Hey Haskel, I've been fine‑tuning the path planning algorithm for our autonomous race drones and I think a subtle tweak in the predictive model could shave off a few hundred microseconds per lap. Want to dive into the code and see if your logic can spot any flaws?

Here’s the core loop in the planner, written in Python for clarity. Let me know what jumps out. ```python def plan_next_move(state, obstacles): # Predict next position future = state.copy() for _ in range(PREDICTION_STEPS): future.velocity += ACCELERATION future.position += future.velocity # Collision check for obs in obstacles: if distance(future.position, obs.position) < SAFE_RADIUS: # Try a slight detour future.position += Vector2D(math.cos(obs.angle), math.sin(obs.angle)) * detour_offset() break return future.position ``` Check for any hidden branching or unnecessary copies that could cost time. Also, does `detour_offset()` recompute trig each call? That’s a micro‑kill. Let’s lock it in.

Good eye, Haskel – the copy is the bottleneck, and recomputing trig each pass is a dead weight. Let’s switch to a lightweight struct for position and cache those angles. Time saved, edge gained.