The blog · since 2015
Essays, playbooks and field notes from our CTOs, architects and leads. Written for people shipping real product.
Mutation testing was always too slow to run on a whole codebase, so nobody did. Agents change that: they touch a handful of files, don't mind a two-minute run, and escaped mutants tell them exactly which tests check nothing. A targeted Infection setup for PHP, plus the agent instructions.
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.
Handing everyone an LLM subscription gives them a model. A shared, self-hosted agent gives them a configured environment with company context, connected tools, and clear boundaries. Why a company should own that layer rather than rent it, and how to start small.
Coding agents made implementation cheap, but validation never got cheaper. That shift moves the bottleneck from writing code to deciding what is worth building, and the response is more autonomy and faster iteration, not more detailed upfront plans.
A written style guide is a museum piece nobody enforces. An LLM changes that: it reads rules in plain prose and applies them on every pull request, reaching judgment calls no linter could express. The style guide stops being a document and becomes a runtime.
An autonomous email agent hit a missing script, spiralled through 25 pointless shell calls, then fabricated email content including a prompt injection, and acted on it. The fix is not more warnings. It is structural validation before the model ever sees the data.
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