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.
Learn how to fine-tune LLMs like Llama 2, Mistral, and GPT models for your specific use case. Includes LoRA, QLoRA, and full fine-tuning techniques.
Compare Terraform, Pulumi, and Ansible for Infrastructure as Code. Learn when to use each tool and how they complement each other in modern DevOps workflows.
Set up comprehensive Linux system monitoring using Prometheus and Grafana. Monitor CPU, memory, disk, network, and application metrics with beautiful dashboards.
The initial cleanup is the easy part. The savings that last come from tagging, per-team budgets, and a monthly review, not a one-time audit.
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 run a fleet of LLM agents on Kubernetes. They're stateful, bursty, and expensive — none of which K8s defaults are good at. Here's what we changed.
