Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Our best engineer quit citing on-call. We rebuilt the whole thing: saner rotations, runbooks that actually help at 3am, and escalation that doesn't punish asking for help.
Our early postmortems quietly assigned blame and taught people to hide mistakes. Here's the template and the facilitation rules that finally made them honest and useful.
We used to ship code and turn it on in the same breath, so every deploy was a bet. Feature flags split those two events apart and made rollbacks a config toggle.
Most SLI dashboards track things nobody notices. Here's how we picked the handful of signals that map to real user pain, and dropped the vanity metrics.
Reliability arguments used to be shouting matches between SRE and product. An error budget turned them into arithmetic. Here's how we made the number drive the roadmap.
When our single LLM provider had a 40-minute outage, every AI feature went dark. A gateway with routing and fallback fixed that, and cut spend 30% as a bonus.
Our failover config looked perfect in the console and did nothing during a real outage. Here's the health-check design that actually flipped regions when it mattered.
Adding a read replica cut primary load 60%, then support tickets rolled in about users not seeing their own edits. Replication lag turned into a correctness bug we had to route around.
A bad deploy used to mean a pager at 2am and a manual rollback. Now Argo Rollouts watches the error rate and aborts the canary itself before anyone wakes up.
Node upgrades, autoscaler scale-downs, and spot reclaims all drain nodes. Without PDBs they can take all your replicas at once. The budgets, probes, and graceful-shutdown handling that keep voluntary disruptions invisible to users.
Cause-based alerts page you for things that don't matter and miss things that do. How we rebuilt alerting around SLO burn rates — multi-window, multi-burn-rate — and cut pages while catching more real pain.
The architectural choice is presented as binary; the practical answer is "depends on the workload." The patterns that earn their place and the failure modes we've hit.
