A measurement worksheet that pairs delivery throughput with DORA stability signals and AI-contribution context, so you can baseline, compare, and investigate tradeoffs with your own data.
A worksheet for connecting coding-tool subscriptions, model usage, review effort, rework, and build activity to accepted changes, using your own invoices instead of borrowed benchmarks.
AI writes code faster than teams preserve why systems work. A typed memory framework covering policies, decisions, owners, and open questions across repos.
Concrete frameworks for translating technical debt into financial metrics that make CFOs approve remediation budgets instead of asking 'can it wait another quarter?'
AI-generated code ships fast but can compound technical debt silently. GitClear's churn data shows why, and a few metrics and gates let you catch the debt before it turns into incidents.
Test coverage percentage is a poor predictor of production reliability. Here are the indicators (Change Failure Rate, Review Depth Score, and rework rate) that actually tell you whether your codebase is healthy.
Traditional code review fails for AI-generated code. Here's the practical governance framework that catches vulnerabilities, manages technical debt, and passes compliance audits.
Slow PR reviews don't just delay shipping. They compound into context switching costs, engineer burnout, and longer wait times. Here's what the research reveals.
