When building local RAG (Retrieval-Augmented Generation) applications, edge agents, or serverless AI pipelines, developers usually hit a wall with standard vector stores: memory overhead. Running a dedicated vector database locally often demands hundreds of megabytes—or gigabytes—of RAM just to ...
Source: [Dev.to](https://dev.to/cteague2018/why-we-built-bitweave-sub-millisecond-hybrid-retrieval-in-11-mb-rss-memory-5bkk)