4.1 / 5

Plate Check

Point at food, get nutrition info and meal logging instantly

Useful

Instant

Privacy

The Problem

Food logging is tedious; people quit calorie counting because entry takes too long

Current Solutions (Not Great)

MyFitnessPal (manual search), Lose It (some photo, cloud-based), giving up

Who Needs This

Dieters, fitness enthusiasts, people with health conditions requiring food tracking, athletes

Calorie counting fails because logging meals is tedious. Plate Check makes it instant: point your camera at your food, and AI identifies what you're eating and estimates nutrition—calories, protein, carbs, fat. No searching databases, no measuring portions, no manual entry. It learns your regular meals and gets better at estimating your typical portions. Works at home, restaurants, anywhere. Your detailed food diary stays completely private—no cloud service knows what you eat.

Honest Take

Food logging is a massive market with high churn—MyFitnessPal, Lose It, and Noom all struggle to keep users engaged. The hard truth is that portion estimation from photos is notoriously unreliable, and people abandon calorie counting regardless of the interface. Privacy angle helps, but you're fighting behavior change, not just tech problems.

Monetization Ideas

Ways to turn this into revenue

Freemium

Free basic, $5-15/mo for pro

Subscription

$5-29/month or $49-199/year

One-Time Purchase

$9-49 per license

Features

Key features that make this app valuable

Food identification from photo
Portion size estimation
Nutrition calculation
Meal logging with one tap
Daily/weekly nutrition summaries
Regular meal learning
Restaurant menu recognition
Barcode scanning for packaged food
Goal tracking (calories, macros)
Trend visualization

Build Prompt

Use this prompt with an AI assistant to start building

Build a React PWA called 'Plate Check' using WebInfer with vision capabilities. UI: camera viewfinder with 'Scan Meal' button, identified foods overlay, nutrition summary card, daily log. Capture: take photo of plate. Vision model analysis with generateObject returns { foods: [{ name: string, portion: string, portionGrams: number, confidence: number, boundingBox: {x,y,w,h} }], mealType: 'breakfast'|'lunch'|'dinner'|'snack', isHomemade: boolean, isRestaurant: boolean, restaurantName?: string }. Nutrition lookup: local database of common foods (USDA data embedded), calculate per identified food. Display: photo with food bounding boxes labeled, nutrition breakdown (calories, protein, carbs, fat, fiber), total meal nutrition. Edit: tap food to adjust portion or correct identification. One-tap log: adds to daily diary. Daily view: meals logged, running totals, goal progress (set calorie/macro targets). Trends: weekly/monthly charts of intake. Smart features: learn 'my usual breakfast' for faster logging, detect restaurant logos and suggest menu items. Barcode mode: scan packaged food, lookup nutrition from local database or AI estimation. All data in IndexedDB, privacy-first. Export: CSV for nutritionist sharing.

Score Breakdown

Pain