Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A practical production playbook for AI systems: evaluation gates, guardrails, observability, cost control, and reliable release management.
A practical field manual for engineering teams who want AI features that survive real users, incidents, and budgets — not just demo day.
We cut our AWS bill by 38% in a quarter. The specific changes that moved the bill, ranked by impact, with what we'd do first.
We run mostly on AWS but use GCP for specific workloads. The honest cost-benefit analysis of multi-cloud, plus the patterns that make it not awful.
A different angle on DR: the planning process — RTO/RPO conversations, dependency mapping, and what we learned about prioritizing what to recover.
Defining monitoring as code: dashboards, alerts, and SLOs in Git. The patterns that survived the migration from clicked-together monitoring.
We cut our largest playbook's runtime from 14 minutes to 4 minutes. The specific changes that mattered, plus the ones that didn't.
We tried Pulumi for a quarter and went back to Terraform. Both are real options. Why we picked one and what would change our mind.
K8s Secrets are barely encrypted. We moved every secret to Vault with the Vault Agent injector and never went back. The setup checklist.
We test infrastructure code with three layers: validation, plan review, and integration tests. The setup that catches real bugs without slowing down PRs.
We have a private module registry with ~25 modules used across 12 accounts. Versioning, interface design, and the over-modularization mistake we keep making.
A container is a process with extra kernel features applied. Walking through namespaces, cgroups, and the actual mechanics — the level of detail that makes "container weirdness" debuggable.
