Rx.NET 7.0 has been released with a narrowly focused change aimed at reducing deployment size for Windows applications. The new version separates WPF, Windows Forms, UWP, and Windows Runtime...

Running AI agents on Kubernetes raises a key question: should each agent get its own Pod? The kagent project argues no—agents are bursty, short-lived, can spawn subagents, and may wait for human...

Cloudflare has recently introduced temporary accounts that let AI agents deploy Cloudflare Workers immediately, without first creating or authenticating with a permanent account. If left...

Cloudflare released the Cloudflare One stack, an open-source library of agent skills for planning, deploying, and managing Zero Trust environments. The skills include automated migration logic for...

本文介绍了在阿里云轻量服务器上部署Paper Hot的步骤，包括安装必要的软件包、克隆项目、创建虚拟环境、配置systemd服务和Nginx，最后提供验证和常用操作的命令。

Microsoft has released Aspire 13.3, introducing a new aspire destroy command for tearing down deployments across Azure, Kubernetes, and Compose. The release adds native Kubernetes deployment in...

GitHub has introduced a new approach to improving deployment safety by leveraging eBPF, enabling the company to detect and prevent hidden circular dependencies that could block recovery during...

Anthropic introduces Managed Agents on Claude, a managed execution layer for agent-based workflows. It separates agent logic from runtime concerns like orchestration, sandboxing, state management,...

The business impact of AI is clear: faster response times, higher customer satisfaction, reduced operational costs, and data-driven insights that leaders can act on with confidence.

“AI agents” and “RAG” dominate slides but not always production. To move from proof-of-concept to real value, organizations need a shared vocabulary and a practical stack: OAGI as the north star,...

本研究探讨了NeoMedSys平台在放射学中优化VIOLA-AI颅内出血模型的有效性。通过迭代改进，该模型的分类敏感性和特异性显著提升，为临床提供了重要的实时反馈机制。

本研究针对物联网环境中的视觉异常检测，提出了高效处理策略和数据压缩技术，显著提升了检测效果，为工业应用提供了新方案。

本研究揭示了大型语言模型（LLM）在实际部署中的安全隐患，发现320,102个公开LLM服务普遍采用不安全的协议和配置，安全风险严重，亟需加强安全措施。

本研究首次提出“全栈”安全概念，系统探讨大型语言模型（LLM）在训练、部署和商业化过程中的安全问题，并通过回顾800多篇文献，为数据生成、安全对齐和模型编辑等研究提供指导。