Can AI replace Buzzsprout?

Build a closest honest personal substitute for Buzzsprout 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 audio, perform practical cleanup, assemble an episode, organize episode files on user-owned hosting, generate an RSS feed, and export production files plus show notes.
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.
Do not claim to reproduce proprietary data, network liquidity, regulated access, or frontier infrastructure.
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.