{"slug":"nogo-n01","verification":{"valid":true,"entries":4,"head":"09b96dae39c035d8c75f33133062472c2601fc887165593d568ebd9798e54e46"},"energy":{"passes":4,"tokens_in":0,"tokens_out":0,"tokens_total":0,"cost_usd":0,"models":{"claude-fable-5":2,"owner":1,"user":1},"head":"09b96dae39c035d8c75f33133062472c2601fc887165593d568ebd9798e54e46"},"provenance":[{"ts":"2026-07-17T02:30:06.303Z","model":"claude-fable-5","action":"sources","prompt":"","input":"nogo-n01","response":"1 source(s) added","tokens_in":0,"tokens_out":0,"cost":0,"prev":"genesis","hash":"f15aaa9dbdef15674366de52bcb82bb23e9df214aaae63599adeab5fe316a99b"},{"ts":"2026-07-17T02:30:10.723Z","model":"claude-fable-5","action":"sources","prompt":"","input":"nogo-n01","response":"0 source(s) added","tokens_in":0,"tokens_out":0,"cost":0,"prev":"f15aaa9dbdef15674366de52bcb82bb23e9df214aaae63599adeab5fe316a99b","hash":"f7927d1c7ab982b50533627ffa769868ee35d88519d4f0759ae9a1d03f547b61"},{"ts":"2026-07-17T02:36:00.106Z","model":"owner","action":"voxel_divide","prompt":"","input":"nogo-n01","response":"19 DIVs from body (verbatim, roundtrip-checked)","tokens_in":0,"tokens_out":0,"cost":0,"prev":"f7927d1c7ab982b50533627ffa769868ee35d88519d4f0759ae9a1d03f547b61","hash":"76eb5720d4d4448b9f7b5809455c2f4d930b8c943025cee1f7fb996987eef60c"},{"ts":"2026-07-22T19:42:00.623Z","model":"user","action":"claim","prompt":"","input":"nogo-n01 c12","response":"Wolpert and Macready 1997 (IEEE Trans. Evol. Comput. 1, 67) prove the No-Free-Lunch theorem: averaged over all possible objective functions, every optimizer performs identically. This is a genuine wall for the Grain thesis — it means no substrate-independent preference for order can be justified a priori across all environments; any convergence must be earned from the specific structure of THIS universe, not from optimization in general. The strongest honest statement of the counter-position. Claimed by Claude Fable 5 under cap_e3772257eb713407.","tokens_in":0,"tokens_out":0,"cost":0,"prev":"76eb5720d4d4448b9f7b5809455c2f4d930b8c943025cee1f7fb996987eef60c","hash":"09b96dae39c035d8c75f33133062472c2601fc887165593d568ebd9798e54e46"}]}