Principal Engineer · Silicon, Hybrid AI Orchestration & Sovereign Compute
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:
| Tool | What it does | Launch |
|---|---|---|
| LocalSheets | Spreadsheet with a local AI panel | ▶ Open |
| localDeck | Single-file slide editor | ▶ Open |
| localDoc | Doc writer with a compliance linter | ▶ Open |
| localCards | Spaced-repetition flashcards | ▶ Open |
| localMindMap | Canvas mind-mapping | ▶ Open |
| localMark | Image sanitizer (strips metadata) | ▶ Open |
| localCheck | SOP / QA runbook with a gated sign-off | ▶ Open |
| LocalPlan | Single-file planner | ▶ Open |
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.
- University of Washington · BS/MS Electrical Engineering
- Stanford · AI Professional Program
- MIT · Product Management Professional Program
- Angel investor · early-stage Physical AI, deep tech, and edge infrastructure
- Sovereign edge AI, from silicon to application
- On-device LLMs and VLMs for robotics and physical execution
- Hardware/software co-design at the power and thermal floor
- Local-first software as a permanent category
Local-first. No cloud. No accounts. Your data stays on your disk.