A Guide to Uploading Lance Datasets on the Hugging Face Hub
Build a multimodal Lance dataset, publish it to the Hub, and query the precomputed vector + FTS indexes in LanceDB, without needing to download the dataset locally.
Why LanceDB Is the Most Natural Memory Layer for OpenClaw
OpenClaw and similar personal autonomous agents need a local-first long-term memory layer. LanceDB fits that role with embedded deployment, filesystem-native storage, and multimodal retrieval.
Lance × Hugging Face: A New Era of Sharing Multimodal Data on the Hub
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!
Keep Your Data Fresh with CocoIndex and LanceDB
Store a multimodal dataset of recipes in LanceDB, a multimodal lakehouse for AI, and keep it fresh with CocoIndex, a declarative data transformation framework for AI with incremental processing capabilities.
One System, Many Workloads: Rethinking What "Multimodal" Means for AI
A practical definition of multimodal complexity, and how LanceDB’s Multimodal Lakehouse is built to address these challenges.
From BI to AI: A Modern Lakehouse Stack with Lance and Iceberg
A comparison of where Iceberg and Lance sit in the modern lakehouse stack. We highlight emerging architectures that are bridging the worlds of analytics and AI/ML workloads using these two formats, while being built on the same data foundation.