@hackproduct9: Everyone memorizes "Quick Sort is O(n log n)." Almost nobody can feel why it beats Bubble Sort. So I put all four on the same shuffled bars and let them run. Same data. Four methods. Watch the work pile up differently: 🫧 Bubble Sort — like the biggest kid drifting to the back of the line. Compare neighbours, swap, repeat. Dead simple. 45 operations. 🃏 Insertion Sort — like sorting cards in your hand: slide each one into place. Brilliant when data's nearly sorted. 56 operations. ⚡ Quick Sort — like splitting a group around one person, again and again. Divide and conquer. 17 operations. 🔗 Merge Sort — like merging two sorted card piles into one. Always O(n log n), and stable. 44 operations. Same input. Quick Sort did it in 17 ops; Insertion took 56. That gap IS the difference between O(n log n) and O(n²) — not a formula on a slide, but real work you can see. Here's the part that matters as we move toward AI engineering: a model will happily generate any of these and tell you it "sorts the array." The skill isn't writing the sort — Python's sorted() already does it (Timsort, O(n log n)). The skill is reading a solution and knowing which one it is, what it costs, and when O(n²) will quietly melt down at scale. Intuition first. Big-O second. Then the notation finally sticks. Which one should I visualize next — Heap Sort, or Timsort itself? #HackProduct #codevisuals #coding #visuals #algorithms

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Monday 29 June 2026 04:37:30 GMT