Add per-user learned machine profile; fix methodology honesty issues
Test and deploy / test-and-deploy (push) Successful in 1m26s
Test and deploy / test-and-deploy (push) Successful in 1m26s
Replaces the flat additive batch-size correction with a per-user multiplicative pace factor learned from each account's own logged roasts (shared/learn.js, GET /api/machine-profile), plus a lot-scoped "last refine" auto-suggestion. Also fixes the reference data being framed as "your own roasts" on a now-multi-user product, and reclassifies the drying/Maillard sanity checks as informational since they're algebraically derived from the DTR check rather than independent (yellow = 0.56 x first crack, not entered separately). Bug fixes found by an adversarial Opus review of the first pass: - Unicode minus sign (U+2212) broke duration parsing against the app's own generated refine-suggestion text - Printed sanity-checks table still showed a bare pass/fail glyph for the now-informational drying/Maillard rows - Printed time-ledger box didn't show the pace multiplication step, so it stopped reconciling by hand once pace != 1 - Field 6.4 (manual batch correction) was double-counted: excluded from the learned-pace fit but added back after the multiplication - Learned pace had no outlier rejection or hard clamp - computeLedger had no test coverage - Batch-size help copy overstated what the pace factor models (it's a single blanket ratio, not conditioned on batch weight) A follow-up Opus pass also caught the per-user profile cache surviving logout/account-switch in a shared browser; fixed by sweeping it alongside the existing plan-draft cleanup. Co-Authored-By: Claude Sonnet 5 <[email protected]>
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