Why memorizing DSA patterns is not enough

After solving 300 DSA problems, here’s what I really learned 👇 🧠 1. Arrays are easy… until they’re not. The moment someone says “rotate,” “merge,” or “two pointers,” my brain quietly disconnects. 😅 2. Debugging builds character. That feeling when you realize the issue wasn’t logic — it was a missing “=” sign. Pain. Growth. Acceptance. ☕ 3. Dynamic Programming is basically trauma with recursion. You think you understand it… until the next problem politely reminds you that you don’t. 🐍 4. LeetCode is like a gym. You skip one day, and suddenly “Easy” problems feel like “Final Boss” mode. 💡 5. The real flex isn’t 250 problems — it’s surviving them. Jokes aside, DSA taught me more than algorithms — it taught me how to think, fail, and keep going. Now onto the next 250… but maybe after a nap. 😴 #DSA #Coding #ProgrammerHumor #LearningJourney #Consistency

The Right Way to Solve a DSA Problem! I’ve been solving at least one DSA problem on LeetCode almost every day for more than a year... hardly a day goes by when I don’t. And during this time, I realized there’s one common mistake that almost everyone (including me) makes. You start solving a problem. You get stuck. You check the first hint... then the second... then the discussion section. Still can’t figure it out. Finally, you open the solution and boom, it looks so simple. You copy it, submit it, feel great for a moment… and move on. But here’s the problem a month later, you see the same question again and get stuck in the exact same place. You vaguely remember the approach but can’t recall the logic. You open the solution again, try to memorize it again… and the same cycle repeats. Welcome to the DSA Trap... where you think you understood the problem, but can’t solve it on your own. 💡 How to Break Out of it! Start by reading the problem statement properly... understand what’s being asked and what the expected output is. Then, give it time. Think through multiple approaches and always begin with the brute force method. It builds confidence and ensures you’ve understood the problem correctly. Write pseudocode before you code. Pseudocode helps you visualize the logic and exposes the pattern behind the solution. If you still can’t solve it, look at others’ solutions, but don’t just memorize. Understand why the solution works. If it’s still unclear, watch a tutorial (I personally recommend this youtube channel @codestorywithMIK). After you understand the concept, immediately solve 2-3 similar problems. This is how you lock the pattern into your brain. One more practical tip: keep an Excel/Notion sheet or a notebook of problems you couldn’t solve on the first try. Revisit it weekly. Once you can solve a problem on your own, remove it. Over time, the list shrinks and your confidence grows. ✨ Hope this adds some value to your DSA journey! #DSA #ProblemSolving #Coding #LeetCode #Learning #Consistency #Programming

Solving DSA with loops, sure... then dont say you really understand DSA. I was solving a DSA problem today— and one thing kept bothering me so I looked up a simple solution in youtube. Everywhere I looked, people used loops, which is sensible since it's easy to implement and undertand. But then I found myself writing multiple long loops.. and lines kept on increasing so much so that i lost focus... But once I started thinking recursively, everything clicked. Suddenly, problems that felt complex — trees, graphs, backtracking — started looking… easy and the code well that was suddenly a tree compared to mountains Less code. More logic. Pure clarity. Sure, recursion gets the blame for stack overflows and confusion but once you get the base case right, it’s pure beauty in motion. So here’s my take: Loops show you how to repeat. Recursion teaches you why repetition works. Now I’m curious — Do you still prefer loops, or are you brave enough to think recursively? #DSA #Programming #Recursion #Developers #Coding #SoftwareEngineering

💭 “𝐈 𝐰𝐢𝐬𝐡 𝐈 𝐡𝐚𝐝 𝐤𝐧𝐨𝐰𝐧 𝐭𝐡𝐢𝐬 𝐛𝐞𝐟𝐨𝐫𝐞 𝐬𝐭𝐚𝐫𝐭𝐢𝐧𝐠 𝐦𝐲 𝐃𝐒𝐀 𝐣𝐨𝐮𝐫𝐧𝐞𝐲…” When I began, I thought DSA was just about solving hundreds of problems. But here’s what experience (and a lot of failed submissions 😅) taught me 👇 🧩 It’s not about speed, it’s about structure. Rushing through problems doesn’t help — but understanding why a solution works builds lasting skill. 📚 Patterns > Problems. Once I started recognizing problem patterns — sliding window, two pointers, binary search — everything began to make sense. 🧠 Logic building is slow — and that’s okay. You can’t “memorize” logic; you develop it by failing, fixing, and rethinking repeatedly. 🚀 Dry running is underrated. Before writing code, walking through an example by hand made my errors vanish before they ever appeared in the compiler. 🎯 DSA isn’t about knowing 100 algorithms — it’s about mastering 10 deeply and applying them confidently. 👉 I’m still improving, but one thing’s for sure — DSA isn’t a race. It’s a mirror that shows how you think, plan, and solve. 👇 What’s one DSA concept that took you the longest to truly understand? #DSA #CodingJourney #ProblemSolving #JavaDeveloper #LearningInPublic #CodingCommunity #GrowthMindset #TechCareers

