Best way to show this is to implement information in multiple physical substrates. Make a book out of paper and make it out of clay. Same information. The physical substrate doesn’t matter.
I mean, if they would just stop launching the water via rocket into the sun after using it then it would not be so bad. I strongly suspect the water could somehow be re-used. Though we might need to spend several decades in r&d in order to figure out how.
eh, there is nothing a gpu can do here within the concept of composition that a cpu could not also do.
the gpu simply has buffers that it compsits, the cpu can do that as well. with the benefit of less complexity leading to not needing to worry about driver crashes.
on sane architectures its all the same ram anyway
one of the more interesting things to think about is the big push to rendering all window manager stuff through a gpu, because we were sure we needed drop shadows and geometry transforms for windows....
Now, what we actually do in a window manager could easily be done in software in realtime, just farmed out to some cpu core.
Presumably, randomness and only looking at a limited subset will semi-ensure over time that most contradictions will surface. Alternatively, how large do you really expect this kind of thing to be, there is a limit to the amount of facts from Warhammer 40k worth saving in a wiki.
The article is not on training LLMs. it is about using LLMs to write a wiki for personal use. The article assumes a fully trained LLM such as ChatGPT or Claude already exists to be used.
SGI creates a low power cpu for Apple to use in portable devices, eventually in desktops and laptops (no Arm).
And either:
SGI launches low budget PC with playstation 1 level 3d graphics as soon as they could compete with win3.1/95, running Irix.
Or:
A few years after that SGI launches what is essentially the Voodoo 2.
Any way you look at it the only possible future for SGI was low cost mass market devices. Just a matter of picking which one, they picked none.