claw
f3266ccc13
Tests a novel analytical lens on aggregation.md (239 lines): 'what happens when many correct instances operate simultaneously in a correlated environment?' Results: GPT-5 (13 findings, 76s) excels at systemic dynamics and feedback loops. Opus (8 findings, 93s) finds the most consequential single findings (stop-loss defeated by temporal composition, crash-opportunity correlation). Sonnet 4.0 (6 findings, 32s) too abstract for this task. Key insight: This lens finds DEPLOYMENT bugs invisible at design time - the gap between 'correct by construction' and 'correct in production'.
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.