"Exploration on the Use of Dynamic Programming in DSA" Over the past few Days, I’ve been exploring Dynamic Programming (DP), and it has been one of the most insightful parts of my DSA journey so far. At first, DP seemed quite intimidating — with all its overlapping subproblems, recursive relations, and the idea of storing intermediate results. But once I started understanding the why behind it, things began to make sense. It’s fascinating how a small optimization in approach can make such a big difference in performance. While solving problems like Fibonacci, 0/1 Knapsack, and Longest Common Subsequence, I realized that DP is not just a concept, it’s a way of thinking. It pushes you to break complex problems into smaller parts and build up the solution systematically. One key takeaway for me has been that DSA isn’t about memorizing algorithms, but about developing problem-solving intuition. DP helps you build that mindset — to think ahead, reuse what you’ve already computed, and find efficient ways to reach the answer. I still have a lot to learn, but with every problem I solve, I feel more confident in identifying patterns and understanding the thought process behind optimization. If you’re also learning DSA, I’d suggest taking your time with DP. Don’t rush it — once the logic clicks, everything else falls into place naturally. #DynamicProgramming #DSA #ProblemSolving #ComputerScience #LearningJourney #Coding

Everyone told me Practice more. No one told me this See it differently. When I couldn’t solve even simple DSA problems, everyone said the same thing Practice more. So, I did. More questions. More hours. More frustration. But nothing clicked. Then I did something weird I stopped coding and started visualizing. I drew the algorithm steps like a flow and suddenly, it clicked. It wasn’t about how much I practiced, it was about how clearly, I understood. Now, even the toughest problems feel easy because I see what’s happening before I code. If you’re stuck on DSA maybe the problem isn’t your logic. Maybe it’s your approach. Try seeing it differently once — you’ll never go back. #DSA #Sorting #algorithms

Just sharing a useful DSA sheet — it’s really helpful for daily practice. You can check it here: https://lnkd.in/gbS-CjHT The website is built by me, and the DSA patterns are taken from the Google Sheet shared by Pratyush Narain . It covers important patterns like Sliding Window, Merge Intervals, Monotonic Stack, and Greedy Algorithms along with many more. Good one for anyone preparing for placements or coding rounds. #DSA #Coding #InterviewPreparation #TargetDSA

⚡ Day 27 of My DSA Journey — Understanding Time Complexity Deeply ⏱️ Today, I dived deep into one of the most crucial DSA concepts — Time Complexity! 💡 If you’ve ever wondered why some programs run faster than others even with similar logic — the answer lies in Time Complexity. It tells us how the runtime of an algorithm changes with input size (n). To make it clearer, here’s a quick breakdown of all the complexities shown in the graph 👇 🔹 O(1) — Constant Time: The fastest complexity — the runtime doesn’t depend on input size. 👉 Example: Accessing any element in an array. 🔹 O(log n) — Logarithmic Time: The input size reduces by half every step. 👉 Example: Binary Search, tree traversal in balanced BST. 🔹 O(n) — Linear Time: Runtime increases directly with input size. 👉 Example: Linear Search, simple traversal of an array or list. 🔹 O(n log n) — Linearithmic Time: Common in optimized sorting algorithms. 👉 Example: Merge Sort, Quick Sort, Heap Sort. 🔹 O(n²) — Quadratic Time: Nested loops — runtime grows rapidly as input increases. 👉 Example: Bubble Sort, Insertion Sort, Selection Sort. 🔹 O(2ⁿ) — Exponential Time: Used in recursive problems where every call splits into two. 👉 Example: Recursion in Fibonacci, Subset Generation. 🔹 O(n!) — Factorial Time: The slowest — used in problems that involve generating all permutations. 👉 Example: Travelling Salesman Problem, Permutations. 📚 I learned these from an amazing article by “Take You Forward” (Striver’s A2Z DSA Sheet) — highly recommend every DSA learner to go through it! 🙌 Every DSA learner must understand this before diving deep — because time complexity is not just math, it’s the soul of efficiency. 🚀 #Day27 #DSA #TimeComplexity #SpaceComplexity #BigO #AlgorithmAnalysis #CodingJourney #LeetCode #Striver #ProblemSolving #Consistency #Programming #TakeYouForward

Today’s progress 💻:Finally understood one of the most important concepts in DSA — Time and Space Complexity.It’s not just about solving problems, it’s about solving them efficiently.Step by step, getting closer to mastering the logic behind algorithms ⚙️#DSA #LearningJourney #TimeComplexity #Coding