Mastering Two Pointer Technique for Efficient Linear Data Structures

🚀 Mastering a Classic Algorithm: Balanced Parentheses (Stack – LIFO) Today I revisited a fundamental problem that every Software Engineer should understand: validating balanced parentheses using a stack. Why it matters: This pattern appears in compilers, interpreters, and even real-world applications like expression parsing. Here’s the idea: 👉 Use a stack (LIFO) 👉 Push opening brackets 👉 Pop and match when encountering closing brackets 👉 Ensure the stack is empty at the end Clean and efficient JavaScript implementation 👇 This is a great reminder that mastering data structures like stacks is key to solving real algorithmic problems efficiently. I’m currently building and documenting algorithm patterns here: 🔗 https://lnkd.in/ej4fNeZs #SoftwareEngineering #JavaScript #Algorithms #DataStructures #Coding #LeetCode #100DaysOfCode

Can’t solve coding problems? You’re probably missing this Big O + Arrays + Strings foundation. Before you solve problems, you need to understand what’s happening under the hood. Let’s start simple. Arrays store data in continuous memory That’s why: • Access is fast → O(1) • Insert/Delete is slow → O(n) (because elements shift) Strings are just arrays of characters So every string problem is secretly an array problem. Now let’s talk about Big O (this is where most people get confused) Big O tells you: “How does my code behave as input grows?” Examples: • O(1) → constant (fastest) • O(n) → loop once • O(n²) → nested loops (slow) • O(log n) → very efficient Here’s the mistake most people make: They memorize Big O, but don’t see it in code. Example: for i in range(n): print(i) That’s O(n) because it runs n times. The goal is simple: Don’t just write code. Understand what it costs. Because once you understand cost, You start writing better solutions naturally. In my next post, I’ll break down the two patterns that solve most problems: → Two Pointers & → Sliding Window Trust me, this is where things start to click. Follow along #Web3 #DataStructures #Algorithms #LearnToCode #TechGrowth

🚀 Day 4 of My LeetCode Journey — Building Problem-Solving Muscle Today I worked on two problems: 🔹 Max Consecutive Ones (LeetCode 485) 🔹 Missing Number (LeetCode 268) 💡 Problem 1: Max Consecutive Ones This problem is all about pattern recognition. 👉 Traverse the array 👉 Keep counting consecutive 1s 👉 Reset when you hit 0 Simple logic, but a great reminder that not every problem needs complex data structures. 💡 Problem 2: Missing Number This one was interesting — multiple ways to solve it: ✔️ Sorting ✔️ HashSet ✔️ XOR ✔️ Sum formula (most optimal) The cleanest approach: 👉 Use the formula: n * (n + 1) / 2 👉 Subtract the actual sum 👉 Boom — missing number found ⚡ 🔥 Key Takeaways from Today: Not every problem needs brute force There’s always a more optimal way — look for patterns Understanding multiple approaches = stronger fundamentals Consistency is slowly turning confusion into clarity 💪 On to Day 5 🚀 #LeetCode #DataStructures #Algorithms #CodingJourney #100DaysOfCode #SoftwareEngineering #Programming #InterviewPrep #JavaScript #CodingLife #TechGrowth #ProblemSolving #Developers #LearnToCode #Consistency #CodeNewbie #DailyCoding

Day 07: Cracking the "Non-Divisible Subset" Logic 🧩 Today was a true test of algorithmic thinking. I tackled a problem that looks like a standard array search but is actually a brilliant exercise in Number Theory and Remainder Math. The Challenge: Given a set of numbers, find the maximum size of a subset where the sum of any two numbers is not divisible by K The Strategy (Remainder Frequency): Instead of checking every possible pair (which would be very slow), I focused on remainders . If two numbers sum to a multiple of K, their remainders (r1+r2)  must sum to K. const s = [19,10,12,10,24,25,22]; const k = 4; function nonDivisibleSubset(k, s) { let freq = new Array(k).fill(0); // count remainders for (let num of s) { freq[num % k]++; } let count = 0; // remainder 0 case if (freq[0] > 0) count++; // check pairs for (let i = 1; i <= Math.floor(k / 2); i++) { if (i === k - i) { // special case when k is even if (freq[i] > 0) count++; } else { count += Math.max(freq[i], freq[k - i]); } } return count; } console.log(nonDivisibleSubset(k,s)) Key Takeaway: When a problem involves divisibility, don't look at the numbers—look at the remainders. It turns a complex pairing problem into a simple counting one! One full week of coding done. The momentum is real! 🚀 #JavaScript #Algorithms #NumberTheory #100DaysOfCode #CodingChallenge #ProblemSolving #SoftwareEngineering

The Logic of Symmetry Day 242 Today Today is day 242 of my coding journey and I am focusing on the Two Pointer technique. This is a very efficient way to solve problems on linear data structures like arrays and strings. The main idea is to use two different indices to move through the data from different ends or at different speeds. I practiced the Opposite Direction pattern today. In this pattern one pointer starts at index zero and the other at the end of the array. They move toward each other until they meet. This logic is perfect for comparison problems like checking if a string is a palindrome or reversing a string. Instead of using extra memory I can just swap or compare elements in place which makes the code much faster. I also worked on the Squares of a Sorted Array problem where I compared values from both ends to build a new sorted list. The Valid Word Abbreviation problem was a great challenge because I had to synchronize two pointers across a word and its abbreviation while handling numbers carefully. Learning these patterns helps me see the logic behind the code instead of just memorizing solutions. LeetCode questions I solved today: LeetCode 125 Valid Palindrome LeetCode 344 Reverse String LeetCode 977 Squares of a Sorted Array LeetCode 680 Valid Palindrome II LeetCode 408 Valid Word Abbreviation #DSAinJavaScript #365daysOfCoding #JavaScriptLogic #LeetCodeDaily #TwoPointers #ProblemSolving #AlgorithmPractice #LogicBuilding #CodingJourney #SoftwareDeveloper #WebDevelopment #DataStructures #JSAlgorithms #CleanCode #TechLearning #ProgrammingGoals #MERNStack #DailyCoding #FullStackDeveloper #CodingCommunity

