cueai-architect 0.1.1

Modular AI Architecture Components based on the Collective Unified Equation (CUE) Framework

CUE-AI Architect

Modular AI Architecture Components based on the Collective Unified Equation (CUE) Framework

Overview

The CUE-AI Architect is a comprehensive Python package that implements 100 modular AI architecture components based on the revolutionary Collective Unified Equation (CUE) Framework by Karl Farah Ambrosius. This framework provides a geometric approach to consciousness-matter unification, offering novel solutions to quantum measurement problems and geometric interpretations of consciousness compatible with known physics.

Key Features

🧠 Consciousness Integration: Native support for consciousness as a geometric structure (DΨ fiber bundle) ⚛️ Quantum-Consciousness Interface: Advanced quantum measurement coupling and decoherence modeling 🌊 RG Flow Dynamics: Complete renormalization group flow implementation with fixed point analysis 📐 Geometric Deep Learning: Fiber bundle neural networks and curvature-aware architectures 🔬 Experimental Interfaces: Direct connections to quantum optics and gravitational wave experiments 🏗️ Modular Design: 100 independent, composable modules across 14 specialized categories

Installation

```bash

Install from PyPI

pip install cueai-architect

Install with experimental dependencies

pip install cueai-architect[experimental]

Install for development

pip install cueai-architect[dev] ```

Quick Start

import cueai_architect as cue

# Initialize the CUE framework
framework = cue.CUEApplicationManager()

# Create consciousness-coupled neural architecture
model = cue.ConsciousnessTransformer(
    consciousness_dim=128,
    fiber_bundle_layers=6,
    rg_flow_enabled=True
)

# Simulate consciousness-matter coupling
simulator = cue.ConsciousnessFieldSimulator()
results = simulator.run_coherence_simulation(
    duration=1000,
    consciousness_coupling=0.1
)

# Analyze RG flow dynamics
rg_analyzer = cue.RGFlowIntegrator()
fixed_points = rg_analyzer.find_critical_points()