It is my own anectodal. Few days something I worked on usually got one shotted or got quality result. Today it is very much going nowhere and is stuck in reasoning loops.

Probably someone should build nerf tracker, because this is quite common that models get substantially worse once PR hype wears off and they quantise them more or simply route requests to older models with system prompt changed to say it is Astra and not Sol etc.

That is not conclusive, because they can detect such tracker and route it through proper not quantised model.

Models do not get nerfed. There has never been evidence of this. This would be trivial to prove if it were true, and such a proof would be a huge story and scandal to a news market hungry for a shred of a signal on AI's downfall.

This is the "your iPhone is listening to you and serving ads based on what you say" of the 2020s.

One theory is that they start serving at full precision, then quantize to save on costs as adoption grows. It's kind of a conspiracy theory atm, but I have definitely felt it - I had a large project done on Opus 4.8 release day, a week later it was struggling to complete partial tasks in the same area.

Yes. They save on compute and customer has to use more tokens to achieve their goal which means more profit.

It’s very far from Fable on benchmarks that matter, like TB4.

no, I mean not really news specifically on the number for terminal bench 2.