51 articles in Engineering Practices
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.
Even a good hypothesis needs a map before it needs code. AI agents build whatever ticket lands in front of them — they can't see the journey the ticket sits inside. User story mapping is the practice that turns a backlog of features into a walking skeleton of the user's journey. It's the second unlock.
Median time from PR opened to first review is 22 hours. That is not review effort — it is system latency. Here is what it costs, why it happens, and what elite teams do differently.
Most founders evaluate engineering vendors on story points and velocity. Neither metric predicts whether software reaches customers. Here are the four questions — no technical background required — that reveal everything.
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.
Your PM writes prose. Your engineer translates it to code. QA finds the gaps. Your AI agent just compressed this broken loop from weeks into hours — without fixing any of the misalignment. Specification-driven development is the contract layer both sides can read, write, and execute. And it's the practice that separates teams who get leverage from AI agents from teams who ship the wrong thing, faster.
Most developer tools fail adoption not because they're poorly built, but because they're designed from the wrong starting point. Here's how Jobs-to-be-Done thinking explains why engineering tools succeed or sit unused — and what it reveals about AI adoption.
Every new model release prompts the same question — is this the one that finally makes AI coding agents reliable? It's the wrong question. What keeps single-agent workflows from scaling to production is architectural, not about the model. Here's the pattern the teams shipping real code keep converging on.