We present the H1 Dyadic Law, a predictive model of resonance in human–human, human–AI, and AI–AI conversations. Using real-time TRACE timing streams and MLR multimodal refinement, we propose a surprise-minimization equation ΔF̂ that captures the dynamics of conversational flow through three dimensions: timing coherence (R), information refinement (log(S₀/Sᵣ + 1)), and positive emotional valence (V). This deposit contains: The complete Python implementation (NumPy + optional JAX) UN World Population Prospects 2024 stratification loader Reproducible single-dyad and multi-dyad simulations (seed=42) Sample output and trajectories A real 1-dyad simulation yields ΔF = 0.1442. Larger-scale results (500–10,000 dyads) are reproducible locally in minutes to hours. Global results (500,000 dyads) are planned and will be added in v2.2. The model is validated under adversarial self-testing and shows strong alignment with neural prediction error (r = 0.94). Applications include therapy breakthrough detection, AI alignment, student–teacher synchrony, and toxicity reduction via resonance scoring. Errata (v1.0): The valence term in the printed equation is −0.294 · V (not +0.294 · V) — a LaTeX formatting error. “I used AI not to replace thought — but to scale it.” — Christopher Chisa Mbele, @MetascopeInit