Working through Karpathy's series builds a foundational understanding of LLMs, providing enough confidence to explore further. A key insight is that LLMs are logit emitters, and their inherent uncertainty compounds dangerously in multi-agent chains, often requiring a human-in-the-loop or a single orchestrator to manage it. Crucially, people confuse word embeddings with the full LLM; embeddings are just the input to a vast, incomprehensible trillion-parameter transformer. The underlying math of these networks is surprisingly simple, built on basic additions and multiplications. The real mystery isn't the math but why they work so well. Ultimately, AI research is a mix of minimal math, extensive data engineering, massive compute power, and significant trial and error.