Page 4 — Technical articles on Kubernetes, K3s, and Proxmox
Kubernetes certificate management is technically a matter of days. What actually takes time is the organizational process of getting sign-off. Here's how cert-manager automates the technical side, and how to design away the operational debt that remains.
Code generated and executed by AI agents can no longer be treated as a trusted, reviewed product. This article explains the limits of container isolation and why Kata Containers' microVM isolation is becoming essential when designing AI agent sandboxes on Kubernetes.
Why doesn't live migrating a VM (KubeVirt) between Kubernetes nodes break the connection? We break down Calico's IP persistence and BGP route convergence, and what it means for teams moving off VMware.
Container image signing explained: tags can be overwritten by anyone, and passing CI tests doesn't guarantee the image running in production is the one you built. Learn how Sigstore and Kyverno work together to reject unsigned images on Kubernetes/K3s, integrated into a GitOps workflow.
World Cup broadcasts duplicate every camera feed across two independent paths and simply discard whichever packet arrives second. That seemingly wasteful design shares the same philosophy as Kubernetes high availability and multi-AZ architecture.
AI-generated Kubernetes manifests pass kubectl apply and 'work' — but getting Kubernetes resource design wrong drives massive overprovisioning. Here's why AI struggles with production-grade requests/limits and what to check before you ship.
Fleet management for K3s edge operations breaks down once you're troubleshooting devices one at a time by hand. Here's how declarative management and Rancher Fleet let you design edge operations that don't depend on any single person.
A single checkout request was quietly triggering five separate service calls behind the scenes. The culprit isn't bad code — it's the "chatty call" architecture that Kubernetes microservices tend to fall into. This article explains the distributed N+1 problem and how to fix it.
AI compute demand has flipped from training to inference, with inference compute projected to reach 1.5x training capacity by 2030. Drawing on KubeCon Japan discussions and the CNCF AI Conformance Program, this article outlines what Kubernetes/K3s clusters need to look like in the inference era.
A field report from a large-scale K3s edge deployment covering 2,000+ devices: why lightweight Kubernetes gets chosen, and the hidden network cost of push-based metrics collection in a hybrid cluster architecture, backed by concrete numbers. For engineers and platform operators.