Series
Some of these were written as a planned run, start to finish. Others are articles that turned out to be one argument spread over a couple of years. Either way, they read best together.
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AI-Native Architecture
A multi-part series helping you to understand the AI-Native architecture guidelines. Design applications around what AI actually is, rather than bolt AI onto what you already built. Learn about probabilistic behavior, inference economics, the context supply chain, and model lifecycle.
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AI and the Developer
What AI actually does to the people who write software. Two years of following the same argument: the productivity claims, the code quality behind them, the costs that show up later, and what is left that only a human developer can do.
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AI Ethics
The AI/ligned run: AI ethics as an engineering discipline, not a philosophy seminar. Ethical risk, algorithmic accountability, the fairness impossibility results, the safety/ethics/governance distinction, and what the EU AI Act actually asks of you.
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Security Fundamentals
The security principles that hold up regardless of what you are building. Trust boundaries between your own services, who is actually responsible for what in the cloud, security zones, and least privilege.
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Availability Blind Spots
The availability you think you have, and the availability you actually have. Every article here is a risk that did not look like a risk: an outage you scheduled yourself, a login you do not own, a single architect, and a priority list where everything is critical.
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Cloud Migration
Moving to the cloud, and the ways it goes wrong in the middle. Why the economics favor the move, the misstep almost everyone makes, how complexity behaves during a migration, and why stopping halfway is the worst place to stand.
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