Lede: A preprint unveils the Universal Constraint Engine, a neuromorphic computing approach that generates emergent behaviors from declarative constraints without neural networks or training. The UCE comprises a Rule Definition Layer, Constraint Solver Layer, Emergent Behavior Engine, and Embodiment Mapper capable of targeting FPGA, spintronic, and quantum hardware, as described in the primary source (https://zenodo.org/records/19600206). This goes beyond conventional neuromorphic systems like IBM TrueNorth, which in 2016 demonstrated a 1 million neuron chip based on spiking neural models but still within the NN framework (Merolla et al., IEEE 2014). What much coverage misses is the complete absence of gradient descent and learned weights, instead using conserved quantities to produce logic, memory, and oscillation from minimal rules. Synthesizing this with Carver Mead's early neuromorphic work emphasizing physical computation (Mead, Proceedings of the IEEE, 1990) and recent spintronics advances for low-power devices (Nature Electronics, 2022), the UCE points to post-NN architectures. Mainstream efforts have focused on optimizing neural accelerators, overlooking how constraint engines might achieve superior efficiency by directly mapping to physical substrates without simulation overhead. This paradigm demonstrates SR latches, biological oscillators, and write-gated memory cells from minimal rule sets (https://zenodo.org/records/19600206), suggesting a broader shift in AI hardware toward symbolic emergent systems, a pattern seen in select physics-based computing explorations but largely ignored in favor of deep learning scalability.