Labs are now putting people in motion suits to capture 'intimate' positions and movements in an effort to train humanoid dolls.

Was this on your 2026 bingo card?

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Qwen3.8-Flash-Next has been officially released, introducing a 6B-active open model that surpasses Claude Opus 4.6 Max on eight out of nine standard benchmarks.

The model applies a highly sparse Mixture of Experts (MoE) framework, featuring 125 billion overall parameters and 51 billion n-gram embeddings, with just 6 billion parameters active per token.

Benchmark results include:
• SWE-bench Pro: 62.5
• SWE-bench Multilingual: 81.0
• CoworkBench: 73.9
• JobBench: 55.7
• Toolathlon: 73.5
• IFBench: 81.3
• GPQA Diamond: 91.7
• LiveCodeBench: 91.9

Qwen3.8-Flash-Next also exceeds the performance of Qwen3.8-27B and DeepSeek-V4-Flash across the majority of evaluated categories.

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🔥 Skild AI has introduced S1

A new robotics foundation model designed to learn new tasks from a single video demonstration without fine-tuning or post-training.

Skild says S1 can execute previously unseen tasks lasting up to 10 minutes, including:

• Making pour-over coffee
• Potting a plant
• Cooking pancakes
• Assembling a kit

The model takes a video demonstration as its prompt and translates it into robot actions for its own environment and embodiment.

Skild says S1 does not simply replay the demonstrated movements, it can recover from mistakes, handle environmental changes, substitute appropriate objects, and sometimes improve on errors made in the original demonstration.

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That pretty much sums up the AI race right now.

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❗️OpenAI’s executive exodus is getting hard to ignore

Another senior executive has left OpenAI.

Chris Malone, who led the company’s data center division, has departed after joining OpenAI in March 2025. He was responsible for a critical part of OpenAI’s massive infrastructure ambitions, including its plans to dramatically scale compute capacity.

And he’s far from the only one.

Since the start of 2026, OpenAI has seen a remarkable number of senior leaders leave, including executives overseeing commercial operations, research, robotics, Sora, marketing, security, risk, ethics, and infrastructure.

Among the departures:

• Head of Data Centers
• Chief Commercial Officer
• Chief Operating Officer
• Director of Communications
• Head of Robotics
• Director of Marketing
• Head of Research
• Head of Sora
• CTO of B2B
• CEO of AGI Deployment
• Head of Risk Assessment
• Chief Futurist
• Head of Security
• Lead AI Ethics Specialist

OpenAI is simultaneously restructuring its leadership, dramatically expanding its compute infrastructure, and preparing for a potential IPO.

The company says its infrastructure organization has been reorganized and that it has experienced leadership in place to execute its plans.

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AI will create jobs they said, starting here 😅

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Neuralink patient, Audrey is now able to create art with her mind.

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Time magazine recently released its 2026 list of “The 100 Most Influential People in AI.” The selection has drawn notice because several prominent leaders in the AI field are absent from the list.

Notable figures such as Jensen Huang, Sundar Pichai, Mark Zuckerberg, Lisa Su, Satya Nadella, Demis Hassabis, and Alex Karp are not included in this year’s ranking.

Time’s annual list highlights individuals recognized for their significant impact on artificial intelligence. The omission of these industry leaders represents a departure from expectations, given their established roles in shaping AI development and deployment.

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Anthropic has launched the Model Hardware Standard (MHS), a new way for AI systems like Claude to control robots and lab equipment using a single interface.

MHS provides a standard method for AI agents to communicate with various lab and factory devices, instead of using different programming interfaces. It uses common drivers to handle device commands, properties, and safety limits.

In the past, bringing different instruments into a lab or factory took a lot of time. MHS simplifies this by using easy read/write drivers and reference files that outline device features and safety guidelines.

AI agents can connect to devices through command-line interfaces or custom code, helping them manage tasks and adapt based on experimental results.

According to Anthropic, early projects have shown that integration times have dropped significantly, going from weeks to just hours or even minutes.

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