Finding Second Largest Number in Array with JavaScript

When Prefix Sums Repeat, Something Interesting Happens 🔁📊 Solved LeetCode 523 today. Problem 🔍 Check if a subarray of length ≥ 2 exists whose sum is divisible by k. Initial thought 🧠 Check every subarray using prefix sums. But that quickly becomes O(n²). Better approach 🚀 Use Prefix Sum + Remainder HashMap. Store the first index where each remainder appears. If the same remainder appears again and currentIndex - previousIndex >= 2, a valid subarray exists. Mental model 🧩 If: prefix[j] % k = prefix[i] % k Then: prefix[j] - prefix[i] is divisible by k. Interview takeaway 🎯 Subarray + divisibility → Prefix Sum + Remainder tracking. Practical insight 💡 Store the first occurrence of each remainder to maximize subarray length detection. This same pattern appears in problems like 974 and 560. #DSA #LeetCode #Algorithms #PrefixSum #HashMap #JavaScript #CodingInterview #InterviewPrep

🔥 Most developers know .flat() exists. Very few know how to implement it from scratch. I just wrote a deep-dive on Flattening Nested Arrays in JavaScript — including the one approach that will impress any interviewer. Here's what's inside: → What nested arrays actually look like in memory → Why you need flattening in real projects (API responses, grouped data) → 4 different approaches: .flat(), .flatMap(), reduce + concat, recursive spread → The iterative stack approach — no recursion, no stack overflow risk → 6 common interview scenarios with the exact problem-solving mindset If you've ever written a nested forEach inside another forEach — this blog is for you. Read the full blog here 👇 🔗 https://lnkd.in/gSvQRSxi Check out my Hashnode profile 👇 🔗 https://lnkd.in/gAwxuryw hashtag #JavaScript #WebDevelopment #CodingInterview #Programming #piyushgarg #chaicode #hiteshchoudhary

Turn Odd Numbers into a Prefix Sum Problem ⚡📊 Solved LeetCode 1248 today. Problem 🔍 Count subarrays with exactly k odd numbers. Initial thought 🧠 Check every subarray and count odds. Works, but becomes O(n²). Better approach 🚀 Convert the array into a binary form: Odd → 1 Even → 0 Now the problem becomes: Count subarrays with sum = k. Use Prefix Sum + HashMap to track frequencies. Mental model 🧩 prefix[j] - prefix[i] = k So if prefixSum - k exists earlier, we found a valid subarray. Interview takeaway 🎯 When a problem says “exactly k occurrences”, convert it into a prefix sum counting problem. Practical insight 💡 Transform the problem first. Once odds become 1 and evens 0, the solution becomes identical to LeetCode 560. #DSA #LeetCode #Algorithms #PrefixSum #HashMap #JavaScript #CodingInterview #InterviewPrep

📘 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

Solved Linked List Cycle II using Floyd’s Cycle Detection Algorithm (Tortoise and Hare). The approach uses two pointers moving at different speeds to first detect the cycle and then identify the exact node where the cycle begins. Once the pointers meet, resetting one pointer to the head and moving both one step at a time leads them to the cycle start node. Key Takeaways: Efficient cycle detection in linked lists O(n) time complexity O(1) space complexity Classic two-pointer technique #DataStructures #Algorithms #LinkedList #JavaScript #LeetCode #ProblemSolving #CodingJourney #SoftwareEngineering

🚀 LeetCode Problem Solved – Reverse String Solved the Reverse String problem on LeetCode using JavaScript with an optimized in-place approach. ✅ Key Idea: Reverse the string by swapping characters from the start and end of the same array, modifying it directly without creating any extra array. 🔹 Time Complexity: O(n) 🔹 Space Complexity: O(1) – No extra space used (in-place modification) 💡 Approach: • Use a two-pointer technique • One pointer starts from the beginning of the array • Another pointer starts from the end • Swap characters and move both pointers toward the center until they meet 📊 Result ✔️ All test cases passed ⚡ Runtime: 0 ms 📦 Space Complexity: O(1) Consistently practicing problems like this helps strengthen Data Structures & Algorithms fundamentals and improves problem-solving skills for coding interviews. #leetcode #javascript #dsa #twopointers #programming #codingpractice #softwareengineering

🚀 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

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

💡 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

I built a free practice section for frontend interview prep and it's live now. Over the past few weeks, I've been curating and organizing frontend interview questions into a structured, topic-wise Q&A format. The questions cover areas like:  • Currying & Closures  • Promises & Async patterns  • DOM manipulation  • Observable & Reactive programming  • …and more Most of the questions are sourced from BFE.dev and GreatFrontEnd two excellent platforms for frontend practice. I've organized them by topic with detailed solutions so you can use this as a quick-reference guide or a structured study plan. Whether you're preparing for your next frontend interview or just want to sharpen your JavaScript fundamentals, I hope this helps. Check it out here: https://lnkd.in/d27Dg6e8 If you find it useful, feel free to share it with someone who's preparing. And if you have feedback or want me to cover a specific topic, drop a comment! #FrontendDevelopment #JavaScript #InterviewPrep #WebDevelopment #CodingInterview #React #OpenSource