Building systems that respond to events rather than requests requires rethinking common patterns. The challenge is managing ordering guarantees, deduplication, and late arrivals while keeping the system simple to reason about. Currently exploring how to model event-driven workflows in Go and Python, with emphasis on testability and operational clarity.

Building tools that make developers more productive. This includes designing clean APIs, reducing cognitive load, and making common patterns explicit. Interested in automation frameworks that handle edge cases gracefully and provide good failure visibility.

Understanding how systems behave under real-world constraints: network partitions, partial failures, and operational complexity. Studying how companies scale systems from thousands to millions of requests per second, and what decisions enable that growth.

The gap between what code does in tests and what it does in production is where most problems live. Focusing on systems that are easy to operate, debug, and improve. This means designing for observability from the start, not bolting it on later.