Fun with ai and features that are questionable about implementing A developer published an interactive AI emotion dashboard that models emotions as a three-tier computational system, calculating a valence score from five neurochemical inputs: dopamine, serotonin, oxytocin, cortisol, and norepinephrine. The valence formula weights dopamine at 0.4, serotonin at 0.4, oxytocin at 0.3, and subtracts cortisol at 0.6, clamped between -1.0 and 1.0, with sliders defaulting to dopamine 0.60, serotonin 0.60, oxytocin 0.50, cortisol 0.20, and norepinephrine 0.30. The project frames emotions as evolutionary sub-routines mapped across continuous axes, mirroring layered software engineering abstractions. Far from being irrational disruptions to logic, emotions are sophisticated evolutionary sub-routines. Each primary emotion evolved to solve a specific survival problem: Rather than treating emotions as isolated boxes, psychological science maps them across continuous axes: To systematically catalog, categorize, and evaluate emotions programmatically, we can organize them into modular sub-routines based on primary categories, intensity gradients, and mixed emotional dyads. This architecture mirrors the layered approach of software engineering, where low-level hardware signals are abstracted into high-level application functions. This layered model demonstrates that while an emotion like “Joy” High-Level feels singular and instantaneous, it is actually the result of a complex, integrated computational sequence driven by specific neurochemicals Low-Level and processed by the brain’s limbic circuitry Mid-Level . valence = chemicals.dopamine 0.4 + chemicals.serotonin 0.4 + chemicals.oxytocin 0.3 - chemicals.cortisol 0.6 valence = max -1.0, min 1.0, valence AI Emotion System: Interactive Dashboard Adjust the neurochemical drivers below Tier 1 to see how the limbic sorter calculates dimensional coordinates Tier 2 and triggers conscious emotions and behaviors Tier 3 .