Kubernetes and AI have become unlikely bedfellows—and the numbers prove it. New data from CNCF and SlashData reveals that two-thirds of organizations running generative AI models have standardized on Kubernetes for orchestration. But here's the thing: it's not because Kubernetes magically solves AI problems. It's because the engineering fundamentals that make Kubernetes valuable—standardization, r
More rules should mean better output. That's the intuition. I spent weeks building a comprehensive CLAUDE.md — 200 lines covering naming conventions, security rules, error handling, architectural patterns, import ordering, type safety requirements, and more. I was proud of it. I'd thought through every scenario. Then I scored the output. 79.0 / 100. My carefully crafted documentation was actively
How Cloudflare Built Resilience: Lessons from Their Infrastructure Overhaul When a single misconfiguration can cascade across a global CDN and take down customer traffic, every deployment becomes a high-stakes decision. Cloudflare recently completed a massive push to make their infrastructure fundamentally more resilient—and their approach offers critical lessons for anyone operating at scale. M
Have you ever looked at code you wrote six months ago and thought: "Who wrote this monster?"? Relax, it happens to all of us. In software engineering, writing code that a machine understands is the easy part. The real challenge is writing code that other humans (including your future self) can understand, maintain, and scale. This is exactly where Software Design Principles come into play. In this
Part 1 of 5 in The New Engineering Contract — what it means to lead engineers when AI is doing more of the coding. SWE-CI tested 18 AI models across 71 consecutive commits. Most broke something on commit 47 they'd already broken on commit 1. That's not an intelligence problem. That's a learning system that isn't learning. A paper made me uncomfortable this month. Not because of what it found about