Principal Engineer · Silicon, Hybrid AI Orchestration & Sovereign Compute
I'm an engineer who works across the whole edge-compute stack, and builds local-first software that runs entirely on your own device.
From the antenna to the AI model, and the power rail to the thermal floor.
15+ years across the full edge-compute stack: RF, analog, baseband, processors, accelerators, and the AI that runs on top.
I've spent 15+ years shipping production silicon and AI infrastructure that runs in billions of devices (RF, modem, NPU). Now I also build local-first tools for the post-cloud transition: software that runs entirely on your device, with no cloud, no account, and no telemetry.
The thesis: AI inference is a physical routing problem.
Cloud-only AI is thermally and economically unsustainable. A fully isolated edge device hits memory-bandwidth walls. The future is a multi-tier hybrid split.
| Tier | Runs | Handles |
|---|---|---|
| Edge / local box | Small models, instant response, tight power envelope | Most routine agentic work: routing, extraction, synthesis |
| On-prem / frontier cloud | Massive memory, custom silicon | The genuinely hard reasoning |
I build the local-first application layer and the deterministic kernels designed to survive that shift. One rule runs through all of it: AI advises, deterministic code decides.
A suite of single-file, local-first tools on one open JSON protocol (localoffice/v1). Local models draft; lightweight deterministic kernels verify. No install, no cloud, your data stays on your disk.
The Hub opens the whole suite, or launch any tool directly:
A presentation editor in a single HTML file. Generate, lay out, and ship decks locally, no cloud suites involved.
A human-first protocol for stateful LLM context and memory. Plain Markdown, Git-versionable, model-agnostic, fully offline.
All MIT licensed. Fork them, inspect the network logs, and own your compute.