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For people building AI systems, the Weights & Biases episode selection focuses on the practical environment around models and agents. Demonstrations, introductions and getting-started videos cover…
For people building AI systems, the Weights & Biases episode selection focuses on the practical environment around models and agents. Demonstrations, introductions and getting-started videos cover managed inference, reinforcement learning, evaluation and agent tools. CoreWeave products feature throughout these episodes, making the selection particularly relevant to viewers exploring how those named services fit into their development work.
A Forge demonstration follows an agent through finding a problem, fixing it and shipping the result. Related material presents Forge as a connected environment for the AI development loop, while introductions to Agent Lens and the Mission Control agent focus on other parts of that working environment. These are concrete product-oriented entries, giving viewers subjects to examine when considering how development tasks are organised and observed.
Inference is another substantial theme. ARENA tutorials address getting started with managed inference, and a separate overview pairs serverless inference with serverless reinforcement learning. A Sandboxes demonstration brings together reinforcement learning, agent tool use and model evaluation. The combination lets viewers move between introductory explanations of a service and examples of particular workflows.
The scope also extends to the infrastructure underneath AI, including a video on closed-loop cooling in data centres. Engineers and technically engaged teams can use the channel to explore the relationship between model work, deployment services and the resources supporting them. Its value in this selection is the specificity of the demonstrations and product introductions, which give practical context to terms that might otherwise remain broad descriptions of AI infrastructure.
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Rapidly Deploying Useful General-Purpose Robots at Walden RoboticsMost Physical AI companies aim to build perfect autonomy before deploying. At Walden, we optimize for deployment first. Our general-purpose robots are doing useful work in production today, thanks to… Most Physical AI companies aim to build perfect autonomy before deploying. At Walden, we optimize for deployment first. Our general-purpose robots are doing useful work in production today, thanks to a full-stack approach that echoes what worked in autonomous driving and builds on recent breakthroughs in Large Behavior Models (LBMs). We see autonomy as a ratio that improves continuously with the most valuable data: deployment data from robots doing real work with human feedback. I'll cover how we scale that loop efficiently, and some of the open challenges.
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