一位开发者发布1-bit WebGPU运行时，可在浏览器运行1.7B大模型
一位开发者推出名为aidekin的项目，基于1-bit WebGPU运行时，使1.7B参数的大语言模型完全在浏览器端运行。用户无需后端服务或API密钥，仅需一个script标签即可集成AI助手。文本聊天模型首次下载约290MB，语音模型约1.6GB，缓存后支持离线使用。所有推理与检索均在用户设备上执行，数据不离开浏览器，避免了云端计费和隐私泄露风险。项目采用MIT开源协议，并支持本地RAG（检索增强生成），可基于用户提供的知识文件回答问题。该方案为AI部署提供了低成本、高隐私的新选择。 #WebGPU #大模型 #AI #开源 #隐私保护 #本地推理 #aidekin #LLM #1bit
一位开发者推出名为aidekin的项目，基于1-bit WebGPU运行时，使1.7B参数的大语言模型完全在浏览器端运行。用户无需后端服务或API密钥，仅需一个script标签即可集成AI助手。文本聊天模型首次下载约290MB，语音模型约1.6GB，缓存后支持离线使用。所有推理与检索均在用户设备上执行，数据不离开浏览器，避免了云端计费和隐私泄露风险。项目采用MIT开源协议，并支持本地RAG（检索增强生成），可基于用户提供的知识文件回答问题。该方案为AI部署提供了低成本、高隐私的新选择。 #WebGPU #大模型 #AI #开源 #隐私保护 #本地推理 #aidekin #LLM #1bit
Anthropic Claude Cowork登陆云端与移动端，超九成会话非编程用途
Anthropic公布数据显示，2026年5月11日至5月31日期间，超60万个组织的120万个匿名会话中，90%以上与软件开发无关，其中业务流程与运营占比33.4%，内容创作与文案占16.4%。新版Claude Cowork支持云上持续运行与跨设备协同，用户可在关闭电脑后继续执行任务，移动端用于接收通知和审批。Anthropic强调桌面端仍保留完整的本地文件及浏览器权限，并将双倍使用限时延长至8月5日。该数据打破了AI代理主要用于编程的刻板印象，凸显企业级自动化办公的潜力。Anthropic正从单一模型提供商向全场景AI工作流平台转型。 #Anthropic #ClaudeCowork #AI代理 #企业自动化 #办公自动化 #人工智能 #生成式AI #云端 #移动端 #数据分析
Anthropic公布数据显示，2026年5月11日至5月31日期间，超60万个组织的120万个匿名会话中，90%以上与软件开发无关，其中业务流程与运营占比33.4%，内容创作与文案占16.4%。新版Claude Cowork支持云上持续运行与跨设备协同，用户可在关闭电脑后继续执行任务，移动端用于接收通知和审批。Anthropic强调桌面端仍保留完整的本地文件及浏览器权限，并将双倍使用限时延长至8月5日。该数据打破了AI代理主要用于编程的刻板印象，凸显企业级自动化办公的潜力。Anthropic正从单一模型提供商向全场景AI工作流平台转型。 #Anthropic #ClaudeCowork #AI代理 #企业自动化 #办公自动化 #人工智能 #生成式AI #云端 #移动端 #数据分析
Anthropic 推出 Claude Cowork 云端版，90% 会话为非编程用途
据 ZDNET 报道，Anthropic 宣布将其 AI 代理助手 Claude Cowork 扩展至 Web 和移动端，正式推出云端版本。数据显示，该平台目前 90% 的会话并非用于编程任务，而是覆盖文档处理、数据分析、客户支持等非编程场景。此次云端化使得用户无需安装本地环境即可使用，进一步降低使用门槛。分析认为，Claude Cowork 正从专业开发者工具向通用企业级应用转型，应用前景更加广泛。 #Anthropic #Claude #AI助手 #云端版 #企业级应用 #科技新闻 #人工智能 #非编程用途
据 ZDNET 报道，Anthropic 宣布将其 AI 代理助手 Claude Cowork 扩展至 Web 和移动端，正式推出云端版本。数据显示，该平台目前 90% 的会话并非用于编程任务，而是覆盖文档处理、数据分析、客户支持等非编程场景。此次云端化使得用户无需安装本地环境即可使用，进一步降低使用门槛。分析认为，Claude Cowork 正从专业开发者工具向通用企业级应用转型，应用前景更加广泛。 #Anthropic #Claude #AI助手 #云端版 #企业级应用 #科技新闻 #人工智能 #非编程用途
Amazon Bedrock AgentCore harness 发布，无服务器图像编辑代理开发更简单
亚马逊云科技推出基于 Bedrock AgentCore harness 的无服务器图像编辑代理方案。用户上传图片后，可用自然语言描述编辑需求，代理基于 Claude Sonnet 4.6 模型分解指令并调用 Stability AI 的多个图像编辑工具完成操作，还能通过微虚拟机执行水印添加等命令。该方案利用 AgentCore harness 自动处理编排循环、工具路由和内存管理等底层逻辑，开发者仅需通过 API 配置代理参数即可，无需编写自定义编排代码。整个方案包含身份认证、加密存储、三种编辑工具及 React 前端，可通过 AWS CDK 一键部署，极大降低了构建 AI 代理的门槛。 #AWS #Bedrock #AgentCore #无服务器 #AI #图像编辑 #自然语言处理 #云计算 #大模型
亚马逊云科技推出基于 Bedrock AgentCore harness 的无服务器图像编辑代理方案。用户上传图片后，可用自然语言描述编辑需求，代理基于 Claude Sonnet 4.6 模型分解指令并调用 Stability AI 的多个图像编辑工具完成操作，还能通过微虚拟机执行水印添加等命令。该方案利用 AgentCore harness 自动处理编排循环、工具路由和内存管理等底层逻辑，开发者仅需通过 API 配置代理参数即可，无需编写自定义编排代码。整个方案包含身份认证、加密存储、三种编辑工具及 React 前端，可通过 AWS CDK 一键部署，极大降低了构建 AI 代理的门槛。 #AWS #Bedrock #AgentCore #无服务器 #AI #图像编辑 #自然语言处理 #云计算 #大模型
亚马逊SageMaker AI与MLflow发布模型监控架构，应对数据漂移
