Devops

Last week I lost a 45-minute Codex session because my thumb grazed the close button on a terminal tab. No warning. No confirmation. Just gone. The session context, chain of thought, and iterative refinements I’d been building all evaporated because of one bad click. If you’ve worked with AI coding agents (Codex, Claude Code, Cursor, whatever), you probably know this feeling. These tools build context over a conversation. Lose that in the middle, and you’re paying for it in both time and mental energy. ...

Kyverno 1.17 landed yesterday, and the big news is that CEL policy types are now GA. If you’ve been running Kyverno with JMESPath-based ClusterPolicy resources, the clock is ticking. They’re officially deprecated and scheduled for removal in v1.20 (October 2026). I spent today migrating a production cluster with about 60 policies. Here is what actually happened. Why This Matters Kyverno has been using JMESPath expressions for years. They work, but they’re Kyverno-specific. CEL (Common Expression Language) is what Kubernetes itself uses for ValidatingAdmissionPolicy since 1.30. By switching to CEL, Kyverno aligns with upstream and gets significantly better evaluation performance. ...

Last month our Artifactory renewal came in at 40% more than last year. No new features we needed, just the usual “enterprise tier” squeeze. Security scanning? Pay more. Replication? Pay more. SSO that isn’t SAML-only? You guessed it. So I spent two weeks building a replacement. Here’s what actually worked, what didn’t, and the gotchas nobody warns you about. What We Were Running Our Artifactory setup handled: Docker images (~800 images, ~12TB total) npm packages (private registry, ~200 internal packages) Helm charts Generic binary artifacts (build outputs, firmware blobs) The big requirements: vulnerability scanning on push, OIDC SSO, and cross-region replication to a DR site. ...

Last month I finally looked at our GPU utilization dashboards properly. What I saw made me physically uncomfortable: 14 A100 GPUs across our cluster, average utilization hovering around 15%. We were paying for dedicated hardware that spent most of its time doing absolutely nothing. This is embarrassingly common. Teams request a full GPU for a workload that uses it for training bursts of 20 minutes, then idles for hours. Kubernetes treats GPUs as integer resources — you either have one or you don’t. There’s no native way to share. ...

I keep seeing the same debugging rabbit hole. A team adds CPU limits, latency gets weird, and the first question is: “Are pods getting killed?” Usually no. That’s memory behavior, not CPU behavior. CPU limits do not kill pods. They throttle them. That one distinction explains a lot of “everything looks fine but users are complaining” incidents. The Misunderstanding A lot of engineers assume this mapping: Memory limit exceeded → pod gets killed (OOMKill) ✅ CPU limit exceeded → pod gets killed ❌ The second one is the trap. The official Kubernetes documentation spells it out: ...

Last week I ran into a familiar mess, pods landing on nodes before the CNI plugin was actually ready. Kubelet marks the node as Ready, scheduler starts placing workloads, then everything sits in ContainerCreating because Calico is still coming up. I have worked around this with init containers and postStart tricks for way too long. I came across the Node Readiness Controller announcement on the Kubernetes blog. It is a new SIG project (v0.1.1), and it is basically what I wanted, custom readiness gates for nodes managed through a CRD. ...

🤖 A New Era of Creation We’re living through something extraordinary. AI isn’t just automating tasks, it’s amplifying imagination. As someone who’s spent years deep in the world of infrastructure, automation, systems, and DevOps, I find myself more excited than ever. Not because AI replaces what we know, but because it lets us build upon it faster than ever before. 💡 Ideas Used to Be Worthless Without Execution For most of IT history, ideas were… expensive. You could have a vision, a product concept, a technical improvement, but without: ...

🧠 The Problem As a DevOps engineer with a Linux-first background, you eventually face this: You’re given a company-issued laptop or you’re working for a client, and their policies require you to use Windows. Fair enough. Modern Windows is solid. With WSL2, you can run a real Linux kernel, your dotfiles work, and tools like Docker, Git, and SSH behave mostly as expected. But then… small things get in the way. Like window switching. ...

Detecting Kubernetes Nodes Running Only DaemonSet Pods, A Deep Dive A real-world story about PromQL struggles, Helm templating, alert design, and operational savings by Dedico Servers. Executive Summary At Dedico Servers, we specialize in building efficient, cost-optimized Kubernetes clusters. In this article, we engineer a Prometheus-based alert to detect nodes running only DaemonSet pods, an operational and financial risk. By tackling this hidden inefficiency, we help our clients save thousands of dollars annually while improving the resilience of their clusters. ...

Managing Kubernetes applications with ArgoCD is already a game-changer, but what if you need to deploy the same app across 10 clusters, or generate dynamic app configs based on Git branches or Helm values? That’s where ApplicationSets step in. 🚀 What is an ApplicationSet? An ApplicationSet is a Kubernetes custom resource that tells ArgoCD how to automatically generate multiple Application resources from a template. It’s like templating your ArgoCD apps, letting you define how they should be generated and where they should go. ...