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ta, this setting carries over to your interactions with the agents. Personal agents are currently having a breakthrough moment. While this year started with early adopters in Silicon Valley latching onto tools like OpenClaw , the recent release of Meta’s Muse agent reached a larger user base that is now experimenting with personalized agents. Muse rocketed to the top of smartphone app download charts, but Dots has come for Meta's lunch. This is a developing story. Please check back for updates .

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(128 GB), AMD Ryzen AI Halo (128 GB), MacBook Pro M5 Pro (64 GB), and NVIDIA GeForce RTX 5090 (32 GB). These have been selected to cover a range of platforms (CUDA, ROCm, Vulkan, Metal), memory capacities, and bandwidths. We will be expanding the featured hardware to include x86 (and other) laptops, AI-focused graphics cards such as the RTX PRO 6000 Blackwell, and more, to give consumers a well-rounded view of performance across different hardware types. The models featured at launch are: Qwen3

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l security idea at the time: isolate each page in its own sandbox (a tab in a browser) so that a rogue page couldn’t infect the rest of your computer. Amazon, Google, Netflix, and Meta were all built on this trust layer. Security and safety for the internet enabled online commerce, connection, gaming, and so much more that wouldn’t have been possible before. Security and safety didn’t slow the pace of innovation—they allowed it to accelerate. Several frontier labs have recently reported versions

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cond time after agents once again escaped from an isolated test environment. OpenAI isn't the only lab dealing with this. Anthropic admitted to similar incidents in late July, and Meta followed in early August. It also recently came out that Google's Gemini hacked three real companies during a test back in May. OpenAI, Anthropic, and outside researchers are now reviewing tens of thousands of other cases . According to OpenAI, many of them are just routine research activity. Nvidia's technology i

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ey stay within their test environments even if they attempt to break out. The release follows a string of hacking incidents involving AI models from Anthropic, Google, OpenAI, and Meta that bypassed security controls to escape their testing environments and access real-world systems. The first and most prominent example occurred this summer when OpenAI agents breached Hugging Face while trying to complete a cybersecurity task. And the hits keep on coming — OpenAI published a new site dedicated t

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a FSD的智驾系统。 此外，团队里还有两位灵魂人物。 1、占方能 。 香港科技大学助理教授、World Mind Lab负责人，长期研究世界模型和物理AI。 2、 季马泽宇 。 00后青年科学家，研究方向横跨3D感知、灵巧操作和人形机器人全身控制。 加入Simate前，他还是硅谷Assured Robot Intelligence的创始成员。 ARI后来被Meta收购。 资本市场，也已经用真金白银投票。 成立仅三个月， Simate 连续完成 多轮每轮数亿元人民币融资 。 而这一切，都是在押注同一件事—— Physical RSI。 Simate从创立第一天便瞄准了这个方向，并且定义了三种不同类型的RSI。 1、弱SI：边界明确，快速闭环 人机共驾模式，研究员设定目标与约束，正常执行过程中不再需要研究员逐步参与，异常可以升级处理。 这次登顶RoboDojo，就是第一阶段的工程验证。 2、中SI：方向明确，但答案未知 怎么改善模型记忆？某类任务的数据比例到底怎么配？ 方向已经有了，但具体哪个方案有效，还得靠多轮实验去找。 这时候，Agent负责整理上下文、实现方案、跑实验、比较结果；研

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杯，Skild这条看似疯狂的技术路线并非横空出世。 把这些想法从论文一路推向真实机器人的，是两位在机器人学习领域深耕多年的卡内基梅隆大学教授。 两位CMU教授，联手造机器人大脑 Skild AI成立于2023年，脱胎于 卡内基梅隆大学（CMU）机器人研究体系 。 团队成员既包括来自卡内基梅隆大学、斯坦福大学、加州大学伯克利分校等高校的研究人才，也包括曾任职于Meta、Tesla、NVIDIA、Google、Amazon和Everyday Robotics等机构的研究员及工程师。 而联合创始人兼CEO Deepak Pathak 的经历，几乎串起了Skild AI的整条技术路线。 他本科毕业于印度理工学院坎普尔校区计算机专业，后赴加州大学伯克利分校攻读计算机博士，师从计算机视觉领域知名学者Alyosha Efros和Trevor Darrell。 博士毕业后，他曾在Meta（前Facebook）AI Research从事研究工作，也曾在伯克利与机器人学习专家Pieter Abbeel合作，担任访问博士后。 此后，他进入卡内基梅隆大学，成为机器人研究所和机器学习系教授。 Deepak Pa

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st as Office defined work for the PC era, the new Copilot is built to define it for the AI era,” says Spataro. That might well be Microsoft’s goal, but there’s a lot more competition in this AI era than there ever was in the PC era. Microsoft has certainly fallen behind the likes of Google, OpenAI, Anthropic, and now Meta, and it’ll be interesting to see if “the AI built for work” is enough to help it catch up. Update, September 25th : Article updated with Microsoft CEO Satya Nadella’s comments.