在生产环境中部署的机器学习模型，其准确性和有效性会因消费者行为变化、新产品发布、传感器技术升级等不可控因素而迅速下降，导致数据漂移和模型漂移。为帮助组织主动监控模型性能，亚马逊云科技介绍了一种基于开源Evidently库和Amazon SageMaker AI with MLflow的模型监控架构。该方案适用于分类和回归等判别式模型，可计算数据漂移和模型漂移，并将结果整合到自定义仪表盘、通过Slack等渠道发送警报，甚至触发自动模型重训练流水线。架构支持批处理和实时推理两种场景，实时端点需启用数据捕获，并可使用AWS Lambda函数按计划或触发运行监控代码。通过这一开源可定制的监控方式，组织能在模型精度下降前及时干预，降低负面影响，同时控制成本并灵活集成到现有观测流水线中。 #亚马逊云科技 #SageMaker #MLflow #模型监控 #数据漂移 #机器学习 #AI #AWS #开源
在生产环境中部署的机器学习模型，其准确性和有效性会因消费者行为变化、新产品发布、传感器技术升级等不可控因素而迅速下降，导致数据漂移和模型漂移。为帮助组织主动监控模型性能，亚马逊云科技介绍了一种基于开源Evidently库和Amazon SageMaker AI with MLflow的模型监控架构。该方案适用于分类和回归等判别式模型，可计算数据漂移和模型漂移，并将结果整合到自定义仪表盘、通过Slack等渠道发送警报，甚至触发自动模型重训练流水线。架构支持批处理和实时推理两种场景，实时端点需启用数据捕获，并可使用AWS Lambda函数按计划或触发运行监控代码。通过这一开源可定制的监控方式，组织能在模型精度下降前及时干预，降低负面影响，同时控制成本并灵活集成到现有观测流水线中。 #亚马逊云科技 #SageMaker #MLflow #模型监控 #数据漂移 #机器学习 #AI #AWS #开源
How AWS Finance teams reclaimed hundreds of hours with Amazon Quick
Every finance professional knows the drill. Monday morning arrives, and your Financial Planning and Analysis (FP&A) team disappears into data compilation. They pull numbers from multiple systems, reconcile sources, build charts, and write commentary. All to answer a question that should be straightforward: what happened with revenue last week, and why? Across AWS Finance, teams were spending hundreds of hours a month on exactly this kind of work. Not analysis. Not strategy. Getting the data ready so the real work could begin. Amazon Quick is a generative AI assistant that connects across all your enterprise data and applications, so business users can search, analyze, and take action through natural language. It handles the complexity of querying millions of rows, running advanced analytics, and automating re
Every finance professional knows the drill. Monday morning arrives, and your Financial Planning and Analysis (FP&A) team disappears into data compilation. They pull numbers from multiple systems, reconcile sources, build charts, and write commentary. All to answer a question that should be straightforward: what happened with revenue last week, and why? Across AWS Finance, teams were spending hundreds of hours a month on exactly this kind of work. Not analysis. Not strategy. Getting the data ready so the real work could begin. Amazon Quick is a generative AI assistant that connects across all your enterprise data and applications, so business users can search, analyze, and take action through natural language. It handles the complexity of querying millions of rows, running advanced analytics, and automating re
curring workflows so your team doesn’t need to. In this post, we show how AWS Finance used chat agents and Flows in Quick to transform two of their most time-consuming workflows. Setting financial targets for strategic customers requires reconciling bottom-up forecasts from business teams with top-down projections from leadership. It also demands enough depth to catch the risks hiding beneath historical data. The team built an Amazon Quick chat agent that connects directly to enterprise data sources and delivers sophisticated insights through natural language conversation. The