Note

This repository is a core component of the Aevov AI Technologies ecosystem. For the complete lineage and orchestration hub, visit the Alexandria Hub.

APL Framework

The World's First Hardware-Native Neurosymbolic Language

APL is a groundbreaking programming language designed simultaneously with its target hardware architecture, creating the world's first zero-overhead neurosymbolic computing platform.

✨ Key Features

• 🔮 Quantum Native: Direct quantum operations with hardware-native superposition and entanglement
• 🧠 Neuromorphic: Spiking neural networks and synaptic learning primitives
• 🧬 Genetic Algorithms: Hardware-accelerated evolutionary computation
• 📚 Symbolic AI: Knowledge graphs and logical reasoning engines
• ⚡ Zero Overhead: Language constructs map 1-to-1 to silicon functional units
• 🎯 Dual Syntax: Write in ASCII or native runic characters - both compile to identical bytecode

🚀 Quick Start

Installation

npm install @aevov/apl

Or via CDN:

<script src="https://cdn.aevov.ai/apl/v1.0.0/apl.bundle.js"></script>

Hello World

const APL = require('@aevov/apl');
const apl = new APL();

await apl.run(`
    print("Hello from APL!")
`);

Quantum Superposition (ASCII)

await apl.run(`
    // Create 2-qubit quantum state
    q = Q.super(2)
    
    // Apply Hadamard gate
    Q.gate(q, "hadamard", 0)
    
    // Entangle qubits
    Q.entangle(q, 0, 1)
    
    print("Quantum state created!")
`);

Quantum Superposition (Runic)

await apl.run(`
    // Create 2-qubit quantum state
    q = ᛩ(2)
    
    // Apply Hadamard gate
    ᛜ(q, "hadamard", 0)
    
    // Entangle qubits
    ᙠ(q, 0, 1)
    
    print("Quantum state created!")
`);

📖 Documentation

ASCII Operations Reference

Operation	ASCII	Runic	Hardware Unit	Description
Quantum Superposition	Q.super	ᛩ	QFU	Create quantum superposition
Quantum Gate	Q.gate	ᛜ	QFU	Apply quantum gate
Entanglement	Q.entangle	ᙠ	QFU	Entangle qubits
Quantum Teleport	Q.teleport	ᛪ	QFU	Quantum teleportation
Genetic Crossover	G.cross	ᚴ	GEU	Crossover operation
Fitness Evaluation	G.fitness	ᚠ	GEU	Evaluate fitness
Mutation	G.mutate	ᚥ	GEU	Apply mutation
Neural Network	N.net	ᚾ	NPU	Create neural network
Pattern Match	N.match	ᛈ	NPU	Pattern matching
Synapse	N.synapse	ᛒ	NPU	Tripartite synapse
Hebbian Learning	N.learn	ᚻ	NPU	Learning rule
Consciousness Φ	C.phi	ᚳ	CU	Integrated information
Information Integration	C.integrate	ᛇ	CU	Integrate information
Symbolic Reasoning	S.reason	ᛊ	SRE	Logical reasoning
Knowledge Graph	S.graph	ᛕ	SRE	Graph operations
Oscillator	R.osc	ᛟ	RU	Create oscillator
Resonance Sync	R.sync	ᚱ	RU	Synchronize
Memory Access	M.access	ᛗ	MU	Memory operations
Distribute	D.dist	ᛞ	COORD	Distribute work
Unify	D.unify	ᚢ	COORD	Unify results
Bind	D.bind	ᛂ	COORD	Bind values

Complete Example: Neurosymbolic AI

const APL = require('@aevov/apl');
const apl = new APL();

// Full AI system combining all paradigms
await apl.run(`
    function ai_system(input) {
        // Quantum preprocessing
        q = Q.super(input.size)
        Q.gate(q, "hadamard")
        
        // Neural processing
        net = N.net(1000)
        patterns = N.match(net, q)
        N.learn(net, patterns, 0.01)
        
