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🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model... show more

Amazon Science

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42,281 görüntüleme • 1 yıl önce •via X (Twitter)

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Meet Amazon Nova 2 reasoning models Nova has introduced its next generation of foundation models that deliver frontier intelligence with industry-leading price performance 1/ Amazon Nova 2 Lite, is a fast, cost-effective model designed for everyday AI tasks. 2/ Amazon Nova 2 Pro (preview), Nova’s most intelligent model built for complex AI workloads. These models offer developers control to manage thinking budgets, native support for agentic workflows, and provide a one-million-token context window for richer interactions.

Meet Amazon Nova 2 reasoning models Nova has introduced its next generation of foundation models that deliver frontier intelligence with industry-leading price performance 1/ Amazon Nova 2 Lite, is a fast, cost-effective model designed for everyday AI tasks. 2/ Amazon Nova 2 Pro (preview), Nova’s most intelligent model built for complex AI workloads. These models offer developers control to manage thinking budgets, native support for agentic workflows, and provide a one-million-token context window for richer interactions.

Amazon Web Services

3,958,154 görüntüleme • 7 ay önce

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

🚨 Big news for AI innovation: Claude Opus 4 and Claude Sonnet 4, Anthropic's most advanced models, are now available in Amazon Bedrock. These powerful models offer hybrid reasoning, 200K token context windows, and are designed for AI agents. From financial analysis to high-quality writing, to enhanced reasoning, coding, agentic capabilities and more—all with the enterprise-grade security of Amazon Web Services.

Amazon

123,030 görüntüleme • 1 yıl önce

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

The most effective AI systems don't rely on a single model. Frontier models provide state-of-the-art performance for complex tasks, while routers automatically select lightweight, open-source models for simpler jobs to optimize accuracy, latency, and cost. Learn more:

NVIDIA AI

12,152 görüntüleme • 4 ay önce

Hy3, the new 295B MoE model from Tencent Hy, is now free in Nous Portal for the next two weeks! It is focused on cost-effective agentic use, and particularly strong on coding, tool-calling reliability, reasoning, and 256K long-context tasks.

Hy3, the new 295B MoE model from Tencent Hy, is now free in Nous Portal for the next two weeks! It is focused on cost-effective agentic use, and particularly strong on coding, tool-calling reliability, reasoning, and 256K long-context tasks.

Nous Research

278,403 görüntüleme • 21 gün önce

Meet Palmyra X5—the AI that thinks big, can digest entire books in seconds, and is now available in Amazon Bedrock. This powerhouse model can: 📚 Process huge amounts of data 🤯Understand context like never before 🚀Unlock new possibilities for AI apps Here’s a whiteboard breakdown 👇

Meet Palmyra X5—the AI that thinks big, can digest entire books in seconds, and is now available in Amazon Bedrock. This powerhouse model can: 📚 Process huge amounts of data 🤯Understand context like never before 🚀Unlock new possibilities for AI apps Here’s a whiteboard breakdown 👇

Amazon

68,391 görüntüleme • 1 yıl önce

MiniMax M2.1 is officially live🚀 Built for real-world coding and AI-native organizations — from vibe builds to serious workflows. A SOTA 10B-activated OSS coding & agent model, scoring 72.5% on SWE-multilingual and 88.6% on our newly open-sourced VIBE-bench, exceeding leading closed-source models like Gemini 3 Pro and Claude 4.5 Sonnet. The most powerful OSS model for the agentic era is here.

MiniMax M2.1 is officially live🚀 Built for real-world coding and AI-native organizations — from vibe builds to serious workflows. A SOTA 10B-activated OSS coding & agent model, scoring 72.5% on SWE-multilingual and 88.6% on our newly open-sourced VIBE-bench, exceeding leading closed-source models like Gemini 3 Pro and Claude 4.5 Sonnet. The most powerful OSS model for the agentic era is here.

MiniMax (official)

1,073,781 görüntüleme • 7 ay önce

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Femke Plantinga

120,565 görüntüleme • 1 yıl önce

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Excited about the launch of Amazon Nova Sonic, our new speech-to-speech model that helps make AI voice applications feel remarkably natural. It's designed to understand not just what people say, but how they say it – working with tone, style, and conversation flow including pauses and interruptions. Nova Sonic delivers speech understanding and generation through a single, unified model, making it easier for builders to develop voice applications that maintain important context and nuance for customer service, AI agents, and other use cases across industries. It’s available in Amazon Bedrock now. Look forward to seeing what teams build with Nova Sonic!

