When Models Learn

Explains test-time training through the analogy of a GPS learning a persistent shortcut around daily traffic rather than a one-time reroute: the model takes a gradient step on the prompt it's answering, so its weights change as it works. Traces three implications, flat memory instead of a linearly growing KV-cache, the provider cost of serving a separate model per user, & faster inference, then states the tension as a tradeoff between serving long context and serving many people, & grounds it in concrete use cases, a coding agent that earns back its per-user cost over a long session versus a one-off query a shared frozen model handles just as well.