First Principles of
Synthetic Intelligence.

When I was 13, scavenging primarily in the Rural Wisconsin dump—after being born in Minnesota and moving to Wisconsin at age 11—I didn't just see a discarded PC tower; I saw a nervous system. The copper traces were the neurons; the capacitors were the synapses holding the charge. I realized early on that if you understand the flow of electricity (the "In"), you can predict the behavior of the system (the "Out").

Today, looking at a Large Language Model (LLM), I see the same thing. The "Magic" of ChatGPT isn't magic at all; it is a trillion-parameter probability engine. It is a biological imperative simulated in silicon. As someone with high-functioning autism, I find comfort in this determinism. The model doesn't "think" like a human; it "predicts" like a storm system. It is mathematical, architectural, and—most importantly—learnable.

In this hub, we are going to dismantle the black box. We will look at Tokens not as words, but as atoms of meaning. We will dissect Weights & Biases to understand how a model "decides" that "King - Man + Woman = Queen." We will walk through the History of AI, from Turing's initial questions to the Transformer architecture that changed everything in 2017.

As a follower of Jesus Christ, I believe that truth is foundational. You cannot build a sovereign future on a foundation of ignorance. If you don't know how the machine learns, you will be a slave to its hallucinations. But if you understand the "Ins and Outs" of the architecture, you become the architect.

Start here. Before you prompt, before you code, before you automate—understand the soil from which this intelligence grows.