13 articles in Code Quality
AI code passes review on day one and ages worst by month six — not because it's wrong, but because the design intent that makes it refactorable was never part of the diff. The fix is encoding intent in artifacts the next iteration must read.
The number of days between a new engineer's start date and their first feature in production is the most honest readout of your codebase's health. Every friction point they hit — environment chaos, unclear boundaries, noisy tests, PR limbo — is a structural problem your team learned to route around. The onboarding clock is not running on your new engineer. It's running on your codebase.
AI coding agents are multipliers. Multiply zero, get zero. Multiply a typical outsourced codebase, and you don't accelerate delivery — you accelerate rework. Here's the founder math on why your AI ROI is landing on the wrong substrate.
Most teams have heard of mutation testing. The reason 90% of them never run it: nobody told them how. This is the operating manual — pick the tool, read the report, kill the surviving mutants, run it fast enough to gate every pull request.
Every team with a legacy codebase hits the same wall: you cannot add tests without refactoring, and you cannot refactor without tests. Characterisation tests are the specific tool — invented by Michael Feathers, amplified by AI — that breaks the loop.
Gene Kim and Steve Yegge argue that vibe coding works for production — if you have preventive, detective, and corrective controls. We agree. We built the platform that enforces those controls structurally. Here's what we've learned about why practices alone aren't enough.
Your dashboards show green. Your test coverage is stable. But three threats are compounding underneath — defects, debt, and non-determinism. AI is making each one worse, faster. Here is how to see them before they see you.
Vibe coding is productive, inevitable, and here to stay. But without guardrails — prevention, detection, and correction — it creates tech debt at warp speed. The teams that win will be the ones who vibe code with discipline, shipping fast AND shipping safely.
Your team adopted GitHub Copilot, spent $180K on licenses, and expected faster delivery. Instead, lead times got longer, defect rates climbed, and your senior engineers are spending more time reviewing AI-generated code than writing their own. Here's why — and what the fix actually is.