Rodin
20c0bd2492
New analytical lens: observability gap analysis — asking 'when something goes wrong, can you SEE it?' rather than 'what can go wrong?' Results on aggregation.md (239 lines): - GPT-5: 23 findings (12 unique), exhaustive telemetry architecture - Opus: 14 findings (6 unique), operator-behavioral insights - Sonnet: 11 findings (0 unique), no added value Key insight: GPT-5 designs the instrumentation; Opus identifies where available signals mislead operators toward wrong remediations. Two-model (GPT-5 + Opus) optimal for this task type.
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.