AI 每日快讯

AI 每日快讯

AI 产品、模型、开源工具和官方动态的时间流。保留历史记录,按分类、日期和标签继续筛选。

2442历史快讯
140开源工具
7当前结果
06 月 17 日 2026-06-17 快讯

AWS Machine Learning 动态:Get back hours every day with autonomous agents in Amazon Quick

原文摘要:Today, Quick gets even more powerful: new autonomous agents that work continuously on your behalf, an activity feed that helps you prioritize your most important work, and the abil 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Amazon SageMaker AI Async Inference now supports inline request payloads

原文摘要:Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpoint 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:Context intelligence for your data and AI agents at scale

原文摘要:Agents are only as intelligent as the context they can reason over. Today, that context is scattered across data lakes, data warehouses, lakehouses, databases, and streams, and in 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

AWS Machine Learning 动态:New in Amazon Bedrock AgentCore: Build agents with broader knowledge and continuous learning

原文摘要:Today we're introducing new capabilities on Amazon Bedrock AgentCore, the platform to build, connect, and optimize agents. In this post, we cover how these capabilities close each 来源:AWS Machine Learning 动态。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

InfoQ AI ML Data Engineering:Presentation: From Hype to Strong Foundations: What the Rise, Fall and Resurgence of Agents …

原文摘要:Aditya Kumarakrishnan explains how to move past the "amnesia phase" of AI. He shares a blueprint for engineering leaders to build modular agent frameworks using CoALA, leverage dec 来源:InfoQ AI ML Data Engineering。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。

InfoQ AI ML Data Engineering:GitHub Copilot Desktop App Targets Parallel Agentic 工作流

原文摘要:GitHub has introduced the GitHub Copilot app, a desktop control centre for agent-native development that aims to keep engineers in charge while AI agents handle more coding work. M 来源:InfoQ AI ML Data Engineering。建议继续查看原文,重点核对它影响的工具入口、成本、风险和真实使用场景。