August 8, 2026
Lost, found, and gloriously roasted
Improving Heuristics for A* Pathfinding
Game pathfinding got a clever upgrade, but the comments stole the show
TLDR: The article shows a clever way to help game characters find routes faster by giving the pathfinder better guesses based on map landmarks. Commenters loved the blog, mocked a very unfortunate typo, and crowned “drunken pathfinding” the unexpected comedy winner.
A deep-dive on making game characters move smarter somehow turned into a mini comment-section event, because while the article explains a clever way to help A* pathfinding make fewer wrong turns, readers were just as obsessed with the vibes around it. In simple terms: instead of only making the search tool faster with better data structures, the author shows how to give it better guesses by using pre-picked “landmarks” on the map. That means characters in games can find routes with less wasted searching, especially when walls and obstacles would normally fool a basic distance estimate.
But the real sparkle came from the crowd. One reader hilariously admitted they had accidentally invented “drunken pathfinding” by adding random weights, making game objects wander around like they’d had one too many at the tavern. Another commenter zeroed in on a typo that totally undercut the article’s big dramatic reveal: the node count supposedly drops from 12693 to 12693 — which, yes, is the kind of math fail that instantly summons the pedants. Meanwhile, others went full fan-club mode, calling Red Blob Games “S-tier” and praising the author’s years-long struggle to explain the idea as nothing less than “Outstanding.”
So the hottest reaction wasn’t really a fight over the method itself — it was a mix of adoration, nitpicking, and nerdy comedy. The article says smarter route guesses can seriously speed things up. The comments say: cool, but also, please fix the number, and never delete drunken pathfinding from our hearts.
Key Points
- •The article argues that improving A*'s heuristic can significantly reduce node exploration, not just optimizations to queues or map data structures.
- •Standard distance-based heuristics can mislead A* because they do not account for walls and obstacles.
- •A perfect heuristic can be computed for a fixed goal and wall configuration, but recalculating or storing it for all goals is impractical.
- •The article introduces landmark-based heuristics that reuse precomputed distances to a landmark and derive lower bounds using triangle inequality.
- •Because one landmark only helps for some goal regions, multiple landmarks can be combined by taking the maximum lower bound.