Basically Yggdrasil?

It can't be successful at that any more than 1+1 can equal 3. Fundamentally, if every token wants to be able to look at every previous token without loss of information, it must be O(n^2); N tokens looking at N tokens is quadratic. Any sub-quadratic attention must hence necessarily lose some information and be unable to support perfect recall on longer sequences.

TLDR: ... I'm getting a comparable experience to NixOS, with all the usual pros a declarative environment brings and without having to put up with Nixlang.

My favorite early chatgpt math problem was "prove there exists infinitely many even primes" . Easy! Take a finite set of even primes, multiply them and add one to get a number with a new even prime factor.

Of course, it's gotten a bit better than this.

So, a set of standard software RPCs (remote procedure calls) and APIs (application programming interfaces) and not another electrical signalling standard. Got it.

Thanks for the correction.

So, I guess the next question is what are you folks actually using at the electrical signalling level to talk? (If you are not allowed to say, I understand.)

It's 32 bits of milliseconds, right? Hm, no, that would overflow much sooner (49.7 days).

I read it as the inefficient part isn't the compute efficiency, the inefficient part is dumping all the resulting heat into the environment without capturing it and using it in some way to generate electricity or do work.

On a related/side note, when there's talk about seti and dyson spheres, and detecting them via infrared waste heat, I also don't understand that. Such an alien civilization is seemingly capable of building massive space structures/projects, but then lets the waste heat just pour out into the universe in such insane quantities that we could see it tens/hundreds of light years away? What a waste. Why wouldn't they recover that heat and make use of it instead? And repeat the recovering until the final waste output is too small to bother recovering, at which point we would no longer be able to detect it.

More recently it's turned out that quantum computers are less useful for molecular simulation than previously thought. See: https://www.youtube.com/watch?v=pDj1QhPOVBo

The video is essentially an argument from the software side (ironically she thinks the hardware side is going pretty well). Even if the hardware wasn't so hard to build or scale, there are surprisingly few problems where quantum algorithms have turned out to be useful.

Could you elaborate?

It's totally plausible that AI codegen produces more bugs. It still seems important to familiarize yourself with these tools now though, because that bug count is only ever going to go down. These tools are here to stay.