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...

Contextual AI is joining the Elastic AI Ecosystem to make its state-of-the-art models available to developers through the Elasticsearch open inference API.

By Dao Mi, Pablo Delgado, Ryan Berti, Amanuel Kahsay, Obi-Ike Nwoke, Christopher Thrailkill, and Patricio GarzaAt Netflix, data engineering has always been a critical function to enable the...

本文探讨了提示工程在Semantic Kernel中的应用，强调其对AI输出质量的影响。通过Few-Shot和Chain-of-Thought技巧，开发者可以优化提示，从而提升AI在.NET项目中的表现。文章提供了C#示例，展示了这些方法在实际项目中的实现，最终提高系统的准确性和效率。

Feature engineering is one of the most important steps when it comes to building effective machine learning models, and this is no less important when dealing with time-series data.

In time series analysis and forecasting , transforming data is often necessary to uncover underlying patterns, stabilize properties like variance, and improve the performance of predictive models.