Show notes
David and Thomas dive into the inner workings of the Transformer architecture behind modern Large Language Models. They break down foundational concepts including vectors, matrices, function approximations, artificial neural networks, and backpropagation. Supported by a live Python code demonstration, they walk through tokenization, word embeddings, sinusoidal positional encodings, and Query-Key-Value attention dynamics. The episode wraps up with a discussion on scaling context windows, KV caching, and the philosophical debate around AI consciousness versus human…
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