{"title": "Cracking the "Anagram" Problem in 4 Different Ways"}

I learned the hard way: details matter. Edge cases can kill you. I'd nailed the overall strategy, but those pesky edge cases cost me. 32 iterations later, I finally got it. The interviewer's hint stuck: "O(1)? Think divide and conquer." Swap those halves, then refine. Reverse Bits, a LeetCode problem, is about reversing bits of a 32-bit unsigned integer. Naive approach: 32 iterations, shifting and ORing. But there's a better way - swap halves, then 8-bit blocks, then 4, 2, 1. Only five steps. You're swapping larger blocks first, refining as you go. Each step doubles the size of properly ordered regions. It's tough. This is a standard bit-manipulation interview question, testing block operations thinking. I had to redo a lot. In Python, use a mask - & 0xFFFFFFFF. Python integers are arbitrary precision, so without masking, you get more than 32 bits. In C/Java, it's all about unsigned right shift. This pattern doesn't often extend to other problems, but it's one drill worth doing - for that "can you do O(1)?" follow-up. Once the bit trick clicks, the O(1) follow-up stops feeling like magic. Take a closer look and start practicing - it'll make you better off in the next coding interview. #LeetCode #OpenToWork #CodingInterview #InterviewPrep

🚀 My Interview Preparation Journey Most people *memorize* star patterns. Today, I tried to **understand the logic behind them.** Here’s what I learned 👇 ✨ Golden Rules: • Rows decide the height (i controls lines) • Columns depend on pattern type → Fixed: j ≤ n → Increasing: j ≤ i → Decreasing: j ≤ n - i ✨ Pyramid Pattern Logic: • Spaces = n - i • Stars = 2*i - 1 ✨ Real Skill (Important 🔥) Whenever you see a pattern, ask: 1. How many rows? 2. What changes per row? 3. How many spaces? 4. Are stars increasing or decreasing? This completely changed how I approach problems. 📌 Sharing my handwritten notes for better understanding. Consistency > Perfection. Let’s grow step by step. 🚀 #learninginpublic #dsa #javascript #python #coding #programming #interviewpreparation #100daysofcode

🔁 Coding Question of the Day: Detect Cycle in a Linked List One of the most elegant problems in data structures — simple to understand, but powerful in interviews. 💡 Problem: Given the head of a linked list, determine if it contains a cycle. 👉 A cycle occurs when a node points back to a previous node instead of "null". --- 🚀 Optimal Approach: Floyd’s Cycle Detection (Tortoise & Hare) Use two pointers: • Slow → moves 1 step • Fast → moves 2 steps If there’s a cycle, they will eventually meet! --- 💻 Python Solution: def hasCycle(head): slow = head fast = head while fast and fast.next: slow = slow.next fast = fast.next.next if slow == fast: return True return False --- ⏱ Complexity: Time: O(n) Space: O(1) --- 🔥 Interview Tip: Want to stand out? Don’t stop at detection. Try finding the starting point of the cycle — a common follow-up! --- #DataStructures #CodingInterview #LeetCode #100DaysOfCode #SoftwareEngineering #TechCareers

🚀 Just found one of the most practical DSA interview prep resources out there. This PDF covers 120+ DSA Interview Questions & Answers across all major topics - from Arrays and Linked Lists to Graphs, Dynamic Programming, Tries, and Complexity Analysis. What I liked most: ✅ Clean explanations ✅ Real interview-style questions ✅ Python code examples for every concept ✅ Time & space complexity included ✅ Covers popular patterns like: Sliding Window Two Pointers BFS/DFS Dynamic Programming Backtracking Monotonic Stack Topological Sort If you're preparing for: 💻 Software Engineering interviews 📚 DSA rounds 🏆 Coding interviews at startups or big tech companies …this is a solid resource to revise core concepts fast. Some standout topics included: 🔹 Kadane’s Algorithm 🔹 KMP Algorithm 🔹 Dijkstra’s Algorithm 🔹 Union-Find 🔹 LRU concepts 🔹 Segment Trees & Fenwick Trees 🔹 Knapsack & LIS 🔹 Graph Traversals 🔹 Binary Search Patterns Honestly, resources like this save hours of searching random tutorials online. Consistency + problem solving = interview success. 🔥 follow JenesisAI for more #DSA #CodingInterview #Programming #SoftwareEngineering #LeetCode #Algorithms #DataStructures #TechInterview #Python #CodingPrep

