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07:05 Hy3 + current Tomo, the 15-task baseline The post-LeetCode Tomo OI baseline runs all fifteen offline SWE-bench-Live tasks on hy3-free at pass@1. It solves five, including Kubernetes Python after a ten-minute first completion, and records 1.72 million provider-reported tokens. That number is a lower bound: 29. 00:35 Tomo OI reverses the Pi LeetCode cost gap The first Luna LeetCode board found Tomo correct but expensive: twelve to fourteen model calls and up to 63 thousand tokens per problem. 00:22 TAOCP partial GPT matrix A deliberately stopped TAOCP solver experiment shows that two full proof audits consumed more tokens and list-equivalent cost than solution generation. The completed paired cases also show no quality gain from slow mode despite 7.57 times the generation tokens.