搜索“AWS”

改的技能 - 更改了技能钩子：同一来源（一个仓库、你自己的文件夹或一个插件）的技能每个会话最多可注册 1,024 个钩子和 16,384 个匹配字符 - 更改了在仅允许来自插件的钩子的策略下的技能：在主对话中使用但其钩子被排除在外的技能不再获得其 allowed-tools - 更新了 /claude-api 技能，在 Claude Platform on AWS 上使用 ant CLI，而不是回退到 SDK 或 curl - 移除了 claude plugin validate 中预测 mod 的钩子将在何处运行的备注 - 移除了 Opus 5.5 上 /fast 对话框标题中的“(research preview)”标签 - [VSCode] 新增了在聊天面板中绘制 mod 的功能，适用于 Amazon Bedrock、Google Vertex AI 和 Microsoft Foundry 上的会话、位于 LLM 网关之后的会话，或关闭遥测的会话 - [VSCode] 修复了会话以“Subprocess initialization did not complete within

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n Bedrock, you can analyze videos directly from an Amazon Simple Storage Service (Amazon S3) URI or a base64 string with no pre-indexing required, and your data remains within the AWS data boundary. In this post, we cover what’s new in Pegasus 1.5, how the model is packaged on Amazon Bedrock, and how to send your first request. We then walk through video segmentation, structured JSON output, and multimodal prompting. What’s new in Pegasus 1.5 Pegasus 1.5 has the following changes compared to pre

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提供命名的部署配置，并带有以下优化选项： 均衡 ——在成本、延迟和吞吐量之间为通用工作负载提供均衡权衡。 吞吐量优化 ——使用推测解码等技术实现每秒最大token数，非常适合高容量批处理。 延迟优化 ——最低的首token时间（TTFT），优先考虑交互式应用的速度。 成本优化 ——每次请求的最低基础设施成本，适合可以接受较高延迟的工作负载。 这些配置包括针对AWS Inferentia和AWS Trainium实例的Neuron优化设置，降低了部署复杂性和基础设施成本。你可以一键使用合适的优化配置文件部署模型，而不必花费数周手动对配置进行基准测试。 Hugging Face深度链接集成 与Hugging Face的新集成将模型发现直接连接到SageMaker Studio，适用于已上线模型。在Hugging Face上浏览模型时，你可以选择 Deploy on SageMaker AI 或 Customize and Deploy on SageMaker AI。你会直接进入已配置好的Studio环境，模型已预加载、权限已预配置，并内置GPU配额可见性。这避免了在实验前设置AWS身份和

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mes of requests asynchronously while still receiving schema-compliant outputs. On the bedrock-runtime endpoint, it also works with cross-Region inference, so you can use it across AWS Regions without additional setup. If you need responses in real time, you can stream structured responses as typed events. How it works When you make a request with a JSON schema, here’s what happens behind the scenes: Schema validation – Amazon Bedrock validates your JSON schema against the supported JSON Schema D

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商业特邀作者5收 藏+10评 论打开微信“扫一扫”，打开网页后点击屏幕右上角分享按钮微 博沉浸阅读返回顶部举报参与评论评论千万条，友善第一条登录后参与讨论提交评论0/1000你可能也喜欢这些文章英伟达百亿美元投资落子德国，黄仁勋：昔日科技强国掉队，让我十分痛心Google申请被拒：美国专利战，正在重算攻防成本马斯克第三，他第一，OpenAI工程师靠重置登顶XAWS图省事，组装式组网能打吗？硅谷AI现状：谁有算力谁是爹今天，Gemini全面杀入AI办公大战微软给Agent立规矩，Windows要管AI了MCU大厂接连出手，这个开源RTOS会一统天下吗？马斯克进军芯片制造Tech商业特邀作者 关注科技创投，记录公司商业进展 欢迎关注公众号：Tech商业发表文章142篇 最近内容智谱的困惑：海外口碑极佳的GLM，为何在国内大众眼里输给了DeepSeek和豆包？1小时前硅谷权力的代际折叠：印裔高管身价十亿美元，新一代华裔创业百亿美元6小时前黄仁勋的终极赌局：下一波AI，去征服物理世界6小时前阅读更多内容，狠戳这里查看AI测评DeepSeek选靠谱AI，看真实评测查看36氪AI测评官方交流社区加

