The 'Beats 100%' LeetCode Solution
Description
This is a two-panel meme featuring the 'Hide the Pain Harold' character, a smiling older man with a pained expression, sitting in front of a laptop. The LeetCode logo is visible in the top left corner. The top panel has the text: 'Solution description: 0ms, 0MB memory, beats 100%, easy understanding, O(n)'. This describes an ideal, highly efficient algorithm. The bottom panel shows Harold with the same strained smile, alongside the text 'THEIR SOLUTION:'. Below this is a small code block containing only the line 'return false;'. The meme satirizes the culture of competitive programming platforms like LeetCode, where users sometimes post misleadingly impressive performance metrics for solutions that are actually just hardcoded to pass the specific test cases of a problem, rather than solving the general algorithmic challenge. The humor comes from the anticlimax and the absurdity of achieving a perfect score with a trivial, non-solution
Comments
9Comment deleted
It's not a hardcoded answer; it's a constant-time, zero-allocation heuristic that correctly predicts the entire test suite will only ever require a falsey value
Sure, it’s O(1) time, O(0) memory, and O(my-god-did-you-even-read-the-prompt) correctness
After 15 years of optimizing distributed systems, I've learned that the fastest code is the code that doesn't run - but I didn't expect LeetCode grinders to take that literally by returning false and somehow beating my carefully crafted O(n log n) solution with proper error handling
When the LeetCode solution claims O(n) time complexity with O(MB) space and 'beats 100%' but the actual implementation is just 'return false;' - a masterclass in technically correct algorithmic analysis where n=0 and the edge case IS the solution. It's the programming equivalent of solving the traveling salesman problem by simply not traveling. Bonus points if this passed all test cases because the problem setter forgot to validate actual logic beyond type signatures
0ms and 0MB is easy when your algorithm is just returning a constant - premature optimization meets perpetual wrongness
Big-O isn't a correctness proof - 'return false' is O(1) and branch-predictor friendly, right up until a hidden test reminds you that wrong answers have infinite latency
O(1)MB memory? Amateurs. Pros ship O(0) lines: return false; - the only solution that scales without tech debt
shouldn't it be O(1) ? Comment deleted
Nah they can't estimate them right, in the end they are the one who write such a descriptions. Comment deleted