[mlyle]

I don't use LLMs to answer general problems for correctness. I use them for text formatting and rewriting superpowers. GPT-4t does a good job if I need it to iterate and change slightly what it does.

For example, to inform the University of California about the content of my courses, I have to go through a course articulation which is several pages long, is written in a formal academic voice, and is pretty time consuming to create. GPT-4t can take my informal course outline and an example of a past articulation that I've written and do the job to a point where I just need to ask it to make small changes for 10 minutes and then make a last couple edits myself. I turn a couple of hours to 10 minutes and 25 cents of API calls.

(Also, sometimes when it's explaining example assignments, it thinks of nice things to include that I hadn't planned on, and I end up shamelessly using them; other times it thinks of garbage and I have to coax it to articulate what I actually meant).

I'd say GPT-4o is slightly better at the task... except it commits so strongly to its answers in the context buffer that it doesn't do effective rewrites/corrections. So I've settled into a workflow of using GPT-4o to do initial work and then use GPT-4t for the final cleanup.