How Lance format and LanceDB's Enterprise feature engineering platform make VLM fine-tuning faster by materializing expensive multimodal features once and training from those columns.

Introducing stable-worldmodel, an open-source platform for reproducible world model research, evaluation, and benchmarking under visual and physical distribution shifts.

A complete walkthrough of building an autonomous vehicle perception model training pipeline on top of LanceDB and the Multimodal Lakehouse.

Announcing native read support for Lance format on Hugging Face Hub. You can now distribute your large multimodal datasets as a single, searchable artifact (including blobs, embeddings and indexes) all in one place!

SemanticDotArt turns art discovery into a multimodal search experience, matching feelings, phrases, and images with LanceDB's hybrid retrieval.

Explore a practical guide to training custom rerankers with practical insights and expert guidance from the LanceDB team.

Discover how AnythingLLM leveraged LanceDB's serverless architecture to eliminate vector database setup complexity, enabling seamless cross-platform RAG and agent workflows with zero configuration required.

Explore late interaction & efficient multi-modal retrievers need more than a vector index with practical insights and expert guidance from the LanceDB team.

Explore a practical guide to fine-tuning embedding models with practical insights and expert guidance from the LanceDB team.

Explore about memgpt: os inspired llms that manage their own memory. Get practical steps, examples, and best practices you can use now.