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Bristol Myers Squibb buys Nvidia AI system for drug discovery

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Bristol Myers Squibb is purchasing an Nvidia DGX SuperPOD built on the chipmaker’s Vera Rubin architecture to support artificial intelligence use across its drug discovery and development operations. The pharmaceutical company said it will be the first life sciences group to acquire a DGX SuperPOD based on Vera Rubin. Nvidia introduced the architecture earlier this […]

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NVIDIA Releases Cosmos 3 Edge: A 4B-Parameter Open World Model That Reasons and Generates Robot Actions On-Device

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NVIDIA has released Cosmos 3 Edge, a 4-billion-parameter open world model built to run on-device. It helps robots and vision AI agents understand surroundings, reason in real time, and generate robot actions locally. The Cosmos 3 family included Cosmos 3 Nano (16B) and Cosmos 3 Super (64B) shipped on May 31, 2026 at GTC Taipei.

NVIDIA Releases Cosmos 3 Edge: A 4B-Parameter Open World Model That Reasons and Generates Robot Actions On-Device Read More »

Kimi K3 open-weight model: China’s biggest AI is a bet on memory, not compute

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Moonshot AI’s Kimi K3 open-weight model has been read almost entirely through its parameter count since it launchedon July 16. At 2.8 trillion parameters, it is the largest open-weight model released to date. Model sizes are usually grouped into rough brackets, and 2.8 trillion rounds into what the industry calls the 3T class. A tier no

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Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared

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A single 24GB card is the practical floor for serious local inference. It is enough for genuinely capable models, and small enough to sit on one GPU. An RTX 3090 or RTX 4090 both land in this tier. The card you own matters less than the models you pick for it. The old hobbyist move

Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared Read More »

Fine-Tuning Qwen3 with LoRA Using NVIDIA NeMo AutoModel: A Complete Single-GPU Google Colab Workflow Tutorial

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In this tutorial, we build an end-to-end NVIDIA NeMo AutoModel workflow in Google Colab and use a single GPU to explore the same configuration-driven training architecture that scales to distributed multi-GPU environments. We verify the available CUDA hardware and precision support, install NeMo AutoModel directly from its source repository, load an official Qwen3-0.6B LoRA fine-tuning

Fine-Tuning Qwen3 with LoRA Using NVIDIA NeMo AutoModel: A Complete Single-GPU Google Colab Workflow Tutorial Read More »

Sakana AI’s Error Diffusion Trains Dale-Compliant Dual-Stream Networks, Reaching 96.7% MNIST and 61.7% CIFAR-10 Without Backpropagation

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Backpropagation dominates deep learning, yet it uses a mechanism the brain likely cannot. Specifically, the backward pass needs exact transposes of forward weight matrices. This is the weight transport problem. Sakana AI’s new paper, Diffusing Blame, confronts this constraint directly. The research team trains networks that obey Dale’s principle while avoiding weight transport entirely. What

Sakana AI’s Error Diffusion Trains Dale-Compliant Dual-Stream Networks, Reaching 96.7% MNIST and 61.7% CIFAR-10 Without Backpropagation Read More »

Thinking Machines Lab Releases Inkling: A 975B-Parameter Open-Weights Multimodal MoE With 41B Active Parameters And Controllable Thinking Effort

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Thinking Machines Lab just released Inkling, their first model trained from scratch, weights are open, fine-tunable on Tinker. The lab pitches it as a base for customization. What is Inkling? Inkling is a Mixture-of-Experts transformer with 975B total parameters and 41B active. It supports a context window of up to 1M tokens. Pretraining covered 45

Thinking Machines Lab Releases Inkling: A 975B-Parameter Open-Weights Multimodal MoE With 41B Active Parameters And Controllable Thinking Effort Read More »

Soofi Consortium Releases Soofi S 30B-A3B: An Open Hybrid Mamba-Transformer MoE Foundation Model For German And English

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A German research consortium has published the pretraining report for Soofi S 30B-A3B. It is an open base model for German and English. Training ran end to end on Deutsche Telekom’s Industrial AI Cloud in Munich. Preview weights are on Hugging Face. It is worth noting that among some of the fully open base models

Soofi Consortium Releases Soofi S 30B-A3B: An Open Hybrid Mamba-Transformer MoE Foundation Model For German And English Read More »

Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment

Agentic LLMs often fail the same way, again and again. A Stanford research team traced this to missing, reusable capabilities. Their system, TRACE, diagnoses those gaps and trains for them directly. TRACE stands for Turning Recurrent Agent failures into Capability-targeted training Environments. It was released open-source under an MIT license. What problem does TRACE solve?

Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment Read More »

Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops

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Most people still use AI like a 2015 search box. You type, you read, you type again. A newer pattern replaces that manual back-and-forth with a loop. This guide explains loop engineering using two verified artifacts. The sources are Andrej Karpathy’s autoresearch repository and the Bilevel Autoresearch paper. The framing follows a write-up by @0xCodila.

Guide to Loop Engineering: How ‘autoresearch’ and ‘Bilevel Autoresearch’ Turn AI Agents Into Autonomous Machine Learning ML Research Loops Read More »