Comment by andai

It probably depends on what you're doing, but my use case is simple straightforward code with minimal abstraction.

I have to go out of my way to get this out of llms. But with enough persuasion, they produce roughly what I would have written myself.

Otherwise they default to adding as much bloat and abstraction as possible. This appears to be the default mode of operation in the training set.

I also prefer to use it interactively. I divide the problem to chunks. I get it to write each chunk. The whole makes sense. Work with its strengths and weaknesses rather than against them.

For interactive use I have found smaller models to be better than bigger models. First of all because they are much faster. And second because, my philosophy now is to use the smallest model that does the job. Everything else by definition is unnecessarily slow and expensive!

But there is a qualitative difference at a certain level of speed, where something goes from not interactive to interactive. Then you can actually stay in flow, and then you can actually stay consciously engaged.