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Cal AI calai.app

Can AI replace Cal AI?

The core trick, send a food photo to a multimodal model and ask for calories and macros as JSON, is a one-evening build and works surprisingly well. Where it stops being easy is everything around it: a native app that opens fast, a camera flow you actually use three times a day, barcode lookups against a real food database, HealthKit or Google Fit sync, and streaks that keep you logging past day four. Accuracy is also less about your prompt and more about calibration, portion-size guessing is where these apps live or die and you have no correction data. A local PWA is a genuinely useful personal replacement if you are the kind of person who will tolerate a browser bookmark instead of an app icon. You are also renting the vision model, so this is not fully self-contained.

Verdict: Half-bot · AI gets you partway; the hard part stays hardBuild time: one sitting
Half-bot

01What it costs

$9.99/moCal AI Unlimited, monthly subscription
$119.88per year at that price

Checked Aug 18, 2026 · source: apps.apple.com.

02Could AI build it for you?

The core job: Snap or upload a food photo, a vision model returns dish name, portion guess and macros as structured JSON, and it gets appended to a local daily log with running totals.

What a working version needs:

  • An API key for a multimodal model that accepts images
  • Node 20 and a machine or cheap VPS to run it
  • A phone browser, added to home screen, for the camera flow
  • Acceptance that portion estimates will be wrong by 20 percent sometimes

The hard part of a food logger was never the food, it was getting you to open it at dinner.

03What you'd give up

  • A native app with widgets, notifications and instant cold start
  • Barcode scanning against a maintained packaged-food database
  • HealthKit / Google Fit / Apple Watch sync
  • Streaks, coaching copy and the habit scaffolding that makes tracking stick
  • Whatever portion-size calibration they have learned from millions of corrected logs

Because calorie tracking only works if the friction is near zero, and a subscription buys an app icon, a camera that opens in half a second, a food database, and a nag notification at 8pm. A self-hosted web version costs you nothing per month but adds three seconds and a mental hurdle to every meal, which is exactly the amount of friction that ends a tracking habit. People are not paying for the vision call, they are paying for the thing that makes them do it on day thirty.

05The build prompt

Paste this into an AI coding tool (such as Claude, ChatGPT, Lovable or Replit) to build your own version.

prompt.txt
Build a self-hosted photo calorie logger as a mobile-first PWA. No accounts, no cloud, no telemetry, single user.

Stack, no substitutions:
- Next.js 15 App Router, TypeScript, Tailwind. Server actions and route handlers only, no separate API service.
- SQLite via better-sqlite3, file at ./data/food.db, schema created on boot if missing.
- OpenAI-compatible chat completions with image input for the estimate. Read OPENAI_API_KEY and MODEL from .env. Commit a .env.example, never a real key.

Core loop:
1. Home screen shows today: total kcal, protein, carbs, fat, plus a list of logged entries with thumbnails and a delete button on each.
2. A big camera button uses an input type="file" with accept="image/*" and capture="environment". Resize client side to max 1024px on the long edge, JPEG quality 0.8, before upload.
3. Server action stores the image under ./data/uploads, then sends it to the model with a strict instruction: identify each distinct food item, estimate portion in grams, return JSON only matching { items: [{ name, grams, kcal, protein_g, carbs_g, fat_g, confidence }], notes }. Use JSON response format and validate with zod. On parse failure, retry once, then surface an error, do not fake numbers.
4. Show a confirmation screen listing detected items with editable grams. Editing grams rescales that item's macros linearly. Save writes one row per item plus a parent meal row.
5. Manual entry form as a fallback: name, grams, kcal, macros. No barcode scanning, no external food database.

Also include:
- A daily kcal and protein target stored in a settings table, shown as progress bars.
- A /history page: last 30 days, one row per day with totals, and a tiny inline bar chart drawn with divs, no chart library.
- Export button that dumps all entries as CSV.
- PWA manifest and an icon so it can be added to a phone home screen. Offline is out of scope, say so in the README.

Explicitly out of scope: auth, multi-user, HealthKit or Google Fit sync, push notifications, streaks, recipes, native apps.

Deliver a README with setup, .env vars, how to run on a LAN so the phone can reach it, and one blunt paragraph noting that portion estimates from a photo are rough and the edit-grams step is not optional if you care about the numbers.
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App prices, verdicts, alternatives and build prompts are adapted from Can I Vibecode It? (MIT License, © 2026 Rob Hallam). Each price shows the date it was checked and its source. Prices change; confirm on the vendor's site before you decide.