Making AI search fast
I help teams build fast search over unstructured data: vector databases, RAG pipelines, and over a decade of web performance craft.
Migrating Self-managed Milvus to Managed Milvus to gain >99% Performance
At small scale, self-managing a vector database is manageable. Once you're in the tens of millions of embeddings you'll probably want to offload the headache to a managed service.
Vector Search, Visualised
SQL makes sense. But when it breaks, you reach for EXPLAIN. Vector search offers no such comfort. Multi-thousand-dimension embeddings, approximate nearest-neighbour indexes, and quantisation tradeoffs make it hard to know what your system is doing, and harder still to diagnose when results quietly degrade. Through interactive visualisations, Simon Hearne shows what embeddings look like in high-dimensional space, what quantisation does to your recall, and how to catch retrieval failures before your agents do. You'll leave with a sharper mental model and a diagnostic toolkit for the production problems hardest to see.
More in the blog and the talk archive.