本文探讨了“复利工程”理念，强调在AI辅助开发中，开发过程应视为闭环系统，重视知识积累而非技术债务。通过规划、执行、审查和沉淀四个步骤，工程师角色转变为系统架构师，从而提升软件开发效率。

Our most specialized reasoning mode is now updated to solve modern science, research and engineering challenges.

You have mastered model.

If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define...

30 techniques from conventional system engineering to supercharge AI Engineering

By Nick HuangA key feature of deep agents is their access to a set of filesystem tools. Deep agents can use these tools to read, write, edit, list, and search for files in their filesystem.In this...

Our Taipei office will be Google’s largest AI infrastructure hardware engineering center outside of the U.S.

A decade of cloud native evolution, a look ahead to AI-Native Platform engineering, technical layers, and key changes. KubeCon NA 2025 signals a new era.

A comparison of TRAE SOLO and VS Code (Copilot, Agent HQ) via the AI Engineering Entity framework, focusing on automation, collaboration, model transparency, and engineering roles.

A comparison of TRAE SOLO and VS Code (Copilot, Agent HQ) via the AI Engineering Entity framework, focusing on automation, collaboration, model transparency, and engineering roles.

Everything Simplicity is Awesome! I would have never guessed that coaching my kid’s LEGO League team would remind me of everything I ever learned about engineering simplicity for startups. But...