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而是一个新的价值体系。” 印奇在解释 3999 元定价时说。购机用户附赠 STEPX Token Plan，连续 12 个月、每月百亿 Token 额度，用于探索和开发智能体应用。阶跃还推出 Amoo 成长计划：前 1 万名任务达标用户将成为 “Builder One”，获得 “星辰船票”，未来五年内凭STEPX Neo可0元置换任意一款阶跃新款终端。 从 Meta、OpenAI 到谷歌、苹果，Personal Agent 正成为巨头押注的新方向。阶跃没有选择为现有手机叠加 AI 助手，而是从模型、操作系统到硬件重构一部手机，用 “模软硬一体” 回答 “智能体应该长在哪” 的问题。3999 元能否撬动用户习惯，生态能否持续生长，智能体终端的争夺战才刚刚开始。 此内容为集微网原创，著作权归集微网所有，爱集微，爱原创 相关推荐 摩尔线程率先完成阶跃星辰Step 5 Preview大模型适配 四部门组织30余家车企座谈 加强汽车产品研发测试验证管理 10-16 22:56 德明利：预计前三季度净利润86.5亿元-89.5亿元 同比扭亏为盈 10-16 22:52 长鑫科技：预计前三季度净利润

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ropout、插值反向和归约的更快路径。Wan2.2 的视频 VAE 解码现在峰值内存为 8 GiB，而非 270 GiB。 从 Python 获得更多控制：公开的 MPS 流和更可靠的事件计时。 更新后的要求：MPS 现在要求 macOS 15 或更高版本；PyTorch 2.15 要求 Python 3.11 或更高版本。 2.16 前瞻：注意力完全在 Metal 上运行、更多 MPSGraph 到 Metal 的迁移，以及更快的 LLM 解码。 这对你意味着什么 如果你在 Mac 上使用 transformers、diffusers 或 ComfyUI 等工具运行模型，以下是 2.15 在实际使用中的变化： 本地 LLM 快得多。混合专家模型解码速度比 2.14 快 5–6.5 倍：在配备 torch.compile 的 M3 Ultra 上，Qwen3.6-35B-A3B 从 9.4 tokens/s 提升到 61.3 tokens/s，LFM2.5-8B-A1B 从 23.6 tokens/s 提升到 123.6 tokens/s。稠密模型也从中受益，得益于更快的线性层和用于

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t 9, 2026 we announced the availability of TwelveLabs’ Pegasus 1.5 on Amazon Bedrock. Pegasus 1.5 is a video language model that turns long-form video into structured, timestamped metadata. You describe the moments that matter to your workflow, and the model returns every matching segment as JSON. Video represents a significant and complex category of unstructured data, yet most of it remains unsearchable and underutilized. Extracting structured, actionable insights from that content has histori

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. Teams can take advantage of Trainium’s capabilities without reinventing their workflows or leaving the PyTorch ecosystem.” – Jana van Greunen, Director of PyTorch Engineering at Meta Getting started: Easy device switching Getting started is straightforward. Run your code on Trainium using the pointer: .to('neuron'). Run a training loop on Trainium model = MyModel().to('neuron') the only change optimizer = torch.optim.AdamW(model.parameters()) for batch in dataloader: optimizer.zero grad() loss

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，亮出首款个人AI助手Underdog，同时官宣获得Anthropic旗下投资联合体、硅谷知名风投公司a16z，以及来自谷歌、OpenAI、Hugging Face的AI大牛等个人及机构的投资。 ▲Conway Research‌创始人Sigil Wen官宣融资的推文（图源：X） 该公司将Underdog称作“迄今为止隐私性最强的Muse竞品”。 9月8日，Meta发布个人AI助手Muse，上线仅10天就超过ChatGPT登顶美国App Store免费应用总榜。Muse可自主拆解用户目标、执行任务，覆盖办公、生活、消费等多场景，每名用户拥有云端独立虚拟机，可后台持续执行任务。 这股热潮迅速扩散，OpenAI发布新一代智能体产品Dot、马斯克发布Grok Bot、Manus推出2.0，还有国内字节、腾讯、蚂蚁、阿里也被曝正进行研发类似产品。 Underdog与Muse这类产品的功能类似，都是帮助用户处理日常生活、工作的任务，最大不同之处在于，Underdog完全运行在用户的设备上，且离线也能跑。 ▲Underdog自动添加日程演示（图源：Conway Research‌） Conway

