Zilliz
PulseAugur coverage of Zilliz — every cluster mentioning Zilliz across labs, papers, and developer communities, ranked by signal.
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Embedding model upgrades can silently break AI retrieval systems
Upgrading embedding models can silently break retrieval systems without throwing errors. This occurs when new query vectors are generated by a different model than the one used for existing document vectors, causing the…
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Vector Databases 2026: Milvus 3.0, Qdrant, and pgvector lead RAG architecture
Vector databases are crucial for RAG pipelines entering production in 2026, with Milvus 3.0, Qdrant, and PostgreSQL's pgvector emerging as key players. Milvus 3.0, presented by Zilliz, offers direct vector retrieval wit…
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Milvus vector database scales to billions of vectors with hybrid search
Milvus is a high-performance, open-source vector database designed to handle billions of vectors, making it suitable for large-scale semantic search applications. It features a distributed architecture with separated st…
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AI agents struggle with coordination, not just models, analyses show
Recent technical analyses suggest that AI agents face coordination challenges rather than solely model limitations. One perspective highlights the significant context window bloat caused by Multi-call Protocol (MCP) ser…
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Milvus 2.5 vector database released with GPU acceleration for 10B+ vectors
Milvus 2.5, an open-source vector database from Zilliz, has been released with enhanced capabilities for handling massive datasets. This new version features GPU-accelerated indexing, a distributed architecture, and tie…
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AI advances toward multimodal systems integrating diverse data types
The AI industry is rapidly evolving towards multimodal systems that integrate diverse data types like text, images, and audio. A key challenge is extracting context from unstructured data, such as video and audio files,…