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From Cockpit to Cloud: One Analogy Is Not Enough

The aviation analogy was a beginning. It gave platform engineering one axis of the AI adoption problem: how to hand authority from humans to machines without catastrophe. But it is not enough. Five other industries navigated the same inflection point, and each left a different blueprint that DevOps has not yet read. In my previous post I drew on aviation’s […]

Beyond the Prompt: Scaling Professional AI with the OpenClaw Framework

In the rapidly evolving landscape of Artificial Intelligence, the gap between a hobbyist using a chatbot and a professional engineer deploying production-ready automations is widening. While many are content with basic prompting, the real value lies in orchestration, security, and local integration. This is where OpenClaw changes the game. OpenClaw isn’t just another AI tool; it is a comprehensive framework […]

How Can We Formalise a “DevOps Calculus”?

1. Decide what DevOps calculus is about First, we need to decide: what are we differentiating and integrating over? In other words, what is the “stuff” of DevOps? At a high level, DevOps calculus would be a formal system for reasoning about: So DevOps calculus is about how changes propagate through a socio‑technical system and how we can reason about […]

🧠 The Abstract Foundations of Cloud DevOps

1. DevOps as a System of Invariants In mathematics and physics, invariants are properties that remain true despite transformations. Cloud DevOps has its own invariants — truths that hold regardless of tooling, cloud provider, or architecture. Examples of DevOps invariants These are not opinions — they behave like laws of nature in distributed systems. 📐 Hypotheses, Theorems, Truths, and Proofs […]

AI-powered Claude Code extensions for DevOps: a comparative security analysis

Ralph Claude Code and Get Shit Done (GSD) both extend Anthropic’s Claude Code to enable autonomous AI development—but they take fundamentally different approaches with distinct risk profiles. For Cloud DevOps engineers, both tools can dramatically accelerate infrastructure-as-code development, pipeline creation, and automation scripting, yet they require careful security controls given that 40-62% of AI-generated code contains security vulnerabilities according to […]