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parallelism. With --attn-dp-size N and --enable-dp-lm-head, the runtime keeps the attention and draft-head work aligned with the DP layout. SGLang initializes target-verification metadata after DP padding establishes the final token shape, including batches with idle peers. We include draft KV memory in the hybrid pool budget so target-cache allocation leaves room for the drafter. Draft capture requires PP=1, and the serving context must fit both the target and the chosen drafter. Beam's full-a

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s $65M in funding to develop 3D computing modules Proofpoint agrees to buy data security company Varonis for $6.3B in cash Ramp raises $1.85B as it expands beyond finance software Meta releases 816B-parameter Muse Spark Open for coding and agent work LATEST STORIES North.cloud bags $60M in funding to help businesses keep runaway cloud costs in check CLOUD - BY MIKE WHEATLEY . 33 MINUTES AGO 8 insights from LogicMonitor’s Elevate event: AI-driven observability moves toward autonomous IT AI - BY C

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0系列显卡就能运行DLSS 5。这样的升级，已经超出了一次常规的性能优化。 要知道，过去几十年，游戏画质的提升很大程度上依赖GPU不断增强传统图形计算能力，从光栅化到光线追踪，都是如此。但从DLSS 4到DLSS 5，神经（网络）渲染技术毫无疑问正在成为一个关键变量，这次就引入了最新的3D引导神经渲染技术。 不止英伟达，不止PC。苹果早在去年WWDC上就通过Metal 4提供了将机器学习整合到实时渲染管线中的能力，并从去年的A19系列和M5芯片开始，在GPU中加入了神经网络加速器；而高通、联发科，也都在新一代旗舰芯片上首次引入了神经渲染技术。 图片来源：苹果 对于已经发展了几十年的计算机图形技术，这或许意味着一场比单纯提升GPU性能更深刻的变化。半年提速10倍，神经渲染究竟改变了什么？ 需要先说明，DLSS 5最新升级的重点在效率。 按照英伟达的说法，通过神经网络模型与渲染管线的共同优化，新模型的参数量已经缩减至约1.18亿，同时将GPU运行开销降低了一半，基本保持了原有的视觉效果。按照测试结果，新模型可以让《NBA 2K27》的帧率最高提升40%。 图片来源：英伟达 DLSS 5也正

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ies in competing bacteria, from cell envelopes to essential intracellular machinery, making it difficult for pathogens to develop resistance. The exponential growth of genomic and metagenomic sequencing has populated global databases with billions of natural amino acid sequences. This pool of natural data has already produced globally used bacteriocins like Nisin, alongside recent genome-mined breakthroughs such as Darobactin, Dynobactin, and Lariocidin. Many, however, remain locked within the u

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widespread file formats for each of its parts, namely: hf dataset stored using Hugging Face datasets library serialization to parquet videos are stored in mp4 format to save space metadata are stored in plain json/jsonl files Dataset can be uploaded/downloaded from the HuggingFace hub seamlessly. To work on a local dataset, you can specify its location with the root argument if it's not in the default /.cache/huggingface/lerobot location. Evaluate a pretrained policy Check out example 2 that ill

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d question that surfaced when Google LLC first debuted its Google Glass wearables more than a decade ago, smart glasses that could record interactions, and persists now again with Meta Platforms Inc.’s smart glasses. Of course, the question still remains: These devices still upload everything to Amazon and users need to stay aware that their lives are being directed to the internet wherever they go. This is true even if Amazon cannot see or access anything without the user sharing it. “Even if t

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frames. Code layers is rolling out in open beta starting today, and will use AI credits. On paid plans, creating and editing within code layers is free through November 4, 2026. Sean Blanton is a product designer at Figma, where he builds agentic tools at the intersection of design and code. Previously, he worked at DoorDash and Meta. Build by design: New workflows for new ways of building Introducing Specs: Markdown as a medium Create, ship, and manage data-backed websites with Payload in Figma

