I build intelligent hardware from silicon to cloud: embedded systems, platform engineering, and applied AI.

I spent roughly twenty years on platform and infrastructure engineering at scale: Kubernetes, CI/CD, observability, and globally distributed teams. Now I also design the hardware underneath, from PCBs in Altium and ESP32 firmware in C to the cloud platform that manages the fleet. Most engineers build one end of that stack. The interesting problems live in owning both.

As CTO of Rising Orchards I build IgorBox, a show control platform for haunted attractions and live venues: cloud-connected controllers that drive the lights, motors, and DMX rigs on the floor. This is production engineering, not a weekend project, and I build every layer of it.

• Firmware: ESP32 in C on ESP-IDF, with OTA updates, WiFi provisioning, and multicast coordination that keeps a venue full of controllers in sync.
• Hardware: schematic and PCB design in Altium, 4-layer boards taken from prototype through production.
• Interfacing: isolated RS-485 and DMX, motor drivers, and LED drivers. Unglamorous work, but it's what makes hardware safe and reliable enough to run a show with a paying audience in the room.
• Regulatory: FCC and ISED certification, the difference between a working prototype and a product you can legally ship.
• Cloud: a Next.js platform on Vercel that authors and sequences shows, then deploys them to a fleet of networked controllers in the field.

Roughly twenty years of platform and infrastructure engineering behind the software. The hardware is where I get to build the whole thing from the copper up.

AI isn't a spectator sport for me. I use it, build with it, and instrument it every day.

• Multi-agent development workflows where coding agents work across multiple repositories, negotiate API contracts with each other, and hand off work through Linear.
• MCP servers that connect agents to running applications, turning AI from a code generator into a developer that can test its own work.
• OpenTelemetry instrumentation on my own AI usage, so workflow decisions come from real token and cost data, not vibes.

I write about all of it: context engineering, how attention bias shapes agent behavior, and what actually works in production.

A few things I've published that people actually use:

• esp-idf-simple-audio-player : I2S WAV playback for ESP-IDF 5.x
• idf_http_rest_client : makes REST APIs bearable in ESP-IDF
• craneoperator : a simple web UI for browsing a Docker Registry

• mikeheijmans.com
• LinkedIn
• Bluesky

I'm open to select advisory and fractional engagements across platform engineering, embedded systems, and applied AI. If that sounds relevant, or you just want to talk shop, reach out.