        // Symbolic reasoning
        knowledge = S.graph(patterns)
        inference = S.reason(knowledge)
        
        // Genetic optimization
        solutions = G.fitness(inference)
        best = evolve(solutions, 100)
        
        // Unify all results
        result = D.unify(patterns, best)
        
        // Measure consciousness
        phi = C.phi(result)
        
        return D.bind(result, phi)
    }
    
    print(ai_system({ size: 100 }))
`);

🎮 Interactive Demo

Check out the interactive playground:

cd examples
open demo.html

Or visit: https://apl.aevov.ai/playground

🏗️ Architecture

Software Layer (Open Source)

• Language Specification: Full APL language grammar and semantics
• Compiler Frontend: Parser, tokenizer, and AST generation
• Classical Optimizer: Traditional compiler optimizations
• Software Simulators: Quantum and neural simulators for development
• Standard Library: Common operations and utilities
• Development Tools: VS Code extension, debugger, profiler

Hardware Layer (Licensed IP)

• .aevQG∞ ISA: Proprietary 5-bit runic instruction encoding
• Quantum Units: Native quantum gate execution
• Neural Cores: Spiking neural hardware
• Hardware Compiler: Native code generation for .aevQG∞
• Performance Optimizations: Secret sauce algorithms

📊 Performance

Workload	Python + PyTorch	APL (Software)	APL (Hardware)
Neural Training	1.0x	10-20x	100-1000x
Quantum Simulation	1.0x	5-10x	50-500x
Genetic Algorithm	1.0x	15-30x	200-2000x
Symbolic Reasoning	1.0x	8-15x	100-800x

🔒 Licensing

Open Source (Apache 2.0)

• APL language specification
• Compiler (frontend + classical optimizations)
• Software simulators
• Development tools
• Standard library

Proprietary (Licensed)

• .aevQG∞ hardware ISA
• Quantum/neural hardware implementations
• Hardware compiler backend
• Performance-critical optimizations

Result: Learn APL for free, license hardware for production performance.

🛠️ Development

Building from Source

git clone https://github.com/aevov/apl.git
cd apl
npm install
npm run build

Running Tests

npm test

Creating Custom Operations

const apl = new APL();

// Register native function
apl.registerNative('myFunction', (arg1, arg2) => {
    return arg1 + arg2;
});

// Use in code
await apl.run(`
    result = myFunction(10, 20)
    print(result)  // 30
`);

🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas We Need Help

• Additional language examples
• VS Code syntax highlighting
• Standard library functions
• Documentation improvements
• Test coverage
• Performance benchmarks

🗺️ Roadmap

v1.1 (Q1 2025)

• VS Code extension
• Debugger integration
• Package manager (apl-pkg)
• More standard library functions

v1.5 (Q2 2025)

• JIT compilation
• WebAssembly backend
• Browser-based IDE
• Hardware emulator

v2.0 (Q3 2025)

• .aevQG∞ hardware launch
• Cloud API access
• Production-ready tooling
• Enterprise support

📚 Resources

• Official Site: https://apl.aevov.ai
• Documentation: https://docs.apl.aevov.ai
• GitHub: https://github.com/aevov/apl
• Discord: https://discord.gg/apl
• Hardware Licensing: hardware@aevov.ai

💬 Community

• Discord: Real-time chat and support
• GitHub Discussions: Long-form technical discussions
• Stack Overflow: Tag questions with apl-lang
• Twitter: @AevovAI

🙏 Acknowledgments

Built on decades of research in:

• Quantum computing (Shor, Grover, Aaronson)
• Neuromorphic engineering (Carver Mead, Kwabena Boahen)
• Genetic algorithms (John Holland, David Goldberg)
• Integrated Information Theory (Giulio Tononi)
• Neurosymbolic AI (Gary Marcus, Yoshua Bengio)

📄 License

• Language & Compiler: Apache License 2.0
• Hardware IP: Proprietary - Contact for licensing

See LICENSE for details.

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