Barre Code: Movement Cipher Lab -Embodied Computing System

A mobile-first learning system that turns choreographic structure into sequence logic, debugging, bounded variation and verifiable completion.

I designed and built an embodied-computing lesson that teaches sequence, dependency, state, debugging and parameter variation through choreographic structure.

Learners arrange six synthetic movement ciphers, compile the phrase against deterministic TypeScript rules, identify a deliberately seeded glitch, make one bounded variation and download a Learning Receipt.

The system deliberately separates authority between code and AI. Deterministic application logic decides whether the sequence is structurally valid. GPT-5.6 is used only as an optional explanation layer after validation and cannot override the result or block lesson completion.

The responsive application was built with Next.js, React and TypeScript and includes accessible non-drag controls, server-side model access, structured output validation, deterministic failure handling and automated boundary tests. No human movement, image, audio, video, biometric, health, identity, client or student data is captured.

My role:

Creator, Choreographer, Product Architect and Full-Stack Developer