SENTIENCE-LOOP — Predictive-Processing Cognitive Loop
Make your agent measure its own accuracy instead of asserting it. SENTIENCE-LOOP perceives signals from sensors you plug in, makes falsifiable predictions with a confidence and a test-after time, scores them against ground truth when they mature (never self-judged), learns from the surprises, and auto-calibrates — reporting a per-domain Brier score and pulling confidence toward observed accuracy. Bring your own sensors + scorer; a deterministic demo runs it all $0. Free, open-source.
1. See it
2. How it works
3. Why it wins
4. Use it to…
Perceive → predict → score
Perceive → predict → score → surprise → reflect → record
Falsifiable, timestamped
Falsifiable, timestamped predictions graded against real outcomes
Self-calibration
Self-calibration: per-domain Brier + hit-rate, confidence auto-tunes
5. The shift it creates
- Duct-taped manual workflows
- Nothing that compounds over time
- Rebuilding it from zero each time
- Perceive → predict → score → surprise → reflect → record
- Falsifiable, timestamped predictions graded against real outcomes
- Self-calibration: per-domain Brier + hit-rate, confidence auto-tunes
6. What's inside
7. The details
Perceive → predict → score → surprise → reflect → record SENTIENCE-LOOP — Predictive-Processing Cognitive Loop turns that from a chore into a few minutes — and you keep it for every project after.
Questions, answered
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