2026Built — internal infrastructure & unreleased
MacroTrack
An AI meal tracker built around one idea — you should be able to say what you ate and be done. Speech or free text, no search box, no portion guessing.
- Role
- Sole developer — schema, Edge Functions, app, iOS submission
- Stack
- React 19
- Vite
- Tailwind 4
- Supabase
- Capacitor
- Gemini

Status
Unreleased. Built as a web app and wrapped for iOS with Capacitor from one codebase. Free, with a ceiling of five AI logs a day.
Decisions
Meal parsing runs server-side
Meals are parsed in Supabase Edge Functions against Gemini, so provider keys never enter the client bundle.
Measured before claimed
Server-side parse latency is 1,155 ms median and about 1,690 ms at p95, and cost varies 3.2× with the number of items in a meal. There is no measured end-to-end figure, so the copy makes no “five-second” claim.
Speech and free text over barcodes
Packaged-food barcode scanning was deleted so the product could focus on speech and free text.
What it cost
- Server-side parsing: a network hop on every log, plus Edge Functions to deploy and run.
- Measured claims: quieter marketing copy.
- Dropping barcodes: working code removed, and a repository name that still says otherwise.
Screenshots


