The science and practice of managing, optimizing, and engineering context for AI coding agents.
A reproducible framework for benchmarking AI coding context engines across codebases, tasks, and session lengths, with vexp vs manual context as a worked example.
65-80% of input tokens in typical Claude Code sessions are irrelevant. Here's where they come from, how to measure them, and how to eliminate them.
Using Cursor, Claude Code, and Codex? Each tool starts from zero every session. Here's how to build shared context across AI coding agents — and why it matters.
Stale context causes AI coding bugs that look like hallucinations but aren't. Here's why it happens, why it's getting worse, and how to detect it.
Every AI coding session starts blank. You re-explain the architecture, the constraints, the decisions. Here's what persistent memory changes and how it works.
Without a code graph, AI agents navigate blind and read 15 files to answer a 3-file question. Here's why it happens and how graph-based context selection fixes it.
Your AI coding agent is only as good as the context it receives. Learn how CLAUDE.md files, memory dir, and graph-based retrieval to eliminate re-explanation overhead and boost first-attempt accuracy.
Most AI coding sessions waste 80%+ of tokens on irrelevant context. Here’s why it happens, how it hurts cost and quality, and how dependency graphs fix it.
Learn how AI context windows work, why long coding sessions degrade, and practical strategies and tools like vexp to keep Claude effective and costs low.