agentic coding in production has been downsized, to the point where it may help with the least critical paths only. And we aren’t particularly AI skeptic, at all.
Also, I feel like progress has stalled in the past couple of years, e.g. Opus latest version doesn’t seem to provide me with any noticeable advantages over the previous one. Are they getting better on paper? I suppose they do, but I couldn’t care less about that if they don’t give me better results.
The thing is, writing code was never the issue, engineering it is. If a machine helps me write code 10 times faster, that saves me maybe a couple hours, which isn’t really meaningful. On the other hand, it increases my workload by forcing me to thoroughly check the work of less experienced devs who rely on them, just to make sure that there aren’t errors that could cause serious harm.
I guess what I’m trying to say is that AI is giving inexperienced people confidence they shouldn’t have in the first place, and that’s not a good thing.
What does it mean? Because just yesterday I saw this guy live streaming a vibe coding session, and he sounded exactly like “Bill”.
If you only need it for development, and will still host the final product in Vercel, why wouldn’t you just test locally?
There are no 30 day windows challenge videos?
There are a bunch of videos about how to configure windows “the right way”, which, to me, seems like a way of coping with the negative side of windows without questioning it too much.
And look, I get that windows users may have trouble with Linux. But LTT have been overly critical and even dismissive of Linux for the longest time. There are videos as recent as 2022 where some of the complains about Linux were straight up “I don’t like how this works”.