Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
We benchmarked four vector databases on the same workload. Each has a place. Here's how we'd pick today.
We've shipped four production RAG applications. Each one taught us something. The end-to-end pattern that works.
Run retrieval-augmented generation at scale. Chunking, caching, and observability.
We cut LLM inference cost 47% over a quarter while improving p95 latency. Six changes, ranked by what each one actually delivered.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
Copilots suggest, agents act. Here's the spectrum between them, where each earns its keep in DevOps, and how to add autonomy without lighting your infra on fire.
I spent 3 weeks chasing an answer-quality regression that turned out to be a tokenizer mismatch in a library upgrade. Here's what I learned about evaluating RAG.
We changed a system prompt for what we thought was a tone improvement and broke a customer-critical extraction overnight. The version control and regression tests we built next.
Learn how to fine-tune LLMs like Llama 2, Mistral, and GPT models for your specific use case. Includes LoRA, QLoRA, and full fine-tuning techniques.
We run a fleet of LLM agents on Kubernetes. They're stateful, bursty, and expensive — none of which K8s defaults are good at. Here's what we changed.
I fine-tuned Llama 3 8B on a single 4090 over a weekend for a side project. Here's what worked, what cost more than expected, and what I'd do differently.
