Activeloop
@activeloop
Followers
4K
Following
1K
Media
266
Statuses
1K
Unlocking Data for AI. Creators of 🌊 Deep Lake.
Mountain View, CA
Joined April 2020
The White House just announced the Genesis Mission to accelerate scientific discovery. Today we’re releasing technology that supports that vision. Search across 25M scientific papers, 400M pages and 175TB+ of data with multimodal AI. Not just text. Charts, molecules, tables,
deeplake.ai
AI agents create 80% of new databases. Shouldn't your DB be built for them?
The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open
1
0
9
We did not expected those results. Built a Software Factory and run it for 15 hours autonomously on our large Deep Lake codebase. Output was 83 lines of highly optimized C++ code. 714 lines of tests. 8:1 test to code ratio. It fixed the bottleneck in a large codebase. Improved
0
0
1
Enabling robots with multimodal data lake? 🤖 Check out Physical AI Hack in SF! Glad to be part of!
Was at the Physical AI Hack in SF today. Absurd talent density with hundreds of people. Every team gets an assigned robot. Energy is off the charts. 🤖🔥 Proud to sponsor with @activeloop and enable teams building on multimodal AI with Deep Lake. So much data to capture.
1
0
2
@activeloop and Pinkbot achieved 9× faster VLM reasoning throughput with @intel newest chips, unveiled at #CES2026. As Physical AI takes on increasingly complex tasks, vision-language models enable robots not just to see, but to perceive and reason. While perception now runs in
0
0
5
Supply chain for Memory is disrupted. Consumer RAM is now more expensive than GPUs. In-memory compute (RAM + fast NVMe) is getting expensive thanks to AI datacenter build-out. That makes memory-limited algorithms far more valuable. Most databases heavily rely on in-memory data
0
2
9
> In-memory compute (RAM + fast NVMe) is getting expensive thanks to AI datacenter build-out. > That makes memory-limited algorithms far more valuable. > Most databases still rely heavily on in-memory data structures and local caches. > Very bullish for Deep Lake in 2026.
A series of events caused RAM prices to explode: - Sam Altman locked up 40% of the world’s DRAM supply in October. - AI chips (GPUs and TPUs) require HBM. Only SK Hynix, Samsung, and Micron can produce it at scale. - Google tried to secure more HBM for TPUs and was told it was
0
2
5
Today we’re launching Deep Lake PG. The unified database for the agentic era. Serverless Postgres for fast state. Deep Lake for multimodal and vector data at lake scale. One database handles both short-term state and long-term multimodal context. Deep Lake PG ships with: •
Today excited to open-source Deep Lake PG = Postgres + Deep Lake Biggest bottleneck of AI having impact on GDP is unlocking data in Enterprises. Every AI team I know is stitching Postgres → Vector DB → Warehouse → Lakehouse → Catalog. All to give their agents basic memory
0
1
3
A Unified Database for Every AI Workload: We believe that the future of AI isn't just about better models; it's about giving those models the right memory and access to reality. With Deep Lake PG, you can build stateful, multimodal agents that instantly recall conversations,
1
1
4
Deep Lake PG achieves state-of-the-art cost efficiency on TPC-H SF100 compared to alternative serverless data warehouses. It is 1.5x cheaper than Snowflake and up to 3x than Databricks.
2
1
5
Why Postgres? LLM learnt PG SQL syntax pretty well given its wide adoption.
1
1
3
You were told to take your data from Postgres, ETL it into a warehouse. Then we said, no, move it into a data lake. Then bolt on a query engine, and let’s call that a Lakehouse. As the number of tables exploded, you unified into a catalog, and branded it a “semantic layer” to
1
1
4
Introducing Deep Lake PG: The Database for AI Deep Lake PG is unifies the database for AI. It combines a fully managed, serverless Postgres (for transactional state) with Deep Lake’s tensor storage (for multimodal data), all accessible via a SQL. It simplifies all aspects
1
1
3
Today excited to open-source Deep Lake PG = Postgres + Deep Lake Biggest bottleneck of AI having impact on GDP is unlocking data in Enterprises. Every AI team I know is stitching Postgres → Vector DB → Warehouse → Lakehouse → Catalog. All to give their agents basic memory
3
8
17
excited! 🤩
1
0
3
The Genesis Mission calls for new ways to accelerate scientific discovery. This is our contribution Multimodal search across 25M papers is a step toward science discovery that moves at the speed of curiosity. Releasing, - Visually indexed scientific paper dataset with open
1
10
58
Feeling cute, might delete later!
0
2
9
Great to partner up with AWS @awscloud on releasing architecture reference for multimodal scientific discovery with Deep Lake and Sagemaker Lakehouse.
@activeloop and @awscloud releasing architecture reference diagram of indexing multimodal scientific data for faster drug discovery as part of Sagemaker Incubator program.
0
0
3