[roadside_picnic]

> Which programmer wants that?!

Agreed.

My LLM usage has quickly become a more efficient way to solve problems that basically require copy/pasting from some documentation I have to look up where me doing it myself is more error prone.

I was recently doing a fairly complex set of data transformations and understanding what the data means remained essential. AI tends to fail spectacularly at really understanding the nuances of data (that often requires understanding the underlying business logic generating the data).

However it's very useful when I have to do a bunch of window functions, where I can describe the operation but don't want to look up the syntax. Or just writing SQL when I can explain exactly what I need it to do.

Similarly working with Pytorch involves a fair bit of what I consider pseudo-boilerplate where the code itself is quite boring, but contains just enough complexity that it can't be simply automated away. Hand rolling this stuff often leads to small errors/bugs, but LLMs can do a spectacular job of quickly generating this, provided you do know exactly what you're looking to build.

What's interesting is that this has still been a major speed boost, because looking up how to do some tedious thing you just forgot how to do can really break flow.