Research institutes like those founded by Terence Tao in our current present feel like they will align to this future almost perfectly on a long enough timeline -- tho I think on a shorter timeline this area of research is almost certain to provide a ton of useful ways to advance our current ai systems as our current systems are still in a state where literally anything that can generate new information that is "accurate" in some way -- like our current theorem prover engines are enormously valuable parts of our still manually curated training loops.

I love determinism and plain old data.

I suspect we'll be doing that sometime in January or February.

I guess forgejo is the easiest migration path? https://forgejo.org/

There is a very interesting thing happening right now where the "llm over promisers" are incentivized to over promise for all the normal reasons -- but ALSO to create the perception that the "next/soon" breakthrough is only going to be applicable when run on huge cloud infra such that running locally is never going to be all that useful ... I tend to think that will prove wildly wrong and that we will very soon arrive at a world where state of art LLM workloads should be expected to be massively more efficiently runnable than they currently are -- to the point of not even being the bottleneck of the workflows that use these components. Additionally these workloads will be viable to run locally on common current_year consumer level hardware ...

"llm is about to be general intelligence and sufficient llm can never run locally" is a highly highly temporary state that should soon be falsifiable imo. I don't think the llm part of the "ai computation" will be the perf bottleneck for long.

This looks interesting but I'm not familiar with NATS

The real world has "actually bad" actors -- not just misaligned incentives.

I don't care about Jimmey Kimmel's jokes nor do I watch his show with any regularity -- but I sure as hell care about his right to make jokes.

Browser standards managed to do this in a lot of ways despite far more complex standards, more complex variations in behavior, and much more rapid continue evolution ...

Structured concurrency libraries like anyio or trio are actually pretty nice -- "stacks" and stack traces are good things. Python multi exception concept is weird --- but also I think probably good ish.

It is still a pita to orchestrate around the gil/how terrible python multiprocessing side effects are wherever cpu bound workloads actually exist ...

Debugger >> language -- next most popular language manifesto slogan (i hope).