Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
We expanded from one Kubernetes cluster to four across two regions. The traffic-routing layer was the hardest piece. Here's what we tried, what worked, and what we'd do again.
We had Datadog for app metrics, Loki for logs, and zero useful insight into what our LLM service was actually doing. Here's the observability stack we built specifically for model serving.
Platform teams own the systems that EVERY service depends on. Our incident response playbook for when the foundation cracks.
We had three months of slow drift between our Terraform code and AWS reality. Here's the daily-cron + Slack workflow that closed the gap.
Set up comprehensive Linux system monitoring using Prometheus and Grafana. Monitor CPU, memory, disk, network, and application metrics with beautiful dashboards.
Discover proven strategies to reduce AWS costs by up to 50%. Learn about Reserved Instances, Spot Instances, right-sizing, and automated cost management.
We deploy LangChain apps in Docker on Kubernetes. The patterns that work, the LangChain-specific gotchas, and what we'd build differently next time.
When everything seems "slow," a baseline gives you something to measure against. The capture-and-compare workflow we use on every Linux host.
HPA, VPA, and Cluster Autoscaler / Karpenter solve overlapping problems badly when you don't understand which one owns what. The mental model that keeps them from fighting.
We replaced three kernel-level monitoring tools with a small set of eBPF programs. What it bought us, what it cost, and where we still use the old stuff.
We removed the corporate VPN, set up workload identity everywhere, and made every service prove who it is on every call. The actual implementation, with what worked and what we abandoned.
We run ~600 GitHub Actions workflow runs per day across 80 repos. The patterns that scale and the ones that hit limits we didn't expect.
