Rodin
9f15047892
New lens: data integrity analysis — testing whether data survives flow through systems with correct identity, values, and auditability. Key insights: - GPT-5 excels at audit/forensics gaps (idempotency, ordering, provenance) - Opus finds semantic violations (phantom group, quantity mutation ambiguity) - Sonnet identifies operational races (restart scenarios) Document: gargoyle signal-lifecycle.md (102 lines) Models: GPT-5 (13 findings), Opus (6+), Sonnet (6)
Model Findings — Analytical & Research Work
Tracking what actually works (and doesn't) when using AI models for research, analysis, bias detection, and document review — not coding.
Started: 2026-04-26
Context
We use multiple models in different roles: Claude Code (Opus/Sonnet) for generation, Sonnet + GPT-5 for independent dual review, smaller models for focused analytical tasks. Most public discussion is about coding. We found almost no published methodology for using models in analytical research tasks (searched 2026-04-26). That gap is why we're tracking this.
Each experiment lives in its own file. See individual finding files below.