One-layer MLPs with hand-coded weights memorized labels for two-token sequences with 90% accuracy. Memory capacity scales linearly with parameter count, mirroring the behavior of trained models. However, the AI Alignment Forum report notes a performance gap in the scaling prefactor. This experiment provides a baseline for understanding how weights store specific facts.