Developer tooling spans the full surface area of software construction — version control, testing, shell ergonomics, AI coding assistants, and platform infrastructure — with a consistent theme: reducing friction without sacrificing correctness or security.

Distributed systems problems — coordination, state management, failure recovery, and observability — recur across cloud infrastructure, durable execution, multi-agent AI, and formal verification research.

Observability spans infrastructure, distributed systems, and AI agents — the practice of making system internals legible through traces, events, and feedback signals so engineers can understand, debug, and improve what they've built.

The engineering decisions that determine how software behaves under real load, covering durability, observability, testing discipline, performance constraints, and the operational costs of failure.

Software engineering spans craft, process, and judgment — how code is structured, tested, reviewed, deployed, and maintained — and the sources collected here collectively interrogate each layer as AI tooling reshapes who does what and why.