Rodrigo Passarelli

kinn is a real-time Bayesian diagnostic interview engine for consultants, founders, and anyone running discovery calls. After every stakeholder answer, Claude Opus 4.7 computes the Expected Information Gain of every candidate next question against a Bayesian belief over the stakeholder's actual situation — and asks the one that collapses the most uncertainty. The frame is BED-LLM (Bayesian Experimental Design with LLM samplers): Opus 4.7 is *both* the answer-distribution sampler (predicting how a stakeholder might respond) and the belief updater (revising priors after each real answer). DSPy/GEPA compiles the prompts offline; runtime is a forced-tool-call loop with prompt caching, two-phase recompile, and dual-algedonic state separation. Built solo across the hackathon week in three iterations (kinn → kinn2 → kinn3). The dual-gate benchmark publishes the honest result (0.852 vs 0.920 target) rather than ship a hidden failure.