Welcome to M5BMachine 5-Minute Briefing

Weaviate v1.39.3 fixes a medium-severity authorization issue in the nearObject GraphQL operator.

Weaviate 1.40 adds PathSeer, a third HNSW filter strategy from a SIGMOD 2026 paper, contributed by the paper's first author. How it works, and how it compares to sweeping and ACORN.

Weaviate v1.39.3 fixes a high-severity credential disclosure issue in the Google modules.

Learn how to use Engram effectively, from writing topic descriptions that control extraction to choosing bounded topics and scopes, selecting a retrieval mode, and placing memories in your prompt without hurting prompt-cache efficiency.

4-bit Rotational Quantization in Weaviate 1.39: the SIMD performance work, a centered tier, scaling analysis and a TurboQuant comparison.

HFresh is Weaviate's disk-based vector index for memory-efficient vector search, combining low heap usage with incremental background maintenance.

PDFs full of charts and tables are notoriously hard to put through a traditional RAG pipeline. In this post, we show how late-interaction multi-vector retrieval lets you search PDFs by what the page looks like: no OCR, no chunking, no text extraction.

When a Weaviate query is slow, the first question is where the time went. Query profiling returns a per-stage, per-shard timing breakdown, making query performance issues visible.

Server-side batching, retries, the blobHash data type, and multimodal ingestion — what to use when, with code.

Weaviate Cloud now supports more granular role-based access control with new Editor and Viewer roles for improved security and organizational management.

Tokenization makes or breaks hybrid search. See how Weaviate's accent folding, custom stopwords, and /v1/tokenize endpoint power multilingual BM25.

Weaviate Shared Cloud is now generally available on AWS in US East and Europe, giving teams a fully managed, AI-native database on the provider and region that works best for them.

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.

This release introduces HFresh vector index (Preview), and brings Server-side Batching, Object TTL, Async Replication Improvements, Drop Inverted Indices, and Backup Restoration Cancellation to general availability.

Learn how to secure your Weaviate vector database with API keys, OIDC, and role-based access control (RBAC). Includes practical examples and setup steps.

Why vector databases are here to stay.

This release introduces Object Time-to-Live (TTL), zstd compression support, flat index RQ quantization, multimodal support with Weaviate Embeddings, runtime configurable OIDC certificates and much more.

Weaviate recognized by AWS Partners in Benelux as leaders in helping customers drive innovation