@lancedb
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The multimodal lakehouse for AI, accelerating large-scale data curation and feature engineering so teams can build better models faster.
San Francisco, CA
Joined April 2023
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LanceDB is in Times Square. 🗽
@msft4startups is featuring us as part of its invite-only Pegasus Program.
A big milestone as we keep building the new foundation for AI data.
na2.hubs.ly/H081dXk0
#MicrosoftForStartups #BuiltWithMfS
LanceDB retweeted
stable-worldmodel got into neurips 2026!
it's basically everything we keep rebuilding for world model research (data loading, baselines, planning, evals) in one place so nobody has to do it again
arxiv.org/abs/2605.21800
8/9 The day opens with @LanceDB CEO @changhiskhan on where multimodal AI is headed.
@arcee_ai's Head of Compute @sloppenheimer delivers the closing keynote on what training and serving open-weight models demand from AI infrastructure.
9/9 Plus panels on physical AI and the autonomous driving data flywheel, connecting scenario discovery, simulation, curation, training, and evaluation.
Join us November 5 in San Francisco.
Explore the agenda and apply to attend. reveriesummit.com/
We teamed up with 🤗 @huggingface on a new guide to using LanceDB as the data backend for LeRobot.
Train directly from object storage, use vector + full-text search for curation and mining, and avoid stitching together separate systems as robotics datasets scale.
huggingface.co/blog/Caroline…
LeRobot now natively supports @lancedb datasets, with fast training and global shuffling directly from HF Storage Buckets - no need to download the dataset first 🚀
From vector & full-text search to curation & mining, discover what you can unlock with LanceDB + LeRobot 👇
Vector search that works at 10M vectors shouldn’t need a redesign at 10B.
LanceDB hit 18.05ms p50 / 21.61ms p99 across 10B vectors, while scaling indexing and letting each query tune precision.
New deep dive from @Yah01_
na2.hubs.ly/H07YzNK0
A great blog written by @loldedxd & @ariG23498 🤗👏 huggingface.co/blog/ariG2349…
funes, by @huggingface, turns past agent sessions into memory your agents can actually use.
It indexes Claude Code, Codex, pi, and Hermes traces into one local Lance dataset, then gives the agent 'recall' and 'get' tools.
The next time a task depends on old reasoning, the agent can pull the original passage back. No LLM summarizing your traces at ingest.
LanceDB retweeted
The best kind of work almost always comes from collaborations!
This time @loldedxd helped me figure out the internal workings on funes. While I knew how to make funes work, he made sure I understand how things run on my system.
It is a fun 30 mins read.
huggingface.co/blog/ariG2349…
Raw text → curated data → 2.43B tokens → trained GPT-2 in 25 minutes on 8 H100s.
@loldedxd built the entire pretraining pipeline on one LanceDB table, streaming + globally shuffling directly from S3 at 3.16M tokens/sec.
Full global shuffle, not a pre-shuffled copy.
na2.hubs.ly/H07Ssr40
Your coding agent forgets everything the moment a session ends. Ask it what it decided last week and why, and it has no idea.
Funes, built by @huggingface, indexes past sessions from Claude Code, Codex, pi, and Hermes into one shared memory your agent can query.
→ Local embeddings, vector + BM25 hybrid search, reranking, recency weighting
→ The memory itself is a Lance dataset
→ Same file whether you're querying locally or publishing it to the Hub for your team
That memory travels with the dataset, not the session.
🔗 Check it out : na2.hubs.ly/H07Qnw40