Exploring Distributed Systems, Machine Learning and Operations, and Engineering Management
Most people think dense vector search works like this: embed your documents store the vectors run cosine similarity Done. This is the biggest misunderstanding in modern AI systems. Dense vector search looks simple, but in real deployments it becomes one of the hardest layers to scale—and often the true bottleneck behind: slow RAG pipelines inconsistent […]
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: […]
(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 […]