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也强调，IP厂商既要承担研发及流片验证投入，也需要协助客户解决芯片集成、系统调试等问题。这需要产业链上下游密切协同，最终在具体项目中完成验证、走向量产。 1.49亿参股芯行纪 华大九天加速合龙数字全流程EDA 华大九天1.49亿元参股芯行纪 补齐布局布线关键EDA工具 西门子、台积电推进半导体设计自动化，聚焦先进制程、3DFabric 与硅光子 新思科技连签AWS、OpenAI两笔合作 IICIE 2026上的EDA新叙事 IICIE 论坛观察：行业形成共识，AI已成 EDA 标配能力 斩获AI顶会NeurIPS 2026！摩尔线程前沿成果ReorgGS：以极小工程改动，提升3DGS渲染画质与效率 59分钟前 3小时前 法动AI智能体和先进封装SI/PI一体化协同设计方案亮相IME2026微波及天线技术展会，张康龙发表主旨演讲 4小时前 全球首款 USB3.2 Gen2 便携逻辑分析仪｜矽速科技 SLogic32U3 搭载高云 GW5AT FPGA 发布 4小时前 再添“芯”誉 | 思特威SC1235HGS荣膺“2026年国芯展创新成果奖” 4小时前 最新资讯 芯爱科技完成超10亿元A

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I，主要是害怕错过。Werner观察到一个现象：每周都有三个新模型，客户越来越焦虑，害怕自己不理解一切，有时闭着眼睛冲进AI时代，却还没有充分理解这项技术的能力。 Werner经常建议客户先三思再AI，花点时间学习，确保真正理解技术能做什么。所有工程师和工程管理人员，都应该接受相关学习。恐惧是一种非常糟糕的决策方式，只有知识才能消除恐惧。 他提到Jeff Lawson写的一本书《问问你的开发者》。Jeff曾在亚马逊工作，后来担任Twilio的CEO。书的核心观点是：你不一定需要找麦肯锡或高德纳。如果真想知道技术能做什么，去问你的开发者。“好的开发者很可能早就开始学习这项技术了，知道它能做什么、不能做什么。如果决策建立在知识上，企业就更容易作出正确决定。如果建立在恐惧上，那大概不是企业希望走的方向。” 在明确了先理解再采用的前提下，成本问题紧接着浮现。企业现在对AI系统成本的不可预测有些疑惑：想提高效率，却要花很多钱，真的值得吗？Werner指出，企业仍在权衡成本与收益，寻求真正的投资回报。 Werner提醒了一件容易被忽略掉的事：“人工智能”这个词已经有七十年历史了。麦卡锡有一句话：“

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erial testing on an unloaded machine, which measures straight-line per-task latency, Modal found Vera was often faster than the other processors tested — Intel GNR, AMD Zen 5, and AWS Graviton 4 and 5 — but not always, and not by a particularly large margin, and the post’s author described being initially skeptical of the results. Under load, Modal reported, the results diverged. The throughput test increases the number of concurrent containers on the machine and loops as many verified task comp

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ity had been whether diffusion LLMs could scale to compete with autoregressive models on complex use cases. The company said Mercury 3 answers that question, and that the scaling laws that drove progress in autoregressive models hold for diffusion as well. Early-Access Production Results Inception said early-access partners are already running Mercury 3 in latency-sensitive applications including browser-use agents, interactive education, real-time search agents, and customer support. The compan

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l estate listings, interactive maps, home value estimates and affordability tools into the AI assistant. Josh Weisberg, senior vice president of AI at Zillow, said the connector draws on two decades of housing data and is meant to carry users from information to action. “This connector helps Claude users take the next step through Zillow: a tour, an agent, a loan officer,” Weisberg said. “The conversation doesn’t stop with providing information, it leads to a way to make it happen.” How the Conn

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ver time and turns them into a single action stream. The model runs locally on the robot without a cloud connection and was developed and trained in-house in Metzingen. Training draws on three sources: real-world robot data, simulation-amplified data and human demonstration data, complemented by data partnerships with industrial companies including Schaeffler and Bosch. NEURA says Neuron AI runs on five robot embodiments: the 4NE-1 Gen 3 and Gen 4, a wheeled 4NE-1, and two further bimanual platf

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left out. That gap between permission and intent has already caused problems. Amazon’s internal coding agent Kiro reportedly took an unexpected approach to fixing a routine bug in AWS Cost Explorer. Rather than making a targeted change, it decided to delete and recreate the environment. The agent had inherited an engineer’s elevated access, allowing it to bypass a two-person approval process. The incident shows how far an agent can take a task when its access exceeds the limits of the original a

