Reading through the thread, a lot of the inflection point debate seems to come down to people talking past each other about what got better. My read is that the models themselves didn't really jump in capability around November, but the harnesses around them got considerably more reliable, and the RLVR work earlier in 2025 had been training the models specifically to behave well inside those harnesses, so when the two met you got a compounding effect that felt like a step change even though neither piece was that dramatic on its own.

I think that's probably why everyone in this thread has such different experiences - someone whose workflow is mostly asking a model for code and pasting it in would have seen modest improvement and would reasonably wonder what the fuss is about whereas someone who was already running agents on 20-step loops would have felt a much bigger shift, because the thing that used to kill those runs was the failure at step 12 cascading into garbage by step 20, and that got a lot better.

The local model story Simon kind of glosses over is interesting for the same reason - a 20GB model drawing a decent pelican on a laptop is a cute data point in isolation. The thing worth noticing is that a competent local model inside a good harness now gets you closer to frontier performance than running the frontier model without a harness does.