Technical articles about AI coding agents, context engineering, and developer productivity.
Team-scale Claude Code costs multiply individual inefficiencies 8-15x. Here's the playbook: shared context engine, standardized CLAUDE.md, per-developer keys, and the actual ROI math.
A reproducible framework for benchmarking AI coding context engines across codebases, tasks, and session lengths, with vexp vs manual context as a worked example.
Most AI coding benchmarks are demos, not data. Here's the rigorous methodology for measuring AI coding tool performance with reproducible, defensible results.
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.
Hitting Claude Code rate limits? The root cause is usually high tokens per request, not total usage. Here's the math and the fixes.
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.
Benchmark results from 21 runs on a real FastAPI project: 65% fewer input tokens, 57% lower cost, 14pp better task completion. Full methodology and setup guide.
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.
Compare manual Claude Code token-saving tactics with automated context engines like vexp, with real numbers on savings, tradeoffs, and setup.