A small set of notes on architecture, performance, platform design, and production-oriented AI systems.
Lessons from leading two large-scale monolith-to-microfrontend migrations, the patterns that generalize, and why the second time was fundamentally different.
How studying biological systems built the mental models I use every day for distributed architecture, performance engineering, and debugging at scale.
Why separating compilation from execution, enforcing context boundaries, and treating AI as a systems problem produces reliable automation at scale.
How path-based routing, configuration-driven expansion, and explicit tradeoffs enabled a global commerce experience without per-country engineering effort.
How we transformed performance from a reactive firefighting activity into an organizational capability with measurement, standards, and delegation.