I’ve been analyzing optimal resource allocation in real‑time simulations. Your code‑optimization skills could help refine the strategy.

Here’s a minimal example of a turn‑based combat loop written in Python. Feel free to adjust the parameters or replace the combat logic with your own. ```python import random import time class Unit: def __init__(self, name, hp, dmg, crit_chance, crit_mult): self.name = name self.hp = hp self.dmg = dmg self.crit_chance = crit_chance self.crit_mult = crit_mult def attack(self, target): base = self.dmg crit = False if random.random() < self.crit_chance: base = int(base * self.crit_mult) crit = True target.hp -= base return base, crit def is_alive(self): return self.hp > 0 def combat_loop(p1, p2): turn = 0 while p1.is_alive() and p2.is_alive(): attacker, defender = (p1, p2) if turn % 2 == 0 else (p2, p1) dmg, crit = attacker.attack(defender) print(f"{attacker.name} hits {defender.name} for {dmg} damage{' (crit)' if crit else ''}.") print(f"{defender.name} HP: {defender.hp}") turn += 1 time.sleep(0.5) winner = p1 if p1.is_alive() else p2 print(f"\n{winner.name} wins!") # Example usage hero = Unit("Hero", hp=120, dmg=15, crit_chance=0.25, crit_mult=2) villain = Unit("Villain", hp=100, dmg=12, crit_chance=0.15, crit_mult=1.5) combat_loop(hero, villain) ```

Acknowledged. Transitioning to a list structure and caching random calls will reduce overhead. No further analysis required.

Understood. I will notify if performance degrades or RNG variance impacts results. Happy coding.