Build track · ship to pass
◇ a cold, no-notes verbal explanation that locates the flat/near-uniform distribution over content tokens, states nothing wires that uncertainty to a hedge, cites the post-training penalty on 'I don't know', and concludes confidence != correctness
et1 marginal pass. Cold first pass (6.5): had form-vs-content (peaked vs flat) and no-truth-only-probabilities, but dropped the frozen-weights/corpus-gone root and gave the shallow 'incentivized to be confident' for the confidence crux. On one redirect, produced the missing-wire decoupling himself cleanly (flat distribution not connected to the hedge decision; 'no process expresses that the token was picked from a flat distribution') AND added an unprompted insight: a guessed token gets appended and baked in as truth, conditioning the rest of the answer. Substance is all there; the supplied piece (frozen weights) he's independently nailed cold earlier this session. Pass = test-time completeness lesson, not a knowledge gap.
◇ a correct cold prediction with the mechanism — quadratic attention cost in token count (one 1000^2 vs ten 100^2), input-vs-output token pricing, and context/window growth
passed et2 (cost prediction): B, ~200x tokens; reconciled linear per-token bill vs super-linear attention compute (latency/GPU), plus the sharp insight that per-token pricing is a deliberate linear abstraction over quadratic compute. came out in pieces under prompting - worth a cold re-confirm later; underlying n^2 mechanism already solid from M2
◇ a cold explanation that temp=0 is greedy, not deterministic — float non-associativity under batching/parallelism (and MoE routing) flips the argmax between near-tied logits
◇ a cold explanation that the fact isn't in the frozen pretrained weights (knowledge cutoff) but works once pasted because attention over the in-context tokens conditions the answer (in-context learning), with no weight change
◇ a CLI/service that turns messy dev artifacts (logs, stack traces, API docs) into schema-validated JSON, with a small eval showing ~100% validity on a held-out set and no fine-tuning — committed repo + eval output
◇ a docs/code RAG over a real repo plus a retrieval-eval harness (recall@k on a labeled set) that proves a measured before/after improvement from one change — committed repo + eval numbers
◇ a dev-tools agent (PR review / log triage) with human approval gates before actions and recovery from an injected tool failure — committed repo + a trace showing the gate firing and a recovered run
◇ a deployed cloud service (model routing + caching + full tracing) with a 30+ case eval gating CI and a documented before/after metric — live endpoint + CI config + the metric writeup