Vibecode Imagen AI

You are building a lean indie version of Imagen 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 =====
# Imagen AI indie build

## Goal

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

## Scope

Catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.

## 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:
- proprietary photographer-trained models, cloud processing, profiles, and workflow integration
- proprietary raw-processing quality
- cloud sync and sharing
- large AI models
- camera and print ecosystem

If those capabilities are essential, use Immich 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 closest honest personal substitute for Imagen AI in an empty repository.
Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks.
The core loop is: catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Index selected folders without moving or rewriting originals and store only catalog data in SQLite.
Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes.
Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare.
Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation.
Render exports to a new folder with explicit color space, quality, size, and metadata choices.
Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out a proprietary raw-rendering engine.
Deliberately leave out hosted cross-device photo sync and client galleries.
Deliberately leave out frontier culling, retouching, and generative models.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- desktop computer
- local photo library
- sufficient disk space
- optional GPU for AI features

## 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 Imagen 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 =====
# Imagen AI indie build

## Goal

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

## Scope

Catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.

## 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:
- proprietary photographer-trained models, cloud processing, profiles, and workflow integration
- proprietary raw-processing quality
- cloud sync and sharing
- large AI models
- camera and print ecosystem

If those capabilities are essential, use Immich 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 closest honest personal substitute for Imagen AI in an empty repository.
Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks.
The core loop is: catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Index selected folders without moving or rewriting originals and store only catalog data in SQLite.
Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes.
Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare.
Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation.
Render exports to a new folder with explicit color space, quality, size, and metadata choices.
Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out a proprietary raw-rendering engine.
Deliberately leave out hosted cross-device photo sync and client galleries.
Deliberately leave out frontier culling, retouching, and generative models.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- desktop computer
- local photo library
- sufficient disk space
- optional GPU for AI features

## 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 Imagen 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 =====
# Imagen AI product brief

## Problem

A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Imagen AI, apply reusable local adjustment presets and keep every edit reversible. The hard boundary is proprietary photographer-trained models, cloud processing, profiles, and workflow integration, plus image pipeline quality, models, and workflow polish.

## Product outcome

Catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.

## Target user

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

## Required capabilities

- desktop computer
- local photo library
- sufficient disk space
- optional GPU for AI features

## Explicit non-goals for v1

- proprietary photographer-trained models, cloud processing, profiles, and workflow integration
- proprietary raw-processing quality
- cloud sync and sharing
- large AI models
- camera and print ecosystem

## 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 closest honest personal substitute for Imagen AI in an empty repository.
Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks.
The core loop is: catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Index selected folders without moving or rewriting originals and store only catalog data in SQLite.
Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes.
Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare.
Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation.
Render exports to a new folder with explicit color space, quality, size, and metadata choices.
Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out a proprietary raw-rendering engine.
Deliberately leave out hosted cross-device photo sync and client galleries.
Deliberately leave out frontier culling, retouching, and generative models.
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 Imagen AI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Imagen AI indie build

## Goal

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

## Scope

Catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.

## 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:
- proprietary photographer-trained models, cloud processing, profiles, and workflow integration
- proprietary raw-processing quality
- cloud sync and sharing
- large AI models
- camera and print ecosystem

If those capabilities are essential, use Immich 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 closest honest personal substitute for Imagen AI in an empty repository.
Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks.
The core loop is: catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Index selected folders without moving or rewriting originals and store only catalog data in SQLite.
Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes.
Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare.
Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation.
Render exports to a new folder with explicit color space, quality, size, and metadata choices.
Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out a proprietary raw-rendering engine.
Deliberately leave out hosted cross-device photo sync and client galleries.
Deliberately leave out frontier culling, retouching, and generative models.
Finish by running the tests and listing the exact commands used.

## Required capabilities

- desktop computer
- local photo library
- sufficient disk space
- optional GPU for AI features

## 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.

# Imagen AI product brief

## Problem

A consolation build is possible, but the paid product's decisive value sits outside a solo rebuild. For Imagen AI, apply reusable local adjustment presets and keep every edit reversible. The hard boundary is proprietary photographer-trained models, cloud processing, profiles, and workflow integration, plus image pipeline quality, models, and workflow polish.

## Product outcome

Catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.

## Target user

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

## Required capabilities

- desktop computer
- local photo library
- sufficient disk space
- optional GPU for AI features

## Explicit non-goals for v1

- proprietary photographer-trained models, cloud processing, profiles, and workflow integration
- proprietary raw-processing quality
- cloud sync and sharing
- large AI models
- camera and print ecosystem

## 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 closest honest personal substitute for Imagen AI in an empty repository.
Use Tauri 2, React, TypeScript, Rust image libraries, SQLite, and local filesystem access; do not offer alternative stacks.
The core loop is: catalog local photos, generate previews, apply reusable non-destructive adjustment presets, keep every edit reversible, and export selected originals or rendered copies.
Make the first run work locally with one documented command.
Store all user data locally by default and make export straightforward.
Put secrets in .env, ship .env.example, and never commit credentials.
Index selected folders without moving or rewriting originals and store only catalog data in SQLite.
Generate thumbnails and previews, read EXIF, detect duplicates by hash, and monitor file changes.
Provide timeline, folders, albums, ratings, flags, tags, search, and side-by-side compare.
Store edits as reversible parameters for crop, rotate, exposure, contrast, white balance, and saturation.
Render exports to a new folder with explicit color space, quality, size, and metadata choices.
Add catalog backup, missing-file repair, integrity scan, and a clear original-safety guarantee.
Include clear empty, loading, success, and recoverable error states.
Add input validation, safe filenames, and graceful handling of unavailable APIs.
Write focused tests for the core transformation and one end-to-end happy path.
Create a README with setup, architecture, permissions, data location, and backup steps.
Do not add accounts, billing, telemetry, analytics, or a hosted control plane.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
Deliberately leave out a proprietary raw-rendering engine.
Deliberately leave out hosted cross-device photo sync and client galleries.
Deliberately leave out frontier culling, retouching, and generative models.
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 Imagen 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