Insights on product thinking and engineering excellence • 10 articles published
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.
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.
AI coding agents are the most powerful tools engineering has ever had. But the teams getting unreal results aren't just prompting — they're combining AI with practices that unlock outcomes nobody else can explain. Hypothesis-driven development is the first unlock.
Harness engineering gives coding agents guides and sensors to make output reliable — the downstream controls that ensure things are built right. Shape is the upstream control that ensures the right thing is being built. Here's what the shape discipline looks like — and why AI teams that skip it ship well-built fragments of the wrong product.
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.
A team of two junior engineers and one mid-level engineer shipped 200,000 lines of production-grade code for an Industrial IoT platform — achieving elite DORA metrics and an 8.8 code health score from day one. No seniors. No rewrites. Here's how Prevention made it possible.
A new framework argues that coding agents need external "harnesses" — feedforward guides and feedback sensors — to produce reliable code. We agree. We also built one. Here's how theory maps to a production system, concept by concept.
DORA metrics tell you delivery is slow. Code coverage says tests are fine. But neither tells you why features take 3x longer than they should. Detection connects the dots — correlating process symptoms to code causes — so you fix what actually matters.
Most test suites look healthy — high coverage, all green. But run mutation testing and you'll discover that 80-90% of tests would still pass even if you deleted the code they claim to test. Here's what decorative tests are, why they're dangerous, and how to fix them.