m4urin/quantized-liquid-state-machines

This project is a low-traction research prototype, likely stemming from academic work or a thesis, as evidenced by its age (nearly 5 years) and minimal engagement (16 stars, 1 fork). While Liquid State Machines (LSMs) and reservoir computing are relevant for low-power edge AI and neuromorphic hardware (like Intel's Loihi), this specific implementation has effectively zero velocity. The defensibility is minimal; the core logic of quantizing a reservoir is a standard optimization rather than a proprietary moat. In the current landscape, it is overshadowed by more robust spiking neural network (SNN) frameworks like snnTorch, Nengo, or Rockpool, which offer significantly better support, hardware backends, and community momentum. Frontier labs are unlikely to compete here as they prioritize massive-scale transformer architectures over niche neuromorphic temporal processing. The primary risk is simple obsolescence—there are better-maintained libraries that implement more advanced quantization-aware training for SNNs and reservoirs.