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

Google (GOOG)(GOOGL) revealed a set of new algorithms today designed to reduce the amount of memory needed to run large language models and vector search engines. Shares of major memory and storage ...

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 ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression algorithm that’s going viral over ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

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

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) ...

Hosted search and discovery platform for enterprise Algolia Inc. today launched NeuralSearch, a vector and keyword search engine using a single application programming interface that provides ...

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 ...