Design patterns for AI agents acting across multi-step tasks, covering how tool access, memory, orchestration topology, and coordination overhead shape whether an agent system works in practice.

AI coding assistants accelerate development but introduce tradeoffs around skill atrophy, codebase design, verification, and security that shape how much value they actually deliver.

Deliberate construction and management of the information fed into an LLM's context window, treated as a first-class engineering problem spanning retrieval strategy, knowledge structure, memory systems, and token efficiency.

Developer productivity spans tooling choices, organizational alignment, and the human skills those tools depend on, with a growing body of sources questioning whether AI-assisted workflows deliver on their promise without eroding the judgment they require.

A broad discipline covering architecture, tooling, testing, and craft decisions that determine how software is built, maintained, and extended — a theme connecting sources on agent reliability, CSS platform primitives, component design, shell scripting, and more.