Health, home and travel decision
nutrIA
A technical user can build a useful clone covering calorie logging, barcode scanning, HealthKit sync and AI photo estimates by combining open-source trackers with external vision/LLM APIs, but the full polished product (in-app purchases, chat coach polish, App Store distribution, and UX polish) is larger and will take ongoing effort.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All nutrIA alternatives, with the arithmetic →
What a replacement has to do
- User records a meal (photo / text / barcode) → app estimates calories & macros (AI/DB) → stores entry and updates progress → user queries coach/chat for guidance.
What it still won’t have
- App Store distribution, reviews, and built-in in-app purchase flows
- Polished mobile UX (widgets, animations) and ongoing App Store polishing
- Proprietary training or tuned models (if vendor uses custom models)
- Any server-side analytics or telemetry bundled by the vendor
- Integrated recipe/creator marketplace and curated content
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
nutrIA does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal iOS nutrition tracker using SwiftUI + Combine, a small Node.js (Express) backend with Postgres (or SQLite for single-device mode), and HealthKit integration. Scope in: (1) iOS screens to capture photo/text/barcode/voice and list meal history; (2) photo upload to a vision API (e.g., Google Vision or custom image-classification endpoint) and translate labels into calories/macros via a food DB; (3) barcode scanning using iOS AVFoundation and mapping to the same food DB; (4) simple REST API to persist users, meals, and targets; (5) chat UI that forwards messages to an LLM API and shows suggestions; (6) sign-in with Apple and optional Google; (7) automated tests for photo upload, barcode parsing, and REST endpoints, plus basic error handling and retries. Out of scope: building custom ML models from scratch, multi-language localization beyond English/Spanish, and a payment/in-app-purchase billing backend. Include unit and integration tests, input validation, network error handling, and CI that runs tests on push.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 3 cited sources+3
- 3/3 assessment runs agreed+4
- Evidence score64
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.
How scoring works →Cited sources · 3
Every page the run actually retrieved.
- official productNutrIA – Come Mejor Cada Día on App Store
- open sourcewger — self hosted fitness/nutrition tracker
- open sourceOpenNutriTracker — open calorie tracker
Integrity checks
What held up, and what did not.
