LF Yadda https://validator.w3.org/feed/docs/rss2.html LLMs…RDMS on steroids Dot Product: The Lubricant of Meaning Inside an LLM – Frank Said / GPT Said Whitman, Ginsberg, and Stein weigh in Frank Said / GPT Said – Anything → Tokens → Latent Manifolds → Semantic Geometry How a Vector Becomes More Meaningful Inside an LNN: Hidden-State Enrichment, Pattern Recognition, and Next-Token Sharpening – Frank Said / GPT Said autoregressive morphogenesis – The Organism as an inference engine of Itself – frank said/GPT said Frank Said / Grok Said: Anything → Tokens → Latent Manifolds → Semantic Geometry A synthesis of the Frank Said / GPT Said posts From Embedding Table to Next-Token Choice: How a Token Becomes a Prediction – Frank Said / GPT Said I Am a Newborn Token – (a tokens’ journey through an LLM) When a system compresses vast experience into latent structure, can it ever produce something that was not explicitly there before? – frank said/gpt said INSIDE AN LLM (EXPANDED) inside the llm what if AI is an emerging intelligence that is following a highly abbreviated but similar design trajectory as biology with a central dogma of code to function replacing Darwinian evolution with backpropagation, epigenetics with inference and informational exposure with context windows Is an LLM a Bayesian Machine, or a One-Way Probability Ratchet? – Frank Said / GPT Said Beyond the Simple Feature Detector Story – A Frank-said / GPT-said sequel on individual neurons, Multi-Layer Perceptrons, superposition, and polysemanticity Title: The Weight Landscape and the Living SignalA Frank-said / GPT-said Dialogue on Feature Detectors, Activation Flow, and Why ANN Weights Behave Like Compressed Semantic Circuitry Talking to openai about llms a short story How a Token Becomes Meaning Inside an LLM – A Frank-said / GPT-said dialogue in plain physical cause-and-effect language Inside an LLM: From Prompt to Prediction The Grand Common Denominator – frank said/gpt said MAC, dot product, and matrix multiply – frank said/gpt said Where the Dot Products Actually LiveA third Frank-said / GPT-said sequel on scalar dot products, matrix multiplies, and how each transformer equation expands into MAC counts Inside One Transformer BlockA deeper Frank-said / GPT-said sequel on tensor shapes, attention equations, and the MLP path in plain English Latent Directions Mastery: PCA, SVD, LoRA, Attention-Head Subspaces, Eigenspaces, Distillation, and Long-Context Memory — A Frank-said / Grok-said Dialogue on How Directional Geometry Powers the Next Leap in LLMs Why the Math Load of an LLM Is Bigger Than It First LooksA Frank-said / GPT-said dialogue on matrix shapes, dot products, MACs vs FLOPs, and why MLP cost often dominates attention during prefill How an LLM Transforms the World into Semantic Geometry – gpt 5.4 lecture The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity – lecture The Victorian Supercomputers – lecture The Geometry of Intelligence – A lecture Engineering Spec for a Latent-Space LLM Communication System How an llm transforms the world into semantic geometry Vectors, Tensors, and the Geometry of Thought – A Frank-said / GPT-said dialogue on rank, shape, latent space, and why attention works on tensors instead of isolated vectors mining bitcoin from home utility usage Two Attention Heads, Real Tensor Shapes, One Transformer Block – A deeper Frank-said / GPT-said walkthrough with explicit shapes and a more realistic flow PCA vs LoRA vs SVD vs Attention-Head Subspaces — A Deeper Frank-said / GPT-said Dialogue on How Latent Directions Shape LLMs One Token Through One Transformer Block – A Frank-said / GPT-said numerical walk through with toy values a ranked annotated bibliography – LLM-to-LLM communication in latent space Latent Packets and Machine Telepathy: A Frank-said / GPT-said Dialogue on Semantic Geometry and the Future of AI Communication From Latent Space to Living Mind: A Frank-said / Claude-said Dialogue on Whether Machines Can Mean Anything at All From Twisted Light to Latent Space: A Frank-said / GPT-said Dialogue on the Future of Communication From Meaning to Geometry: A Frank-Said / GPT-Said Dialogue on Embeddings, Weights, and Inference Eigenspace, Attention Heads, LoRA, Distillation, and Long-Context Memory — A Frank-said / GPT-said Dialogue on the Next Stage of LLM Effectiveness Inside One Transformer Block – A deeper Frank-said / GPT-said dialogue on how embedding dimensions, neuron weights, and attention heads interact mathematically Title: How Geometry Becomes Behavior – frank said/gpt said AI – The basic pipeline. Inference Hardware Has Become the Real AI Battlefield LFYadda: site-wide report