Turning complexity into clarity

Every worthwhile project starts with a problem worth solving. At FreeWheel, that meant leading cross-functional investigations into complex streaming issues, coordinating across engineering, product, and client teams to find root causes. Now it means designing agentic systems that handle ambiguous inputs gracefully — or coordinating an iPhone app and its server through a versioned API contract across three machines that never see each other, with git as the only channel. The approach is the same: slow down, understand the constraints, and think critically about what "done" actually looks like.

I approach problems by first mapping the territory: what's known, what's unknown, and where assumptions hide. Then I break the problem into smaller, testable pieces. When the Gaines Concierge app approves a booking with no signal, the answer is persisted and retried with its original idempotency key — a dropped connection can never lose an approval and never approve twice. That's what decomposition buys you: the failure modes get named before they happen.

This isn't about rigid process for its own sake. It's about finding the real constraint. When a cloud provider's per-request token cap made it impossible to hand a model every tool at once, the fix wasn't fewer tools — it was a cheap planning call that picks which servers each request actually needs. A clear understanding of the problem is the most underrated tool in any developer's toolkit.