Earlier quoted context omitted.
> LLMs are like anti-compression algorithms when used like that, a simple idea gets expanded into a bloated mess by an LLM, I think that's the answer: LLMs are primarily useful for data and text translation and reduction, not for expansion. An exception is repetitive or boilerplate text or code where a verbose format is required to express a small amount of information.
There is one other very useful form of "expansion" that LLMs do. If you aren't aware: (high-parameter-count) LLMs can be used pretty reliably to teach yourself things. LLM base models "know things" to about the same degree that the Internet itself "knows" those things. For well-understood topics — i.e. subjects where the Internet contains all sorts of open-source textbooks and treatments of the subject — LLMs really…
Why is the angle called m? Why is a combination nPr * (1/r)? What is 1/r doing there?
I use mathacademy.com as my source of practice. Usually that’s enough but I tend to fall over if small details aren’t explained and I can’t figure out why those details are there.
In high school this was punished. With state of the art LLMs, I have a good tutor.
Also it’s satisfying to just upload a page in my own handwriting and it understands what I did, and is able to correct me there.