Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A bad deploy used to mean a pager at 2am and a manual rollback. Now Argo Rollouts watches the error rate and aborts the canary itself before anyone wakes up.
Twenty-three clusters, one app, and a folder of near-identical Application YAMLs that drifted constantly. ApplicationSets killed the copy-paste and the drift.
Node upgrades, autoscaler scale-downs, and spot reclaims all drain nodes. Without PDBs they can take all your replicas at once. The budgets, probes, and graceful-shutdown handling that keep voluntary disruptions invisible to users.
Cause-based alerts page you for things that don't matter and miss things that do. How we rebuilt alerting around SLO burn rates — multi-window, multi-burn-rate — and cut pages while catching more real pain.
Most CI caches either miss constantly or restore stale junk. The cache-key discipline, scope boundaries, and measurements that turned our pipeline cache from theatre into real minutes saved.
Default-deny, namespace isolation, egress control — the patterns we use, the gotchas around DNS, and where Cilium changed our calculus.
Most post-mortems produce a document and no follow-through. The format, the discipline, and the cultural moves that actually convert incidents into engineering improvements.
Horizontal and vertical autoscalers solve different problems and break in different ways. The thresholds, cooldowns, and conflicts we learned the hard way.
Vault + Kubernetes auth + Vault Agent Injector. The setup, the failure modes during pod startup, and the patterns that beat raw Kubernetes Secrets.
Picking partition counts and keys decides whether your Kafka consumers scale linearly or hit a wall. The patterns that survived rebalances, partition-count changes, and consumer-group ops.
Production monitoring catches user-facing issues. CI failures stay invisible until someone notices the merge queue is stuck. The metrics and alerts that make pipelines observable.
Static thresholds on error rate produce noisy alerts. Burn-rate alerting flips the question to "are we burning the error budget faster than we can sustain?" — and pages only on real problems.
