Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
We invalidate ~6% of LLM outputs before they reach a downstream system. Here's how we structure prompts and validators to catch malformed responses early.
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.
We ran Pulumi in TypeScript and Terraform in HCL side by side across 60+ services. Each won different categories of work. Here's the breakdown.
We deleted every static GCP service account key in our org over six weeks. Here's the migration plan, the gotchas, and the policies we now enforce.
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.
We ran the same RAG workload across three vector stores for a quarter each. Here's what we learned about latency, cost, and operational overhead.
Every hook on this list caught a bug or a security issue in the last twelve months. The configs are short. The savings have been considerable.
We moved a 60-node production EKS cluster to Auto Mode. Some pain points evaporated, others got harder. The cost picture is more nuanced than the marketing suggests.
We ran the same workload on both for half a year. The break-even point isn't where most blog posts say it is — and the latency story has more nuance than throughput-per-dollar charts admit.
We've been running the OTel Collector at the edge of every cluster for 18 months. The config patterns that lasted, the ones we ripped out, and a few processors that quietly saved us money.
