[throwawayReLU]

It probably depends on the definition of "expert" here. Based on my definition, experts are people who write the LLM papers I read (some of them are my colleagues), people who implement them, people that push the field forward and PhD researchers blogs that go into depth and show understanding of how attention and transformers work, including underlying math and theory. Based on my own knowledge, experience (I'm working on LLMs in the field) and my discussions with people I consider experts in my day job I wouldn't add you to this category, at least not yet.

Based on my reading of some of your blogs and reading your discussions with others on this site, you still lack technical depth and understanding of the underlying mechanisms at what I would call an expert level. I hope this doesn't sound insulting, maybe you have a different definition of "expert". I also do not say you lack the capacity to become an expert someday. I just want to explain why, while you consider yourself an expert, some people could not see you as an expert. But as I said, maybe it's just different definitions. But your blogs still have value, a lot of people read them and find them valuable, so your work is definitely worthwhile. Keep up the good work!

[simonw]

Yup, I have a different definition of expert. I'm not an expert in training models - I'm an expert in applications of those models, and how to explain those applications to other people.

AI engineering, not ML engineering, is one way of framing that.

I don't write papers (I don't have the patience for that), but my work does get cited in papers from time to time. One of my blog posts was the foundation of the work described in the CaMeL paper from DeepMind for example: https://arxiv.org/abs/2503.18813