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By projecting input manifolds into a high-dimensional, high-entropy \"Galactic\" space ($d \\gg 784$), we demonstrate that complex features can be untangled without the thermodynamic cost of backpropagation. Utilizing the Moore-Penrose pseudoinverse to solve for the output layer in a single step, VoodooNet achieves a classification accuracy of \\textbf{98.10\\% on MNIST} and \\textbf{86.63\\% on Fashion-MNIST}. Notably, our results on Fashion-MNIST surpass a 10-epoch SGD baseline (84.41\\%) while reducing the training time by orders of magnitude. We observe a near-logarithmic scaling law between dimensionality and accuracy, suggesting that performance is a function of \"Galactic\" volume rather than iterative refinement. 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