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.

AI safety covers the technical and behavioral risks of deployed AI systems, from sycophantic belief distortion to misaligned model behavior, and the tooling built to detect and constrain those failures at inference time.

A broad category of platforms, libraries, and infrastructure spanning version control, CI systems, language toolkits, AI coding agents, and operational dashboards, increasingly shaped by AI-native patterns and the MCP ecosystem.

The ecosystem of tools for running, serving, and organizing knowledge for LLMs spans local inference runtimes, documentation platforms, and structured knowledge bases, with transparency and context efficiency as recurring concerns.