Friends, welcome back to Arc Tutorials. In this episode of my AI Masterclass Series (Part 11), I break down a super important beginner concept: model parameters. If you’re anywhere near training, retraining, reinforcement learning, or even just trying to understand why one model “feels smarter” than another, this is a topic you can’t miss. I explain parameters as the “knobs” an AI learns during training—basically the patterns and behaviors the model picks up, and what ultimately controls the knowledge and outputs you get at inference time. I also share my simple “digital intern” analogy: fewer parameters means a smaller intern who only knows a few patterns and gives simpler answers. More parameters means a bigger intern—more brain capacity—so the model can capture complex patterns, reason better, and handle nuanced questions more elegantly. From a developer point of view, I tie it to model size: more parameters often means better generalization, but the trade-off is higher compute and memory requirements (think tiny JS function vs a large framework). And yes—you can still run smaller open-source models locally, like Llama, if you want practical setups on your own machine.