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

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

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

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

Talented physics inventors and researchers in the house! Thanks to everyone who is coming to discover something novel in physics using AI. Aditya P. Advani Armin Hamrah Bhavye Khetan bidhan Emanuele Fenocchi @inservixeofx Gautam Goswami Adam (هومن) Ilia Zintchenko @guanniqu

I let it run for 14 hours autonomously. Output was 83 lines of highly optimized C++ code. 714 lines of tests. It fixed the bottleneck in a large codebase. Improved the benchmark 2x. Verified memory leak using ASAN. Spent $150 of LLM calls. I was not in front of screen whole

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.

The worlds largest physical AI hackathon in SF Letsgoo

Remember to be kind to Claude and ChatGPT if you don’t want to get into trouble in the future 🤖🦾

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Last weekend, I had the pleasure of taking part as a judge in a Physical AI hackathon that recorded over 800 registrations. Activeloop supported the event by providing Deep Lake, its GPU-based multimodal database, enabling high scalability and low latency for the development

Keep scrolling if you have ocd and want coffee

Flash Attention 4 moved to a Python DSL to make life easier. Last night, I rewrote it back into raw C++ CUTE just to get it running on my DGX Spark. Here’s the 5x optimization arc from an sm_121 CUDA mismatch to matching PyTorch's speed from scratch.

The path to solving last-mile delivery is built on real-time perception. With #IntelCoreUltra Series 3 processors and Activeloop’s Deep Lake GPU database, Pinkbot increased VLA throughput by 9x and improved delivery outcomes. Learn more about Intel Core Ultra Series 3 at

Jensen just announced the start of the GPU-accelerated database era at #GTC26. AI runs on GPUs. But your data still runs on CPUs. That mismatch is breaking the AI stack. For the last two months, we’ve been busy solving this problem. Excited to announce Deeplake becoming the

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Here is a banger article on how to make Postgres serverless for AI Agents. The rules: - spin up per request - scale reads and writes - drop to zero when idle - no state tied to machines - keep Postgres, rethink storage engine What we got: ~14s cold start → ~1s