Reader change: Instead of repeatedly typing “What now?” the reader learns to design a loop that specifies an activation trigger, an observable numeric stop condition, an independent evaluator, and a default failure action. With that design the AI runs autonomously each day; the article supplies a concrete, ready‑to‑use blueprint (tested in Japanese and English) that the reader can copy and deploy.
Evidence supporting the claim:
Remaining uncertainty / limitation: The loop approach only pays off for repetitive, well‑defined judgments; it is not cost‑effective for one‑off tasks. Its success still hinges on the human‑crafted stop criteria and evaluator logic—if the evaluator crashes or is mis‑configured, the system must have an explicit default‑failure policy, otherwise low‑quality outputs could be emitted. Moreover, the Anthropic 97 % result is tied to a specific experimental setup and has not been independently reproduced, so the figure cannot be universally generalized.