Vibecode Luma Dream Machine

You are building a lean indie version of Luma Dream Machine.

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 =====
# Luma Dream Machine indie build

## Goal

Build the smallest trustworthy replacement for the core Luma Dream Machine workflow for one developer or a tiny team.

## Scope

Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.

## 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:
- voice or likeness safety systems
- low-latency inference infrastructure
- licensed data, avatars, and production templates
- high-fidelity color, format, and export handling
- frontier generation quality

If those capabilities are essential, use ComfyUI 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 the closest honest consolation tool inspired by Luma Dream Machine; do not claim to replace its structural moat.
Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
Primary job: Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: voice or likeness safety systems; low-latency inference infrastructure; licensed data, avatars, and production templates.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.

## Required capabilities

- GPU-capable machine or model API key in .env
- Python 3.12
- FFmpeg
- Explicit README warning that this is a consolation build, not a production replacement

## 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 Luma Dream Machine.

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 =====
# Luma Dream Machine indie build

## Goal

Build the smallest trustworthy replacement for the core Luma Dream Machine workflow for one developer or a tiny team.

## Scope

Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.

## 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:
- voice or likeness safety systems
- low-latency inference infrastructure
- licensed data, avatars, and production templates
- high-fidelity color, format, and export handling
- frontier generation quality

If those capabilities are essential, use ComfyUI 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 the closest honest consolation tool inspired by Luma Dream Machine; do not claim to replace its structural moat.
Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
Primary job: Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: voice or likeness safety systems; low-latency inference infrastructure; licensed data, avatars, and production templates.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.

## Required capabilities

- GPU-capable machine or model API key in .env
- Python 3.12
- FFmpeg
- Explicit README warning that this is a consolation build, not a production replacement

## 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 Luma Dream Machine.

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 =====
# Luma Dream Machine product brief

## Problem

Do not mistake the interface for the product. Luma Dream Machine's durable value is proprietary model, inference, safety, which a solo one-shot build cannot reproduce responsibly. The prompt therefore builds only the closest honest personal consolation tool.

## Product outcome

Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.

## Target user

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

## Required capabilities

- GPU-capable machine or model API key in .env
- Python 3.12
- FFmpeg
- Explicit README warning that this is a consolation build, not a production replacement

## Explicit non-goals for v1

- voice or likeness safety systems
- low-latency inference infrastructure
- licensed data, avatars, and production templates
- high-fidelity color, format, and export handling
- frontier generation quality

## 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 the closest honest consolation tool inspired by Luma Dream Machine; do not claim to replace its structural moat.
Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
Primary job: Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: voice or likeness safety systems; low-latency inference infrastructure; licensed data, avatars, and production templates.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.

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

# Luma Dream Machine indie build

## Goal

Build the smallest trustworthy replacement for the core Luma Dream Machine workflow for one developer or a tiny team.

## Scope

Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.

## 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:
- voice or likeness safety systems
- low-latency inference infrastructure
- licensed data, avatars, and production templates
- high-fidelity color, format, and export handling
- frontier generation quality

If those capabilities are essential, use ComfyUI 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 the closest honest consolation tool inspired by Luma Dream Machine; do not claim to replace its structural moat.
Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
Primary job: Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: voice or likeness safety systems; low-latency inference infrastructure; licensed data, avatars, and production templates.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.

## Required capabilities

- GPU-capable machine or model API key in .env
- Python 3.12
- FFmpeg
- Explicit README warning that this is a consolation build, not a production replacement

## 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.

# Luma Dream Machine product brief

## Problem

Do not mistake the interface for the product. Luma Dream Machine's durable value is proprietary model, inference, safety, which a solo one-shot build cannot reproduce responsibly. The prompt therefore builds only the closest honest personal consolation tool.

## Product outcome

Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.

## Target user

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

## Required capabilities

- GPU-capable machine or model API key in .env
- Python 3.12
- FFmpeg
- Explicit README warning that this is a consolation build, not a production replacement

## Explicit non-goals for v1

- voice or likeness safety systems
- low-latency inference infrastructure
- licensed data, avatars, and production templates
- high-fidelity color, format, and export handling
- frontier generation quality

## 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 the closest honest consolation tool inspired by Luma Dream Machine; do not claim to replace its structural moat.
Use exactly this stack: Python 3.12 + FastAPI + FFmpeg + React.
Primary job: Build the closest honest personal AI video generation workflow using one user-selected local or API model, job history, preview, and export.
Start from an empty folder and create the complete working project.
Make the default mode single-user and private.
Store user data locally unless the core job requires the declared self-hosted database.
Do not add analytics, telemetry, ads, or third-party accounts.
Put every secret and external credential in .env and provide .env.example.
Use realistic sample data that is clearly labelled and easy to delete.
Implement the smallest polished interface that completes the core loop end to end.
Include clear empty, loading, validation, success, and failure states.
Add import and export so the user is not trapped in the app.
Use accessible keyboard navigation, labels, focus states, and sensible contrast.
Validate untrusted input and never log secrets or private file contents.
Deliberately exclude these paid-product advantages: voice or likeness safety systems; low-latency inference infrastructure; licensed data, avatars, and production templates.
Do not fake integrations, network effects, proprietary data, model quality, compliance, or security claims.
Where an external API is optional, keep the app useful without it and explain the degraded mode.
Write focused unit tests for the data model and the most important workflow.
Add one end-to-end smoke test that proves the core loop works.
Create a README with setup, permissions, architecture, data location, backup, and limitations.
Add scripts for install, development, test, build, and a production-style local run.
Run the tests and build before finishing, then fix errors rather than merely describing them.

## 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 Luma Dream Machine 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