Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
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'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.
Fine-tuning is rarely the right answer. We've fine-tuned three times in two years; few-shot or RAG was correct for everything else. The decision criteria.
Standard APM doesn't tell you when your LLM-powered features are silently degrading. The signals we track and the dashboards that catch the regressions standard tools miss.
