A personal fitness operating system that consolidates fragmented health data — calories, activity, steps, workouts, weight — into a single, unified view. Built end-to-end as a non-technical founder, with AI doing the technical lift.
Steps in one app, calories in another, workouts in a third, weight in a fourth. The single number that actually matters — energy in versus energy out — never existed in one place. AI Fitness OS was built to close that gap.
A single energy-balance view that pulls every source together, replacing multiple disconnected apps.
Steps, activity and workouts flow in automatically through a webhook ingestion endpoint — no manual entry.
Snap a meal and an AI vision model estimates its calories — logging food without hunting through a database.
I brought the product vision and research; AI handled architecture, code generation and documentation. The work moved from idea to a running application through a tight, repeatable loop.
Nailed the core job-to-be-done — one energy-balance number — before writing a line of code, so every feature laddered back to it.
Generated a complete Next.js + Supabase application, delivered as a downloadable package with a step-by-step setup guide.
Built the Apple Health webhook and the OpenAI Vision flow for food photos, with row-level security on all stored data.
Moved into live setup — resolving environment, keys and integrations — treating each hiccup as configuration, not failure.
Chosen for speed, security and a clean path to deployment — the kind of stack a lean product ships on.
A fully scaffolded, deployable application now exists and runs — the fragmented-data problem solved by design. It proves a non-technical operator can ship a real full-stack product by pairing clear product thinking with AI execution.