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We think #GenAI will transform virtually every customer experience. That’s why we’re doubling down on all three layers of what’s called the GenAI “stack.” What do we mean by stack? Simple. Three layers, bottom to top. The “bottom layer” means the infrastructure. Think chips, servers, networking – the software,...

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47,421 görüntüleme • 2 yıl önce •via X (Twitter)

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⬇️ Read more about Amazon's AI leadership:

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My goodness… I buy stuff from you and then I find out this company is antisemite. Disgusting!

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Naturalized Deer 🇮🇱2 yıl önce

@ajassy your employees mocking hostages. In Arabic Hudhaifa wrote: "She went to bring her father's drum and came back pregnant. -- Egyptian proverb" This is beyond disgusting.

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Bring him home!!!

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Ellakay2 yıl önce

You guys temporarily locked my account for no reason and no way of contacting customer service and I think it’s high time I give up on Amazon

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WTF? Does my family need to stop ordering from Amazon every day?

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We just launched some exciting new #GenAI tools to accelerate seller growth and enhance the customer shopping experience. What are they? First up is Project Amelia, a GenAI assistant that acts as a personal Amazon expert and partner to sellers, is in beta to an initial set of U.S.-based sellers. Project Amelia learns seller’s unique businesses and provides immediate access to tools and services they use every day, in addition to tons of personalized recs and insights.
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Our co-founder Pratyush Kumar highlighted Sarvam's "contrarian bet" of being a full-stack AI platform. That means we build every layer: the models, how they run (inference), how agents work with each other (orchestration), and the AI applications people use. This lets us build AI that actually works for India's unique context and diverse language needs.

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Today, we’re open-sourcing Agentex, the agentic infrastructure layer in the Scale GenAI Platform. Built for developers everywhere, Agentex gives the community transparency and control to help shape what the future of agent infrastructure looks like.
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We asked James Wang why Cerebras can run large models so quickly. His answer: Inference speed is mostly a memory problem. Instead of constantly pulling weights from external memory, Cerebras splits the model across multiple wafers and pipelines the layers together. “Inference is all about memory bandwidth.” “You don’t want to store weights external to the wafer, because the second it’s outside, it becomes much slower.” “We put the weights on the inside.” “The team built a new software stack that basically lets us split the models by layer and store them layer by layer across multiple wafers.” “That allows us to never have to read from external memory, and that’s what makes it so fast.” “For context, Claude is about 100 tokens per second.”

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As AI becomes more capable and agentic, models themselves become more of a commodity, and all value gets created by how you steer, ground, and finetune these models with your business data and workflow — and how they compose with the UI layer of human to AI to human interaction. That’s why today we are announcing wave two of Microsoft 365 Copilot.

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