Hey everyone,

I’ve been working on a new AI architecture called Vortex, which is a wave-based, phase-synchronization-driven alternative to traditional transformer models like GPT. Unlike transformers, which require massive computational power, Vortex runs efficiently on low-end hardware (Intel i3, 4GB RAM) while maintaining strong AI capabilities.

How Does Vortex Work?

Vortex is based on a completely different principle compared to transformers. Instead of using multi-head attention layers, it uses:

The Vortex Wave Equation:

A quantum-inspired model that governs how information propagates through phase synchronization.

Equation:

This allows efficient real-time learning and adaptive memory updates.

AhamovNet (Adaptive Neural Network Core):

A lightweight neural network designed to learn using momentum-based updates.

Uses wave interference instead of attention mechanisms to focus on relevant data dynamically.

BrainMemory (Dynamic Memory Management):

A self-organizing memory system that compresses, prioritizes, and retrieves information adaptively.

Unlike transformers, it doesn’t store redundant data, meaning it runs with minimal memory overhead.

Resonance Optimization:

Uses wave-based processing to synchronize learned information and reduce computational load.

This makes learning more efficient than traditional backpropagation.