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.

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.

The practical discipline of building, evaluating, and operating systems that use large language models, spanning knowledge architecture, agent control flow, inference optimization, and the human and organizational costs of getting it wrong.

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.