@hackproduct9: 🧠 Save this one. The difference between grinding 300 problems and actually getting good isn't more problems — it's pattern recognition. Here's the honest take most prep advice won't tell you: interviewers aren't testing whether you've seen the problem. They're testing whether you can look at a fresh one and know which tool to reach for in the first 60 seconds. That's a decision, not a memory. 🎯 So instead of memorizing solutions, memorize the fork in the road 👇 📦 Array / String — "fast lookup?" → HashMap · "sorted?" → Binary Search · "contiguous subarray?" → Sliding Window · "top-K?" → Heap · "pair from both ends?" → Two Pointers 🔗 Linked List — "cycle or middle?" → Fast & Slow · "reverse a part?" → In-place Reversal · "merge sorted?" → Min-Heap 🌳 Tree — "level by level?" → BFS · "path / diameter?" → DFS · "lowest common ancestor?" → DFS + parent track · "validate BST?" → Inorder 🕸️ Graph — "shortest unweighted?" → BFS · "weighted, no negatives?" → Dijkstra · "negative weights?" → Bellman-Ford · "dependency order?" → Topological Sort ♟️ DP — "overlapping subproblems?" → Memoization → Tabulation · "local choice = optimal?" → Greedy (prove it!) ❓ Other — "prefix match?" → Trie · "dynamic connectivity?" → Union-Find · "median of a stream?" → Two Heaps Still stuck? Brute-force it, then optimize. That's a valid first move, not a failure. 💪 And here's the part that matters even after interviews: this is the exact skill that lets you read AI-generated code. When an assistant spits out a solution, pattern recognition is what tells you whether it reached for the right approach — or just confidently wrote O(n²) when a heap would do. Recognize the pattern, judge the code. 🧩 💬 Which branch trips you up most — Graph or DP? Pattern recognition > code writing. #HackProduct #codevisuals #coding #visuals #algorithms
