Google has announced the Google Colab CLI, a command-line tool that allows developers and AI agents to interact with remote Colab runtimes directly from a local terminal. By Daniel Dominguez

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents...

The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows how, to...

Discord has detailed how it rebuilt its database operations around a new internal orchestration framework called the Scylla Control Plane (SCP), enabling its small infrastructure team to automate...

Anthropic has introduced a new feature called Routines for Claude Code, allowing developers to configure automated coding workflows that run on schedules, through API calls, or in response to...

The rise of AI coding agents has transformed how we think about developer tools. Large language models like Claude, GPT, and others are remarkably effective at using command-line...

Agentic AI is increasingly a part of shopping, but not all transactions will be automated in the same way. Here’s what agents will handle—and the situations that will call for human involvement.

As hardware and software advance, general-purpose robots may evolve from a niche curiosity to an integral part of the workplace.

New analysis helps quantify the impact of technological disruption and shows how consumer companies can turn automation’s potential into value.

本研究提出了多智能体框架MLZero，旨在解决现有自动机器学习系统在处理多模态数据时需要大量手动配置的问题。MLZero通过大型语言模型实现端到端自动化，显著减少人类干预，并在多个基准测试中表现优异，展示了其在机器学习自动化领域的重要潜力。

本研究提出了Mobile-Agent-V框架，通过视频指导提高移动自动化的操作效率，性能提升36%。

EcoAgent框架通过云端与边缘智能体的协作，解决了云端移动智能体在高延迟和高成本方面的问题。研究表明，该框架提高了移动自动化效率，确保了高任务成功率，并显著减少了大语言模型的令牌消耗。

本研究提出Zad系统，通过外部资源丰富公共漏洞数据库中简短描述的问题，提高漏洞信息的质量和实用性。

本研究提出了一种离线仿真框架，结合大语言模型和公开脚本指南，降低了软件脚本自动化的编程门槛。实验结果表明，该框架提高了自动化成功率，优化了响应时间和运行成本，展示了利用执行反馈生成技能的潜力。

本研究提出了一种三元人类与人工智能协作框架，旨在解决自动驾驶中人类与AI的实时协作问题。框架定义了三种AI角色（顾问、共同驾驶者和守护者），能够动态适应人类需求，为人机协作策略的发展奠定基础。