agent queries millions of rows across Amazon Redshift data tables instantly while also searching external data signals. Screenshot showing Quick presenting a scenario analysis and creating a 5-sheet Microsoft Excel worksheet. Here’s what changed: Before: Analysts could deep-dive roughly a third of strategic customers in the time available between bottoms-up inputs and when top-level targets are due. The rest got surface-level coverage. A single customer analysis consumed up to 6 hours of manual work, including extracting data, running models, and documenting findings. After: The Quick agent evaluates statistical forecasts, runs regression analysis, Monte Carlo simulations, and performs scenario modeling across multiple factors in approximately 10 minutes per customer. It surfaces risks and opportunities that manual analysis missed. The team now covers their entire customer portfolio with even greater depth than before. “We have expanded from deep-diving a third of our strategic customers to covering our entire portfolio. Our finance team now spends time on what matters: partnering with the business to drive revenue, not compiling data or writing complex queries.” What makes this work: An analyst asks a question in natural language: “Run an opportunity and risk assessment for our top strategic accounts.” Quick then queries millions of rows, runs advanced analytics, and synthesizes structured data with unstructured insights from field reports and pipeline data. The agent does bull versus bear analysis by reviewing accounts with upside potential based on contract renewal timing and pipeline strength, and flags accounts with risk exposure. These are insights that traditional models missed. Because there’s no coding barrier, every finance professional on the team becomes a data analyst. Teams customize agents for different regions or business units, and the insights refresh automatically. If target setting is a periodic deep dive, regular business reviews are the recurring ritual that occupies FP&A teams everywhere. At AWS, every week, insights on revenue performance need to be compiled, analyzed, and packaged for leadership. And every week, that preparation consumes an entire Monday. The same AWS Finance team solved this by deploying Amazon Quick chat agents specific to each geographic region, connected through Flows to automate workflows that run on a set cadence without manual intervention. Video showing a blank Revenue Performance Analysis Flow that helps automate weekly business review workflows. Here’s what changed: Before: Every Monday, FP&A analysts spent a full morning compiling data from multiple systems, analyzing trends, manually reaching out to sales leads for customer anecdotes, and preparing talk tracks so leaders could understand what happened with revenue and why. The process was manual, repetitive, and left little time for strategic work. After: