Vibecode MealWise

You are building a lean indie version of MealWise.

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 =====
# MealWise indie build

## Goal

Build the smallest trustworthy replacement for the core MealWise workflow for one developer or a tiny team.

## Scope

Turn one natural-language meal request into a structured recipe with ingredients, portions, estimated costs and cooking steps, then save it locally.

## 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:
- native iPhone interaction and voice dictation
- server-side authentication, quota enforcement and abuse protection
- optional cross-device sync
- recipe refinement and regeneration polish
- ongoing AI-quality, food-safety and operational maintenance

If those capabilities are essential, use RecipeSage 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 personal MealWise-like recipe generator in an empty repository.
Use Next.js 15, TypeScript, Tailwind CSS, IndexedDB, and the OpenAI Responses API; do not offer alternative stacks.
Make the first run work with npm install and npm run dev.
Add an .env.example containing only OPENAI_API_KEY.
Create one mobile-first page with a natural-language meal request box.
Let the request include ingredients on hand, budget, servings, dietary preferences, exclusions, time and equipment.
Send it to the model with a strict JSON schema.
Render one complete recipe with title, servings, ingredients, estimated ingredient costs, total cost, cost per person, steps and a practical tip.
Clearly label all costs, allergen handling and nutrition-related content as estimates to verify.
Save accepted recipes locally in IndexedDB.
Include a searchable saved-recipe library and deletion controls.
Provide loading, empty, offline, malformed-model-response and retry states.
Validate input and safely render all model output as text.
Keep the API key server-side and never log meal requests or recipes.
Write focused tests for schema parsing and per-person cost calculation.
Create a README with setup, data location, privacy behavior and limitations.
Do not add accounts, billing, analytics, cloud sync, voice input, push notifications or background jobs.
Do not claim live grocery prices, dietary safety, allergy safety, nutritional accuracy or food-safety guarantees.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- an OpenAI-compatible API key
- a modern browser

## 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 MealWise.

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 =====
# MealWise indie build

## Goal

Build the smallest trustworthy replacement for the core MealWise workflow for one developer or a tiny team.

## Scope

Turn one natural-language meal request into a structured recipe with ingredients, portions, estimated costs and cooking steps, then save it locally.

## 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:
- native iPhone interaction and voice dictation
- server-side authentication, quota enforcement and abuse protection
- optional cross-device sync
- recipe refinement and regeneration polish
- ongoing AI-quality, food-safety and operational maintenance

If those capabilities are essential, use RecipeSage 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 personal MealWise-like recipe generator in an empty repository.
Use Next.js 15, TypeScript, Tailwind CSS, IndexedDB, and the OpenAI Responses API; do not offer alternative stacks.
Make the first run work with npm install and npm run dev.
Add an .env.example containing only OPENAI_API_KEY.
Create one mobile-first page with a natural-language meal request box.
Let the request include ingredients on hand, budget, servings, dietary preferences, exclusions, time and equipment.
Send it to the model with a strict JSON schema.
Render one complete recipe with title, servings, ingredients, estimated ingredient costs, total cost, cost per person, steps and a practical tip.
Clearly label all costs, allergen handling and nutrition-related content as estimates to verify.
Save accepted recipes locally in IndexedDB.
Include a searchable saved-recipe library and deletion controls.
Provide loading, empty, offline, malformed-model-response and retry states.
Validate input and safely render all model output as text.
Keep the API key server-side and never log meal requests or recipes.
Write focused tests for schema parsing and per-person cost calculation.
Create a README with setup, data location, privacy behavior and limitations.
Do not add accounts, billing, analytics, cloud sync, voice input, push notifications or background jobs.
Do not claim live grocery prices, dietary safety, allergy safety, nutritional accuracy or food-safety guarantees.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- an OpenAI-compatible API key
- a modern browser

## 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 MealWise.

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 =====
# MealWise product brief

## Problem

A useful personal version is absolutely one-shottable: a small local app can turn a typed meal request, ingredients, budget, servings and time into a structured recipe. What does not arrive in one session is MealWise's native iPhone finish, voice input, authenticated quota enforcement, optional cross-device sync, and the ongoing work of keeping AI output reliable and safe to use.

## Product outcome

Turn one natural-language meal request into a structured recipe with ingredients, portions, estimated costs and cooking steps, then save it locally.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- an OpenAI-compatible API key
- a modern browser

## Explicit non-goals for v1

- native iPhone interaction and voice dictation
- server-side authentication, quota enforcement and abuse protection
- optional cross-device sync
- recipe refinement and regeneration polish
- ongoing AI-quality, food-safety and operational maintenance

