Exploring Distributed Systems, Machine Learning and Operations, and Engineering Management
A systems-level explanation for engineers, architects, and anyone building RAG, search, or agent infrastructure. Dense retrieval looks clean on paper. You take an embedding model, generate vectors, drop them into a vector database, and let an ANN index handle the rest. But once you go beyond a single machine, dense search becomes something very different: […]
Why dense search becomes a routing, sharding, and distributed-systems problem Vector search looks simple when everything fits on one machine. It becomes a different discipline entirely when you need to serve: millions to billions of vectors, across multiple nodes, with predictable latency, and high recall, while RAG or agent pipelines depend on you staying under […]
(Where Dense Search Becomes a Distributed Systems Problem) Most content about vector databases focuses on the glamorous part: fast queries, clever indexing, tight cosine similarity loops. But if you operate these systems in production, you learn something uncomfortable: Your system’s correctness, performance, and scalability are defined far more by the write path than by the […]
The Hidden Failure Modes of Dense Vector Search, ANN Indexes, and RAG Infrastructure Most engineering teams learn this the hard way: vector databases don’t fail like relational systems or search indexes. They fail in quiet, geometric, and catastrophic ways that often go unnoticed until correctness, latency, or agent performance collapses. Dense vector search systems built […]