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AI News Hub tracks Weaviate Blog AI updates with visible source status, reuse boundaries, collection method, and published articles.
Official vector database and AI infrastructure feed; confirm reuse terms before full body display.
This release brings the HFresh disk-based vector index and the built-in MCP Server to general availability, rebuilds cluster-wide async replication to run from a single scheduler (on by default), and adds two previews: the Boost API and Nested Object Filtering.
Most vector database prototypes fail at ingest. This guide covers server-side batching, error handling, blobHash data type, and multimodal ingestion for Weaviate, with code examples and production-ready patterns.
Weaviate Cloud now offers free tiers across its entire product suite, including the managed database, Query Agent, and Engram, with no credit card required and no time limit, enabling users to build prototypes and use them indefinitely.
Weaviate announces the general availability of Engram, a managed memory and context service for agentic applications. It addresses long-context degradation, messy raw data, and multi-agent context fragmentation through asynchronous pipelines, templates, and built-in scopes, helping agents compound value over time.
Use Weaviate's built-in MCP server to give Claude Code, Cursor, and VS Code hybrid search over your codebase and docs. No glue code.
A researcher argues retrieval quality is the most critical factor in RAG systems, outweighing model size or prompt design. Poor retrieval leads to undetectable hallucinations. The article identifies five common failure modes and offers practical tips for improving retrieval, including hybrid search, cross-encoder re-ranking, and continuous evaluation.
This release introduces the built-in MCP Server, Extensible Tokenizers, Diversity Search (MMR), and Query Profiling as previews, along with Incremental Backups, Gemini audio support for multi2vec-google, and the new BlobHash property type.
A deep dive into Engram, our managed memory service for agents which is simple to get started but adaptable to any use case.
Weaviate Shared Cloud is now generally available on AWS in US East and Europe, providing teams with a fully managed, AI-native database on the provider and region that works best for them.
Two weeks of dogfooding Engram, Weaviate's memory product, in daily Claude Code sessions. This surfaced where a dedicated memory product adds value, and the specific mechanics that prevent integration with coding assistants from working well.
Multimodal embeddings allow AI systems to search and reason across text, images, audio, and video in their native formats. This blog covers the key intuitions behind how this all works and walks through three practical implementations using Weaviate and Gemini.
The Weaviate Managed .NET Client brings Entity Framework Core-like experience to C# developers, enabling attribute-driven schema, type-safe queries, and automatic migrations for vector databases.
A comprehensive guide to securing Weaviate enterprise deployments using OIDC, RBAC, multi-tenant isolation, audit logging, and network security, illustrated through the fictional MedVector Health case study.
Learn how we built a production-ready, end-to-end RAG application in just 36 hours using the Query Agent and the new Weaviate Agent Skills library. The post explains the architecture, comparison with naive RAG, and step-by-step instructions.