Optimizing Array Problem Solution with O(n) Time Complexity

Just crossed 100 problems on LeetCode. Before anyone says “nice” — yes, I know that’s basically beginner level. At the start, my approach was: Solve → get TLE → look at solution → convince myself I understood it. Now it’s slightly more structured: → Recognizing patterns (sliding window, DFS/BFS, monotonic stack… sometimes correctly) → Thinking about constraints before writing code → Reducing blind brute force attempts → Actually understanding why a solution works What improved: • ~60 mins → ~20 mins per problem • Debugging is less guesswork now • Patterns are starting to repeat (finally) What still needs work: • Hard problems (only 4 so far… yeah, not great) • Stronger grasp on DP and graphs • Writing cleaner, more optimized code One thing I’ll take seriously: 95-day streak > motivation Consistency did more than any “trick” or “hack”. Next target: 200 problems — but with actual depth, not just counting questions. If you're doing LeetCode: Don’t just solve. Understand why you failed. Still learning. Still getting humbled regularly. #leetcode #datastructures #algorithms #problemSolving  #100DaysOfCode #consistency #softwareengineering #developer

Day 42 on LeetCode — Merge Sorted Array (Two Pointer Approach) ✅ Today’s problem focused on efficient in-place array manipulation using the two-pointer technique. 🔹 Approach Used in My Solution The key insight was to compare elements from the back of both arrays instead of the front. Since nums1 already has extra space at the end to accommodate elements from nums2, we can fill the array from the last index backwards. Key points in the logic: • Initialize pointers at the end of the valid elements of nums1 and nums2 • Compare the elements and place the larger one at the back of nums1 • Move the pointers accordingly until all elements are merged • This allows the final sorted array to be stored directly in nums1 This strategy avoids unnecessary shifting of elements and keeps the solution efficient. ⚡ Complexity: • Time Complexity: O(m + n) • Space Complexity: O(1) (in-place merge) 💡 Key Takeaways: • Learned how working from the back can simplify in-place merges • Strengthened understanding of the two-pointer technique • Practiced optimizing array operations without using extra space #LeetCode #DSA #Algorithms #DataStructures #TwoPointers #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

🚀 Day 53 of #100DaysOfCode — Mastering In-Place Problem Solving Today’s focus: LeetCode #48 — Rotate Image🧠 At first glance, this problem looks simple… until you hit the constraint: 👉 You must rotate the matrix in-place (no extra space allowed). That’s where the real learning begins. 💡 What I learned today: * Difference between brute force vs optimized approach * Why space complexity matters in real-world systems * How to break a problem into steps: 1. Transpose the matrix 2. Reverse each row * Improved understanding of 2D array manipulation 🔍 Key Insight: Most problems aren’t about coding — they’re about *thinking in transformations. Once you visualize the pattern, the solution becomes elegant. 💻 Tech I’m sharpening daily: * Data Structures & Algorithms (DSA) * Problem-solving mindset * Writing clean & optimized C++ code 📈 Consistency Update: Even on days when it feels tough or slow, I’m showing up. Because growth isn’t about speed — it’s about discipline. 🎯 Goal: Become a strong problem solver ready for real-world engineering challenges. If you're also on a coding journey, let’s connect and grow together 🤝 #Day53 #100DaysOfCode #DSA #LeetCode #CodingJourney #SoftwareEngineering #ProblemSolving #CPlusPlus #TechCareers #LearningInPublic #Consistency #FutureEngineer

Day 44 of #100DaysOfCode Consistency is starting to compound — and today’s problem was a perfect example of how structured thinking leads to efficient solutions. I worked on the “Product of Array Except Self” problem on LeetCode, which initially looks simple but pushes you to think beyond brute force. 🔍 What makes this problem interesting? * You cannot use division * You must achieve O(n) time complexity * You need to optimize space usage 💡 My Approach & Learnings: Instead of calculating the product repeatedly, I broke the problem into two smart passes: 1. Prefix pass → storing product of all elements to the left 2. Suffix pass → multiplying with product of elements to the right This helped me: ✔ Avoid redundant calculations ✔ Improve efficiency ✔ Write clean and optimized code ⚙️ Skills Strengthened: * Problem decomposition * Optimized array traversal techniques * Space-time tradeoff understanding * Writing production-level clean C++ code 📈 Why this matters? Problems like these build the foundation for tackling real-world scenarios where performance and scalability are critical. It’s not just about solving — it’s about solving efficiently and elegantly. Every day of this journey is improving how I think, not just how I code. Looking forward to pushing further #Day44 #100DaysOfCode #LeetCode #DSA #CodingJourney #SoftwareEngineering #ProblemSolving #Algorithms #Cplusplus #TechSkills #DeveloperJourney #PlacementPreparation #CodeDaily #LearnToCode #GrowthMindset #FutureEngineer #Consistency #CodingLife

🚀 Day 4 of 100 Days LeetCode Challenge. Problem: Special Positions in a Binary Matrix Today’s problem focused on matrix traversal + counting logic—simple concept, but requires careful observation. 💡 Key Insight: A position (i, j) is special if: mat[i][j] == 1 All other elements in the same row and column are 0 🔍 Efficient Approach: Count number of 1’s in each row Count number of 1’s in each column A position is special only if: Row count = 1 Column count = 1 👉 This avoids unnecessary repeated checks and improves efficiency. 🔥 What I Learned Today: Preprocessing (row & column counts) simplifies problems Avoid brute force → think in terms of frequency/counting Clean logic > complex code 📈 Challenge Progress: Day 4/100 ✅ Staying consistent! LeetCode, Matrix Problem, Arrays, Counting Technique, DSA Practice, Coding Challenge, Problem Solving, Algorithm Thinking, Optimization #100DaysOfCode #LeetCode #DSA #CodingChallenge #Matrix #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

