Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
AI Inference Cost Optimization. Practical guidance for reliable, scalable platform operations.
Practical game day scenarios for CI/CD: broken rollbacks, permission issues, and slow feedback loops—and how we fixed them.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
A real story of removing console-only changes, adding drift detection, and getting Terraform back in charge.
SLO-Based Monitoring for APIs. Practical guidance for reliable, scalable platform operations.
