AI engineer — LLMs, local inference, and developer tooling. I build end-to-end systems and contribute upstream to the tools I depend on.
Now: on-device speech for Apple Silicon — shipping localvoxtral and contributing the Voxtral realtime inference path to mlx-audio-swift.
- localvoxtral — native macOS menu-bar app for realtime, fully local dictation: words appear while you're still speaking, with optional on-device LLM polishing. Runs Mistral's Voxtral Mini 4B Realtime on Apple Silicon, or against any OpenAI Realtime-compatible backend.
Swift - mlx-audio-swift — contributor to the Voxtral realtime streaming path: incremental mel/conv front end (O(N²) → O(N) per utterance), fp16 dtype fixes, Metal buffer-pool policy, and quantized tied-embedding loading — teaching the engine to load a 4-bit tied-head checkpoint I publish, which cuts the per-token LM-head projection ~10× (~30 ms → ~3 ms on an M1 Pro). Streaming went from ~1.9× slower than realtime to a flat 0.76 RTF.
Swift - llama.cpp #20120 — merged: preserve Anthropic thinking blocks through the server's message conversion.
C++ - fastcontext — read-only repository-exploration agent that coding agents delegate to over bash: cited
file:lineanswers, not context noise.Python - voxmlx — realtime Voxtral transcription in MLX; my fork adds an OpenAI-Realtime-compatible WebSocket server and Metal memory caps. 4-bit conversions on Hugging Face.
Python - pi-intl-segmenter-fallback — diagnosed a V8 segfault (null
icu::BreakIteratoron small-ICU Node builds, nodejs/node#51752), filed it upstream with a proposed patch, and shipped this pure-JSIntl.Segmenterfallback to npm the same day.TypeScript - pi-llamacpp-provider — pi extension that registers a llama.cpp server as a provider and auto-discovers every model it exposes.
JavaScript - skills — skills that let a coding agent delegate work to another coding CLI and keep itself in sync from git.
Shell
LinkedIn · Hugging Face · npm




