Lohnt sich dein eigenes Swell AI?

Build a personal replacement for Swell AI in an empty repository.
Use Python 3.12, FastAPI, ffmpeg, SQLite, and an HTMX interface; do not offer alternative stacks.
The core loop is: import spoken-word episode audio, perform practical cleanup, generate transcripts, show notes, articles, and social posts, and export production files.
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.
Create an upload queue and preserve originals in a read-only media folder.
Generate waveforms and non-destructive edit markers instead of rewriting source files.
Implement silence trimming, loudness normalization to -16 LUFS, fades, and noise-gate presets.
Add chapter markers, intro and outro slots, and a simple two-track timeline.
Produce a transcript and draft title, description, chapters, and social excerpts.
Export MP3, WAV, transcript, chapters JSON, and a complete project manifest.
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.
Deliberately leave out real-time remote recording.
Deliberately leave out podcast hosting and directory analytics.
Deliberately leave out licensed stock music and voice cloning.
Finish by running the tests and listing the exact commands used.