I build AI systems,
and applications with them.

The operator surface for Tower: live node health, running and finished agent jobs with the evidence each returned, and tiered retrieval over a private corpus — summary cards first, a separate vector index second, raw sources only for an audit.

A self-hosted photo culler with its own ML pipeline: face clustering, scene and aesthetic scoring across roughly 80,000 frames, all running on owned GPU hardware. The demo is the real front end over a fictional library, so it behaves exactly like the logged-in app.

A self-hosted job application tracker. A browser extension captures a posting into structured fields as you look at it, normalises inconsistent markup through a per-site adapter registry, and writes into a versioned SQLite schema. Local-first, because that record is not data to hand to a third party.

A house-hunting workspace built on immutable capture. Listing sites edit and pull their own pages, so every capture is archived as evidence first and the comparable-property database rebuilds from it. A delisted property keeps its record.

A study and credential workspace that answers “what should I study next” from recorded progress rather than a static plan, pairing a durable milestone store with a corpus-and-coaching engine behind one authenticated origin.

A browser extension that turns one lump grocery charge into every line item at the price actually paid, read from the order page already open in the tab. No stored passwords, nothing leaves the browser. Kroger and H-E-B today, with a Safari version in progress. The demo is its price-history dashboard over an invented household's orders.

Pricing, deploying and auto-scaling a web app on AWS, then moving its data to a second cloud: the cloud-degree capstone, written up with the numbers.