The Lambda deep learning blog

Open Jarvis: making local LLMs work as agents

caia.costello@lambdal.com (Caia Costello) — Wed, 30 Sep 2026 14:49:50 GMT

Running an open-weight model is only half the job. The other half is the harness: what tells the model what to do, which tools it can use, and how to learn from its mistakes.

Lambda to build new data center in Mayes County, Oklahoma, generating half a billion dollars in tax revenue over next decade

Lambda — Sun, 27 Sep 2026 14:01:36 GMT

Facility will be built in MidAmerica Industrial Park & deploy closed-loop cooling technology to preserve and recycle water; all energy costs paid in compliance with Oklahoma’s ratepayer protections

Building multimodal models for spatial reasoning

Jianwen Xie — Thu, 24 Sep 2026 19:58:32 GMT

What is spatial reasoning?

AI is moving beyond the digital world. The next generation of AI systems needs to understand and interact with physical environments, from robots to autonomous systems navigating complex spaces.

From structures to dynamics: scaling AI for molecular dynamics in drug discovery

Jianwen Xie — Mon, 21 Sep 2026 17:26:34 GMT

Why molecular dynamics matters for drug discovery

Molecular dynamics (MD) is an important tool in modern drug discovery. While structure prediction and molecular docking can show how a potential drug might fit into a protein, they provide only a snapshot. Molecules are constantly moving. MD simulations allow researchers to see how a drug and its target behave over time: whether the interaction remains stable, how the protein changes shape, and how the molecular system evolves.

Closing the loop: agentic evaluation for image editing foundation models

Jianwen Xie — Thu, 17 Sep 2026 16:29:33 GMT

Why evaluating image editing models is both critical and challenging

Instruction-based image editing is becoming a core capability of multimodal foundation models. Users can increasingly edit images simply by describing what they want: “remove the person in the background,” “make the car red,” or “move the chair next to the table.”

MLPerf Inference v6.1: pioneering agent, VLM benchmarks

Lambda — Wed, 16 Sep 2026 15:02:49 GMT

First agentic workload on datacenter hardware in MLPerf, and the first model over a trillion parameters. Plus 8.85% more throughput on identical hardware since v6.0.

Lambda signs the White House Ratepayer Protection Pledge, reinforcing commitment to community benefits and grid reliability

Lambda — Mon, 14 Sep 2026 20:03:57 GMT

SAN FRANCISCO, CA, September 14, 2026 — Lambda, Inc. (“Lambda”), the Superintelligence Cloud, today announced it has signed the White House Ratepayer Protection Pledge, affirming its commitment to ensuring that the infrastructure supporting America's artificial intelligence (AI) future delivers meaningful benefits for local communities, while protecting consumers and ratepayers.

OpenResearcher: a reproducible and scalable pipeline for training deep research agents

Jianwen Xie — Thu, 10 Sep 2026 14:38:02 GMT

A deep research agent has to do more than answer a question. It plans, searches, opens documents, gathers evidence, reasons across sources, and keeps going across dozens or hundreds of tool calls before it lands on an answer. Training one means teaching all of that. That's where most teams hit a wall.

Giving robots 3D vision without depth sensors

Jianwen Xie — Wed, 09 Sep 2026 18:34:13 GMT

Giving robots better 3D perception

Robots are becoming increasingly capable of learning manipulation skills directly from images. Most robot policies still rely on monocular cameras, though, which struggle to estimate depth accurately. That makes precise tasks such as grasping objects in cluttered environments, inserting parts, or manipulating complex geometry hard to pull off.

How SPREEAI trains the model behind photorealistic virtual try-on

Lambda — Tue, 08 Sep 2026 19:03:22 GMT

A shopper uploads one photo. Ten seconds later, they’re looking at themselves wearing a garment from a brand's catalog, rendered photorealistically rather than approximated on an avatar. That’s the product. Most of the difficulty sits underneath it.