Recursion is like telling a story step by step—it’s clear and easy to understand. But when the story gets too long, it starts repeating itself, and that’s where DP comes in to save the day! The key? Spot when your recursion is solving the same problem over and over. That’s called overlapping subproblems, and DP helps you store those results so you don’t waste time recalculating them. Examples: - Fibonacci: Naive recursion recalculates the same numbers, but DP stores them for reuse. - 0/1 Knapsack: Recursion explores all possibilities, but DP optimizes by remembering past choices. - Longest Increasing Subsequence (LIS): Recursion checks every path, but DP keeps track of the best one. Pro Tip: If your recursion feels slow or you notice it’s solving the same thing repeatedly, it’s time to switch to DP! Let’s Discuss: - What was your “aha!” moment when you realized recursion could become DP? - Do you prefer memoization (top-down) or tabulation (bottom-up)? Why? - Share a problem where DP turned a slow solution into a fast one! Hot take: “All DP problems start as recursion problems.” Do you agree? Let’s chat! 👇

Ever looked at a grid or matrix and thought, “This feels like a graph problem”? You’re not wrong! Grids are just graphs in disguise—each cell is a node, and movements (like up, down, left, right) are the edges connecting them. Even fancier moves, like diagonal traversal, or weighted cells (think terrain costs in games) are just edge weights in graph terms. Once you see grids this way, problems like finding the shortest path or counting connected regions become way easier to tackle! Examples: - Shortest path in a binary maze (BFS) - Counting islands (DFS) - Rotting oranges problem Pro Tip: If the problem involves "movement directions" (up/down/left/right) or "connected cells", it’s likely a graph in disguise! Let’s Discuss: - What matrix problem shocked you as a graph problem? - BFS or DFS—which do you default to for grids? - Share a matrix puzzle that graphs simplified! Quick poll: Are you team BFS or team DFS for grids? Let’s hear it! 👇

Linked lists are everywhere in coding interviews—and for good reason! They’re perfect for testing how well you understand pointers and handle edge cases. Plus, they’re a great way to see if you can think step by step without getting lost in the links. Whether it’s reversing a list, detecting cycles, or designing something like an LRU Cache, linked lists are a goldmine for interview questions. If you’re struggling, check out this awesome resource that breaks down 90% of linked list problems into types: LeetCode Linked List Guide. Examples: - Reversing a list in-place - Detecting cycles (Floyd’s Algorithm) - Designing an LRU Cache Pro Tip: If the problem involves "constant-time insertions/deletions" or "pointer manipulation", think linked lists! Let’s Discuss: - What’s your favorite linked list problem? - Which interview question made you rethink pointers? - Share a time when dummy nodes saved your code! Quick question: Do you prefer drawing diagrams for linked lists or just keeping it all in your head? Let’s hear your approach! 👇

When you need to compute cumulative sums or optimize repeated calculations, think about Prefix/suffix arrays. Examples: - Calculating range sums in O(1) time - Solving "Product of Array Except Self" - Finding longest palindromic substrings Pro Tip: Look for keywords like "range query", "cumulative", or "without using division"—these often hint at prefix/suffix tricks. Think of it like pre-baking calculations to avoid repetitive work! Let’s Discuss: - What’s a problem where prefix/suffix arrays surprised you? - Do you prefer prefix or suffix arrays for specific scenarios? - Share a tricky problem that became trivial with these arrays! How do you spot when to use prefix/suffix arrays? Drop your strategies below!

Tries are often overlooked, but they're incredible for prefix-based operations and autocomplete features. Some real-world uses: - IP routing tables - Autocomplete suggestions - Spell checkers Here's what clicked for me: When you're dealing with strings and need to check prefixes frequently, tries can turn O(n*m) operations into O(m). It's like having a tree-shaped dictionary where each path spells out a word! Let's discuss: - What's your favorite trie application? - How do you handle trie memory optimization? - Share a problem where trie outperformed other data structures What's your approach to trie node design?