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在线的智能体、自动化任务。 Git那些适合分布式离线工作的特性，反倒成了仓库扩容的阻碍。那如果从头设计一套源码控制系统，原生就考虑智能体永远在线这个前提会是什么样？ 02.六大设计原则，部分思路与Git截然不同 基于上述现状，他们在构建Scion时在几个核心原则上采取了与Git截然不同的方法，同时其借鉴了大型单体仓库成熟的源代码控制技术，如谷歌的Piper和Meta的Sapling。 其构建Scion的几个核心原则是： 客户端始终在线：Git默认客户端离线工作并且需要仓库的完整副本。由于Anthropic几乎所有的客户端都是智能体和自动化作业，他们围绕一个始终可访问的服务器设计了Scion的客户端和协议，让客户端在需要时获取数据。 客户端-服务器不对称：在Git中，每个客户端都能够充当功能齐全的服务器。在Scion中，服务器负责回答有关文件历史记录和blame的问题，创建提交，执行合并和变基等操作。 客户端维护一个有界本地对象缓存，在磁盘上布局工作副本文件，监控文件系统变更，并为用户提供符合人体工程学的服务器交互方式。客户端自身计算能力有限，但作为交换，它所需的本地数据量也少得多。 一

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ack office applications are being infused with AI routines, and this is driving usage up the hockey stick handle at a rapid pace. That is why Microsoft is putting the pedal to the metal with its Maia family of custom, homegrown XPU accelerators, and letting customers know Microsoft is working on the problem is why the company has rolled out the feeds and speeds on its future “Clea” Maia 300 accelerator as the Open Compute Project Summit 2026 kicks off this week. The Maia 300 chip is a much-impro

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ps smooth out the visible steps that can show up in gradients like skies when SDR footage is stretched to a wider range. The output is a standard ComfyUI image with no color-space metadata, so downstream Create Video nodes need to be told whether the data is HDR or HDR PQ. The color management and EXR support in this release also handle that step. Nvidia VFX Nodes Workflow Better EXR support for VFX pipelines In addition to Nvidia’s VFX nodes, we’re improving support for EXR files format. EXR is

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for everyday tasks. Tech giants are preparing for a potential future in which people get things done by speaking or typing to AI agent rather than by operating apps and websites. Meta recently launched its Muse personal assistant, along with a new slate of smart glasses designed to give users easier access to its new AI helper. OpenAI is also developing several hardware gadgets. Apple is betting that its focus on privacy and the fact that consumers already run their digital lives on its iPhones

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ion.json. It does not optimize benchmark ECE or select checkpoints. NLL-optimal temperature need not minimize ECE on another dataset. Calibration artifacts are bound to checkpoint metadata hashes; regenerate one after changing weights. If relocating a checkpoint, update its artifact's checkpoint path only after checking all stored file hashes. Browser demo and HTTP API Open http://127.0.0.1:7860. The demo supports typed questions, multiple image uploads, probabilities, and calibrated confidence.

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goals were that it should be simple to encode/decode, extensible, have selectable encoding precision, and allow random access. RAD file structure, consisting of an extensible JSON metadata header followed by randomly-seekable chunks. Each chunk is a RADC file with JSON header and the properties of a 64K chunk of splats, encoded and compressed in column-order. The file structure is simple: the header RAD0, the byte size of the header metadata, the metadata JSON, followed by one or more chunks of

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ts own tools through skills: Tool creation: Agents can write new tools (Python modules, Bash scripts, etc.) and register them into a hierarchical, file-system-based structure with metadata describing interfaces and dependencies; Tool management: Tool-specific container-based execution isolation supports heterogeneous tools with conflicting dependencies, while a shared workspace enables data sharing across containers; Asynchronous execution: Agents can decide to run selected tools in the backgrou

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ent. The judge model reviews and scores each transcript for the target behavior plus secondary qualities that help contextualize the score. These scores and summaries then go to a meta-judge, which produces a report with an overall suite summary, a breakdown of different scenarios, elicitation strategies and their success, and any other details you request Seed Configuration Bloom's configuration system is highly adaptable—you can tailor evaluations to elicit a wide range of failure modes. Confi

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