
Until now, voice mode has only been available on Claude Haiku, Anthropic's faster but less powerful model. Now the company is making its Opus and Sonnet models available in voice mode, and extending its reach into apps like Gmail, Slack, and Canva. When Anthropic launched voice mode last year, it was primarily focused on delivering answers to quick questions with minimal delay. But in a blog post, the company said people immediately started using voice mode for far more than ca
Anthropic has expanded voice mode access for its Claude AI assistant to its more powerful Opus and Sonnet models, in addition to making voice capabilities available through apps like Gmail, Slack, and Canva. Previously, voice mode was only available on Claude Haiku, a faster but less capable model that users found limited for complex work. Sonnet and Opus are designed for deeper problem-solving and can deliver more complex responses, perform analysis, and take actions on behalf of users, such as creating documents or adjusting schedules. The expansion also includes voice mode support in multiple new languages beyond English, including French, German, Spanish, Hindi, Indonesian, Italian, Japanese, Korean, and Portuguese.

In this tutorial, we explore FAIRChem v2 and the UMA universal machine-learning interatomic potential as a unified framework for atomistic simulation across molecular chemistry, catalysis, and inorganic materials. We configure an environment, authenticate with Hugging Face to access the gated UMA model weights, and initialize task-specific calculators for the omol, oc20, and omat domains. We then apply the same pretrained potential to a broad set of computational chemistry workflows, including

Most agents that learn from video need to know what action produced each frame. Induction Labs is arguing that this requirement is the bottleneck. Last week, they released imagination models, a foundation model architecture that pretrains on raw video with no action labels at all. Their test system is Photon-1, a sparse 106B-A5B mixture-of-experts (MoE) transformer trained on 18 years of computer demonstration video. On an internal computer use benchmark, Induction Labs reports that Photon-1

The KwaiKAT Team at Kuaishou has introduced the KAT-Coder-V2.5. It is a coding model trained to operate inside real, executable repositories rather than emit single-turn code. The served model is available through StreamLake. An open-weight variant, KAT-Coder-V2.5-Dev, was released separately on Hugging Face under Apache-2.0. AutoBuilder: environments that actually run the intended tests The research frames a verifiable task as a triplet. It needs a precise task description, an executable
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