Nvidia dropped 13 open models at their CES 2026 event. NVIDIA’s open models — trained on NVIDIA’s own supercomputers — are powering breakthroughs across healthcare, climate science, robotics, embodied intelligence and autonomous driving.
Google today announced a new open standard, called the Universal Commerce Protocol (UCP) for AI agent-based shopping, at the National Retail Federation (NRF) conference.
The standard, developed with companies like Shopify, Etsy, Wayfair, Target, and Walmart, lets agents work across different parts of customer buying processes, including discovery and post-purchase support. The core idea is that the standard could facilitate these various parts of the process instead of requiring connections with different agents.
Anthropic has released Claude Code v2.1.0, a notable update to its "vibe coding" development environment for autonomously building software, spinning up AI agents, and completing a wide range of computer tasks, according to Head of Claude Code Boris Cherny in a post on X last night.
Joining the ranks of a growing number of smaller, powerful reasoning models is MiroThinker 1.5 from MiroMind, with just 30 billion parameters, compared to the hundreds of billions or trillions used by leading foundation large language models (LLMs).
But MiroThinker 1.5 stands out among these smaller reasoners for one major reason: it offers agentic research capabilities rivaling trillion-parameter competitors like Kimi K2 and DeepSeek, at a fraction of the inference cost.
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By abandoning the pure Transformer orthodoxy in favor of a hybrid architecture, TII claims to have built a 7-billion parameter model that not only rivals but outperforms competitors nearly 7X its size — including the 32B and 47B variants of Alibaba's Qwen and Nvidia's Nemotron.
When initially experimenting with LLMs and agentic AI, software engineers at Notion AI applied advanced code generation, complex schemas, and heavy instructioning.
Quickly, though, trial and error taught the team that it could get rid of all of that complicated data modeling. Notion’s AI engineering lead Ryan Nystrom and his team pivoted to simple prompts, human-readable representations, minimal abstraction, and familiar markdown formats. The result was dramatically improved model performance.