950-line open-source project tries mimicking AI using only a 1906 Markov formula

This project takes a probability-prediction formula devised by Markov in 1906 (originally used to predict the next letter in a sequence) and tests whether the exact same formula, unchanged, can handle byte prediction, word prediction, decision-making, causal reasoning, reinforcement learning (Q-learning), hierarchical planning, selective attention using four signals, persistent memory via SQLite, self-modifying parameters, and automatic generation of new modules. Called MCR, the whole system is just 950 lines of code and is light enough to run on an ordinary laptop. Its core argument is that intelligence may depend less on model size and more on how many levels a single formula can be layered across.