## 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 personal MealWise-like recipe generator in an empty repository.
Use Next.js 15, TypeScript, Tailwind CSS, IndexedDB, and the OpenAI Responses API; do not offer alternative stacks.
Make the first run work with npm install and npm run dev.
Add an .env.example containing only OPENAI_API_KEY.
Create one mobile-first page with a natural-language meal request box.
Let the request include ingredients on hand, budget, servings, dietary preferences, exclusions, time and equipment.
Send it to the model with a strict JSON schema.
Render one complete recipe with title, servings, ingredients, estimated ingredient costs, total cost, cost per person, steps and a practical tip.
Clearly label all costs, allergen handling and nutrition-related content as estimates to verify.
Save accepted recipes locally in IndexedDB.
Include a searchable saved-recipe library and deletion controls.
Provide loading, empty, offline, malformed-model-response and retry states.
Validate input and safely render all model output as text.
Keep the API key server-side and never log meal requests or recipes.
Write focused tests for schema parsing and per-person cost calculation.
Create a README with setup, data location, privacy behavior and limitations.
Do not add accounts, billing, analytics, cloud sync, voice input, push notifications or background jobs.
Do not claim live grocery prices, dietary safety, allergy safety, nutritional accuracy or food-safety guarantees.
Finish by running the tests and listing the exact commands used.

## 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 MealWise capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# MealWise indie build

## Goal

Build the smallest trustworthy replacement for the core MealWise workflow for one developer or a tiny team.

## Scope

Turn one natural-language meal request into a structured recipe with ingredients, portions, estimated costs and cooking steps, then save it locally.

## 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:
- native iPhone interaction and voice dictation
- server-side authentication, quota enforcement and abuse protection
- optional cross-device sync
- recipe refinement and regeneration polish
- ongoing AI-quality, food-safety and operational maintenance

If those capabilities are essential, use RecipeSage instead of pretending the gap is solved.

# 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

## Original build brief

Build a personal MealWise-like recipe generator in an empty repository.
Use Next.js 15, TypeScript, Tailwind CSS, IndexedDB, and the OpenAI Responses API; do not offer alternative stacks.
Make the first run work with npm install and npm run dev.
Add an .env.example containing only OPENAI_API_KEY.
Create one mobile-first page with a natural-language meal request box.
Let the request include ingredients on hand, budget, servings, dietary preferences, exclusions, time and equipment.
Send it to the model with a strict JSON schema.
Render one complete recipe with title, servings, ingredients, estimated ingredient costs, total cost, cost per person, steps and a practical tip.
Clearly label all costs, allergen handling and nutrition-related content as estimates to verify.
Save accepted recipes locally in IndexedDB.
Include a searchable saved-recipe library and deletion controls.
Provide loading, empty, offline, malformed-model-response and retry states.
Validate input and safely render all model output as text.
Keep the API key server-side and never log meal requests or recipes.
Write focused tests for schema parsing and per-person cost calculation.
Create a README with setup, data location, privacy behavior and limitations.
Do not add accounts, billing, analytics, cloud sync, voice input, push notifications or background jobs.
Do not claim live grocery prices, dietary safety, allergy safety, nutritional accuracy or food-safety guarantees.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- an OpenAI-compatible API key
- a modern browser

## 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.

# 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.

# MealWise product brief

## Problem

A useful personal version is absolutely one-shottable: a small local app can turn a typed meal request, ingredients, budget, servings and time into a structured recipe. What does not arrive in one session is MealWise's native iPhone finish, voice input, authenticated quota enforcement, optional cross-device sync, and the ongoing work of keeping AI output reliable and safe to use.

## Product outcome

Turn one natural-language meal request into a structured recipe with ingredients, portions, estimated costs and cooking steps, then save it locally.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- an OpenAI-compatible API key
- a modern browser

## Explicit non-goals for v1

- native iPhone interaction and voice dictation
- server-side authentication, quota enforcement and abuse protection
- optional cross-device sync
- recipe refinement and regeneration polish
- ongoing AI-quality, food-safety and operational maintenance

## 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

## Starting brief

Build a personal MealWise-like recipe generator in an empty repository.
Use Next.js 15, TypeScript, Tailwind CSS, IndexedDB, and the OpenAI Responses API; do not offer alternative stacks.
Make the first run work with npm install and npm run dev.
Add an .env.example containing only OPENAI_API_KEY.
Create one mobile-first page with a natural-language meal request box.
Let the request include ingredients on hand, budget, servings, dietary preferences, exclusions, time and equipment.
Send it to the model with a strict JSON schema.
Render one complete recipe with title, servings, ingredients, estimated ingredient costs, total cost, cost per person, steps and a practical tip.
Clearly label all costs, allergen handling and nutrition-related content as estimates to verify.
Save accepted recipes locally in IndexedDB.
Include a searchable saved-recipe library and deletion controls.
Provide loading, empty, offline, malformed-model-response and retry states.
Validate input and safely render all model output as text.
Keep the API key server-side and never log meal requests or recipes.
Write focused tests for schema parsing and per-person cost calculation.
Create a README with setup, data location, privacy behavior and limitations.
Do not add accounts, billing, analytics, cloud sync, voice input, push notifications or background jobs.
Do not claim live grocery prices, dietary safety, allergy safety, nutritional accuracy or food-safety guarantees.
Finish by running the tests and listing the exact commands used.

## 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.

# 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.

# 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

## 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 MealWise 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