Vibecode Cal AI
track this build5 steps, step by step0%KINDA · weekend project
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.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Cal AI.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== README.md =====
# Cal AI indie build
## Goal
Build the smallest trustworthy replacement for the core Cal AI workflow for one developer or a tiny team.
## Scope
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.
## Quick start
1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.
## Honest limits
This build deliberately does not replace:
- 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
If those capabilities are essential, use Cal AI instead of pretending the gap is solved.
===== AGENTS.md =====
# Agent instructions
- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".
===== BUILD_PLAN.md =====
# Build plan
## Original build brief
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.
## Required capabilities
- 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
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.
===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.
APP_ENV=development
# Add only values required by the selected implementation.You are building a lean indie version of Cal AI.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== README.md =====
# Cal AI indie build
## Goal
Build the smallest trustworthy replacement for the core Cal AI workflow for one developer or a tiny team.
## Scope
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.
## Quick start
1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.
## Honest limits
This build deliberately does not replace:
- 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
If those capabilities are essential, use Cal AI instead of pretending the gap is solved.
===== AGENTS.md =====
# Agent instructions
- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".
===== BUILD_PLAN.md =====
# Build plan
## Original build brief
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.
## Required capabilities
- 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
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.
===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.
APP_ENV=development
# Add only values required by the selected implementation.You are building a production product version of Cal AI.
Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.
===== PRODUCT.md =====
# Cal AI product brief
## Problem
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.
## Product outcome
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.
## Target user
A serious builder who needs a maintainable product foundation rather than a one-off demo.
## Required capabilities
- 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
## Explicit non-goals for v1
- 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
## Success criteria
- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.
===== ARCHITECTURE.md =====
# Architecture
## Starting brief
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.
## Boundaries
Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.
## Production baseline
- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.
## Decision records
For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
===== AGENTS.md =====
# Agent instructions
- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.
===== MILESTONES.md =====
# Delivery milestones
## M0 — Decisions and scaffold
- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.
## M1 — Core workflow
- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.
## M2 — Trust layer
- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.
## M3 — Operability
- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.
## M4 — Release gate
- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
===== OPERATIONS.md =====
# Operations
## Before release
- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.
## Incident checklist
1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.
## Launch constraint
Do not market omitted Cal AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.README.md
# Cal AI indie build ## Goal Build the smallest trustworthy replacement for the core Cal AI workflow for one developer or a tiny team. ## Scope 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. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - 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 If those capabilities are essential, use Cal AI instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan
## Original build brief
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.
## Required capabilities
- 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
## Delivery order
1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.
## Done when
- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden..env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Cal AI product brief ## Problem 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. ## Product outcome 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. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - 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 ## Explicit non-goals for v1 - 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 ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture
## Starting brief
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.
## Boundaries
Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.
## Production baseline
- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.
## Decision records
For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Cal AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent · this prompt is generated from the build plan · improve it via PR
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.
xA native app with widgets, notifications and instant cold start
xBarcode scanning against a maintained packaged-food database
xHealthKit / Google Fit / Apple Watch sync
xStreaks, coaching copy and the habit scaffolding that makes tracking stick
xWhatever portion-size calibration they have learned from millions of corrected logs
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Cal AI ($9.99/mo) with one prompt"
Vibecode Cal AI
Kinda. The core of Cal AI is buildable in a weekend with the prompt on this page, but there are real gaps: A native app with widgets, notifications and instant cold start, Barcode scanning against a maintained packaged-food database. Read the honest list above before committing.
How much does Cal AI cost?
Cal AI costs about $9.99/month (Cal AI Unlimited, checked 2026-08-18), which is $119.88 per year.
What do I lose by replacing Cal AI?
Honestly: 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. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Cal AI?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.