
We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.
Google DeepMind has released three new AI models in its Gemini series designed to make artificial intelligence agents more efficient and cost-effective at scale. Gemini 3.6 Flash reduces output token usage by 17% compared to its predecessor while improving performance in coding and knowledge work tasks, Gemini 3.5 Flash-Lite prioritizes speed and low cost for high-volume operations, and Gemini 3.5 Flash Cyber is a specialized model for identifying and fixing cybersecurity vulnerabilities. These releases address developer needs for lower latency, higher efficiency, and more reliable performance when building production AI agents that can perform multi-step tasks autonomously.

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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