2025-10-23
A new framework developed by researchers at Google Cloud and DeepMind aims to address one of the key challenges of developing computer use agents (CUAs): Gathering high-quality training examples at scale.
The framework, dubbed Watch & Learn (W&L), addresses the problem of training data generation in a way that doesn’t require human annotation and can automatically extract demonstrations from raw videos.
Their experiments show that data generated W&L can be used to train or fine-tune existing computer use and foundation models to improve their performance on computer-use tasks. But equally important, the same approach can be used to create Discover Copy
2025-02-13
We called 2023’s Galaxy Watch 6 a “modest upgrade” from the Galaxy Watch 5, which itself also got described as “very similar” to its predecessor, the Watch 4. So it’s perhaps not surprisin [...]
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The most obvious question is “Why?” <br /> Framework builds modular, repairable laptops that anyone can take apart and put back together again. It’s a big deal in an era where laptops are [...]
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As if early June wasn't already going to be a wild enough time in the gaming world with the arrival of the Nintendo Switch 2, that's also when a whole host of showcases takes place as part o [...]
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Imagine you do two things on a Monday morning.First, you ask a chatbot to summarize your new emails. Next, you ask an AI tool to figure out why your top competitor grew so fast last quarter. The AI si [...]
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Microsoft’s multi-agent framework, AutoGen, acts as the backbone for many enterprise projects, particularly with the release of AutoGen v0.4 in January. However, the company aims to harmonize all o [...]
2025-05-06
You might know the story by now: Framework makes repairable, modular laptops where you can sub in new components for old or broken ones. It’s been two years since the company debuted an AMD mainboar [...]
2025-10-07
Some of the largest providers of large language models (LLMs) have sought to move beyond multimodal chatbots — extending their models out into "agents" that can actually take more actions [...]