Algonur’s Post

🚀 Recursion: The Foundation of Problem Solving Recursion is not just a concept — it’s a way of thinking. When you truly understand recursion, you start seeing patterns everywhere. Problems that once looked complex begin to break down into smaller, manageable pieces. 💡 Why recursion matters: It is the backbone of solving Trees & Graphs (DFS, traversals, backtracking) It builds the intuition needed for Dynamic Programming DP is simply optimized recursion with memory But the real benefit goes beyond just concepts… 🧠 Recursion trains your brain You learn to approach problems from multiple angles You stop getting stuck on a single approach You naturally start thinking in terms of subproblems and structure The more problems you solve using recursion, the sharper your problem-solving mindset becomes. And that’s where growth happens. 👉 Don’t just learn recursion — practice it deeply. Because once it clicks, it unlocks Trees, Graphs, DP… and a whole new level of thinking. #Recursion #DataStructures #Algorithms #DynamicProgramming #CodingJourney #ProblemSolving

🚀 Understanding Time Complexity — The Backbone of Efficient Code Ever wondered why some programs scale beautifully while others slow to a crawl? The answer lies in Time Complexity. 🔍 Here’s a quick breakdown: ✅ Best Case (Ω) – The minimum time an algorithm takes 👉 Example: Finding an element instantly → O(1) ⚖️ Average Case (Θ) – Typical performance across inputs 👉 Balanced scenario between best and worst ⚠️ Worst Case (O) – Maximum time taken 👉 Example: Searching through all elements → O(n) 💡 Common Time Complexities: • O(1) → Constant (Fastest) • O(log n) → Logarithmic • O(n) → Linear • O(n log n) → Efficient sorting • O(n²) → Quadratic (gets slow quickly) • O(2ⁿ), O(n!) → Exponential (avoid when possible!) 📈 Key Insight: As input size grows, inefficient algorithms become bottlenecks. Choosing the right approach can mean the difference between milliseconds and minutes. 🔥 Pro Tip: Don’t just make your code work — make it scale. #DataStructures #Algorithms #TimeComplexity #Coding #SoftwareEngineering #Tech #Learning #Programming

Using tools like Cursor and Antigravity made me realize something: The biggest productivity boost isn’t the tool itself — it’s knowing how to use the tool well. AI IDEs can speed up: ✅ Refactoring ✅ Boilerplate generation ✅ Debugging ideas ✅ Learning unfamiliar codebases But architecture decisions, problem solving, and clean code thinking still come from the developer. Feels like we’re moving from “writing every line” to “guiding code intelligently.” Anyone else exploring AI-powered dev tools in their workflow? 👇 #Cursor #Antigravity #AICoding #DeveloperTools #Programming

🚀 Day 94 / 100 — Two Pointers Is Not a Technique, It’s a Mindset I used to think the two pointers approach was just another pattern… But today I realized — it’s actually a way of thinking. Earlier, my mindset was: 👉 “How should I write the loop?” Now it’s: 👉 “Can I shrink the search space intelligently?” 💡 Key Insight: Two pointers is not just about moving indices… It’s about eliminating unnecessary work. Instead of checking every possible pair (brute force), we move pointers based on conditions — drastically reducing time complexity. ⚡ What I learned: • Sorted / monotonic arrays are a strong hint for two pointers • One pointer anchors, the other explores • Movement is always condition-driven, not random • It reduces time complexity from O(n²) → O(n) 🎯 Realization: Brute force checks everything. Two pointers decide what NOT to check. 📍 Learning at Parul University 💻 Practicing daily on LeetCode 🔥 Day 94 complete. #LeetCode #DSA #Algorithms #Coding #ProblemSolving #CompetitiveProgramming #TwoPointers #DeveloperJourney #100DaysOfCode #Tech #CodingLife #GrowthMindset

🚀 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 | 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞 | 𝐖𝐨𝐫𝐝 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 📌 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭: A dictionary application learns from the user’s text. Whenever a word appears more than once, the application stores that word in the dictionary and later uses it as a suggestion in future typing sessions. The task is to identify all repeated words and print them in lexicographical order. Example: Input: 5 cat bat cat rat bat Output: bat cat 💡 𝐎𝐩𝐭𝐢𝐦𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Read all the words from the input. Use a hash map to store the frequency of each word. Traverse the map and collect words with frequency greater than 1. Sort those words lexicographically. Print the result. Why Hashing? Hashing allows us to count word frequencies efficiently in nearly O(1) average time for each insertion and lookup. ⏱ Time Complexity: Frequency Counting: O(n) Sorting Repeated Words: O(k log k) Where: n = total number of words k = number of repeated words 🔗 Optimal Approach Code Link: https://lnkd.in/gWJ8QwtG #CompetitiveProgramming #Hashing #CPP #Coding #DSA #ProblemSolving #Algorithms #Programming

