Not me, if I'm going to take the time to read something, I want it to have been written, reviewed and edited by a human. There is far too much high fidelity information to assimilate that I'm missing out on to put in low fidelity stuff
This, IMO, is the actual biggest problem with LLMs training on whatever the biggest text corpus us that's available: they don't account for the fact that not all text is equally worthy of next-token-predicting. This problem is completely solvable, almost trivially so, but I haven't seen anyone publicly describe a (scaled, in production) solution yet.
Can you explain your solution?