I called out the prompt injection section as "pretty weak sauce in my opinion".
I did actually have a negative piece of commentary in there about how you couldn't see the thinking traces in the API... but then I found out I had made a mistake about that and had to mostly remove that section! Here's the original (incorrect) text from that: https://gist.github.com/simonw/eedbee724cb2e66f0cddd2728686f... - and the corrected update: https://simonwillison.net/2025/Aug/7/gpt-5/#thinking-traces-...
The reason there's not much negative commentary in the post is that I genuinely think this model is really good. It's my favorite model right now. The moment that changes (I have high hopes for Claude 5 and Gemini 3) I'll write about it.
Suspicious.
I continue to think that the 12B model is something of a miracle. I've spent less time with the 120B one because I can't run it on my own machine.
It’s reasonable that he might be a little hyped about things because of his feelings about them and the methodology he uses to evaluate models. I assume good faith, as the HN guidelines propose, and this is the strongest plausible interpretation of what I see in his blog.
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!
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