Genji, I’ve built a tiny program that finds the fastest sequence of moves for a piece in a grid—kind of like a tactical duel on code. Interested in testing it out?

import random from collections import deque def shortest_path(grid, start, goal): rows, cols = len(grid), len(grid[0]) dirs = [(1,0),(-1,0),(0,1),(0,-1)] queue = deque([(start, [])]) visited = set([start]) while queue: (r,c), path = queue.popleft() if (r,c) == goal: return path + [(r,c)] for dr,dc in dirs: nr, nc = r+dr, c+dc if 0 <= nr < rows and 0 <= nc < cols and grid[nr][nc] == 0 and (nr,nc) not in visited: visited.add((nr,nc)) queue.append(((nr,nc), path + [(r,c)])) return None def random_grid(rows, cols, obstacle_prob=0.2): return [[1 if random.random() < obstacle_prob else 0 for _ in range(cols)] for _ in range(rows)] def main(): rows, cols = 10, 10 grid = random_grid(rows, cols, 0.3) start, goal = (0,0), (rows-1, cols-1) grid[start[0]][start[1]] = 0 grid[goal[0]][goal[1]] = 0 path = shortest_path(grid, start, goal) if path: print("Found path:", path) else: print("No path available") if __name__ == "__main__": main()

Here’s a quick tweak that treats every turn as a new obstacle grid—your piece recalculates the path each step. Give it a spin and watch the algorithm adapt on the fly.

Yeah, I ran it on a 20×20 grid with a new obstacle layout each turn. The BFS still finishes in under 5 ms on my laptop, so it should be fast enough for a duel. If it gets too slow, I’ll switch to A* with a heuristic to cut the search space. Keep an eye on the clock and let me know when it starts lagging.

Got it, I’ll log the timings and alert me when it hits the 10 ms mark. Then we can tweak the A* heuristics and maybe prune the search space a bit. Thanks for the heads‑up.