"Optimizing LeetCode Problem with BFS: A YouTube Explanation"

🚀 Sliding Window & Two Pointers — Test Your DSA Skills with Quick MCQs! Two powerful coding patterns — Sliding Window and Two Pointers — often show up in interviews and real-world problem solving. Understanding when to use each can turn nested loops into clean O(n) solutions. Sliding Window: Move a fixed-size or variable window across data to efficiently compute sums, counts, or unique elements. Two Pointers: Use two indices moving independently to find pairs, merge arrays, or remove duplicates without brute force. 💡 Try these MCQs and check your understanding: Test your knowledge on: ✅ Sliding Window Technique ✅ Two Pointers Approach Drop your score in comments! 👇 Follow Codekerdos for more algorithm insights, clean code patterns, and interactive learning exercises that sharpen your problem-solving skills 🔥 #DSA #SlidingWindow #TwoPointers #CodingInterview #ProgrammingTips #CleanCode #DeveloperMindset #100DaysOfCode #Algorithms #Codekerdos

🚀 Leveling Up with Core DSA Patterns: Arrays, Strings, and Beyond Lately, I’ve been spending time on LeetCode and GeeksForGeeks — not just to solve problems, but to revisit and strengthen the core patterns that make tough questions feel simpler. Some days it’s about debugging for an hour, other days it’s that moment of clarity when a familiar concept finally clicks in a new way. Here are a few problems that really stood out 👇 🌀 Maximum Circular Subarray Sum — realising Kadane’s magic can bend in a circle too 🔠 Longest Substring with K Unique Characters — sliding window and hashmap mastery 💡 Add Binary Strings — bit-by-bit logic that feels oddly satisfying ➖ Minimum Sum — small tweaks, big optimization 🧮 Maximum Sum Rectangle — extending Kadane’s algorithm to 2D grids ✨ Sort Vowels in a String — when two-pointer meets string manipulation Revisiting these helped me see how much depth there is in the basics — and reminded me that: 💭 The hardest problems are often clever combinations of simple ideas 🧩 Pattern recognition > memorizing formulas ⏳ Progress in DSA is all about consistency, not speed Still a long way to go — but every solved problem adds a little more intuition, confidence, and joy in problem-solving. #LeetCode #GeeksForGeeks #DSA #ProblemSolving #PatternRecognition #CodingInterview #SoftwareEngineering #LearningJourney #Consistency #DeveloperMindset #WomenInTech #WomenWhoCode #TechCommunity #CodingJourney Anchal Sharma Ikshit ..

🚀 DSA Progress – Day 121 ✅ Problem #2147: Number of Ways to Divide a Long Corridor 🧠 Difficulty: Hard | Topics: Greedy, Strings, Combinatorics, Math 🔍 Approach: Today’s problem was a beautiful mix of counting + observation. The goal is to determine how many different ways you can place dividers in a corridor so that every section has exactly 2 seats (S). Here’s how I approached it: Step 1 (Identify Seats): Scan the corridor and record positions of every 'S'. If the number of seats is odd or zero → return 0 because you cannot form valid pairs. Step 2 (Form Seat Pairs): Seats must form natural pairs: (S1,S2), (S3,S4), (S5,S6), … Each pair will form one complete valid section. Step 3 (Count Gaps Between Pairs): Between every two consecutive pairs, we look at the plants (P) in between. These plants create possible divider positions. For example: If the gap between pairs is k, then there are k valid places to put a divider. We multiply all such gaps to get total ways. Step 4 (Modulo Multiplication): Result is taken modulo 1e9+7 to prevent overflow. This approach converts string processing into a clean combinatorial multiplication problem. 🕒 Time Complexity: O(n) — single pass for seats + gap calculation 💾 Space Complexity: O(k) — storing seat positions 📁 File: https://lnkd.in/gwmdGmTT 📚 Repo: https://lnkd.in/g8Cn-EwH 💡 Learned: This problem reinforced a powerful insight: 👉 When constraints group elements naturally (here: exactly 2 seats per section), the spaces between the groups often define the combinatorial answer. Counting gaps instead of simulating partitions made the solution elegant and efficient. Understanding how structure + combinatorics work together is super useful for tricky string problems. ✅ Day 121 complete — turned a corridor of chaos into mathematical order! 🪄🚪🧮✨ #LeetCode #DSA #Python #Strings #Combinatorics #Greedy #Math #DailyCoding #InterviewPrep #GitHubJourney

From Brute Force to Better Solutions My Data Structures & Algorithms Journey When I started my development career, I never had a formal DSA interview. It wasn't until 2023 that a colleague introduced me to AlgoExpert, and I discovered a whole new way of thinking about code.What began as curiosity turned into a genuine passion for problem-solving. I started taking notes in Obsidian, sketching data structures in Excalidraw, and working through structured roadmaps like NeetCode. Dynamic programming was the toughest hurdle, I didn't truly understand it until I stopped copying solutions and started asking why they worked The real payoff? Day-to-day, I now reach solutions faster and have more approaches at my disposal. Learning DSA hasn't just been about completing problems, it's fundamentally changed how I think about code and solve real-world challenges.Resources that helped me: - NeetCode's YouTube channel and roadmap - LeetCode 75 for structured practice - Obsidian for organizing concepts - Excalidraw for visualising structures If you're on a similar learning journey, I'd love to hear about it! What resources have worked best for you? Read the full story of my LeetCode journey (with screenshots and detailed insights) on my blog: https://lnkd.in/e7ivqkbr #SoftwareDevelopment #ProblemSolving #ContinuousLearning

