Insights on product thinking and engineering excellence • 18 articles published
When a test requires 40 lines of setup and three mocks for four lines of assertion, engineers blame the test. The test isn't the problem. It's the first thing honest enough to say the design is wrong.
The feature shipped. Tests passed. Ticket closed. Three months later someone needs to change it — and the original author is the only person who understands it. That is not done. That is a liability with a time delay.
Your CI went red. You clicked Retry. It went green. You merged. This happens dozens of times a month on most teams. Nobody is counting. The cost is not the retries — it is what the retries teach your team about what red means.
Someone wrote "we should refactor this" in a PR comment nine months ago. Three more features have been built on top of it since. The comment is still there. The code is still there. This is not a discipline problem. It is a system that produces the wrong outcome reliably.
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.
When two services integrate, an implicit agreement is formed — a field name, a response shape, an error code. That agreement was never written as a test. Both services keep shipping. The agreement does not update. Contract testing makes the assumption explicit, verifiable, and enforced by CI before a breaking change reaches production.
AI can generate a complete test suite — descriptive names, 94% coverage, green CI — in the same session it writes the implementation. The gap is behavioral: tests written after code verify what the code does, not what it should. Mutation testing makes this visible.
59% of developers ship AI-generated code they don't fully understand. The output looks professional. The reasoning underneath is absent. Here's what broke, why it matters, and how engineering discipline rebuilds the apprenticeship AI bypassed.
Most teams deploy agents at stage five speed on stage two or three foundations. AI amplifies whatever it's applied to — strong process gets faster, broken process gets more broken, faster. The question before expanding agent use is not whether. It's where your codebase actually sits.