uhm… you don’t have to hope…? You can literally check which driver is installed. You can even install preferable driver version via your package manager and delete mesa drivers if you don’t want your system to ever use the integrated gpu.

It’s almost as if neither “ai can do everything for me” nor the ai witch hunting are healthy approaches, but, as always, people keep polarizing themselves to hate on the opposite tribe.

I’m so fucking tired of this bullshit…

Lutris seems to be the solution for your problem. Also, for older games, often than not, you gotta try few different proton versions.

Except even this is no longer an issue. There’s nvidia-open driver, and, at least to my knowledge, it works without any problems.

it’s not always a hypercube tho! It can be other shapes too!

Mine was a form of a hyper-dyno-shaped nuggie!:D

What even is a “professional developer”? I mean, given that you aren’t an egg-headed RnD developing new technology for someone like Nvidia, the job of a developer is 95% mind-numbing routine and typical tasks solved by applying ready-made patterns.

i’ve heard of the dataset poisoning and degradation caused by llm-generated content present in the dataset myself, but i’m not sure whether it was a practical observation, or a mere experiment. And I still fail to see how new datasets are really useful for developing a new llms, or how it’s a problem for the devs to switch back to the older datasets.

And the cornerstone stays the same: to have any significant effect on the final LLM quality, shouldn’t the poisoned (either by llm-produced content, or by intentional poisoning) data portion be… well, statistically significant?

i think that sort of issues will no longer be relevant, considering that there are official semi-opensource nvidia-open drivers and that even wayland supports nvidia gpus just fine nowadays.

except for poison to take in, it should be a pretty significant part of the dataset. Also, ngl, i’m not much informed on the topic, but aren’t all the datasets, if we’re talking about generic diffusion models and LLMs, already been formed? From what i gather, the innovation in AI mainly comes from utilizing new architectures, rather than training a model on something unique.

not really related to the post, but NGL, it kinda fascinates me, how with the advent of LLMs, they became the ultimate punching bag for whenever something works bad, as if people didn’t write even more horrible things without them (my regards to javascript and python).

just to clarify, he’d not so much called LLMs “the new compilers” as he compared both to each other in a sense that an LLM is just another layer of analysis tooling between the developer and the final machine code, which, IMO, sounds much more reasonable than calling LLMs “the new compiler”.

it’s not so much “bending” as it is treating ai as a potentially-useful tool when done right. Which it totally is.