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10月2日周五
  1. AWS Machine Learning Blog

    Add secure Web Search to Claude Desktop with Amazon Bedrock AgentCore

    中文摘要

    Amazon Bedrock 上的 Claude Desktop 仅限于该模型的知识截止日期,不支持网络搜索。在本文中,我们将逐步介绍如何使用 Amazon Bedrock AgentCore 网关将 Claude Desktop 连接到网络搜索,通过 AWS IAM Identity Center 和 Amazon Cognito 进行基于 JWT 的入站身份验证。

    英文原文

    Claude Desktop on Amazon Bedrock is limited to the model's knowledge cutoff without web search. In this post, we walk through connecting Claude Desktop to Web Search using Amazon Bedrock AgentCore Gateway, with JWT-based inbound authentication through AWS IAM Identity Center and Amazon Cognito.

  2. AWS Machine Learning Blog

    Fine-tune a search agent with multi-turn RL on Amazon SageMaker AI

    中文摘要

    微调可以教会一个小型搜索代理你的工具和环境,使其在更低的延迟和成本下,具备前沿模型的可靠性。在本文中,我们使用多轮强化学习(MTRL)在Amazon SageMaker AI上对一个由大型语言模型驱动的搜索代理进行微调,并分享我们在检索质量和可靠性方面测量到的提升。

    英文原文

    Fine-tuning teaches a small search agent your tools and environment, giving it the reliability of a frontier model at lower latency and cost. In this post, we fine-tune an LLM-powered search agent with multi-turn reinforcement learning (MTRL) on Amazon SageMaker AI and share the gains we measured in retrieval quality and reliability.

  3. TechCrunch · AI

    Pope Leo XIV is not a fan of AI-generated art

    中文摘要

    “在艺术与机器通过统计计算基于他人创作的数百万张图像生成的东西之间,甚至在审美层面之前,存在一种本体论上的差异,”教皇写道。“算法缺乏人性的火花。”

    英文原文

    "There is an ontological difference, even before an aesthetic one, between art and what a machine can generate through statistical calculation based on millions of images created by others," the pope wrote. "Algorithms lack the spark of humanity."

  4. GitHub Blog · AI & ML

    AI is changing developer work. Here are three skills to strengthen.

    中文摘要

    学习如何指导AI代理,批判性地审查它们的输出,并将技术判断置于工作流程的中心。后AI时代正在改变开发人员的工作方式。以下是三项需要加强的技能。首次出现在 The GitHub Blog 上。

    英文原文

    Learn to direct AI agents, critically review their output, and keep technical judgment at the center of your workflow. The post AI is changing developer work. Here are three skills to strengthen. appeared first on The GitHub Blog .

  5. Latent Space

    Inside-Out AI: Rebuilding Airbnb Behind the Scenes and Across the Guest Experience

    中文摘要

    在领导了Meta的Llama模型之后,阿哈迈德·阿尔-达赫勒现在正在用人工智能改变爱彼迎(Airbnb),从团队开发产品的方式到服务客人的方式都在发生变化。

    英文原文

    After leading Meta’s Llama models, Ahmad Al-Dahle is now transforming Airbnb with AI — from how its teams develop products to how it serves guests.

  6. NVIDIA Blog

    NVIDIA DGX Spark 64GB Gives Developers More Ways to Build and Scale Local AI

    中文摘要

    本地人工智能正变得越来越有用。随着AI代理从实验阶段进入日常开发,功能越来越强大的开源模型正在缩小,以适应更多设备,使开发者能够在本地运行更多内容。本月即将推出,NVIDIA DGX Spark将通过顶级制造商合作伙伴——Acer、……提供64GB的统一内存。

    英文原文

    Local AI is becoming more useful by the token. As AI agents move from experiments into everyday development, increasingly capable open models are shrinking to fit on more devices, giving builders more to run locally. Coming this month, NVIDIA DGX Spark will be available with 64GB of unified memory from top manufacturer partners — Acer, […]

  7. MIT Technology Review · AI

    Don’t be fooled—LLMs don’t reason

    中文摘要

    2016年3月,首尔的一个下午,我看着一个由我参与设计的程序在围棋棋盘的第五行放了一颗棋子,看起来像是送给其人类对手的一份礼物。在五局比赛的第二局中,第37步看起来如此荒谬,以至于一些评论员认为这是一次……

    英文原文

    On an afternoon in Seoul in March 2016, I watched a program I helped build put a stone on the fifth line of a Go board in what looked like a gift to its human opponent. Move 37 in game two of the five-game match looked so absurd that some commentators thought it was a…

  8. Latent Space

    Academia is for Ambition — Alex Zhang, MIT

    中文摘要

    我们首先采访了第一作者Alex Zhang,他是MIT博士,就Jev、博士masxing以及安全带的未来进行了交流。

    英文原文

    We catch up with RLM first author Alex Zhang, MIT PhD, on Jev, PhD masxing, and the future of harnesses.

  9. OpenAI News

    Chatham scales its capital markets expertise with OpenAI

    中文摘要

    Chatham Financial 使用 Codex 和 GPT-5.6 来构建技术和重新设计工作流程,将交易验证时间从 30 分钟减少到不到 4 分钟。

    英文原文

    Chatham Financial uses Codex and GPT-5.6 to build technology and redesign workflows, cutting trade validation from 30 minutes to under 4.

