JavaScript Sort() Gotcha: Lexicographical Sorting

🚀 𝗡𝗲𝘄 𝗣𝗥 𝗠𝗲𝗿𝗴𝗲𝗱 I recently tackled the challenge of reversing an array in-place. The core 🧩 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 was to modify the original array directly without creating a new one, which requires careful index management. To solve this, I implemented the two-pointer technique in JavaScript. I used one pointer starting at the beginning of the array and another at the end. I then swapped the elements pointed to by these pointers and moved them towards the center until they met or crossed. This ⚙️ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 ensures O(1) space complexity. My 🐞 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 involved dry runs to trace the pointer movements and element swaps. Visualizing the array's state at each step was crucial. I also occasionally used the debugger to step through the code and confirm my understanding of the logic. A 📚 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 from this exercise was the power of in-place algorithms for optimizing memory usage, especially when dealing with large datasets. You can see the implementation in my latest PR: https://lnkd.in/d5rAw_CN What are your favorite in-place algorithms or techniques? 📦 Repo: https://lnkd.in/d5rAw_CN #DataStructures #Algorithms #JavaScript #TwoPointerTechnique #ProblemSolving #InPlaceAlgorithm #CodingChallenge #SoftwareDevelopment #ArrayManipulation

🚀 𝗡𝗲𝘄 𝗣𝗥 𝗠𝗲𝗿𝗴𝗲𝗱 Just tackled a fun logical challenge: finding the intersection of two arrays. The goal was to identify elements present in both input arrays. I approached this using JavaScript. My strategy involved iterating through the first array and checking for the existence of each element in the second array. To optimize this lookup, I leveraged a Set data structure, which provides average O(1) time complexity for checking membership. During the 🐞 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀, I found dry runs and visualizing the data flow particularly helpful. Stepping through the code with a debugger allowed me to pinpoint exactly where my logic was diverging from the expected output. A 📚 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 for me was the significant performance improvement gained by using a Set for lookups compared to nested loops or Array.prototype.includes within a loop. Check out the implementation and contribute to the discussion here: https://lnkd.in/dvQbUFGK How do you typically ⚙️ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 array intersection problems? 📦 Repo: https://lnkd.in/dvQbUFGK #Algorithm #JavaScript #ProblemSolving #DataStructures #Set #CodingChallenge #Developer #Tech #InterviewQuestion #LogicalThinking

📘 𝐉𝐚𝐯𝐚𝐒𝐜𝐫𝐢𝐩𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐌𝐨𝐝𝐮𝐥𝐞 (𝐁𝐚𝐬𝐢𝐜) 𝐒𝐞𝐜𝐭𝐢𝐨𝐧 7: 𝐒𝐭𝐫𝐢𝐧𝐠 1.What is string? 2.How to declare string? 3.String Declare Special way or Special purpose string? 4.String Check type? 5.String length Check? 6.String Access index? 7.What is empty string?  8.When to use empty string? 9.String is immutable or mutable?  10.String methods? 11.String concatenation? 🎯 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐄𝐱𝐭𝐫𝐚) 1.Difference between slice and substring? 2.What is template literal? 3.Why string is immutable? 4.What are the benefits of Template Literals (Backticks ``)? 5.Why are strings immutable in JavaScript? How does it work in memory? 𝐒𝐞𝐜𝐭𝐢𝐨𝐧 8: 𝐍𝐮𝐦𝐛𝐞𝐫 & 𝐃𝐚𝐭𝐞 1.What is number? 2.Number Types? 3.Math methods? 4.How to find date? 5.Date methods? 🎯 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐄𝐱𝐭𝐫𝐚) 1.Difference between Math.floor and Math.round? 2.How to generate random number between range? #DotNet #AspNetCore #MVC #FullStack #SoftwareEngineering #ProgrammingTips #DeveloperLife #LearnToCode #JavaScript #JS #JavaScriptTips #JSLearning #FrontendDevelopment #WebDevelopment #CodingTips #CodeManagement #DevTools

🚀 Learning by Building: Mastering Frequency Patterns in JavaScript Today I worked on a classic algorithm problem: finding the most frequent element in an array using a HashMap (Map in JavaScript). Here’s the idea: 👉 Traverse the array 👉 Count occurrences using a Map 👉 Track the maximum frequency in real-time This approach is efficient (O(n)) and widely used in real-world scenarios like data analysis, caching, and optimization problems. 💻 Example result: Input: [1, 3, 2, 1, 4, 1, 2, 1, 5, 3] Output: { value: 1, count: 4 } I’m currently practicing patterns and strengthening my problem-solving skills step by step. 📌 Check out my full exercises here: https://lnkd.in/ej4fNeZs Consistency > Talent. #JavaScript #Algorithms #ProblemSolving #SoftwareEngineering #100DaysOfCode #CodingJourney

