Excellent HN-esque innovation in moderation: immediate improvement in S/N ratio, unobtrusive UX, gentle feedback to humans, semantic signal to machines.

How was the term "rug" chosen, e.g. in the historical context of newspaper folds?

Really well done article.

I'd note, when I gave the input/output screenshot to ChatGPT 5.2 it failed on it (with lots of colorful chain of thought), though Gemini got it right away.

Thanks for sharing; you clearly spent a lot of time making this easy to digest. I especially like the tokens-to-embedding visualisation.

I recently had some trouble converting a HF transformer I trained with PyTorch to Core ML. I just couldn’t get the KV cache to work, which made it unusably slow after 50 tokens…

What an excellent write-up. Thank you!

Amazing article. I was under the misapprehension that temp and other output parameters actually do affect caching. Turns out I was wrong and this explains why beautifully.

Great work. Learned a lot!