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OpenAI unveils GPT-Rosalind
PLUS: Anthropic releases Opus 4.7 ahead of Mythos
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howdy, it’s Barsee again.
happy friday, AI family, and welcome back to AI Valley.
here are the biggest things worth knowing today:
Anthropic releases Opus 4.7 ahead of Mythos
Tencent open-sources a model that can generate full 3D worlds
OpenAI launches GPT-Rosalind
A new robot brain can figure out tasks it was never taught
Plus trending AI tools, posts, and resources
A newLet’s dive into the Valley of AI…
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THROUGH THE VALLEY
Anthropic has released Claude Opus 4.7, upgrading its flagship model while keeping its more powerful Mythos system limited to a small group of security partners. The update focuses on reliability, with the model designed to plan, verify, and check its own outputs before responding, leading to stronger performance across coding, instruction following, memory, and visual understanding. It also improves agentic workflows, tool use, and long-running tasks, alongside higher-resolution multimodal input, and can now handle real-world workflows like automating CAD, where you describe a design and the model builds it inside the software. While Mythos remains out of reach, 4.7 offers a clearer look at how Anthropic is preparing for more capable systems.
Tencent has released HY-World 2.0, a multimodal system that can generate complete 3D environments from text, images, and video, with outputs designed for real production use. Instead of producing short clips or static scenes, it creates editable assets like meshes, point clouds, and Gaussian splats that can be exported into engines like Unity and Unreal. The system works through a multi-stage pipeline that constructs panoramic scenes, plans navigation, expands environments, and assembles them into coherent worlds, making it possible to generate maps, prototype levels, and build assets far more quickly than traditional workflows.
OpenAI has introduced GPT-Rosalind, a model focused on life sciences, built to support research across biology, drug discovery, and translational medicine. It combines stronger reasoning across chemistry, protein engineering, and genomics with improved tool use, helping researchers move from literature review to experimental planning more efficiently. The aim is to speed up early-stage discovery, where timelines often stretch over 10 to 15 years, by enabling scientists to explore more hypotheses, connect complex data, and design better experiments earlier in the process.
Physical Intelligence has introduced π0.7, a new model that allows robots to perform tasks they were never explicitly trained on by adapting to instructions through verbal guidance instead of retraining. While it still struggles with fully autonomous multi-step tasks from a single command, the system shows early progress toward more flexible, general-purpose robotics. It remains in the research phase, but points to a shift away from rigid training toward systems that can learn and adjust in real time, which is critical for scaling automation in real-world environments.
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