Learn how to build an OCR pipeline for handwritten medical notes using DSPy, GEPA, and LanceDB to manage images, labels, outputs, metrics, and retrieval.

Turn information-rich PDFs into a local, inspectable, searchable evidence store with LlamaIndex's LiteParse library and LanceDB.

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 a new LanceDB-backed memory plugin that gives Hermes Agent durable, semantic recall across sessions, with benchmarks and a hands-on remember/recall/forget walkthrough.

How LanceDB uses the Lance format's flexible data evolution features to enable scalable feature engineering for multimodal datasets.

Learn how LanceDB turns raw multimodal data into reproducible, training-ready datasets with search, filtering, deduplication, sampling, and versioned curation workflows.

A hands-on walkthrough showing how LanceDB and DuckDB work together to query multimodal data in SQL, join against multiple tables, and materialize results back into LanceDB.

Build a structure-aware PDF QA agent with LiteParse, LanceDB, and Claude to answer complex questions over visually rich documents.

Benchmarking three OpenClaw memory plugins on the LOCOMO dataset

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.

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.

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!

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.

A practical definition of multimodal complexity, and how LanceDB’s Multimodal Lakehouse is built to address these challenges.

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.