Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
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.
A field report from rolling out retrieval-augmented generation in production, including cache bugs, bad embeddings, and how we fixed them.
Multi-agent systems are mostly hype. The patterns we've seen actually deliver value, plus the ones we'd avoid until the tooling is more mature.
We tried four quantization techniques on Llama-3 and Mistral models. The quality vs cost trade-offs we found, plus what works for production inference.
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.
