Autonomous systems that plan, act, and verify across tool calls and multi-step workflows, with active debate over architecture choices, coordination costs, memory design, state management, and the governance infrastructure needed to make them reliable.

Context engineering is the practice of deliberately constructing what an LLM receives in its context window — structuring, compressing, persisting, and retrieving information so agents produce reliable output across tasks and sessions.

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.

The infrastructure, utilities, and integration layers built around large language models, spanning local inference runtimes, context management, MCP servers, knowledge organization, and provider-agnostic design patterns.

Open-source tools span compilers, CLIs, design libraries, version-control workflows, and AI agent SDKs; what unites them is public availability of source, enabling inspection, customization, and community-driven improvement.