Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Build MLOps pipelines for training, evaluation, and deployment. Reproducibility and monitoring.
We started with a single Celery worker handling everything. Eight months and three architecture changes later, here's what scaled and what we learned about queue design.
We cut our average CI build time from 28 minutes to 6 minutes. The changes that mattered, ranked by impact.
We scan every container image in CI and at runtime. Trivy + Cosign + admission controllers. The setup that earns its place and what we wish we'd known.
We migrated 40+ services to GitOps with Argo CD. Two years in, here's what works and what required workarounds.
How a packet actually gets from the internet to a pod, walked layer by layer. Plus the things that surprise people the first time they hit them.
Design serverless apps for reliability, cold start, and cost. Event-driven patterns and observability.
We've shipped three end-to-end ML systems. The pieces that look obvious in slides and turn out to be the actual work.
We started routing 90% of LLM traffic through a small internal gateway. The gateway wasn't planned — it emerged from solving the same problem in 5 places. Here's the shape it took.
Prompt injection, data leakage, jailbreaks, and the boring controls that actually keep production AI features safe. The threat model that matters once you ship.
We benchmarked six embedding models on the same retrieval task. The results that surprised us, and how we'd pick today.
We cut our monthly LLM bill from $11,200 to $2,300 with seven specific changes. The ones that worked, the ones that didn't, and what we'd do first.
