1/7 🎨 In a world of infinite scroll, discovering art still feels like searching for a needle in a haystack. With SemanticDotArt, we flipped the question: What if you searched by mood, not just metadata? See how we did this in @lancedb 👇🏽

This is a big milestone for Lance format. The F3 paper ( https://t.co/hVREwxykSn) verified that Lance has THE fastest random access, essential for search, shuffle, and many other AI workloads. But it incorrectly assumed it was because of lack of compression. With 2.1, we show

Join us for our webinar on Apache Spark™ and Lance Spark Connector with Jack Ye (@lancedb) on September 25! 👏 Learn how the Lance Spark Connector enables Apache Spark™ to work with Lance’s AI-native multimodal storage. ✅ We’ll look at how Spark can handle embeddings, images,

When building a columnar file reader, it becomes clear that 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗻 𝗮𝗯𝘀𝘁𝗿𝗮𝗰𝘁 𝗰𝗼𝗻𝗰𝗲𝗽𝘁. ( https://t.co/9TGr34de1n) It is the set of rules that determines how every byte of data is stored and accessed on disk. A few months ago,

The data prep bottleneck for fine-tuning LLMs is a common challenge. 𝗢𝘂𝗿 𝗻𝗲𝘄 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗠𝗲𝘁𝗮'𝘀 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗞𝗶𝘁 𝗵𝗲𝗿𝗲 𝘁𝗼 𝗳𝗶𝘅 𝘁𝗵𝗮𝘁! It simplifies the entire workflow with a 𝘀𝘁𝗿𝗮𝗶𝗴𝗵𝘁𝗳𝗼𝗿𝘄𝗮𝗿𝗱 𝗖𝗟𝗜 for

🚀 Video from @TMLS_TO : @character_ai x @LanceDB on building a unified multimodal data lake , a single system for text, audio, video & image retrieval. @changhiskhan @ryanvilim Simpler pipelines, lower infra costs, faster AI dev. 🎥 Watch: https://t.co/bt21gm8dwZ #AI #LLM

@swyx @jxmnop - built by solid db people and hackable (we have a contributor at nomic to it) - used by top ai companies / labs / products for it's nice properties when used in a training loops (e.g. midjourney has been using it since 2023) so probably not going anywhere - feels like the right

q from the audience: "Is Lance the next big thing in data?" answer: "Yes" 👀

We just published a 𝗻𝗲𝘄 𝗯𝗹𝗼𝗴 ( https://t.co/nT0lF1sbmH) on what the 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 actually does. The Lakehouse is 𝗳𝗼𝗿 𝘁𝗵𝗼𝘀𝗲 working with a mix of text, images, audio, and structured data - 𝘄𝗵𝗼 𝘄𝗶𝘀𝗵 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱 𝘁𝗵𝗲 𝗽𝗮𝗶𝗻 of

Today we’re announcing our $30 million Series A. This round is led by @Theoryvc with support from @CRV , @ycombinator, @databricks, @runwayml , @ZeroPrimeVC , @swift_vc, and more. Your belief in a future powered by multimodal data brings us one step closer to that reality.

Missed Ethan’s talk at @DataCouncilAI 2025? 🎤 He shares how @RunwayML tackles multimodal data challenges—and how LanceDB helps store, query, and retrieve it all efficiently. 🎥 Watch here: https://t.co/MhNXKY7sx0 Ethan's slides: https://t.co/sxWcAfnWUb #LanceDB

Live at #DataAISummit from @databricks @DbrxMosaicAI and @lancedb . A joint talk by @changhiskhan and Zero Qu Congrats to both teams on the newly announced storage optimized vector search. Now we take billion vector scale to the moon!

Join @character_ai and @lancedb at the upcoming @TMLS_TO for a joint talk on "𝘼 𝙐𝙣𝙞𝙛𝙞𝙚𝙙 𝙈𝙪𝙡𝙩𝙞𝙢𝙤𝙙𝙖𝙡 𝘿𝙖𝙩𝙖 𝙇𝙖𝙠𝙚 𝙛𝙤𝙧 𝙉𝙚𝙭𝙩-𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣 𝘼𝙄" @changhiskhan will be there! Time: June 13th, virtual talk Register: https://t.co/XJlr77Lo3E Btw,

We just released a walkthrough on how to ingest the 𝗳𝘂𝗹𝗹 𝗪𝗶𝗸𝗶𝟰𝟭𝗠 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 — that's 𝟰𝟭 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝗿𝗼𝘄𝘀 𝗼𝗳 Wikipedia — into @lancedb ~11 minutes. 🔧 What you’ll learn: • How to generate embeddings at scale • Ingest massive datasets into LanceDB Cloud

🔐 LanceDB is now SOC 2 Type II, HIPAA, and GDPR compliant. We’re built for secure, privacy-conscious AI applications — from startups to enterprises. 🔐 #AIInfrastructure #DataPrivacy #GDPR #HIPAA #SOC2

The weather is supposed to be much nicer tmr in NYC - who wants to hang out near Bryant Park?

🧠 Monthly Newsletter update from LanceDB: ✅ Research paper on arXiv ⚔️ New Lancelots knighted ⚙️ Guides on rerankers + embeddings 💼 Case studies: @continuedev & @AnythingLLM 🔧 Big product upgrades https://t.co/iFPQBIi6tc

The AI infra team at @character_ai is particularly special to me personally. When I joined LanceDB, this was the first frontier modal company that I worked closely with. An exceptionally talented team that saw the value in what we were building at @lancedb. Thank you guys :)

📄 Read the full paper here:

This work not only addresses the challenges faced by current storage solutions but also sets the stage for future innovations in data management. If you're interested in the intersection of AI, data storage, and performance optimization, I invite you to read our paper and explore