Rodin
b9036401c2
New analytical lens examining implicit assumptions about broker APIs, market behavior, network conditions, and timing. Document: gargoyle's feeds-and-instruments.md (115 lines) Models: GPT-5 (24 findings), Opus (15), Sonnet (15) Key insight: External system assumptions benefit more from reasoning depth than internal architecture analysis. GPT-5's exhaustive coverage of broker implementation details and network failure modes justifies the token cost for critical external interfaces. Union of all models finds ~30 distinct assumptions vs ~24 max single model.
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.