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A local-first music intelligence desktop app that analyzes your audio library with machine learning — BPM, key, genre, mood, and semantic embeddings — so producers can filter, search, and discover reference tracks by sonic characteristics. Everything runs on your machine, with no cloud dependency.
For the past few years I've been getting more serious about music production. Not just listening — actually producing. And one of the things producers do constantly is work with reference tracks: comm
Two days ago I wrote about building a music intelligence app in five days with two AI coding agents. That post described a working MVP — a private prototype called music-intelligence that could scan a
Welcome to the first official binary release of Deep Cuts! This release marks a major milestone: a 100% offline, private, and zero-dependency studio audio analysis and music intelligence desktop appli
Just yesterday, I released the first binary release (v0.1.0) for Deep Cuts—an offline-first, local music exploration tool built using Rust, Tauri, and Svelte 5. It is powered by local machine learning
Pulsar by Vangelis is a 12-minute track. The first seven minutes are slow, evolving, near-silence synthesis. Then it erupts. Deep Cuts was telling me it sounded like AC/DC. The bug was in how I'd been
I've been running two AI coding assistants in parallel. Claude for architecture and implementation — it reasons carefully about code structure and catches edge cases. Gemini for research, reading long