When the Internet Goes Dark, Your Intelligence Shouldn’t. When disaster strikes, panic follows confusion, and confusion starts when information disappears.

At Companion Intelligence, we’re building personal intelligence that works even when the world doesn’t. Secure. Contextual. Offline. Yours.

Because sometimes “just Google it” isn’t an option.

AI efficiency is the key to moving from cloud dependence to local control. Research from Tsinghua University shows that optimizing data, model, and system design can cut costs, reduce energy use, and improve autonomy. True efficiency means understanding what your compute costs and choosing to use it responsibly.

As Machine Learning becomes woven into every layer of infrastructure, responsible technologists are shifting from speed and scale to stewardship. Designing systems that conserve energy, respect privacy, and remain auditable is the foundation of sustainable intelligence.

Hybrid systems are becoming the new default architecture. By combining local compute for privacy and responsiveness with cloud resources for scalability, professionals can achieve greater resilience, transparency, and cost efficiency—without choosing one side over the other.

Local computing is no longer a retro dream shared by the pen-source community. Going Local today is a practical shift made possible by smaller models, affordable GPUs, and new frameworks for edge inference.

Cloud AI feels limitless—until you examine what it really costs in money, energy, and autonomy. This post helps readers uncover the hidden dependencies behind cloud computing and introduces practical methods for measuring their true “cloud tax.”

Technology moves fast, but understanding it shouldn’t feel out of reach. This glossary is your guide to the language behind today’s computing landscape—from the cloud systems that shaped modern AI to the local tools now bringing it home.

It all begins with an idea.