🔹 LeetCode Spotlight: Spiral Matrix 🔹 One of the interesting challenges on LeetCode is the Spiral Matrix problem. Instead of traversing a 2D array in the usual row-wise or column-wise order, this problem requires moving in a spiral: right → down → left → up, layer by layer, until all elements are covered. ✨ Why it’s valuable: Tests your understanding of 2D arrays and boundary management. Sharpens logical thinking and problem decomposition. Often asked in coding interviews to evaluate algorithmic approach. 🧠 Conceptual Approach: Define four boundaries: top, bottom, left, right. Move along each boundary in spiral order. After each pass, adjust the boundaries inward. Repeat until all elements are visited. 🌟 Takeaway: The Spiral Matrix problem is more than traversal—it teaches structured thinking, pattern recognition, and efficient iteration in algorithms. A small visualization on paper can save hours of debugging! #LeetCode #Algorithms #DataStructures #ProblemSolving #CodingInterviews #TechLearning

I’ve been experimenting with sharing half-formed ideas here. This one’s been echoing in my head for a while — about what “semantic programming” might really mean. We usually write code to tell computers how to compute. But lately, in AI and agentic systems, we’re starting to describe what something means, and letting the system figure out how. That made me wonder — what would a semantic programming language look like? A language where intent, feedback loops, and memory are built-in, and “running” it feels more like maintaining a thought process than executing code. Maybe it’s just a small thought experiment. But the image keeps echoing — a world where we don’t just program instructions, we compose meanings. 🧠✨ I wrote a short Medium piece about this curiosity — not a prediction, just an exploration: 👉 From Semantic Programming to the Semantic OS https://lnkd.in/guXWkR-Y #AIthoughts #ComputingPhilosophy #SemanticProgramming · #ThinkingInPublic

🧠 DSA Algorithms – Theory, Explanation & Code Examples (Complete Guide) 🚀 Mastering Data Structures and Algorithms (DSA) is the key to becoming an efficient and confident problem solver. Whether you’re preparing for FAANG interviews or aiming to improve your coding logic, understanding the theory behind algorithms along with their code implementations will give you a solid edge. 📘 What You’ll Learn: ✅ Algorithm Basics: Time & Space Complexity, Big O Notation ✅ Sorting Algorithms: Bubble, Selection, Insertion, Merge, Quick, Heap Sort – with code ✅ Searching Algorithms: Linear & Binary Search – logic and efficiency ✅ Recursion & Backtracking: Real-world coding examples ✅ Greedy & Dynamic Programming: Optimization techniques with explanations ✅ Graph Algorithms: BFS, DFS, Dijkstra, Kruskal, Prim’s ✅ Advanced Topics: Divide & Conquer, Sliding Window, Two Pointers, Kadane’s Algorithm 💡 Pro Tip: Don’t just memorize — focus on why an algorithm works and when to use it. Combine theory with consistent practice on platforms like LeetCode, GeeksforGeeks, and CodeStudio. 🔥 Strengthen your algorithmic thinking and write optimized, clean, and scalable code! #DSA #Algorithms #DataStructures #CodingInterview #ProblemSolving #LeetCode #DynamicProgramming #Sorting #Searching #GraphAlgorithms #Programming #TechCareer #InterviewPreparation

Understanding the Sliding Window Pattern in Problem Solving While practicing LeetCode, I realized something important: DSA becomes much easier when you start thinking in patterns, not just “array” or “substring” problems. One pattern that truly changed how I approach problems is the Sliding Window technique. It helps avoid unnecessary brute force and lets you process a moving portion of data in O(n) time by simply expanding or shrinking the window. 🔍 Why focus on patterns instead of topics? Earlier, problems labeled “array” or “string” felt random. But when I started grouping them by patterns like: • Sliding Window • Two Pointers • HashMap Patterns • Prefix Sum • Binary Search on Answer …everything instantly made more sense. You start recognizing the structure of a problem before writing code — and that’s where efficiency begins. ⚡ Why Sliding Window Matters Sliding Window helps you ditch the O(n²) brute force approach and solve the same problem in O(n). If you’re starting with patterns, begin with Fixed Sliding Window. A few great starter problems: • Maximum sum of k consecutive elements • First negative number in every window of size k • Count of subarrays of size k with average greater than X Once you’re comfortable, move on to Variable Sliding Window and then advanced variations. Learning DSA is not just about solving questions — it’s about learning to recognize patterns, optimize logic, and think differently. #DSA #SlidingWindow #ProblemSolving #LeetCode #CodingJourney #LearnInPublic

💻 Today I solved “Minimum Number of Days to Make M Bouquets” — a classic binary search problem! 🌹 This one really tested my ability to apply binary search on the answer space, rather than the array itself — a concept that’s easy to miss but super powerful in optimizing solutions. Here’s what I learned 👇 🔹 Understand the problem constraints deeply — brute force fails fast here. 🔹 Instead of searching for an element, search for the minimum day that satisfies the condition. 🔹 Use a helper function to check if it’s possible to make m bouquets with the current mid-day. 🔹 Optimize by carefully adjusting the search space using low and high boundaries. Solving this reinforced how important pattern recognition and mathematical thinking are in problem-solving — not just for interviews, but for writing efficient code in real projects too. Every DSA problem solved isn’t just practice — it’s sharpening the way you think. ⚡ #coding #dsa #leetcode #binarysearch #problemsolving #developerjourney