Nevaarize

Native JIT Performance, Zero Dependencies

A modern programming language that compiles directly to x86-64 machine code. Designed for AI engineering, model serving, and high-performance computing.

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⚡

TRUE JIT Compilation

Compiles Nevaarize code directly to x86-64 machine code at runtime. Achieves 500M+ operations per second.

🎯

Zero Dependencies

Built entirely with C++23. No LLVM, no external libraries. Just pure, self-contained performance.

🧠

AI-Ready

50+ SIMD-accelerated AI functions, neural network training, and model deployment with CLI tools.

📦

Module System

Clean import system with stdlib support for math, time, IO, and file-based module imports.

🔄

Async/Await

First-class async support for concurrent operations without callback hell.

🎨

Clean Syntax

Python-inspired syntax that's easy to read and write. No semicolons, no curly bracket debates.

A Taste of Nevaarize

// Hello, Nevaarize!
print("Hello, World!")

// Define a function
func fibonacci(n) {
    if (n <= 1) {
        return n
    }
    return fibonacci(n - 1) + fibonacci(n - 2)
}

// Use it
for (i in Range(1, 10)) {
    print("fib(", i, ") =", fibonacci(i))
}

Async Operations

import stdlib time as t

async func fetchData(url) {
    print("Fetching:", url)
    t.sleep(100)  // Simulate network delay
    return "Data from " + url
}

// Fetch multiple resources
result1 = await fetchData("api/users")
result2 = await fetchData("api/posts")

print("Results:", result1, result2)

Native JIT Performance

// Benchmark: 500M+ operations per second
iterations = 100000000

result = nativeSumLoop(iterations)
sum = result[0]
opsPerSec = result[1]

print("Sum:", sum)
print("Performance:", int(opsPerSec / 1000000), "M ops/sec")

Performance

Benchmark	Performance	Notes
Native JIT Loop	505M ops/sec	Pure x86-64 machine code
SIMD Sum Loop	4B+ ops/sec	AVX2 vectorization
Interpreter	3.5M ops/sec	Tree-walk evaluation
Matrix Multiply	50+ GFLOPS	Cache-blocked SIMD