Unless you're coding or stress-testing benchmarks, the "latest and greatest" usually won't change how you use AI.

The life of Pi.

LFM2.5-230M proves that while 3-billion-parameter models like VibeThinker are solving advanced calculus, a ...

I was excited by the Radxa Cubie A5E's impressive hardware, but the lack of OS options and old, problematic software made it ...

The M5Stack CardputerZero is a tiny, portable computer that’s about the size of a small stack of business cards. But despite its compact size, it’s a full-fledged PC with a display, keyboard, and ...

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TinyLlama delivered the strongest responsiveness on the Pi, making it the most usable option for lightweight local inference. DeepSeek-R1 produced richer reasoning output but incurred much longer ...

If you’ve tinkered with Raspberry Pi boards for a while, you probably know the default choice: Raspberry Pi OS. It’s lightweight, optimised, and works flawlessly for most DIY projects. But what if you ...

Running AI models locally can reveal surprising insights about cost, performance and usability. In her latest explainer, Joyce Lin examines how the DeepSeek R1, a 1.5-billion-parameter ...

The Pi 500+ led most benchmark categories, including single-core, multi-core, compression, graphics, and networking. Compared with the Pi 400, newer BCM2712-based models delivered roughly double CPU ...

Equipped with a quad-core CPU and up to 8GB or 16GB RAM, it can handle computer vision workloads using OpenCV, TensorFlow Lite, or lightweight YOLO models. For applications that demand higher speed ...