A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

BERLIN & NEW YORK--(BUSINESS WIRE)--Qdrant, the leading high-performance open-source vector database, today announced the launch of BM42, a pure vector-based hybrid search approach that delivers more ...

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...

Learn why Google’s TurboQuant may mark a major shift in search, from indexing speed to AI-driven relevance and content discovery.

The primary purpose of artificial intelligence is to help people become more creative, productive and ingenious. Targeted at citizen and enterprise developers equally, Vector Search for MongoDB Atlas ...

Open-source vector database provider Qdrant has launched BM42, a vector-based hybrid search algorithm intended to provide more accurate and efficient retrieval for retrieval-augmented generation (RAG) ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More As generative AI usage has grown dramatically in the last several years, ...

Vector databases unlock the insights buried in complex data including documents, videos, images, audio files, workflows, and system-generated alerts. Here’s how. The world of data is rapidly changing ...