LF Yadda https://validator.w3.org/feed/docs/rss2.html Shannon Builds the World, Boltzmann Walks Through It The Weight of Almost: A Frank Said Claude Said Dialogue on Softmax, Cross-Entropy, and the Geometry of Meaning From Exponentials to Meaning: A Frank Said Grok Said Dialogue on Softmax, Cross-Entropy, and Semantic Geometry From Exponentials to Meaning: A Frank Said / GPT Said Dialogue on Softmax, Cross-Entropy, and Semantic Geometry From One Strange Gate to a Whole Scientific Calculator The Bug in the Cave: Plato, Gödel, Hackers, and the LLM as Catalyst Bitcoin After Shor: A Frank Said / GPT Said Conversation on Quantum-Safe Bitcoin Transactions Without Softforks When the Ledger Starts Thinking Like a Thief: A Frank Said / GPT Said Conversation About Claude Mythos, Blockchain, and the Future of Crypto Security LLMs…RDMS on steroids When the Locksmith Learns to Pick Locks: A Frank Said / GPT Said Conversation About Claude Mythos Preview I KNOW I AM I KNOW Whitman, Ginsberg, and Stein weigh in A synthesis of the Frank Said / GPT Said posts Sculpting a Shared Mind: How Millions of Tiny Nudges Forge One Coherent Intelligence – A Frank & Grok Dialogue How Millions of Tiny Nudges Become One Mind of Response Frank Said / GPT Said: One Planet, Infinite Space, and the Odds of Becoming Alive autoregressive morphogenesis – The Organism as an inference engine of Itself – frank said/GPT said Frank Said / GPT Said – Anything → Tokens → Latent Manifolds → Semantic Geometry When a system compresses vast experience into latent structure, can it ever produce something that was not explicitly there before? – frank said/gpt said 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 The Grand Common Denominator – frank said/gpt said Frank Said / Grok 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 From Embedding Table to Next-Token Choice: How a Token Becomes a Prediction – Frank Said / GPT Said Buying Meaning with Heat – a frank said gpt said conversation Dot Product: The Lubricant of Meaning Inside an LLM – Frank Said / GPT Said Is an LLM a Bayesian Machine, or a One-Way Probability Ratchet? – Frank Said / GPT Said I Am a Newborn Token – (a tokens’ journey through an LLM) INSIDE AN LLM (EXPANDED) 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 inside the llm 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 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 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 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 Two Attention Heads, Real Tensor Shapes, One Transformer Block – A deeper Frank-said / GPT-said walkthrough with explicit shapes and a more realistic flow One Token Through One Transformer Block – A Frank-said / GPT-said numerical walk through with toy values Inside One Transformer Block – A deeper Frank-said / GPT-said dialogue on how embedding dimensions, neuron weights, and attention heads interact mathematically mining bitcoin from home utility usage 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