I build small, useful things with AI.

Products, agents, and the occasional piece of hardware. Below is what I’ve built, and what I learned building it.

Work

12 entries
  1. 01

    Algonkian Starter

    An 18-hole model of a real golf course, built from OpenStreetMap, for keeping a busy Saturday on pace.

    3D simulationPrototype
  2. 02

    Home Concierge

    A mobile home-buying guide that turns every choice into a monthly cost, then walks you through a lit 3D house.

    Web appPrototype
  3. 03

    Gonkbot

    Log golf rounds in ChatGPT or Claude while you play: scorecards, trends, and handicap math.

    MCP serverLive
  4. 04

    Sky Lab

    A narrated iPad lab that shows young kids how helicopters fly. Blow into the microphone to make wind.

    Learning appLive
  5. 05

    Apex Arena: 1776

    A first-person shooter in snowy 1776 Trenton where every reload is a history question. Miss one and your musket stays empty.

    Game · Unreal EnginePrototype
  6. 06

    Agent Macropad

    A hand-wired 14-key pad with a knob for steering coding agents. Case based on a design by Newtle Kim.

    HardwarePrototype
  7. 07

    Birdcam

    A Raspberry Pi feeder camera. Claude names the species and deletes the frames without a bird.

    HardwarePrototype
  8. 08

    Sock Sorting Bot

    A robot arm learning to sort socks. Claude finds each sock on a grid mat; a policy trained on recorded demos picks it up.

    RoboticsBeta

Code and experiments

  1. 09

    Family Steward

    A household agent you text. The repo is its open-source core: state, approvals, and an audit log the model can’t skip.

    AI agentOpen-source core
  2. 10

    Hermes Agent

    Six merged PRs that made the SMS channel of Inkbox’s Hermes fork dependable. The fork has since been archived in favor of a plugin.

    Open source6 PRs merged
  3. 11

    Drone lab

    An MCP server that lets an agent fly PX4 drones in simulation: missions, failsafe tests, and telemetry checks.

    Robotics · simulationPrototype
  4. 12

    Local model lab

    Benchmarking and fine-tuning open models on one GPU. Teaching a model to use a lookup tool beat training more facts into it.

    ML experiments

Also here

Ideas I keep coming back to

Keep the policy out of the model.

The model proposes. Approvals, retries, and the audit log live in plain code it can’t talk its way around.

Ship tools, not inference.

Gonkbot runs no model at all. The golfer’s own assistant does the thinking; the server keeps the books.

Read the essay →

Cheap hardware, smart model.

A Pi, a camera, and a vision model go a long way. The birdcam trains nothing: Claude does all the looking.

Guardrails come from failures.

Every rule my deploy agent follows is there because it broke something first.