Before you write another line of complex code, read this. As the co-creator of Go, Rob Pike knows a thing or two about writing efficient software. His classic 5 Rules of Programming recently resurfaced in developer circles, and the core message remains timeless: simplicity and data structures matter most. Instead of jumping to fancy algorithms that are prone to bugs, Pike reminds us to focus on the basics. Rule 5 says it all: 'Data dominates.' If you design your data structures properly, the algorithms will reveal themselves. Furthermore, never guess where your code will bottleneck—always measure first. Do you prioritize data structures over complex algorithms in your daily work? Let's discuss! #SoftwareEngineering #Programming #Coding #TechInsights #DeveloperCommunity

🚀 Day 3 of 180 — Spiral Matrix III ✅ Yesterday I tried this problem. Today I solved it. Let's get into it. LeetCode 885 — Spiral Matrix III You start from a given cell (rStart, cStart) on a rows × cols grid and spiral outward. The goal is to collect all valid cells in the order you visit them. The catch — the spiral goes beyond the grid boundaries. You keep moving but only collect a cell if it actually exists inside the grid. My thought process: I used a direction array to handle movement cleanly: dir = 0 → East ( 0, +1) dir = 1 → South (+1, 0) dir = 2 → West ( 0, -1) dir = 3 → North (-1, 0) The most important pattern to notice in a spiral — step count increases after every 2 turns. East → 1 step South → 1 step West → 2 steps North → 2 steps East → 3 steps ... and so on So whenever direction is East or West, I increment the step count. At every cell — check if it's inside the grid. If yes, collect it. If no, just keep moving. The spiral never stops, we just skip invalid cells. Start → add (rStart, cStart) directly while(collected < rows * cols): if dir == East or West → step++ move 'step' times in current direction → inside grid? collect it turn clockwise → dir = (dir+1) % 4 One thing this problem taught me — sometimes the movement pattern is more important than the boundary logic. Once I saw the step pattern clearly, everything else followed. Day 3 done. 177 to go. 🔥 #180DaysDSA #Day3 #LeetCode #SpiralMatrix #Java #DSA #Arrays #Matrix #DSAJourney #CodingJourney #Programming #DataStructures #Algorithms #ProblemSolving #BuildInPublic #CodeNewbie #LearnToCode #100DaysOfCode #SoftwareDevelopment #Developer #StudentDeveloper #TechCommunity #LinkedInTech #CompetitiveProgramming

🚨 Developers, your code review might be out of date GitHub Copilot 2.0 Launches with Real Time Debugging 🚀 GitHub released Copilot 2.0 this week, adding live debugging and contextual AI suggestions that run directly inside VS Code. The update also supports TypeScript, Python, and Go out of the box, and it now learns from your commit history. Why it matters This means developers can catch bugs before they hit production, reducing the mean time to resolve issues by up to 30% according to a recent internal benchmark. For agencies, it speeds up sprint cycles and cuts rework. My take as a 9‑year veteran As someone who has built 10+ production sites, I see this as a big win. The real test will be how well the AI keeps up with complex legacy codebases. I’m excited to experiment, but I’ll keep an eye on false positives. What do you think? 💡 Overhyped or game changer? Check if your current IDE can integrate Copilot 2.0 and start a trial. #TechNews #WebDevelopment #AI #GitHub #Copilot #VSCode #Debugging #DeveloperTools #Productivity #Coding #Innovation #SoftwareEngineering #DigitalTransformation #TechTrends #FutureOfWork

Day 5 of 100 Days of LeetCode – Improving Problem-Solving Skills Problem Solved: Move Zeroes Today’s problem focused on rearranging an array such that all zeroes are moved to the end while maintaining the relative order of the non-zero elements. 🔍 Approach: Instead of creating a new array or performing multiple shifts, the optimized solution uses a two-pointer technique. One pointer tracks the position where the next non-zero element should be placed, while the other pointer traverses the array. Whenever a non-zero element is encountered, it is swapped to the correct position. This ensures all non-zero elements remain in order while pushing zeroes toward the end efficiently. ⚡ Time Complexity: O(n) ⚡ Space Complexity: O(1) 💡 Key Takeaways: • Learned how the two-pointer technique simplifies array manipulation • Importance of in-place operations to reduce extra space usage • Strengthening understanding of efficient array traversal Each problem solved helps build stronger algorithmic thinking and coding discipline. #Day5 #100DaysOfLeetCode #LeetCode #DataStructures #Algorithms #ProblemSolving #CodingJourney #SoftwareDevelopment

𝗖𝗹𝗲𝗮𝗻 𝗰𝗼𝗱𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗳𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴.... For me, it’s really simple:  • Easy to read and understand  • Easy to change and update  • Easy to debug If your code needs a meeting to explain it, it’s probably not clean code. Write code for humans first, machines second. What’s your definition of clean code? 👇 #CleanCode #CodingBestPractices #SoftwareEngineering #CodeQuality #ProgrammingTips #ReadableCode #MaintainableCode #DevLife #CodeForHumans #CleanArchitecture #SoftwareDevelopment #TechLeadership #ProgrammingMindset #CodeReview #DevTips