Day 23 Mirror Distance Problem (Coding Insight) Worked on a simple yet interesting problem involving number manipulation. The task is to compute the mirror distance of an integer — defined as the absolute difference between the number and its digit-reversed form. 💡 Key Idea: Reverse the digits of the number Compute the absolute difference: |n - reverse(n)| 📌 Example: Input: 25 → Reverse: 52 → Output: 27 Input: 10 → Reverse: 1 → Output: 9 This problem highlights fundamentals like digit operations, edge cases (like trailing zeros), and clean logic implementation. #Coding #ProblemSolving #Algorithms #Programming #TechInterview

Solved a great DP problem today: Count Binary Strings Without Consecutive 1’s. Given a length n, the task is to count all distinct binary strings such that no two 1s are adjacent. Example: n = 3 → 000, 001, 010, 100, 101 → 5 n = 2 → 00, 01, 10 → 3 The key insight: 1. Strings ending with 0 can be formed by appending 0 to all valid strings of previous length. 2. Strings ending with 1 can only be formed by appending 1 to strings that previously ended with 0. This leads to a simple DP relation: 1. zero[i] = zero[i-1] + one[i-1] 2. one[i] = zero[i-1] So the pattern follows a Fibonacci-style sequence, and the problem can be solved in: O(n) time and O(1) space I enjoy how problems like this look simple at first, but the right state definition makes them very elegant. #DataStructures #Algorithms #DynamicProgramming #Coding #ProblemSolving #Cpp #Programming #SoftwareDevelopment

Recover the Tree ?? by providing water! Approach (Recover BST) Do inorder traversal (BST should be sorted) Track previous node If order breaks (prev > current), it’s a violation First violation: first = prev middle = current Second violation (if exists): last = current Time Complexity: O(n) → you traverse all nodes once using inorder Space Complexity: O(h) → recursion stack (h = height of tree) Worst case (skewed tree): O(n) Best case (balanced tree): O(log n) #LeetCode #DSA #DataStructures #Algorithms #Coding #Programming #binarysearchtree

Solved the Reverse Integer problem without using any built-in functions 💻 Implemented a digit-by-digit reversal approach while carefully handling overflow using boundary checks. This ensures the solution is both safe and efficient. 🔹 Time Complexity: O(log n) 🔹 Space Complexity: O(1) Glad to see it pass all test cases with optimal performance 🚀 Sometimes, sticking to fundamentals is the best way to strengthen problem-solving skills. #Coding #DataStructures #Algorithms #ProblemSolving #CProgramming #LeetCode

💻 Day 51/100 – LeetCode Challenge. Today’s problem: 560. Subarray Sum Equals K. Finding the total number of continuous subarrays whose sum equals a target k. 🔹 Approach (Brute Force). 1)Iterate over every starting index i. 2)Keep adding elements from i to the end (nums[j]). 3)If the running sum equals k, increment the count. Time Complexity: O(n²) | Space: O(1) 🔹 Key Takeaways. 1)Brute force helps understand the core logic. 2)For larger arrays, optimize with Prefix Sum + HashMap → O(n). 3)Step by step learning is key to mastering algorithms! 🚀. #100DaysOfCode #LeetCode #Programming #Algorithms #CodingChallenge #ProblemSolving 

Vector indexes don't have to break the bank. This video breaks down DiskBBQ — a disk-first vector index that keeps large embedding collections on disk, minimizes RAM usage, and preserves query performance so your vector DB costs don't scale linearly with size. Short, practical explainer with architecture highlights and benchmarks. Read more: https://lnkd.in/efQhhz7F #programming #ai #database https://lnkd.in/eG-yVXbg

🚀 Mastering Binary Search Beyond Basics! Binary Search isn’t just about finding an element — it’s about finding the right answer efficiently. From lower & upper bounds to rotated arrays and monotonic functions, these patterns unlock powerful problem-solving techniques. 💡 Key takeaway: Turn brute force O(n) into optimized O(log n) Level up your DSA game by recognizing patterns, not just memorizing code! #BinarySearch #DSA #ProblemSolving #CodingPatterns #TechSkills #Programming #InterviewPrep #DataStructures #Algorithms #LearnToCode

📌 We learn stacks as a simple concept: Last In, First Out 👉 That makes it easier to recognize using stack as a pattern for problems such as “Parantheses matching” or “Undo/Redo” But then you come across a problem like Daily Temperatures 🌡️ Why would you even think of using a stack here? 🤷♀️ And more importantly — how? 🤔 That’s where the idea of a “Monotonic stack” comes in. 📌 Instead of using a stack just for insertion order, we use it to maintain a condition. 👉 Elements are kept in a specific order (increasing or decreasing) 👉 The moment that order is violated, we start popping So the stack isn’t just storing elements — it’s helping us eliminate unnecessary comparisons and keep only what’s useful ✔️ And that shift is what makes the solution efficient ⚡ Why this problem captures my interest is because: 👉 Not every problem clearly signals the pattern you need, the real skill is identifying which structure helps you track and optimize information efficiently👍 #softwareengineering #leetcode #algorithms #dsa #stack #coding #programming #problemSolving #computerscience

Explain about a AVL Trees? An AVL tree is a self-balancing binary search tree (BST) where the height difference (balance factor) between left and right subtrees is at most 1 for all nodes. It guarantees time for search, insertion, and deletion by using four rotation types (LL, RR, LR, RL) to restore balance. #AVLTrees #DataStructures #Algorithms #BalancedTrees #TreeTraversal #ComputerScience #Programming #TechLearning #DSA #BinaryTrees #AlgorithmDesign #CodingInterview #SoftwareEngineering #Coding #DeveloperLife

Day 65 on LeetCode Sort Characters By Frequency 🔤📊✅ Today’s problem focused on combining hash maps with custom sorting. 🔹 Approach Used in My Solution The goal was to sort characters in a string based on their frequency in descending order. Key idea in the solution: • Use a hash map to count frequency of each character • Store (character, frequency) pairs in a vector • Apply custom sorting based on frequency (descending order) • Build the result string by repeating characters according to their frequency This approach efficiently organizes characters by importance (frequency). ⚡ Complexity: • Time Complexity: O(n log n) (due to sorting) • Space Complexity: O(n) 💡 Key Takeaways: • Practiced frequency counting using hash maps • Learned how to apply custom comparators in sorting • Reinforced building results based on frequency-driven logic #LeetCode #DSA #Algorithms #DataStructures #HashMap #Sorting #Strings #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney