Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Most post-mortems produce a document and no follow-through. The format, the discipline, and the cultural moves that actually convert incidents into engineering improvements.
Production monitoring catches user-facing issues. CI failures stay invisible until someone notices the merge queue is stuck. The metrics and alerts that make pipelines observable.
Static thresholds on error rate produce noisy alerts. Burn-rate alerting flips the question to "are we burning the error budget faster than we can sustain?" — and pages only on real problems.
Single-provider LLM apps fail when the provider does. Multi-provider routing isn't just resilience — it's also a cost lever. The patterns we run.
Wrong SLI metrics mean green dashboards while users churn. The discipline of picking signals that move with what users actually feel, and the ones that look reliable but lie.
We run a chaos game day each quarter. The scenarios that surfaced real problems, the ones that didn't, and the operational discipline that makes the practice pay back.
A real-world model fallback guide for customer-facing AI systems, covering how one team preserved response quality and support SLAs during a partial provider degradation.
A practical artifact promotion guide for CI/CD teams that were tired of hearing 'it passed in staging' after production behaved differently because the release was rebuilt.
A hands-on RDS restore drill guide for small cloud teams that thought backups were covered until a timed restore test exposed missing steps, DNS confusion, and stale credentials.
A practical systemd drop-in guide built from a real operations problem: vendor unit files kept changing, but the team still needed consistent restart, environment, and logging behavior.
A practical embedding model upgrade guide for RAG systems, built from a real support-search migration that initially reduced answer quality instead of improving it.
A real-world multi-cluster traffic routing guide for SaaS teams that have outgrown a single Kubernetes cluster and need safer rollout control without a service-mesh science project.
