i'm an AI engineer. i build systems around models: agents, tools, evals, interfaces, data flows, and the infrastructure that turns a promising demo into something people can actually use.
i'm interested in the parts that decide whether AI systems survive contact with reality: clean boundaries, observable behavior, good data, measured trade-offs, failure modes that are easy to find before users find them, and feedback loops that actually close.
- agentic systems, tool use, evals, and human-in-the-loop workflows
- software that is simple enough to reason about and boring where it should be
- AI systems that make their assumptions visible and their failures debuggable
- reproducible experiments, useful traces, and less hand-waving
- physics, biology, music, coffee, travel, and cultures that keep the map expanding
start with the problem. build the smallest thing that can teach me something. measure what matters. delete what is pretending to help.
i like systems with clear seams: where data comes from, where decisions are made, where humans can intervene, and where the whole thing gives you enough signal to improve it.
make it work.
make it legible.
make it reliable.
then make it interesting.