Byte-size guides on DevOps, Cloud, and AI

🛠️ Pipelines in the Wild #1 Byte size summary After reading this article, you will know how to implement a blue/green deployment pipeline on OpenShift that uses HAProxy-backed Route weight splitting for traffic control and Flagsmith for feature flag management — and more importantly, you will know where the implementation breaks silently. Specifically: the HAProxy propagation gap that lets your smoke tests lie to you, the partial rollout state that puts two versions in production simultaneously, and why the standard approach of patching a Route weight and immediately proceeding has cost teams I’ve worked with entire migrations. The implementation uses GitHub Actions for orchestration, oc commands for OpenShift-specific traffic control, and Flagsmith as the feature flag service. The patterns apply to AKS, EKS, and GKE with platform-specific variations called out. ...

🤖 AI in the Stack #1 Byte size summary After reading this article, you’ll have a framework for evaluating AI tools in platform engineering contexts — not by capability type, but by where in your workflow the tool actually changes the outcome. You’ll understand why the tools that sound most compelling are still hype, where genuine productivity gains exist today, and what governance infrastructure you need in place before any AI component gets near production. This article is the foundation for the series; subsequent articles implement each touch point against real OpenShift infrastructure. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI I Still Remember the Sound Forklifts beeping in reverse. Conveyor belts humming. Cold warehouse air hitting my face as I stood on the floor of a Delphi plant in 2002. I was staring at a maze of pallets, racks, and production lines, trying to redesign the entire material movement system. I had a chemical engineering degree, a head full of equations, and absolutely no idea how this moment would shape the next 20 years of my career. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI The Meeting That Changed How I Prepare I used to spend hours before important workshops and client meetings pulling together materials. Downloading presentation decks, saving links, reading through documents, trying to hold enough in my head to answer questions confidently in the room. Then I discovered NotebookLM. NotebookLM is a generative AI tool from Google that lets you upload your own documents, presentations, and links and then have a conversation with all of that material as if it were a knowledgeable colleague who had read everything perfectly. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI I Was Selling AI Before Most People Knew What It Was A decade ago I was selling predictive and prescriptive analytics solutions to enterprise clients. Tools like SPSS Modeler — IBM’s data science platform for predicting future outcomes — and CPLEX, the optimisation engine we talked about in Article 6, which solved complex scheduling and logistics problems for supply chain and warehouse operations. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI The Problem With Doing It By Hand Early in my Cloud and Infrastructure career I watched a colleague spend three days manually building out a production environment on Azure. Clicking through dashboards, configuring virtual networks, setting up security groups, deploying OpenShift, installing operators. Three days of careful, methodical work. Two weeks later, we needed an identical environment for testing. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI From Supply Chain to Container Orchestration When IBM acquired Red Hat, I was working as a technical seller trying to position IBM’s data science platform to clients. Our internal team was containerising CPLEX — a powerful optimisation engine used in warehouse management and supply chain applications — and running it on OpenShift. I had seen CPLEX solve complex scheduling problems in the real world. I understood inputs, equations, constraints, and outputs. But pods? Nodes? Dockerfiles? It felt like a science project. I could not connect what I was seeing on screen to anything that could work in real production. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI The First Time I Saw a Pipeline Run I still remember the first time I watched a CI/CD pipeline run from start to finish. A developer pushed their code to GitHub. Within seconds, a series of automated steps fired off on their own — tests ran, the application was packaged into a container, and it was deployed to a live environment. Nobody pressed a button. Nobody sent an email saying “please deploy this.” It just happened. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI The Moment Someone Finally Explained Containers to Me When IBM acquired Red Hat, my world changed overnight. Suddenly everyone around me was talking about containers. Kubernetes. Pods. Orchestration. I was nodding along in meetings while internally having absolutely no idea what any of it meant. My background was in supply chain and logistics. I understood how physical goods moved around the world — warehouses, pallets, shipping routes. But containers in software? That meant nothing to me. ...

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI The Day I Nearly Broke Production Early in my Cloud career, I was making what I thought was a small change to a configuration file. I’d been working on it locally, testing it, feeling confident. Then I committed and pushed — straight to the main branch. Within seconds, a CI/CD pipeline picked up the change and started deploying it to production. My phone buzzed with an alert. My colleague looked over and said, very calmly, “Did you just push to main?” ...