Live · 2026-02 → ongoing
Ableton Mentor
AI augmentation in a non-traditional place — an MCP server that listens to my music sessions and tells me why track 7 sounds dark and mono.
Role: Solo build · Python / MCP / Ableton LOM / real-time DSP
The bet
AI augmentation isn’t just for code. A producer’s tools should reason about music, not just transport bytes. The same patterns work anywhere a domain expert wants software to think alongside them.
What it is
A daemon that traverses the Ableton Live Object Model, analyses audio in real time across three threads, and returns specific musical observations — not generic mix feedback.
Decisions that mattered
- Three-thread architecture. LOM read, audio analysis, MCP server — separated so analysis never blocks the transport read.
- Reference-track memory. Sessions remember the reference. New tracks evaluated against what the user said sounded right.
- Specific over generic. Output is “EQ8 notch at 3.09 kHz” — not “the mid-range needs work.” The system either has the data or stays silent.
What’s next
A larger reference corpus. Maybe a way to teach the system new genres without retraining. Built for me; shared because it’s fun.