@hackproduct9: A min-heap is just a machine that always keeps the smallest thing on top. 🫧 Say you've got K sorted lists and need one merged sorted list. The simplest move: dump every value into one min-heap, then keep pulling the top until it's empty. The heap does all the comparing for you — you never scan for the minimum yourself. The trick in this version: each value is pushed as a tuple (val, i, j) — the number plus which list it came from and its position. That i, j isn't decoration. It breaks ties so equal values never collide, and it keeps each list's original order intact. The animation shows every value leaving its list (struck off) and dropping into the heap as a live binary tree — then the heap drains smallest-first into the output. Push everything, pop in order. O(N log N). When an AI assistant writes this, check one thing fast: is it pushing a comparable tuple, or just (val, node)? The second one crashes the moment two values match — and models make that exact mistake constantly. Read the heap key before you trust the output. Which should we visualize next — Top-K Frequent or Binary Search? #HackProduct #codevisuals #coding #visuals #algorithms

HackProduct

Open In TikTok:

Region: US

Saturday 20 June 2026 16:50:06 GMT