Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A practical risk-management framework for release timing, Friday deployment policies, progressive delivery, and how elite teams protect reliability and people.
Cut Kubernetes spend without hurting reliability using a practical FinOps playbook for rightsizing, autoscaling guardrails, showback, and weekly waste cleanup.
A practical way to define SLOs and error budgets, connect them to release decisions, and avoid reliability debates without data.
How to implement Backstage with real templates, scorecards, and golden paths so internal platform work reduces delivery friction.
A practical pattern for monorepo CI with path filters, matrix builds, caching, and deployment guards that keep feedback fast as teams scale.
A production-focused guide to Azure DevOps: standardized YAML templates, secure service connections, rollout safety, and measurable delivery reliability.
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.
