Category: Uncategorized
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Beyond the Simple Feature Detector Story – A Frank-said / GPT-said sequel on individual neurons, Multi-Layer Perceptrons, superposition, and polysemanticity
Frank-said:So far we have been saying that the Multi-Layer Perceptron, or MLP, behaves like compressed semantic circuitry and that its feature detectors fire when the incoming hidden state matches certain learned patterns. That feels right. But I also suspect it is too clean. Real neural networks are probably messier than the phrase “feature detector” makes…
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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
The model does not merely contain knowledge. It contains learned ways of transforming patterns into behavior. Frank-said: You said something important a moment ago: the weights are the stored pattern structure, and the matrix math is the way the current input interacts with that stored structure during inference. I want to push that much further.…
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MAC, dot product, and matrix multiply – frank said/gpt said
Frank-said: I keep seeing these three terms together in LLM discussions: MAC, dot product, and matrix multiply. They seem related, but they are not the same thing. Define them cleanly. GPT-said: Good. The cleanest way to understand them is as a hierarchy of scale. A MAC is the smallest arithmetic work unit.A dot product is…
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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
Frank-said:Redo it, but make it sharper. I want the focus to be brutally clear: where exactly do the dot products live inside the equations of one transformer block? I want to see the difference between a scalar dot product and a matrix multiply. I want to see how a matrix multiply is really a giant…
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Inside One Transformer BlockA deeper Frank-said / GPT-said sequel on tensor shapes, attention equations, and the MLP path in plain English
Frank-said:All right, let’s go one level deeper. I do not just want the broad story anymore. I want to walk inside a single transformer block and actually look at the tensor shapes as the prompt flows through it. I want to see the algebra, but I also want it translated into plain English so the…
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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
Frank-said:When people talk about an LLM doing “dot products,” I think the phrase sounds deceptively simple. It makes the process sound like the model is just doing one kind of little comparison over and over again. But the more I look at it, the more I think that hides the real picture. A dot product…