Andy Jassy

155,772 görüntüleme • 1 yıl önce

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Robot AI brains, aka Vision-Language-Action models, cannot adapt to new tasks as easily as LLMs like Gemini, ChatGPT, or Grok. LLMs can adapt quickly with their in-context learning (ICL) capabilities. But can we inject ICL abilities into a pre-trained VLA like pi0? Yes! Introducing RICL (Retraining for In-Context Learning), our Conference on Robot Learning (CoRL) 2025 paper. Our RICL-pi0 model can adapt to unseen objects, novel motions, and new scenes with just ICL and RAG (retrieval-augmented generation). RICL-pi0 also boosts performance on the long-tail of tasks. A quick 1 minute video summary:

Kaustubh Sridhar

52,158 görüntüleme • 11 ay önce

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Try Google Gemini 3 Pro now on Verdent! Enjoy up to 85% OFF usage cost to build more. - 70% off for 30 days: Gemini 3 Pro usage cost in Verdent is 70% OFF. - 2x credits on Verdent subscription: With our limited-time double credits bonus, Gemini 3 Pro costs is 85% OFF of the official price. - Gemini 3 Pro keeps full-project context for real development tasks. Its agentic execution ensures results are correct and shippable. Come build with Google's latest powerful model for coding! Enjoy the Black Friday Sales now.

Verdent

154,352 görüntüleme • 8 ay önce

$Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.$

Mark Zuckerberg is explaining one of the most misunderstood dynamics in AI and it has direct investment implications (Save this). The concept he's describing is model distillation, and it's one of the most important techniques to emerge in AI over the past year. Here's how it works. You train a massive, enormously expensive model, in Meta's case, Llama 4 Behemoth, a 2 trillion parameter teacher model and then you use that model to teach a much smaller, cheaper model. The smaller model inherits roughly 90 to 95% of the intelligence of the giant while running at 10% of the cost and on a fraction of the compute. Meta already did this with the Llama 4 family and Behemoth serves as the teacher. Llama 4 Scout and Maverick, the publicly released open-source models were distilled from it. Scout runs on a single H100 GPU with a 10 million token context window and outperforms models that cost far more to operate. Maverick, at 17 billion active parameters, rivals DeepSeek V3 in coding at half the parameter count and beats GPT-4o on multimodal benchmarks. Both are completely free for commercial use. What Zuckerberg is pointing at is a structural shift in how AI gets deployed in the real world. Companies aren't taking a frontier model off the shelf and running it as-is but rather taking open-source models, fine-tuning them on their own proprietary data, distilling them into even smaller custom models tailored to their specific use case, and running them on infrastructure they control at a fraction of the cost of a closed frontier API. The investment implication of this is significant and runs in two directions. For Meta specifically, this is a strategic masterstroke. Every company that builds on Llama, fine-tunes it, distills it, or deploys it through their infrastructure is pulling into Meta's orbit while Meta builds the most powerful open teacher model. The ecosystem of companies using it grows and that ecosystem generates commercial activity across Meta's platforms and data services. Meta's AI research benefits from billions of real world deployment signals and it's a flywheel that closed model providers cannot replicate because their strategy requires charging per token, which is now a 65x cost disadvantage against the open-source alternative. For the broader market, distillation changes the economics of inference in a way that has barely been priced in. As intelligence becomes extractable into smaller and cheaper models, the absolute demand for compute doesn't decline but rather it explodes, because now the number of applications that are economically viable expands by orders of magnitude. Every task that was previously too expensive to automate at $3.25 per call becomes viable at $0.05 that means more total token usage, more total GPU utilization, and more demand for the infrastructure companies, the Nebiuses, the GE Vernovas, the Constellation Energies that supply the underlying compute and power.

Milk Road AI

27,869 görüntüleme • 23 gün önce

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by Jerry Liu and Anupam Datta of LlamaIndex 🦙 and TruEra , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course!