Every time Claude Code makes a new request, it sends your entire codebase context from scratch. For complex projects, Graphify's knowledge graph reduces this overhead by up to 71%. #claude #claudecode

🚀 DSA Day 17: From Theory to Code – Building the Foundation 🏗️ Today was a busy one, but I made sure to squeeze in some growth time. I focused on the "blueprint" of a Singly Linked List: ✅ The Node Factory: Created a constructor to give every piece of data its own value and a next pointer. ✅ The Structure: Initialized my MyLinkedList class with a head, tail, and a length tracker to keep things organized. It’s a simple start, but it’s the skeleton that makes everything else possible. 🔜 Coming up next: The "heavy lifting" begins! I’ll be tackling: 🔹 Get: How to find a value at a specific index (the "walk"). 🔹 Insert: Adding new nodes without breaking the chain. 🔹 Delete: Removing nodes and re-linking the pointers. The goal isn't just to write the code, but to visualize how those pointers shift in memory. 🧠✨ #Day17 #JavaScript #DSA #CodingJourney #LinkedList #LearningInPublic #WebDev #DataStructures #Consistency

Day#17 💡Cracked LeetCode #202 – Happy Number! Ever wondered if a number can be happy? 😄 Turns out, in programming… it can! 🔍 Problem Statement: A number is called Happy if: You replace the number by the sum of the squares of its digits Repeat the process If it eventually becomes 1 → it's a Happy Number If it falls into a loop → Not Happy ❌ 🧠 Key Insight: This problem is not about math… it's about cycle detection! We keep transforming the number: 👉 If we reach 1 → Done ✅ 👉 If we revisit a number → Loop detected 🔁 Approaches: 1️⃣ Using HashSet Store visited numbers Detect loop easily Time: O(log n), Space: O(log n) 2️⃣ Floyd’s Cycle Detection (Fast & Slow Pointer) 🚀 Same concept as Linked List cycle No extra space needed Time: O(log n), Space: O(1) 💻 JavaScript Code (Floyd’s Approach): var isHappy = function(n) {      const getNext = (num) => {          let sum = 0;          while (num > 0) {              let digit = num % 10;              sum += digit * digit;              num = Math.floor(num / 10);          }          return sum;      };      let slow = n;      let fast = getNext(n);      while (fast !== 1 && slow !== fast) {          slow = getNext(slow);          fast = getNext(getNext(fast));      }      return fast === 1; }; 🔥 What I Learned: Cycle detection isn’t just for linked lists Mathematical problems often hide pattern recognition Always think: Can this loop forever? 📌 Example: 19 → 82 → 68 → 100 → 1 ✅ (Happy Number) #LeetCode #DSA #ProblemSolving #CodingInterview #JavaScript

🚀 𝗗𝗮𝘆 𝟮𝟳/𝟯𝟬 – 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Consistency builds skill. Skill builds confidence. 🚀 As part of my 30-day challenge, I’m focused on solving real-world problems while strengthening core development concepts. 🧠 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗪𝗲𝗮𝘁𝗵𝗲𝗿 𝗔𝗽𝗽 (GUI) I built a Python-based Weather Application using Tkinter that fetches real-time weather data and displays it in an interactive graphical interface. ✨ 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This project goes beyond CLI and introduces GUI development — making applications more user-friendly and visually interactive while still leveraging real-time API data. ⚙️ 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: • Real-time weather data by city 🌍 • Displays temperature, humidity, and condition 🌡️ • Dynamic weather icons (sun, cloud, rain) 🖼️ • Clean and responsive Tkinter UI 💻 • Error handling for invalid inputs ⚠️ 💡 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗲𝗱: • API integration using `requests` • GUI development with Tkinter • Handling JSON data • Image processing using Pillow • Writing modular and clean Python code 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/dSqTtREe 📌 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Moving from CLI to GUI is a big step — it changes how users interact with your application. Building such projects helps understand not just logic, but also user experience. On to Day 28. 🔥 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #APIs #Tkinter #SoftwareDevelopment #Coding #Learning #OpenSource #Projects

🚀 Day 28 / 128 – Coding Challenge Progress Today I worked on the classic "Square Root (x)" problem from LeetCode. 🔍 Problem Summary: Given a non-negative integer x, compute and return its square root rounded down to the nearest integer — without using built-in exponent functions. 💡 Approach I Used: I implemented a Binary Search solution to efficiently find the integer square root. Instead of checking every number, I narrowed down the answer by: Picking a middle value Comparing mid * mid with x Adjusting the search range accordingly ⚡ Why Binary Search? It reduces time complexity from O(n) to O(log n) — making the solution much faster and scalable. 🧠 Key Learning: This problem reinforced how powerful binary search is, even beyond sorted arrays — especially for mathematical computations. 📌 Progress: Day 28 complete, staying consistent and learning something new every day! #128DaysOfCode #CodingChallenge #LeetCode #JavaScript #ProblemSolving #BinarySearch #DeveloperJourney