What happens when you give AI coding agents a lab full of robotic arms, some compute resources, and a “generous token budget” for teaching the robots various tasks? The agents can apparently figure out a training regimen that teaches the robots to successfully cut zip ties and even insert GPUs into thin sockets on motherboards.

That glimpse into how AI can act in a fully autonomous way to automate robot training was made possible by a new agent harness framework—software that wraps around AI models to enable their use of various tools while also providing capabilities such as memory, context, constraint, and feedback loops. That agentic harness, called ENPIRE, was developed by robotics researchers at the Nvidia GEAR (Generalist Embodied Agent Research) lab alongside collaborators from Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

“A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post. “We just read the reports in the morning.”

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Chipmaker Nvidia is planning to sell $25 billion of investment-grade debt in the US on Monday, its first bond sale in five years, in a test of investor appetite for further exposure to the AI sector.

In a marquee seven-part bond offering, the company will issue a wide range of maturities from two years to 30 years, according to a term sheet seen by the FT.

The issuance was upsized from $20 billion after receiving more than $85 billion in orders by early afternoon in New York, according to people familiar with the deal.

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