[R] I built a cognitive architecture that learns like a brain — no backprop, no GPU, no forgetting

Most AI systems are built on three assumptions: you need backpropagation, you need GPUs, and you need to carefully prevent catastrophic forgetting. I wanted to see what happens if you throw all three out.

RAVANA is a research prototype that:

• Learns through prediction errors — like Friston's free energy principle, the system feels "pressure" when predictions fail and self-organizes to reduce it
• Never forgets — a biologically-inspired sleep cycle (SWS for consolidation + REM for creative recombination) eliminated catastrophic forgetting entirely in our tests
• Runs on CPU — pure NumPy, works on a laptop
• Has emotions — a 3D Valence-Arousal-Dominance engine modulates how the system learns and infers
• Learns continuously from the web — curiosity-driven exploration, no retraining needed
• Supports multi-user beliefs — a BeliefStore tracks who believes what and merges across users

I'm at the stage where I need community feedback, discussion, and contributors. The codebase is substantial (~25k lines across 3 packages) with 1250+ tests and published on PyPI.

This is not a product — it's a research project exploring whether pressure-driven self-organization can work as a genuine alternative to gradient-based learning. Would love to hear thoughts from this community.

Code: https://codeberg.org/oxiverse/ravana | https://github.com/oxiverse-ecosystem/ravana