- How Claude is accelerating protein design and analytical chemistry Anthropic
We ran Opus 4.8 and Mythos Preview in multi-target mode with 48 hours of wall time and up to 12,500 NVIDIA H100 hours of compute for running specialized protein design and folding models. We also ran Mythos Preview in single-target mode with 24 hours of wall time and up to 2,500 NVIDIA H100 hours of compute for each target.5
Source: How Claude is accelerating protein design and analytical chemistry Anthropic
- Guide to Preferred Sources in Google Search for Web Publishers | Google Search Central | Documentation | Google for Developers
If you’re a website owner, you can help your audience find your publication as a preferred source in Google Search. When a user selects your site as a preferred source, your content is more likely to appear in “Top Stories”, highlighted with a “preferred” badge. In AI Mode and AI Overviews, your content can be highlighted with a “preferred” badge for users who have selected your site as a preferred source.
- eviews-mcp — Drive EViews from Python and AI assistants
Drive EViews 13 from Python — or let an AI assistant drive it for you. A Model Context Protocol server and a Python library, sharing one engine. Every number comes from EViews itself.
Source: eviews-mcp — Drive EViews from Python and AI assistants
- [2602.22631] TorchLean: Formalizing Neural Networks in Lean
Neural networks are increasingly deployed in scientific, safety critical, and mission critical pipelines, yet verification and analysis are often performed outside the programming environment that defines and runs the model. This creates a semantic gap between the executed network and the analyzed artifact: guarantees can depend on implicit conventions about operator semantics, tensor layouts, preprocessing, floating-point behavior, graph transformations, accelerated kernels, and external certificates. We present TorchLean, a unified framework for formalizing, executing, and verifying neural networks in Lean 4.
Source: [2602.22631] TorchLean: Formalizing Neural Networks in Lean
- TorchPhysics – Deep Learning for PDEs
Welcome to TorchPhysics, an open-source PyTorch library that implements deep learning methods for partial differential equations, enabling engineers and applied mathematicians to use modern AI-based methods in their usual workflow.