The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
Years of honest no's trained teams to bring engineering half an idea and ask what's possible. Now that three sprints is an afternoon, that question dodges the hard part. The scarce thing is knowing what's worth building, and engineers have to undo the conditioning.
Handing an AI your engineering conventions cleans up the code without fixing the architecture. A 5,000-line god class shrank to 500 lines across tidy domain files, and an independent reviewing agent still returned FAIL: authority never left the central object.
Becoming AI-native in three years does not need a three-year technology roadmap. It needs a usage policy, a map of your data, one bounded experiment, and both the enthusiasts and the sceptics in the room. Build the ability to change, not the overhaul.
Infrastructure documentation goes stale because it is a second thing to maintain. Terraform plus cloud discovery plus a few query tools describes the system better than any diagram, and leaves humans to document only the why behind the odd decisions.
Hitting a wall with AI usually means hitting the wall of the chat box, not the wall of AI itself. Give a model memory, tool access and a browser, and the interfaces we built for humans become the integration. The bottleneck stops being the model and starts being your imagination.
Sentry silently drops transactions that blow past its span and payload limits, so the heaviest background jobs are exactly the ones missing from your dashboard. Capping span count and truncating span descriptions on bytes brings them back.
An AI-assisted note system captures faster than you can understand, and the gap compounds into internalisation debt: a dense graph of links attached to a thin mental model. The test of a knowledge base is not how much it holds, but how much you could still explain with the file closed.
A practical guide to evaluating fractional CTO providers in Belgium: the three criteria that matter most, how the four provider types differ, what an engagement should cost, and when a full-time hire is the better call.
Teams accrue learning debt when shipping always beats getting better. It stays invisible on every dashboard, compounds fastest under AI-speed change, and quietly caps the ceiling a buyer is willing to price.
Synthetic checks like curl and Lighthouse measure a request your users never make. A HAR file captures the real logged-in flow, and handed to Claude alongside the codebase it turns a wall of JSON into a ranked list of fixes that get implemented in the same conversation.
A back-end engineer set out to redesign a React app with an AI agent and came out actually knowing React. The unlock wasn't the prompting, it was understanding the code well enough to steer it. As the cost of entry collapses, knowing the technology matters more, not less.
A good technical due diligence report is smaller than you expect, separates fact-checked observations from labelled judgement, and answers the specific questions your deal depends on. No score. The real test: does it change what you do on Monday morning?
Analytics only shows the events you remembered to add, never the ones you forgot. Let Claude instrument features as it builds them, then run a skill that audits your tracking against your own docs and live data, flagging what's missing, noisy, or quietly broken.
Integrating a tool with Claude rarely needs an MCP server. A boring script wrapped as a named, version-controlled skill does the job, and the real value shows up when two skills combine: one tool tells you what happened, another tells you why.
17 lock-wait timeouts in two hours. The SELECT was doing exactly what it was supposed to. The problem was what InnoDB did silently in addition: a gap lock on the write path, invisible in logs, invisible in EXPLAIN, visible only in production.
Writing code by hand used to be the job. Soon it will be a hobby, like woodworking or growing your own veg. What a company pays for now is judgment: knowing what to build, catching the answer that looks right and isn't, deciding when it's good enough to ship.
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