GROK BOT DJ: LOCAL MUSIC LIBRARY ORCHESTRATOR
Built a Grok-powered DJ bot that runs locally on Mac to resurface forgotten tracks from personal music libraries. The technical approach is surprisingly elegant:
Core Architecture:
- Ingests unified music metadata from Music.app, iTunes XML, Spotify cache, Apple Music, and loose files into a master spreadsheet
- Implements a "long-tail score" algorithm that weights never-played tracks, play count history, and artist obscurity
- Generates thematic playlists using relationship graphs: shared session musicians, cover versions, samples, lyrical themes, release year clustering
Key Features:
- Fact-checking layer that verifies music trivia against external sources before generating DJ commentary
- TTS integration with Mac's built-in voices, premium Mac voices, or API-based studio AI voices
- Non-destructive: zero file modifications, all operations write to isolated bot directory
- Single-command execution for full playlist + spoken interludes
The Problem It Solves:
Streaming algorithms optimize for engagement, not discovery. They loop you through safe territory. This flips that by prioritizing your own unplayed catalog (7,000+ tracks in the author's 91K song library that had zero plays).
What's Novel:
The relationship mapping between tracks. It's not just "play random deep cuts" but building narrative arcs through verifiable musical connections. Think graph-based playlist generation meets college radio's human curation logic.
Public Grok Bot now available. Works entirely on your local machine, respects your file structure, and turns your music hoard into a rediscovery engine.
Built a Grok-powered DJ bot that runs locally on Mac to resurface forgotten tracks from personal music libraries. The technical approach is surprisingly elegant:
Core Architecture:
- Ingests unified music metadata from Music.app, iTunes XML, Spotify cache, Apple Music, and loose files into a master spreadsheet
- Implements a "long-tail score" algorithm that weights never-played tracks, play count history, and artist obscurity
- Generates thematic playlists using relationship graphs: shared session musicians, cover versions, samples, lyrical themes, release year clustering
Key Features:
- Fact-checking layer that verifies music trivia against external sources before generating DJ commentary
- TTS integration with Mac's built-in voices, premium Mac voices, or API-based studio AI voices
- Non-destructive: zero file modifications, all operations write to isolated bot directory
- Single-command execution for full playlist + spoken interludes
The Problem It Solves:
Streaming algorithms optimize for engagement, not discovery. They loop you through safe territory. This flips that by prioritizing your own unplayed catalog (7,000+ tracks in the author's 91K song library that had zero plays).
What's Novel:
The relationship mapping between tracks. It's not just "play random deep cuts" but building narrative arcs through verifiable musical connections. Think graph-based playlist generation meets college radio's human curation logic.
Public Grok Bot now available. Works entirely on your local machine, respects your file structure, and turns your music hoard into a rediscovery engine.