Top 10 Algorithms in Development: A Quick Review

Hot take: Using stochastic LLMs to write imperative, step-by-step code is like using a probability engine to calculate 2+2. Sure, it works... but are we applying the wrong tool to the problem, or are we still thinking about programming in outdated paradigms?

🚀 Tried breaking down Pascal’s Triangle in the simplest way 👇 We’ve all seen this pattern(Pascal's Triangle) somewhere but have you ever wondered how it’s actually generated? 🤔 The question is simple: 👉 Given an integer n, generate the first n rows of Pascal’s Triangle. For example, when n = 3 💡 How this approach clicked: While thinking about how each element is formed, I realized that every value in Pascal’s Triangle is just a combination (nCr) and this relation C(n,r)=C(n,r−1)∗(n−r+1)/r , is connecting each next element with the previous one computed. This not only makes the logic cleaner but also eliminates redundant factorial computations. so, here is the code and the dry run of the solution , once you see it this way, Pascal’s Triangle feels surprisingly simple! 😄 #C++ #DSA #ProblemSolving #CodingJourney #PascalTriangle #Programming #Learn #CodeVisualization #WomenInTech

🔥 Day 21 | LeetCode Challenge – Unique Paths (Problem #62) Today’s challenge focused on finding the number of unique ways a robot can move from the top-left corner to the bottom-right corner of an m x n grid — with the restriction that it can only move right or down at any step. 💡 Concept Overview: The task is a classic Dynamic Programming problem that leverages the principle of counting paths using cumulative sub-solutions. Each cell’s path count is derived from the sum of paths from the top and left cells — as those are the only directions available to reach it. 📘 Approach Used: Used a 1D DP array to optimize space. Initialized the first row with one path since there’s only one way to move horizontally. Iteratively updated the DP array to accumulate paths from top and left cells. The final value in the array represents all unique paths to the target. ⚙️ Algorithm Used: Dynamic Programming (Space Optimized) 🧾 Language: Java ⚡ Runtime: 0 ms (Beats 100%) 💾 Memory: 41.86 MB 🧠 Key Insight: Efficient path-counting problems often reveal that simple cumulative logic and state reuse can drastically minimize memory usage while maintaining optimal performance. #Day21 #LeetCode #UniquePaths #DynamicProgramming #JavaCoding #ProblemSolving #100DaysOfCode #DataStructuresAndAlgorithms #CodingChallenge #TechLearning #ProgrammingJourney #CodeOptimization

🔥 Day 79 of #100DaysDSAChallenge 📌 Topic: Array — Minimum Moves to Equal Array Elements ✅ Problem Solved on #LeetCode: 453. Minimum Moves to Equal Array Elements (🟠 Medium) 💡 Key Learnings: • Understood that incrementing (n−1) elements is equivalent to decrementing one element. • Learned how to derive the formula using the smallest element as a reference point. • Strengthened problem-solving with array manipulation and mathematical reasoning. • Enhanced understanding of optimization — solving in O(n) time and O(1) space. 🚀 Stay Consistent: Daily practice turns effort into skill and skill into success. 🏷️ #Day79 #100DaysChallenge #100DaysDSAChallenge #LeetCode #Java #CodingChallenge #ProblemSolving #DataStructures #Algorithms #Arrays #Programming #LearningJourney #10kCoders #10000Coders #Consistency #LogicBuilding

🧩 Day 64 of #100DaysOfCode 🧩 🔹 Problem: Remove All Adjacent Duplicates in String – LeetCode ✨ Approach: Used a stack-based approach to efficiently remove adjacent duplicates. For each character, if it matches the stack’s top element, pop it — otherwise, push it. A simple yet powerful way to process strings in O(n) time while maintaining clean logic. ⚡ 📊 Complexity Analysis: Time Complexity: O(n) — each character is processed once Space Complexity: O(n) — for the stack and output string ✅ Runtime: 23 ms (Beats 54.52%) ✅ Memory: 45.26 MB (Beats 85.43%) 🔑 Key Insight: Sometimes, solving problems isn’t about brute force — it’s about using the right data structure to make every step count. 💡 #LeetCode #100DaysOfCode #ProblemSolving #DSA #Stack #StringManipulation #CleanCode #CodingChallenge #AlgorithmDesign #LogicBuilding #CodeJourney #Programming

Achieving Atomicity and Isolation in Concurrent Rust: Channels vs. Mutexes Here's the thing about concurrent programming in Rust: the moment you try to share data between threads, the compiler forces you to pick a side. You can't sit on the fence. Either you embrace the "one owner at a time" philosophy of channels, or you accept the "share but wait your turn" reality of... https://lnkd.in/e5MbGnwC By Amrit Singh

On Day 299 of my #300daysofcode challenge, I'm sharing my solution to LeetCode Problem 275, "H-Index II." The key to solving this problem efficiently is recognizing that the sorted input array creates a monotonic property that enables Binary Search. My video provides a clear walkthrough of the $O(\log N)$ search logic, a highly valued skill for technical interviews focused on algorithmic optimization. #DataStructures #Algorithms #LeetCode #CodingInterview #Programming #BinarySearch #300daysofcode

🌳 Day 82 of #100DaysOfCode Today, I tackled the Lowest Common Ancestor (LCA) problem in a Binary Tree — a classic interview and concept-building question in tree algorithms 🌲 🧩 What I learned: 👉 The LCA of two nodes is the deepest node that is an ancestor of both. 👉 If the current node matches either p or q, we return it. 👉 Then, we recursively search in both left and right subtrees. 👉 If both calls return non-null, the current node is the LCA — it’s the point where the paths to both nodes meet. 👉 Otherwise, we propagate the non-null child upwards. 💡 This problem beautifully demonstrates recursion, tree traversal, and how divide-and-conquer logic works in hierarchical data structures. #DSA #BinaryTree #Recursion #Coding #CPlusPlus #100DaysOfCodeChallenge #Programming #ProblemSolving #TechJourney

On Day 295 of my #300daysofcode challenge, I'm sharing my solution to LeetCode Problem 981, "Time Based Key-Value Store." This problem requires designing a specialized data store for version control. My video provides a clear walkthrough of the optimal design: using a HashMap to quickly find the key and then applying Binary Search on the timestamps to retrieve the correct historical value. This is a valuable skill for advanced data structure implementation and system design interviews. #DataStructures #Algorithms #LeetCode #CodingInterview #Programming #SystemDesign #300daysofcode

Day 8 of Mastering DSA Patterns: Container With Most Water : Another day, another DSA challenge conquered. Today's focus was on the Two Pointer technique, and I solved the LeetCode classic: Container With Most Water. This problem is a fantastic illustration of how a clever choice of algorithm can drastically reduce time complexity. Instead of checking every pair of lines an O(n^2) brute-force solution), the Two-Pointer approach offers an efficient O(n) solution: 1.Start pointers at the farthest ends of the array. 2.Calculate the area. 3.The area is limited by the shorter line. To potentially find a larger area, you must try to increase the height. Therefore, move the pointer of the shorter line inward. Moving the longer line's pointer would only decrease or keep the height the same, offering no guaranteed improvement. 4.Repeat until the pointers meet. It's a subtle but powerful optimization! #DSA #DataStructures #Algorithms #ContainerWithMostWater #LeetCode #CodingChallenge #TwoPointers #Java #Programming #Tech