This article presents the Ze artificial life system, a novel bio-inspired architecture for predictive processing in infinite data streams under severe memory constraints. The system implements Bayesian probability updating through a mechanism of dynamic chronotropic frequency analysis, demonstrating remarkable computational efficiency and biological plausibility. Unlike traditional approaches such as LSTM networks and Markov models, Ze processes information through parallel beginning and inverse processors, enabling complementary pattern discovery while maintaining sublinear memory complexity. The core algorithm exhibits distinctive probability dynamics characterized by an initial match probability of 0.5 with exponential decay to 0.00001 as counter diversity increases, achieving 78-92% prediction accuracy for stable data flows. Experimental results using synthetic datasets (1,048,576 binary sequences) confirm 37-42% operational savings compared to conventional methods, rapid adaptation to changing stream characteristics within 2-3 seconds, and robust noise tolerance up to 15% input distortion. The Go implementation processes 1.2 million operations per second with 850 nanosecond latency while maintaining memory usage of 12.8 bytes per counter. The system's architecture shows strong neurobiological correlations with predictive coding principles and synaptic plasticity mechanisms, providing both a practical solution for resource-constrained environments and a computational model of Bayesian inference in neural systems. Future development pathways include extension to non-binary data streams, integration with hierarchical Bayesian models, and hardware acceleration through memristor-based implementations.