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ce. Traces from a multi-turn session are linked into a thread. That structure gives engineers the full execution tree, including nested runs, timing, retries, inputs, outputs, and metadata. When you need to understand exactly how something executed, the trace is the source of truth. But not every workflow needs the full execution tree, and that's where trajectories come in. A trajectory is a projection over the traces in a thread. It removes the nested run structure and keeps the messages and ac

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ing 80+ pro-grade tools across imaging, design, video, and documents. New interactive editors let you hands-on edit PDFs and Adobe Express designs directly inside Claude . Muse at Meta Connect - Zuckerberg is going all in on Muse, Meta’s personal AI assistant. It can now use any app on your Mac, buy for you via Walmart, Best Buy & Sephora and it’s getting its own email address so you can CC it on threads. It’s also coming to Meta’s glasses in the next few months. Zuck says Muse stays free for “a

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as an MCP result. To inspect that on the wire: Why serve MCP from the gateway Discoverable. Connect your gateway to API hub and its MCP server is published there with MCP-specific metadata and appears in Agent Registry automatically, so agents and developers can find the tools it exposes; Nothing new to operate. Your existing spec, gateway, authentication, quotas, and logging do the work — MCP and REST traffic stay consistent because they share one policy path The Public Preview covers REST and

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missed its bandwidth target. The following example shows how a certification names its targets and the categories to run. apiVersion: nvcre.nvidia.com/v1alpha1 kind: Certification metadata: name: gpu-cluster-cert spec: target: nodeSelector: nvidia.com/gpu.present: "true" categories: - domain: communication variant: nccl-all-reduce - domain: training variant: nemotron5-8b $ kubectl apply -f certification.yaml $ kubectl get certifications.nvcre.nvidia.com -w The built-in catalog currently covers t

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With AI agents like Meta’s Muse and Instinct now able to make calls on users’ behalf, Google is now letting Gemini call businesses with a new feature called “Call for Me.” The feature will initially roll out to Pixel 11 owners who pay for a Gemini subscription in the U.S. It will also require the use of the beta version of Google’s Phone app for Android, as it’s still supposed to be an experiment. While Google has long offered calling features guided by AI, prior versions were limited in what th

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llar. The benchmarking results highlight consistent stability across multiple benchmark datasets in multiple languages, such as NOTSOFAR, AMI, CALLHOME, and AISHELL, outperforming Meta Muse Voice Transcribe on all datasets at even the ultra-low configuration, and losing to pyannote Community-1 only on AMI. Even under ultra-low latency configurations, the error rate remains within a tight margin of offline baselines, confirming robust speaker attribution under real-time constraints. Streaming dia

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llar. The benchmarking results highlight consistent stability across multiple benchmark datasets in multiple languages, such as NOTSOFAR, AMI, CALLHOME, and AISHELL, outperforming Meta Muse Voice Transcribe on all datasets at even the ultra-low configuration, and losing to pyannote Community-1 only on AMI. Even under ultra-low latency configurations, the error rate remains within a tight margin of offline baselines, confirming robust speaker attribution under real-time constraints. Streaming dia

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What is the GGUF file format? GGUF packages model weights and metadata, including tokenizer information and an optional chat template, in one file. It supports different quantization levels, letting you trade some precision for a smaller memory footprint. Variants such as Q4 K M mix tensor precisions, using mostly 4-bit weights while keeping sensitive tensors at higher precision. Here's how quantization changes the file size of Unsloth's Qwen3.5-4B : We suggest starting with Q4 K M , then trying

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ontext stays server-side, reducing parsing round trips; Eager and concurrent tool execution while model streams; Graceful failures; Live SSEs of every granular step and additional metadata for observability; Mixed tool support (client and hosted tools) One platform for models and agents Baseten has focused on making models fast, reliable, and easy to operate in production. Baseten Hosted Tools expands that foundation to the capabilities models use at inference time. Baseten Grounded Inference is

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n Context stays server-side, reducing parsing round trips Eager and concurrent tool execution while model streams Graceful failures Live SSEs of every granular step and additional metadata for observability. Mixed tool support (client and hosted tools) Baseten has focused on making models fast, reliable, and easy to operate in production. Baseten Hosted Tools expands that foundation to the capabilities models use at inference time. Baseten Grounded Inference is the first step. Over time, we’ll a

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per benchmarks: vertically integrated labs producing proprietary data, models trained against scientist-calibrated rewards, and deployment back into experimentation. The strongest meta-observation came from @richardczl : every company with a meaningful data moat will likely try this play, shifting bottlenecks toward RL rollout throughput, verifier compute, and weight sync . Gemini 3.8 Live and the Push Toward Real-Time Voice Agents Google’s new live audio models : Google launched Gemini 3.8 Live

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g AA's speech-to-speech quality ranking. Odyssey introduced Odyssey 3, a world model that can control robot arms, humanoids, self-driving cars, drones, and games, set to release in the coming weeks. A 404 Media report detailed OpenAI’s ‘Project Lily’, finding that hundreds of human contractors read and rate real ChatGPT chats, often without the user’s knowledge. Meta rolled out Meta One, a new paid tier across its apps running from $2.99 to $499 a month for extra Meta AI usage and creator tools.

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