Rps With My Childhood Friend V100 Scuiid Work 🔔

And that’s the truth of it: some things are better together. Rock Paper Scissors. Childhood friends. Even a V100 and a messy ID system.

print(Counter(results)) # should be near 33% each

Back then, we didn’t know about , Nash equilibrium , or pseudo-random number generators (PRNGs) . We just knew that Alex had a tell: he almost always opened with rock. I countered with paper. He called it "betrayal." I called it "strategy." Part 2: Growing Apart, Then Reconnecting Through Code Life happened. College, jobs, moves. Alex went into AI research; I fell into backend development. We exchanged memes, not emotions. Years passed. rps with my childhood friend v100 scuiid work

import random, time from collections import Counter def rps_result(p1, p2): # 0 = tie, 1 = p1 wins, 2 = p2 wins if p1 == p2: return 0 if (p1, p2) in [(0,2), (1,0), (2,1)]: return 1 return 2 moves = [0,1,2] results = [] for _ in range(1_000_000): a, b = random.choice(moves), random.choice(moves) results.append(rps_result(a,b))

A SCUIID generator typically combines timestamps, machine IDs, and counters to create unique values. But Alex noticed a bias: certain IDs appeared more often in certain time windows. That hinted at poor entropy — i.e., not random enough. And that’s the truth of it: some things

We added a nostalgia feature: every 1 million rounds, the program printed a memory from our actual childhood RPS games. "Round 1,000,000: Alex used scissors to cut my paper – just like 3rd grade art class."

“Still can’t beat me,” he said.

I was intrigued. Not just by the tech, but by the chance to play RPS with my childhood friend again — even if through a terminal. The NVIDIA Tesla V100 is not your everyday GPU. With 640 Tensor Cores, 5120 CUDA cores, and 32GB of HBM2 memory, it’s designed for AI training, molecular simulations, and massive parallel computing. Alex had access to a V100 node through his university lab.