Claude and Copilot fail on real codebases because they lack context. Here's why AI coding tools break down—and what actually works for complex engineering tasks.
Real answers to hard questions about making AI coding tools actually work. From context windows to team adoption, here's what nobody tells you.
Cyclomatic complexity is a lie. Here's how to actually measure code health by combining complexity, churn, and ownership data that predicts real problems.
Serverless or Kubernetes? This guide cuts through the hype with real tradeoffs, cost breakdowns, and when each actually makes sense for your team.
Engineering teams lose 20-35% of developer time to context acquisition. This invisible tax is baked into every estimate and accepted as normal. It shouldn't be.
How to use discovered features, competitive gaps, and team capabilities to build data-driven roadmaps instead of opinion-driven ones.
Knowledge concentration is a ticking time bomb. When a key engineer leaves, the blast radius extends far beyond their code.