🚀 Now Available

AI-First Programming Language Built for Performance

Name: AquaLua
Rating: 4.8 (150 reviews)
Author: Abdullah Zeyad Alshaikh

AquaLua combines Python-like syntax with Rust-like type safety and a high-performance C backend. Built specifically for AI and machine learning development with 2-5x speedup over pure Python.

👀 See Examples 💻 GitHub

2-5x Faster

35+ Examples

Zero Dependencies

Created by Abdullah Zeyad Alshaikh

hello.aq

fn main() {
    print("Hello, AquaLua!")
    
    // Variables and types
    let name: string = "AquaLua"
    let version: float = 1.0
    const MAX_ITERATIONS = 1000
    
    // AI/ML operations
    tensor weights = random([10, 5])
    tensor input_data = ones([1, 10])
    tensor result = matmul(weights, input_data)
    tensor output = relu(result)
    
    // Control flow
    if output.sum() > 0 {
        print("Positive activation detected!")
    }
    
    print("AI computation complete!")
}

Why Choose AquaLua?

Designed from the ground up for AI development with modern language features

⚡

High Performance

C runtime backend delivers 2-5x speedup over pure Python with automatic fallback

🧠

AI/ML First

Built-in tensor operations, neural networks, and ML primitives out of the box

🐍

Python Integration

Seamless interop with Python libraries and ecosystem using ast_exec

🎨

Modern Syntax

Clean, readable syntax inspired by Python and Rust with type safety

💻

Professional IDE

Full-featured development environment with syntax highlighting and debugging

📦

Zero Dependencies

Precompiled executables require no additional installations or setup

See AquaLua in Action

From simple scripts to complex AI models

basic.aq

// Variables and functions
let name = "AquaLua"
let age = 25
const MAX_SIZE = 1000

fn greet(name: string) -> string {
    return "Hello, " + name + "!"
}

fn main() {
    let user = "Developer"
    let message = greet(user)
    print(message)
    
    // Control flow
    if age >= 18 {
        print("Adult user")
    } else {
        print("Young user")
    }
    
    // Loops
    for i in range(0, 5) {
        print("Count: " + i)
    }
}

neural_network.aq

fn main() {
    // Create tensors
    tensor weights = random([10, 5])
    tensor biases = zeros([5])
    tensor input_data = ones([1, 10])
    
    // Tensor operations
    tensor result = matmul(weights, input_data)
    tensor output = add(result, biases)
    
    // Activation functions
    tensor activated = relu(output)
    tensor probabilities = softmax(activated)
    
    // Create neural network layers
    layer dense1 = dense(input_size=784, output_size=128, activation="relu")
    layer dense2 = dense(input_size=128, output_size=10, activation="softmax")
    
    // Forward pass
    tensor hidden = dense1.forward(input_data)
    tensor predictions = dense2.forward(hidden)
    
    print("AI computation complete!")
    print("Predictions shape: " + predictions.shape)
}

python_integration.aq

fn main() {
    print("AquaLua + Python Integration")
    
    // Execute Python code for data analysis
    ast_exec("
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Create sample data
data = np.random.randn(1000)
df = pd.DataFrame({'values': data})

# Statistical analysis
print(f'Mean: {df.values.mean():.2f}')
print(f'Std: {df.values.std():.2f}')

# Visualization
plt.figure(figsize=(10, 6))
plt.hist(data, bins=50, alpha=0.7, color='blue')
plt.title('Data Distribution')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.grid(True, alpha=0.3)
plt.show()
    ")
    
    // Continue with AquaLua operations
    let result = sum([1, 2, 3, 4, 5])
    print("AquaLua sum result: " + result)
    
    print("Integration complete!")
}

Documentation & Resources

📚

Language Syntax

Complete syntax reference and language guide

🔧

API Reference

Built-in functions and operations reference

🏗️

Architecture

System design and internal components

💡

Examples

Code examples and tutorials

AI-First Programming Language Built for Performance

Why Choose AquaLua?

High Performance

AI/ML First

Python Integration

Modern Syntax

Professional IDE

Zero Dependencies

Benchmark Results

Matrix Multiplication

Tensor Operations

Neural Network Forward

See AquaLua in Action

Documentation & Resources

Language Syntax

API Reference

Architecture

Examples

Ready to Get Started?

AquaLua v1.0.0