Lohnt sich dein eigenes Imagen AI?

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.