Large Language Models (LLMs ) are everywhere, but how exactly do they work under the hood? [Miguel Grinberg] provides a great explanation of the inner workings of LLMs in simple (but not simplistic) terms that eschews the low-level mathematics of how they work in favor of laying bare what it is they do.
At their heart, LLMs are prediction machines that work on tokens (small groups of letters and punctuation) and are as a result capable of great feats of human-seeming communication. Most technical-minded people understand that LLMs have no idea what they are saying, and this peek at their inner workings will make that abundantly clear.
Be sure to also review an illustrated guide to how image-generating AIs work. And if a peek under the hood of LLMs left you hungry for more low-level details, check out our coverage of training a GPT-2 LLM using pure C code.
The Tandberg unit is beautifully finished in wood and metal, a style of construction that’s fairly rare these days. It’s got big, chunky controls, and a certain level of heft that is out of vogue in modern electronics. Heavy used to mean good — these days, it means old. That’s not to say it’s indestructible, though. It’s full of lots of old plastic pulleys and fasteners that have aged over the decades, so it’s a little fragile inside.
BreadboardOS is built on top of FreeRTOS. It’s aim is to enable quick prototyping with the Pi Pico. Don’t confuse operating system with a graphical environment — BreadboardOS is command-line based. You’d typically interface with it via a serial terminal emulator, but joy of joys, it does support color!
