AI-Native Engineering
How software gets built when AI is the default: agents in production, coding with Claude, testing non-deterministic systems, and the new SDLC. 12 essays, from 25 years at the seam between the boardroom and the codebase. This is the thinking behind AI-native transformation work.
Stop Prompting Claude. Start Staffing It.
Almost everyone runs Claude as one freelancer with amnesia: open a chat, paste a task, close the tab, re-explain everything next time. Anthropic quietly open-sourced the repo that turns it into a department instead.
Read →Build a System That Prompts Itself
You're not supposed to prompt Claude. You're supposed to build a system that prompts itself. The leverage was never in the wording. It's in the wiring.
Read →Ten Coding Agents, One Laptop
Running six to ten coding agents at once was never a model problem. It's an environment problem, and the moment you solve it, the constraint moves to the one thing you can't refactor: the RAM on your desk.
Read →The Pentest Comes Too Late. Put OWASP in Every Pull Request.
A pentest twice a year tells you what was broken months ago. Wire Claude Code's GitHub Action into your pipeline for everyday code review, then add a second, explicit security step that reads every diff against the OWASP Top 10 and blocks the merge when it finds a hole. Security stops being an event you survive twice a year and turns into something your pipeline checks on every commit.
Read →Professional Vibe Coding: Reclaiming a Phrase the Industry Loves to Mock
Vibe coding earned its bad reputation honestly. But the speed it hints at is real, and with real guardrails, it becomes the most powerful way to build software I've seen in 25 years. This is part one of a series.
Read →Your CI Was Green. The Model Just Swore at a Child.
You can't test an AI agent by asserting on strings, and you can't trust a green build either. You test the behavior, by replaying real histories, injecting the exact RAG context, and grading the tool calls, and you test it adversarially, because a determined nine-year-old is a better red team than your pipeline.
Read →Every Engineer Is a Manager Now
The job is no longer to write the code. It's to break the work down, hand it to a team of agents, and be accountable for what comes back. Every individual contributor is quietly becoming an engineering manager of synthetic staff, and the same shift is coming for every other role.
Read →Waterfall Is Coming Back, and It's Not a Joke
Agile was a hedge against the high cost of change. AI collapsed that cost, and with it the reason to slice everything into two-week confetti. When a three-month body of work costs what a ticket used to, the constraint moves back upstream, to thinking. That is waterfall's old home.
Read →AI Agents in Accounting: Build a Graph, Not a Swarm
Accounting is reading, computing against the rules, and filling in forms, exactly the work AI does faster than any analyst. Here's the agent architecture I'd build for it, and why it's a graph, not a swarm.
Read →AI Agents in E-Commerce: Finding Margin in the Cost of Fulfillment
In a commodity market the price is fixed and the product is undifferentiated, so profit hides in the cost of fulfilling each order. Here's the agent architecture I'd build to find it, across both owned inventory and drop shipping.
Read →Design Your AI Agents Like Very Stupid Employees
The easiest way to think about agent design isn't to build one brilliant generalist. It's to hire a team of narrow, slightly dim specialists who each do exactly one job, and nothing else.
Read →Agile Is Dead. The Word Is Still Twitching.
Agile was a workaround for the slow, expensive nature of building software in 2001. AI repriced the building. The ceremonies that compensated for the old expense are now a tax paid in the exact currency they were invented to protect: speed.
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