Advanced Acceleration Logistics Technology

import time
import inspect

def accelerant_optimizer(func):
    """
    A decorator that monitors code efficiency and 
    triggers a 'growth' event if thresholds aren't met.
    """
    def wrapper(*args, **kwargs):
        # 1. Measure starting performance
        start_time = time.perf_counter()
        
        # 2. Execute the current technology logic
        result = func(*args, **kwargs)
        
        end_time = time.perf_counter()
        duration = end_time - start_time
        
        # 3. Efficiency Threshold (Example: 0.5 seconds)
        if duration > 0.5:
            print(f"--- [LATENCY ALERT] ---")
            print(f"Function: {func.__name__} took {duration:.4f}s")
            print(f"ACTION: Triggering AI-Refactor to optimize logic...")
            
            # Get the raw source code to send to an optimizer
            source = inspect.getsource(func)
            
            # In a live system, 'source' would be sent to an LLM API 
            # to return a more efficient version of the same logic.
            optimize_logic(source)
        else:
            print(f"Performance Optimal: {duration:.4f}s")
            
        return result
    return wrapper

def optimize_logic(code_snippet):
    """Placeholder for an AI-driven code rewriting engine."""
    # Logic: Analyze Big O notation -> Rewrite -> Unit Test -> Deploy
    pass

# --- Example Usage ---
@accelerant_optimizer
def heavy_computation_task(data_size):
    # Simulating a slow, inefficient process (Nested Loop)
    total = 0
    for i in range(data_size):
        for j in range(data_size):
            total += (i * j)
    return total

# Running the code
if __name__ == "__main__":
    print("Starting tech-growth simulation...")
    heavy_computation_task(5000)