  10. NVIDIA Blog

    How NVIDIA GPUs Help Accelerate OpenAI’s GPT-6 Astra Ultrafast

    中文摘要

    GPT-6 Astra Ultrafast 现已在 OpenAI API 上推出,并可供符合条件的 ChatGPT Work 和 Codex 用户使用。通过 OpenAI 的模型利用 NVIDIA Blackwell 架构的功能,借助推理优化,Ultrafast 的令牌生成速度比 Astra 标准模式快多达 8 倍。对于开发者来说,[…]

    英文原文

    GPT-6 Astra Ultrafast, running on NVIDIA Blackwell GPUs, is available now in the OpenAI API and to eligible ChatGPT Work and Codex users. Accelerated by inference optimizations through OpenAI’s models that tap into the capabilities of the NVIDIA Blackwell architecture, Ultrafast offers up to 8x faster token generation than the Astra Standard mode. For developers, […]

  11. AWS Machine Learning Blog

    Scaling cloud migrations with agentic AI on Amazon Bedrock AgentCore

    中文摘要

    了解AWS专业服务如何利用基于Amazon Bedrock AgentCore的多智能体框架,实现企业级云迁移的端到端自动化。专门构建的AI智能体负责发现、基础设施即代码生成、组合治理以及迁移后操作,将IaC开发时间从数周缩短至数分钟。

    英文原文

    Learn how AWS Professional Services uses a multi-agent framework built on Amazon Bedrock AgentCore to automate enterprise cloud migrations end to end. Purpose-built AI agents handle discovery, infrastructure as code generation, portfolio governance, and post-migration operations, reducing IaC development time from weeks to minutes.

  12. AWS Machine Learning Blog

    Serve live, governed data in AI-built apps with Amazon Quick

    中文摘要

    在 Amazon Quick 中,使用实时数据的应用程序,AI 构建的应用程序会实时查询您受控的 Quick Sight 数据集,而不是静态的、在构建时拍摄的快照。每次查询都会以查看应用程序的人员身份运行,因此行级和列级的安全性会根据每个读者进行应用。了解如何使用自然语言构建、发布和共享实时数据应用程序。

    英文原文

    With Live Data in Apps in Amazon Quick, AI-built apps query your governed Quick Sight datasets in real time instead of static, build-time snapshots. Each query runs as the person viewing the app, so row-level and column-level security apply per reader. Learn how to build, publish, and share a live-data app using natural language.

  13. AWS Machine Learning Blog

    Build agent memory with NVIDIA NeMo Agent Toolkit and Amazon S3 Vectors

    中文摘要

    了解如何在NVIDIA NeMo Agent Toolkit(NAT)中使用Amazon S3向量作为持久化内存层,该工具在Amazon Elastic Kubernetes Service(Amazon EKS)上部署。本文将展示NAT的内存子系统的工作原理,并通过一个多元代理投资研究的用例,说明如何使用Amazon S3向量作为自定义内存提供程序。

    英文原文

    Learn how to use Amazon S3 Vectors as the persistent memory layer within the NVIDIA NeMo Agent Toolkit (NAT), deployed on Amazon Elastic Kubernetes Service (Amazon EKS). This post shows how NAT's memory subsystem works and how to implement Amazon S3 Vectors as a custom memory provider, using a multi-agent investment research use case.

  14. Simon Willison

    pwasm 0.2a0

    中文摘要

    发布:pwasm 0.2a0 pwasm 是我的一个愚蠢项目——一个完全通过 vibe 编码的纯 Python WebAssembly 引擎,我在一月份第一次陷入人工智能狂热期间开发了它。自从一月份以来,我再也没有碰过它,所以我决定让 Claude Opus 5.5 来处理它,看看它是否能做出任何显著的改进:评估 pwasm 当前的状态——然后考虑要让它运行研究仓库中的 MicroPython 和 micro JavaScript 实验需要做些什么——以及要让它提速需要做些什么。在 42 次提交之后(仅需最少的后续提示),它现在几乎可以处理所有的 WASM 规范,而 PyPI 上的 wheel 包含了 MicroPython、QuickJS 和 Micro QuickJS 的工作型 WASM 构建。我完全不信任这个东西——因此打上了 alpha 版本标签——但看到今天的模型能改进 10 个月前模型的工作成果,这很有趣。 标签:python、webassembly、vibe-coding

    英文原文

    Release: pwasm 0.2a0 pwasm is one of my folly projects - an entirely vibe-coded pure Python WebAssembly engine that I built in January during my first bout of AI mania . I hadn't touched it since January, so I decided to let Claude Opus 5.5 loose on it and see if it could make any significant improvements: Evaluate current state of pwasm - then consider what it would take to get the MicroPython and micro JavaScript experiments from the research repo working under it - and what it would take to speed it up 42 commits later (with minimal follow-up prompting) it now handles almost all of the WASM specification, and the wheel from PyPI bundles working WASM builds of MicroPython , QuickJS and Micro QuickJS . I wouldn't trust this thing at all - hence the alpha version tag - but it's interesting seeing how today's models can improve on the work of models from 10 months ago. Tags: python , webassembly , vibe-coding