I have a theory that soon enough every code library will ship a CLI and a very tiny finetune for that specific lib alongside it

81%is nothing. It is not hard to be more than 3x better than Postgres [1]. And you don’t need a model to do that, let alone a 4B model. How much additional compute is needed to just run that model?

[1] https://github.com/datalevin/datalevin/tree/master/benchmark...

It's certainly a good idea to train a neural network to find good query plans but... an LLM??

An 8GB dataset? That’s literally a few seconds worth of records generated in my world, and any speedup at that scale is completely meaningless.

Let’s talk when you are looking at double digit TB at a minimum.

Funny enough I was thinking about something very similar to this based on the Jev model posted yesterday.

Suppose you run a platform, and you run a couple thousand different queries of different types throughout the day. It would make sense to have an auto-optimizer that would read long queries, ponder over them with an LLM, come up with some good plans, and store them as hints. This seems like quite a good idea? Is there a product for this?

such a (visually) beautiful blogpost.

Mentioned elsewhere but classic ml seems the right tool for this problem

> How hard can it be?

> As it turns out: enormously hard.

This exactly tracks me learning everything

Really creative use of distillation here.