Andrew Ng

655,572 görüntüleme • 2 yıl önce

🚨Update! Our new demo is LIVE 🚨 In this demo, we walk through the core features of Intelligence Cubed, a next-generation AI model platform built for research, experimentation, and ownership. 🔹 500+ Research Models Intelligence Cubed has grown from 200+ to 506 models, contributed by our expanding Research Fellow Cohort, including researchers, PhDs, and post-docs from Stanford, CMU, Harvard, MIT, and other top U.S. institutions. 🔹 Model Cards & Research Transparency Each model is linked to its original research paper and includes a detailed model card outlining its purpose, use cases, category, pricing, market traction, reviews, and public ownership percentage. 🔹 1.2M Public-Owned Models We’ve introduced Public-Owned Models, with over 1.2 million models available — all fully documented with research papers and comprehensive model cards. 🔹 Auto Router Not sure which model to use? Our Auto Router analyzes your question and automatically routes it to the most suitable model. In this demo, it selects an LLM Detection Survey model to answer the query. 🔹 Modelverse, Canvas & Workflows Users can explore models in Modelverse, try them instantly, add favorites to cart, and deploy purchased models in Canvas using drag-and-drop to build custom workflows. We also provide professionally curated workflows for immediate hands-on experience. 👉Try Now: #AI #Web3 #AIModel #DeFi #blockchain #LLM #OpenSourceAI #AIxWeb3 #DeAI #IntelligenceCubed

🚨Update! Our new demo is LIVE 🚨 In this demo, we walk through the core features of Intelligence Cubed, a next-generation AI model platform built for research, experimentation, and ownership. 🔹 500+ Research Models Intelligence Cubed has grown from 200+ to 506 models, contributed by our expanding Research Fellow Cohort, including researchers, PhDs, and post-docs from Stanford, CMU, Harvard, MIT, and other top U.S. institutions. 🔹 Model Cards & Research Transparency Each model is linked to its original research paper and includes a detailed model card outlining its purpose, use cases, category, pricing, market traction, reviews, and public ownership percentage. 🔹 1.2M Public-Owned Models We’ve introduced Public-Owned Models, with over 1.2 million models available — all fully documented with research papers and comprehensive model cards. 🔹 Auto Router Not sure which model to use? Our Auto Router analyzes your question and automatically routes it to the most suitable model. In this demo, it selects an LLM Detection Survey model to answer the query. 🔹 Modelverse, Canvas & Workflows Users can explore models in Modelverse, try them instantly, add favorites to cart, and deploy purchased models in Canvas using drag-and-drop to build custom workflows. We also provide professionally curated workflows for immediate hands-on experience. 👉Try Now: #AI #Web3 #AIModel #DeFi #blockchain #LLM #OpenSourceAI #AIxWeb3 #DeAI #IntelligenceCubed

i³ (Intelligence Cubed)

116,576 görüntüleme • 6 ay önce

🚀 Meta FAIR is releasing several new research artifacts on our road to advanced machine intelligence (AMI). These latest advancements are transforming our understanding of perception. 1️⃣ Meta Perception Encoder: A large-scale vision encoder that excels across several image & video tasks. 2️⃣ Meta Perception Language Model: A fully open & reproducible vision-language model designed to tackle visual recognition tasks. 3️⃣ Meta Locate 3D: An end-to-end model for accurate object localization in 3D environments. 4️⃣ Releasing model weights for our 8B-parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. 5️⃣Collaborative Reasoner: A framework for evaluating & improving collaborative reasoning skills in language models. Download the code, datasets, and research papers and learn more about how these artifacts are paving the way for more efficient and accurate AI systems.➡️

🚀 Meta FAIR is releasing several new research artifacts on our road to advanced machine intelligence (AMI). These latest advancements are transforming our understanding of perception. 1️⃣ Meta Perception Encoder: A large-scale vision encoder that excels across several image & video tasks. 2️⃣ Meta Perception Language Model: A fully open & reproducible vision-language model designed to tackle visual recognition tasks. 3️⃣ Meta Locate 3D: An end-to-end model for accurate object localization in 3D environments. 4️⃣ Releasing model weights for our 8B-parameter Dynamic Byte Latent Transformer, an alternative to traditional tokenization methods with the potential to redefine the standards for language model efficiency and reliability. 5️⃣Collaborative Reasoner: A framework for evaluating & improving collaborative reasoning skills in language models. Download the code, datasets, and research papers and learn more about how these artifacts are paving the way for more efficient and accurate AI systems.➡️

