Health, home and travel decision
Calchi AI
A technical user can build a working local calorie tracker with USDA mapping and basic photo-based recognition using third-party vision APIs, but reproducing the product's claimed fast, polished AI food-recognition, peptide management polish, and mobile-store readiness would be costly and time-consuming.
Visit website↗Built by Tony Conte, who ships 4 products in this index
Not priced
No pricing page we fetched carried a figure, so there is nothing to compare against. The build side is still real.
$100one-off140 h to build
$50/mo6 h/mo upkeep
No published price to break even against.
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 Calchi AI alternatives, with the arithmetic →
What a replacement has to do
- User takes a photo or types a meal → app identifies items and estimates calories/macros → store entry and update daily totals; optional: log peptide dose and schedule reminders.
What it still won’t have
- Proprietary, production-grade food image model and the vendor's claimed 2s avg scan time
- Polish of a published iOS/Android app (store listings, reviews, continuous UX improvements)
- Any proprietary mappings or curated food-item disambiguations beyond USDA matching
- Built-in badges, habit heuristics, and cross-user analytics or personalization
What remains hard
- Product polish and ongoing maintenance
First-year cost
No published price
Calchi AI 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
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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 cross-platform mobile calorie-tracking app using React Native (Expo), SQLite for local storage, Firebase Auth (optional) and Cloud Functions, and Google Cloud Vision (or an on-device Vision model) for food recognition. In scope: (1) camera/photo picker upload to Vision API and parse detected items, (2) map detected items to USDA nutrition entries and compute calories/macros, (3) local logging of meals and daily aggregates (calories left, macros, streaks), (4) manual chat-style text entry fallback, (5) peptide dose logging with local scheduled notifications and low-stock alert. Out of scope: training a custom food-recognition model, multi-user analytics, and app-store publishing polish. Include error handling for network/vision failures, input validation, unit/integration tests for mapping and aggregation logic, and CI to run tests.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 2 cited sources+1
- 3/3 assessment runs agreed+4
- Evidence score62
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 · 2
Every page the run actually retrieved.
- official productCalchi AI · Calorie tracking, made effortless
- open sourcesimonoppowa/OpenNutriTracker
Integrity checks
What held up, and what did not.
