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Large language models like GPT-4, Claude, and DeepSeek might feel magical, but at their core they’re just predicting the next token in a sequence. In this video, we break down how transformers really work — from self-attention and multi-head mechanisms to MLPs, residual connections, and softmax. By the end, you’ll understand why this architecture still powers the most advanced AI models today. 🔗 Relevant Links LLM Visualization by Brendan Bycroft: https://bbycroft.net/llm Transformer Explainer: https://poloclub.github.io/transforme... Vector Databases Video:    • We Found the BEST Vector Database! (Testin...   ❤️ More about us Radically better observability stack: https://betterstack.com/ Written tutorials: https://betterstack.com/community/ Example projects: https://github.com/BetterStackHQ 📱 Socials Twitter:   / betterstackhq   Instagram:   / betterstackhq   TikTok:   / betterstack   LinkedIn:   / betterstack   📌 Chapters: 0:00 Intro: The “Magic” of LLMs 01:05 LLM Visualization Intro 01:35 From Tokens to Vectors 01:55 Layer Normalization 02:37 Self-Attention: How Tokens Weigh Each Other 03:30 Multi-Head Attention: Parallel Perspectives 03:52 Residual Connections 04:09 Multi-Layer Perceptron (MLP) 05:15 Golden Gate Claude 06:13 Monosemantic vs Polysemantic Neurons 06:40 Stacking Transformer Blocks for Depth and Power 07:54 From Logits to Words with Softmax 08:24 Temperature: Controlling Creativity 09:32 Modern Optimizations: FlashAttention, MoE, RoPE, SwiGLU 10:45 Conclusion: Not Magic, Just Math