TempoDance AI is a local-first prototype that turns a supplied tutorial into a source-synchronized 30 FPS COCO-17 coach, identifies the learner's lowest-scoring tracked limb, and evaluates the next loop's target delta. It teaches upper body, lower body, then the complete move to limit cognitive overload; session memory and policy events remain visible. A deterministic Demo mode works without a camera, pose-model download, or cloud dependency. Video tutorials can replay a move, but they cannot see why a learner keeps missing it. Most movement products give everyone the same instruction and one opaque score, leaving beginners to guess whether timing, an arm line, or a weight transfer is holding them back. TempoDance turns each practice loop into an evaluated coaching trial. It finds the lowest-scoring tracked body segment, gives one focused correction, and records the next loop's target delta. Each reliable loop can initialize, retain, or revise a predefined focus and cue strategy; insufficient evidence holds it. In the documented localhost setup, frames go to the local FastAPI process for in-memory inference and are not persisted by application code. The hackathon prototype makes no medical or rehabilitation claims.