[datadrivenangel]

Yeah. The speed is the biggest issue. The intelligence of open models is good enough for serious work (though still worse than the frontier models), but the cloud models are often 3-7 times faster, and you can get more parallelization and so get speeds on the order of hundreds of tokens per second, which makes things fast!

[freeopinion]

Even extremely slow LLMs can generate Part B faster than I can audit Part A. So the LLM can generate Part A while I look over my email. Then it can worry over Part B while I look over Part A.

It can worry over Part C while I have my 10:30 group meet. And it can worry over Part D while I do whatever other silly, time-wasting thing all humans do in almost all organizations. Then I still haven't reviewed Part B, yet, so the extremely slow AI is waiting on me.

Maybe someday I'll be good enough to need faster AI so I can rewrite something like Bun in a few days. Right now, slow and local fits my use case very well.

[quietsegfault]

I don’t think it matters if you’re “good enough” or not. Much of AI development is iterative. If you context switch between A from project 1 to B from project 2 back to check A, then maybe C while B finishes up, you will lose the flow state that AI assistance can enable with speed for those who are not fluent coders.

Sure, I can wait hours for my local model to finish, or I can spend basically as much and get the answer right away

There’s a lot of exciting stuff with local LLMs despite the speed, but for me I don’t have the discipline and working memory to jump from project to project.

[bcjdjsndon]

> Even extremely slow LLMs can generate Part B faster than I can audit Part A.

Depends what part a and b are...because workloads are different