🚀 𝐂𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 | 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 | 𝐥𝐨𝐧𝐠𝐞𝐬𝐭 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐬𝐮𝐛𝐚𝐫𝐫𝐚𝐲 𝐰𝐡𝐨𝐬𝐞 𝐬𝐮𝐦 𝐢𝐬 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐞𝐪𝐮𝐚𝐥 𝐭𝐨 𝐊. 🔹 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭: You are given an array A of size N and an integer K. Your task is to find the length of the longest continuous subarray whose sum is exactly equal to K. 🔹 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐌𝐚𝐭𝐭𝐞𝐫𝐬? A brute force approach (checking all subarrays) takes O(N²) time, which is inefficient for large inputs. To optimize this, we use Hashing + Prefix Sum, reducing the complexity to O(N). 🔹 𝐎𝐩𝐭𝐢𝐦𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐔𝐬𝐢𝐧𝐠 𝐇𝐚𝐬𝐡𝐢𝐧𝐠) 1️⃣ Compute Prefix Sum while traversing the array 2️⃣ Use a Hash Map to store the first occurrence of each prefix sum 3️⃣ At each index i, check: 👉 If (prefixSum - K) exists in the map 4️⃣ If it exists, a valid subarray is found 5️⃣ Update the maximum length accordingly 💡 Key Idea: Store only the first occurrence of prefix sums to maximize subarray length. 🔹 Example Input: A = [2, -1, 2, 1, -2, 3], K = 3 Output: 4 Explanation: Subarray [2, 1, -2, 3] has sum = 3 and length = 4. 🔹 Complexity Analysis ⏱ Time Complexity: O(N) 📦 Space Complexity: O(N) 🔹 Code (Optimal Approach) 🔗 https://lnkd.in/dckHGMiW 🔥 𝐊𝐞𝐲 - 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 This problem is a perfect example of how hashing transforms brute force into optimal solutions. Mastering such patterns is crucial for cracking coding contest and excelling in competitive programming. #CompetitiveProgramming #DSA #Hashing #Codingcontest #ProblemSolving #Cpp

Day 66 on LeetCode — Binary Search 🔍✅ Today’s problem focused on one of the most fundamental and powerful techniques in DSA — Binary Search. 🔹 Approach Used in My Solution The goal was to find the index of a target element in a sorted array. Key idea in the solution: • Maintain two pointers: low and high • Calculate mid = low + (high - low) / 2 to avoid overflow • Compare nums[mid] with target:   – If equal → return index   – If greater → search left half   – If smaller → search right half • Continue until the search space is exhausted This approach efficiently reduces the search space by half in every step. ⚡ Complexity: • Time Complexity: O(log n) • Space Complexity: O(1) 💡 Key Takeaways: • Mastered the core concept of divide and conquer • Learned how to safely calculate mid to avoid overflow • Reinforced understanding of searching in sorted arrays efficiently 🔥 Binary Search is a must-know pattern for interviews and advanced problems! #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #Arrays #DivideAndConquer #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 80 on LeetCode Next Greater Element I 🔍📈✅ Not the optimal solution this time, but the focus remains the same consistency over perfection 💯 🔹 Approach Used in My Solution (Brute Force) The goal was to find the next greater element of each value in nums1 based on its position in nums2. Key idea: • For each element in nums1 → find its position in nums2 • From that index, scan to the right • The first element greater than current → that’s the answer • If none found → return -1 A straightforward approach that gets the job done. ⚡ Complexity: • Time Complexity: O(n * m) • Space Complexity: O(1) (excluding output) 🔹 Optimal Insight (For Next Time) • Use a monotonic decreasing stack on nums2 • Precompute next greater for all elements • Store results in a hash map for O(1) lookup 💡 Key Takeaways: • Even brute force builds understanding of the problem • Learned how nested traversal simulates next greater search • Not every day is about optimization — discipline matters more 🔥 Streak > Speed. Showing up daily is the real win. #LeetCode #DSA #Algorithms #DataStructures #Stack #MonotonicStack #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #Consistency #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Cursor's code review agent just got smarter. It now learns from PR activity in real time. The result: 78% of issues found are resolved by the time the PR is merged. How it works: → Reviews your code automatically → Learns from feedback on PRs → Self-improves with every review → Catches issues before merge Why it matters: Fewer bugs in production. Faster PR cycles. Less back-and-forth. Learn more: cursor.com/code-review Cursor #Cursor #CodeReview #AI #SoftwareDevelopment #DevTools #Programming #Engineering

📚 Day 17/130 — What is Big O Notation? Today in my Daily Tech Learning Series, let’s understand a fundamental concept in algorithms 👇 🔹 What is Big O Notation? Big O Notation is used to describe the performance or efficiency of an algorithm in terms of time or space as input size grows. 🔹 Simple Understanding: 👉 Big O = How an algorithm scales with more data It focuses on the worst-case scenario 🔹 Why is it Important? • Helps compare algorithms 📊 • Identifies efficient solutions ⚡ • Crucial for coding interviews • Used in real-world systems 🔹 Common Big O Examples: • O(1) → Constant (best) • O(log n) → Very efficient • O(n) → Linear • O(n log n) → Good (sorting) • O(n²) → Slow 🔹 Key Idea: 👉 Ignore constants & small terms 👉 Focus on how the algorithm grows Example: 2n + 5 → O(n) 🔹 Real-Life Analogy: 👉 Finding a contact in: Sorted phonebook → faster (log n) Unsorted list → slower (n) 📊 See the diagram below for better understanding. 📌 Tomorrow’s Topic: 👉 Best, Average & Worst Case Complexity #BigO #Algorithms #DataStructures #Programming #Coding #TechLearning #LearningInPublic #Students #Developer