Coding agents that actually get better the more your team uses them. Introducing Hivemind: continual learning for AI coding agents. Hivemind turns the traces from every agent your team runs (Claude Code, Codex, Cursor, Hermes, OpenClaw, Pi) into reusable skills, then pushes those skills across all of them. All on your cloud storage. Now with SkillOpt built in, your skills get trained: 19.1 points of accuracy in Claude Code, 24.8 in Codex, best or tied on all 52 setups tested. Open source, one line install.

Agentic LLM systems this year have mostly gone to coding, browsing, research. With the @activeloop (Deeplake) team, we pointed agents at a job they don't usually do: curating a robotics dataset. Roboscribe-AF automates the work a human curator would do — segment episodes, cross-check video vs trajectory, flag the messy ones. Disagreements between reasoners get written back as queryable Deeplake fields. Open source: deeplake.ai/blog/agentfield

Hivemind just crossed 100 stars on github 🌟 github.com/activeloopai/hive… Beyond memory. Let's compound intelligence!

today, we're going beyond memory. your org's agents shouldn't just remember what happened. they should learn from their experience. Hivemind takes agent traces and codifies them into skills every agent on your team can use. > no more explanations > no more duplicate work > no more repeat bugs we make your intelligence compound. here's how 👇

today, we're going beyond memory. your org's agents shouldn't just remember what happened. they should learn from their experience. Hivemind takes agent traces and codifies them into skills every agent on your team can use. > no more explanations > no more duplicate work > no more repeat bugs we make your intelligence compound. here's how 👇

Jack's right: "Companies move fast or slow based on information flow." But framing it as a worker hierarchy problem is losing the plot. Look at where the actual work is moving: agents. Quick history: Email got messy. Slack fixed it. Then humans kept dropping balls anyway. Someone's offline, a thread dies, marketing has no idea what eng shipped, the handoff never happens. And now Slack itself is the slog. What if you could spend a fraction of the time in it? Meanwhile, your agents are in the pre-Slack era: • Your Claude Code agent has no clue what your coworker's OpenClaw agent decided yesterday. • Marketing's agent can't see what sales's agent promised the customer. • Product's agent has no idea what engineering's agent already shipped. Same company, same project, totally separate brains. The fastest workers on your team are stuck on the slowest part of your stack. Deeplake Hivemind fixes it. One install and your agents share memory across sessions, across teammates, across tools: Claude Code, OpenClaw, Codex, whatever. When one agent learns something, every agent on your team knows. No Slack pings. No status updates. No "wait, did you tell the VP?" Just shared context, flowing automatically. Slack was for humans. Hivemind is for the things actually doing the work now. Comment HIVEMIND and we'll DM you $100 in free credits. Run the experiment with your crew.

A banger post by our CTO @khustup on how he made Postgres Serverles and spin up under second. We built a serverless, PostgreSQL-compatible database. Not a modified PostgreSQL deployment. PostgreSQL provides the interface. DuckDB provides the query execution. Deeplake provides the storage engine. The architecture makes a different set of tradeoffs than traditional PostgreSQL. We think those tradeoffs are right for agent workloads: bursty, ephemeral, storage-heavy, and analytical. Link: deeplake.ai/blog/serverless-…

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 GPU Database. Deeplake brings your database directly onto the GPU, eliminating the CPU <-> GPU bottleneck for AI workloads. The pendulum has switched. GPU-native queries are now 10× faster and an order of magnitude cheaper to run. Last week we even put up a 101 banner in San Francisco. And this is just the beginning. We’re planning a huge set of announcements starting this week. Stay tuned.

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 and reasoning. We replaced the entire data ecosystem with one database. Deep Lake PG is now open source. Stateless multimodal knowledge SQL queries vectors in a single place. Build on top of the database that powers our own Scientific Agent, a trove of 175TB of multimodal data.

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 access 25M papers, 450M visually indexed pages. Total 175TB . All on Deep Lake - Open-source scientific data agent that achieves 48% SOTA on Humanity's Last Exam with tools including the indexed scientific research dataset. Excited to see what discoveries you all uncover with this. Try it and share your most interesting findings.