Install Node.js LTS and Claude Code, then create GitHub, Supabase (Mumbai region for India), Vercel, Claude API and Razorpay accounts. About twenty minutes; every one has a free tier or test mode.
Build an AI Calorie Tracker App
Build an AI calorie tracker that analyzes food photos, estimates calories and macros, stores meals and supports subscriptions using Claude Code, Next.js, Supabase and Razorpay.
AI tools you’ll use.
Step-by-step guide.
Sketch the six screens: onboarding, today, scan, editable result, trends and paywall. Hand the picture to Claude Code with the build prompt.
Run the database prompt and check what comes back against the reference schema. Then log in as two test accounts and confirm neither can read or write the other's rows.
Give the agent the whole build prompt: magic-link and Google sign-in, private photo storage, a server-side scan route, the editable result screen and 3 free scans a day.
Build one food file of about 8,300 foods from the Indian Nutrient Databank and USDA FoodData Central, then check the top match for common foods like rajma, roti and dal.
The server sends each photo to Claude with this prompt. The model names each food and its grams, and the calories come from the food table.
Create ₹300 monthly and ₹2,400 yearly plans in Razorpay, then add subscriptions in test mode. Test a success, a failure and a duplicate webhook.
Push to GitHub, import into Vercel and add the environment variables there. Point the Razorpay webhook at the live domain, test on a real phone on mobile data, and switch Razorpay to live mode last.
The prompts that built it.
Design the Postgres schema for this app in Supabase and write it as one migration file.
TABLES
profiles one row per user: sex, date_of_birth, height_cm, weight_kg, activity_level,
goal, target_calories, target_protein_g, target_carbs_g, target_fat_g,
locale, timezone
meals one row per logged meal: user_id, eaten_at, local_date, photo_path,
source ('photo' | 'search' | 'manual'), name, calories, protein_g,
carbs_g, fat_g,
…How you can monetize it.
Languages and tech stack.
More AI projects to build.
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