I'm thinking the best end result would come from custom-built models. An 8 billion parameter generalized model will run really quickly while not being particularly good at anything. But the same parameter count dedicated to parsing emails, RAG summarization, or some other specialized task could be more than good enough while also running at crazy speeds.

Well of course. In the long run AI will do almost all tasks that can be done from a computer.

I completely do not buy the human's story.

> all I said was “you should act more professional”. That was it. I’m sure the mob expects more, okay I get it.

Smells like bullshit.

> Claude is definitively trained on the process of coding not just the code, that much is clear.

Nuance like this is why I don’t trust quantitative benchmarks.

I'm impressed GitHub managed to handle this beast:

https://github.com/a11ce/docker-lisp/actions/runs/2216831271...

500+ container invocations to compute factorial(3)

I don't wanna know how much containers it spins up for fibonacci(3)

Or A(4,2).

People are rapidly learning how to improve model capabilities and lower resource requirements. The models we throw away as we go are the steps we climbed along the way.

For the moment it’s best practice to run it and all of your dev stuff in a VM.

People keep talking about automating software engineering and programmers losing their jobs. But I see no reason that career would be one of the first to go. We need more training data on computer use from humans, but I expect data entry and basic business processes to be the first category of office job to take a huge hit from AI. If you really can’t be employed as a software engineer then we’ve already lost most office jobs to AI.

Ah but that was before he saw the comp packages. But no judgement. The tool is still open source. Seems like a great outcome for everyone.