Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Bad resource requests waste money or trigger OOMs. The methodology we use to right-size requests based on actual usage, and the gotchas the autoscalers don't fix.
Argo CD ships your manifests; Argo Rollouts ships them gradually with automated quality gates. The setup, the analysis templates that earn their place, and what we measure.
Helm gives you a lot of rope. The patterns we used that backfired, the ones we replaced them with, and what to skip if you're starting today.
After two years of running Karpenter on production EKS clusters, the NodePool patterns that survived, the ones we replaced, and the tuning that matters.
Run your first three Kubernetes objects — Pod, Deployment, Service — on a local cluster, then understand why each one exists and how they fit together.
GitOps in plain words — what it actually is, the workflow it enables, and a hands-on demo using Argo CD on a local Kubernetes cluster.
Three layers of pooling, three different jobs. We learned the hard way which to use when. Real numbers from a 8k-connection workload.
We launched Backstage in October. Six months in, 80% of services are catalogued, on-boarding takes a third of the time, and we mostly know what owns what.
We started using eBPF tooling for ad-hoc production debugging six months ago. Three real incidents where it cut investigation time from hours to minutes.
A two-line config change to an Argo Rollouts analysis template caught a regression that would have cost ~$40k in API spend before we noticed. Here's the pattern.
Three production OOM incidents that taught us how kubelet, containerd, and the kernel actually decide which process dies. With debugging commands you'll wish you had earlier.
Bills hit $3,400/mo for runner minutes. We moved to self-hosted on EKS spot. The savings were real; the surprises were too.
