I built a framework-free prototype learner that lets local LLMs learn and correct facts instantly (1.6x–4x faster than backprop)[R]

A developer has created a framework-free prototype learner enabling local LLMs to instantly learn and correct facts, achieving speeds 1.6x–4x faster than backpropagation. The system, built using pure NumPy (jayce_tokens.py) and native Java (JayceMemory.java) without PyTorch or TensorFlow, runs entirely offline on consumer hardware with a local Qwen3-4B GGUF. The developer is seeking feedback on this innovative approach.

This prototype learner is notable for its framework-free design, using only NumPy and native Java, unlike most LLM development that relies on PyTorch or TensorFlow.