Insights on product thinking and engineering excellence • 12 articles published
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.
When an AI agent works on your codebase, your test suite is the only artifact of intent it consistently sees. Tickets, Notion pages, and Slack threads do not enter the loop. If your tests describe what the system should do, the agent is constrained. If they only describe what the code already does, the agent ships whatever it generated.
AI agents and offshore teams break in exactly the same way. They are execution accelerators, not thinking substitutes. The work that doesn't compress — specs, architecture, test design, critique — is the work you have to keep in-house. Outsource the typing. Keep the thinking.
AI didn't take your job. It exposed it. The work was never typing characters into an editor — it was translating ambiguous business intent into something that adds value to users. Code was always the byproduct. The translation was always the work.
When AI generates code that breaks production, ownership doesn't transfer to the model — the post-mortem still names the human who clicked merge. Vibe engineering is the craftsmanship answer: you delegate the typing, but never the understanding, the design intent, or the accountability.
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 say they "do CI/CD" because they have a pipeline that builds, tests, and deploys. That pipeline is automation — Continuous Delivery is a discipline. Here are the practices that turn one into the other.
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.