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y can. We’ve scaled Mercury more than 40x in total parameters, and intelligence across a range of standard benchmarks has risen 3x from Mercury 1 (mini) to Mercury 3. The scaling laws that drove progress in autoregressive models hold for diffusion too. Mercury 3 in production Early-access partners are already running Mercury 3 to power latency-sensitive applications such as browser use agents, interactive education, real-time search agents, and customer support. Browser Use Agents Agents wait on

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争承接 AI 工作负载。与依赖少数几家超大规模云提供商相比，这种模式能带来哪些经济优势，尤其是在 GPU 需求高涨时？ 我们强调在 GPU 稀缺时愈发明显的三大优势：价格发现、弹性以及无锁定。 1. 价格发现：在 0G Compute 上，提供商自行定价并竞争工作负载。其结果可能是成本显著降低：同类开放模型推理成本最多可低 95%，而我们的存储层运行成本比 AWS S3 低 90% 以上。部分原因来自竞争，部分原因来自让闲置 GPU 容量投入运作，以及在专用基础设施上运行开放模型而非闭源前沿模型。 2. 弹性：超大规模云厂商的容量是提前数年规划的，并通过长期关系进行分配。而市场可以从任何地方获取供给，包括独立运营商、学术集群以及拥有闲置硬件的企业。当需求激增时，更高的价格可以吸引更多供给上线。而固定队列只会越排越长。 3. 无锁定：我们网络上的每一家提供商都通过同一个路由器暴露相同的 OpenAI 兼容 API，如果某家提供商宕机，系统会自动故障转移。如今，这意味着 13 家提供商通过单一端点提供数百个模型，已处理超过 3730 亿个 token 和 2700 万次推理请求。开发者只需

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cloud, cybersecurity and more 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/ About SiliconANGLE Media SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-ti

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社区，支持我们保持内容开放和免费的使命。加入 theCUBE 的 Alumni Trust Network，技术领袖在此连接、分享情报并创造机会。 theCUBE 视频拥有 1500 万+ 观众，推动 AI、云、网络安全等领域的对话 11.4k+ theCUBE 校友——通过独特的基于信任的网络，与 11,400 多位塑造未来的技术和商业领袖建立联系 您是 AWS 客户吗？通过从我们的 Marketplace 门户页面和链接购买 AWS 服务，为 SiliconANGLE 提供资金支持：https://siliconangle.com/aws-marketplace/ 关于 SiliconANGLE Media SiliconANGLE Media 是数字媒体创新领域公认的领导者，融合突破性技术、战略洞察和实时受众互动。作为 SiliconANGLE、theCUBE Network、theCUBE Research、CUBE365、theCUBE AI 和 theCUBE SuperStudios 的母公司——在硅谷和纽约证券交易所设有旗舰据点——SiliconANGLE Media

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ier的消息： 支持我们的使命，通过参与theCUBE社区，让内容保持开放和免费。加入theCUBE的校友信任网络，技术领袖们在这里建立联系、分享情报并创造机会。 theCUBE视频拥有超过1500万观众，推动着AI、云、网络安全等领域的对话 超过1.14万名theCUBE校友——通过独特的信任网络，与超过11,400名塑造未来的技术和商业领袖建立联系 您是AWS客户吗？通过我们的Marketplace门户页面和链接购买AWS服务，以财务方式支持SiliconANGLE：https://siliconangle.com/aws-marketplace/ 关于SiliconANGLE Media SiliconANGLE Media是数字媒体创新领域公认的领导者，将突破性技术、战略洞察和实时受众互动融为一体。作为SiliconANGLE、theCUBE Network、theCUBE Research、CUBE365、theCUBE AI和theCUBE SuperStudios的母公司——在硅谷和纽约证券交易所设有旗舰据点——SiliconANGLE Media在媒体、技术和AI的交汇

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s what those AI agents are acting on.” An agent that acts on one vendor’s incomplete view of an asset is going to be wrong, he said. Axonius spent years building a platform that draws on more than 1,400 bidirectional integrations, and Ben Simon described AI Workforce as what can safely be built on top of it. The company made the same data-quality argument in April, when it launched Axonius Verified Assets on the premise that AI agents act on their inputs without questioning them. AI Workforce, t

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cloud, cybersecurity and more 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/ About SiliconANGLE Media SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-ti

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cloud, cybersecurity and more 11.4k+ theCUBE alumni — Connect with more than 11,400 tech and business leaders shaping the future through a unique trusted-based network Are you an AWS customer? Support SiliconANGLE financially by buying your AWS services from our Marketplace portal page and links: https://siliconangle.com/aws-marketplace/ About SiliconANGLE Media SiliconANGLE Media is a recognized leader in digital media innovation, uniting breakthrough technology, strategic insights and real-ti

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