Just solved the Binary Search problem in JavaScript. Binary Search is a great example of the Divide and Conquer approach. Instead of checking every element, the algorithm looks at the middle of a sorted array and eliminates half of the search space each step. Because the array is divided in half every time, the time complexity becomes O(log n), which makes it much faster than linear search O(n) for large datasets. Small problem, but a great reminder of how powerful algorithmic thinking can be. #JavaScript #Algorithms #BinarySearch #CodingPractice #LeetCode #SoftwareEngineering

I thought JavaScript arrays worked like C arrays… I was wrong. ❌ I believed: 👉 Arrays are stored in contiguous memory 👉 Indexing is just base + offset 👉 Everything sits neatly in one block But JavaScript doesn’t work like that. Here’s the real mental model 👇 ✅ The variable (arr) lives in the STACK ✅ It stores a reference (not the actual array) ✅ The actual array lives in the HEAP ✅ The array stores: • metadata (like length) • pointer to elements storage Now the interesting part: 👉 Elements can be: * primitives (numbers, booleans) * references (strings, objects) 👉 Strings/objects are usually stored separately in memory 👉 Arrays can be dynamic, sparse, and optimized internally ⚠️ Most important: JavaScript arrays are NOT guaranteed to be contiguous in memory. That means: They are closer to dynamic objects than low-level arrays. 💡 Actual storage depends on the JS engine (like V8), which heavily optimizes things under the hood. I made a diagram to simplify this 👇 Course Instructor: Rohit Negi | Youtube Channel: Coder Army #JavaScript #webdevelopment #buildinpublic #learninginpublic #fullstackdevelopment.

Just solved a classic algorithm problem and recorded my approach. I started with a linear search (O(n)) solution — simple, clear, and a great baseline for understanding the problem. Walking through it step by step really helped reinforce how time complexity grows with input size. Then I explored how to improve it to O(log n) using binary search when the array is sorted — a great reminder of how powerful optimization can be. Key takeaway: Start with a working solution Then optimize for performance Sharing my thought process in this video — would love your feedback and how you usually approach problems like this. #coding #javascript #algorithms #leetcode #softwareengineering #100daysofcode

💡 Understanding Two Sum — It’s Not About the Numbers, It’s About Timing Today I revisited the classic Two Sum problem and realized something simple but powerful: 👉 The algorithm doesn’t try to find the best pair 👉 It doesn’t look for largest numbers 👉 It doesn’t check all possibilities It simply returns the first valid pair it encounters during traversal Key Insight: The result depends entirely on the order of iteration Example: nums = [1, 2, 4, 5, 6, 8] target = 10 We might expect: 2 + 8 = 10 → indices [1, 5] But the algorithm returns: 4 + 6 = 10 → indices [2, 4] Why? Because: 4 is seen earlier 6 appears soon after The algorithm stops immediately when it finds the first match The Rule to Remember: 🧠 Hash map solution returns the first valid pair based on traversal order Takeaway: This isn’t just about solving Two Sum — it’s about understanding how algorithm behavior is shaped by execution flow, not just logic. Once you see this, you stop memorizing solutions and start actually understanding them. #JavaScript #Algorithms #CodingInterview #SoftwareEngineering #ProblemSolving

📌 #71 DailyLeetCodeDose Today's problem: 69. Sqrt(x) – 🟢 Easy Finding the integer square root of a number – meaning the largest integer whose square is less than or equal to the given number – can be done in a very straightforward way. You could simply start from 0, keep adding 1, and check if the square exceeds the number. This works perfectly for small numbers and is easy to understand. However, there's a way to improve efficiency by using 𝐛𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐚𝐫𝐜𝐡. Instead of checking every number one by one, binary search allows us to quickly narrow down the range and find the result much faster, especially for large numbers. https://lnkd.in/eURbjQ8g #DailyLeetCodeDose #LeetCode #JavaScript #Algorithms #ProblemSolving #Coding

🚀 LeetCode Problem Solved - Best Time to Buy and Sell Stock Today I solved the Best Time to Buy and Sell Stock problem on LeetCode using JavaScript with an optimized single-pass approach. 📊 Approach The goal is to determine the maximum profit from buying and selling a stock given its daily prices. The key idea is to: • Track the minimum price encountered so far • Calculate the potential profit for each day • Update the maximum profit whenever a higher profit is found This allows us to compute the result in one pass through the array. ⚡ Complexity 🔹 Time Complexity: O(n) – Traverse the prices array once 🔹 Space Complexity: O(1) – No extra data structures used ✅ Result ✔️ All test cases passed 📦 Constant space complexity Problems like this reinforce the importance of array traversal, state tracking, and optimizing brute-force solutions into efficient linear algorithms. Consistent practice with these problems strengthens DSA fundamentals and problem-solving skills for coding interviews. #leetcode #javascript #dsa #algorithms #codingpractice #softwareengineering #webdevelopment