What if attention didn’t need matrix multiplication?

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I built a cognitive architecture where all computation reduces to three bit operations: XOR, MAJ, POPCNT. No GEMM. No GPU. No floating-point weights. The core idea: transformer attention is a similarity computation. Float32 cosine computes it with 24,576 FLOPs. Binary Spatter Codes compute the same geometric measurement with 128 bit operations. Measured: 192x fewer ops, 32x less memory, ~480x faster. 26 modules in 1237 lines of C. One file. Any hardware: cc -O2 -o creation_os creation_os_v2.c...