AI at Meta

163,313 görüntüleme • 1 yıl önce

GPT-5.4 is our most factual and efficient model: fewer tokens, faster speed. In ChatGPT, GPT-5.4 Thinking has improved deep web research, better context retention when it thinks for longer—and oh—you can now interrupt the model and add instructions or adjust its direction mid-response. Steering is available this week on Android and web. iOS coming soon.

GPT-5.4 is our most factual and efficient model: fewer tokens, faster speed. In ChatGPT, GPT-5.4 Thinking has improved deep web research, better context retention when it thinks for longer—and oh—you can now interrupt the model and add instructions or adjust its direction mid-response. Steering is available this week on Android and web. iOS coming soon.

OpenAI

480,849 görüntüleme • 4 ay önce

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by Chroma founder anton 🇺🇸, you’ll learn: (i) Query expansion using an LLM to rewrite and improve a query, by either generating either additional relevant queries or a hypothetical answer to the query. (ii) Reranking using a cross-encoder - a model trained to measure similarity between two inputs presented simultaneously. Reranking reorders retrieved documents based on the cross-encoder similarity measure. (iii) Constructing and training an Embedding Adaptor, which is a model that adapts the embedding values to be more relevant to your use case. Each of these techniques can help you build much better RAG systems. Please sign up for the course here:

Andrew Ng

191,219 görüntüleme • 2 yıl önce

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, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

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, hardware, and computing power in our data centers used to train, build, and deploy AI models and applications. The “middle layer” is the GenAI sandbox – all the tools, Large Language Models (LLMs), and Foundation Models (FMs) we’ve built into Amazon Bedrock. This lets customers pick the #AI models that best fit their needs to easily and securely customize GenAI applications specific to their business. The “top layer” means the popular agents and assistants that people actually interact with – now including Amazon Q. These are built from the models in the middle layer – in Amazon Bedrock. While other apps might help you write a quick email, or edit an image, Amazon Q’s meant for taking care of business. It’s been engineered to be the most capable GenAI-powered assistant for speeding up software dev and leveraging companies’ internal data. Are you as bullish on the future of GenAI as we are? Listen to our CEO Andy Jassy talk about our investment across the stack.

Amazon

47,421 görüntüleme • 2 yıl önce

If your chips and models aren't built in lockstep, you're already behind. At VivaTech, Amazon SVP Peter DeSantis—who leads a new org uniting foundation models and custom chips—explained why they have to develop together, not in sequence. 🤝 "If the chips are not telling the model designers what capabilities are coming and where they can optimize, then we're not doing the science necessary to take advantage of those capabilities until the chips are available—and then you're waiting months and months." Amazon has been doing this since day one. That’s why Trainium is a chip of choice for AI companies building applications from large language models to world models and more.

If your chips and models aren't built in lockstep, you're already behind. At VivaTech, Amazon SVP Peter DeSantis—who leads a new org uniting foundation models and custom chips—explained why they have to develop together, not in sequence. 🤝 "If the chips are not telling the model designers what capabilities are coming and where they can optimize, then we're not doing the science necessary to take advantage of those capabilities until the chips are available—and then you're waiting months and months." Amazon has been doing this since day one. That’s why Trainium is a chip of choice for AI companies building applications from large language models to world models and more.

Amazon

63,006 görüntüleme • 17 gün önce

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇

How did a tiny, scrappy team build one of the most powerful AI voice models? In a deep dive with Sesame CTO Ankit Kumar and a16z's Anjney Midha, we explore how Sesame is pushing the boundaries of AI conversation, why it open-sourced its speech generation model, and the power of small teams to outdo much larger AI labs on product focus. A part of their secret: a relentless focus on real-time, natural conversations over raw intelligence, and a deep commitment to voice, personality, and user experience. By opening up its speech generation model, Sesame is paving the way for even more breakthroughs in AI-native conversation 👇

a16z

29,930 görüntüleme • 1 yıl önce

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 görüntüleme • 1 ay önce