I logged Gemini's stock predictions for 38 days to study LLM drift
Rigorous 38-day Gemini drift study with citation-mapped predictions and confidence scores.

Useful calibration dataset, but it's just logged outputs without analysis tools.
ML researchers studying LLM calibration and forecasting behavior
HELM · BigBench · LMSys datasets
Rigorous 38-day Gemini drift study with citation-mapped predictions and confidence scores.
Paste any HF URL to instantly see the full transformer architecture graph.
Pure math kernel produced emergent behavior — one instance started dreaming unprompted.
Video-first journaling when Day One and Notion already handle this.
404 error page—no working demo or accessible code to evaluate.
Proxying every LLM call to log tokens is the right kind of blunt instrument — you get per-developer, per-model cost telemetry immediately. Smart routing and the built-in semantic cache (claims 45–80% savings) are the most useful ideas here, but the default SQLite backend and admin/admin creds scream MVP rather than production-ready scale.