A lot of people struggle with DSA… not because it’s hard — but because they miss the pattern. One developer solves 200 problems. Still gets stuck in interviews. Another solves just 20… but recognizes the pattern instantly. The difference? Pattern recognition. Take this problem: 🔥 Count Number of Nice Subarrays At first glance, it feels confusing. Subarrays… odd numbers… count exactly k… Most people try brute force → ❌ O(n²) But the real trick is 👇 💡 Prefix Sum + HashMap pattern 🧠 How it works • Convert problem into: → Count subarrays with sum = k • Treat odd numbers as 1, even as 0 • Use prefix sum to track cumulative count • Use HashMap to store frequency of prefix sums • Check: currentSum - k ⚡ Why this is powerful This same pattern works for: • Subarray sum equals K • Binary arrays • Count problems • Even/odd transformations 📚 Hard truth You don’t need to solve 500 problems. You need to master: → Sliding Window → Two Pointers → Prefix Sum + HashMap → Binary Search That’s where interviews are cracked. #DSA #LeetCode #CodingInterview #SoftwareEngineering #ProblemSolving #DataStructures #Programming #TechLearning

Struggling with subarray problems? This one pattern can solve most of them 👇 If you’re preparing for coding interviews or improving your problem-solving skills, the Prefix Sum technique is a must-know pattern. 💡 What is Prefix Sum? It’s a way to store the cumulative sum of elements so that you can quickly calculate the sum of any subarray in constant time. 👉 Example: For array [1, 2, 3, 4] Prefix sum becomes → [1, 3, 6, 10] 🎯 Why use it? . Reduces time complexity from O(n²) → O(n) . Helps solve problems like: - Subarray sum = k - Longest subarray with given sum - Count of subarrays 🧠 Core Idea: Instead of recalculating sums again and again: sum(i to j) = prefix[j] - prefix[i-1] 🔥 Power Boost with HashMap Combine prefix sum with a hashmap to: - Track previously seen sums - Solve complex problems in one pass ⚡ Key Patterns to Remember: - currentSum += nums[i] - Check currentSum - k - Store sum in hashmap - For longest → store first occurrence - For count → store frequency 💬 Learning Tip: Don’t just memorize the code — understand why we store sums and how we use them. 📌 Prefix Sum is not just a trick — it’s a pattern that appears again and again in interviews. Keep practicing. Keep improving. 💪 #DSA #CodingInterview #Programming #Python #SoftwareEngineering #Learning #100DaysOfCode

🚨 Interview Experience Today 👇 Problem: Input: [0,1,0,3,12] Output: [1,3,12,0,0] ⚠️ Constraint: Do not create new/another list ⸻ 💭 My attempt (under time pressure): print(f”[{nums[1]}, {nums[3]}, {nums[4]}, {nums[0]}, {nums[0]}]”) Output: [1, 3, 12, 0, 0] ❌ Rejected ⸻ 💡 Approach shared after: nums = [num for num in nums if num != 0] + [0] * nums.count(0) print(nums) 🤔 I pointed out that this still creates a new list ⸻ 🧠 My Question: 👉 Would this still be considered valid given the constraint? 👉 Or should the expectation strictly be an in-place solution? ⸻ 💬 Curious to hear how you would approach this! #InterviewExperience #Python #CodingInterview #DataStructures #Learning #BackendDevelopment #SoftwareEngineering

🚀 𝗚𝗿𝗼𝘂𝗽 𝗔𝗻𝗮𝗴𝗿𝗮𝗺𝘀 – 𝗙𝗿𝗼𝗺 𝗕𝗿𝘂𝘁𝗲 𝗙𝗼𝗿𝗰𝗲 𝘁𝗼 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 (𝗗𝗦𝗔 𝗝𝗼𝘂𝗿𝗻𝗲𝘆) Grouping anagrams is one of the most common interview problems — and a great way to understand hashmaps, strings, and optimization. Let’s break it down step by step 👇 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗱𝗲: https://lnkd.in/g5pP4F4j 🔴 𝗕𝗿𝘂𝘁𝗲 𝗙𝗼𝗿𝗰𝗲 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 👉 Compare each word with every other word 👉 Use character frequency to check anagrams 📌 Time Complexity: O(n² * k) 💡 Good for understanding logic, but not efficient 🟡 𝗕𝗲𝘁𝘁𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿 𝗖𝗼𝘂𝗻𝘁) 👉 Use a 26-length array (a–z) 👉 Convert it into a tuple as hashmap key 📌 Time Complexity: O(n * k) 💡 Faster and interview-friendly 🟢 𝗢𝗽𝘁𝗶𝗺𝗮𝗹 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 (𝗦𝗼𝗿𝘁𝗶𝗻𝗴) 👉 Sort each word and use it as key Example: eat → aet tea → aet ate → aet 📌 Time Complexity: O(n * k log k) 💡 Clean, simple, and widely used ⚡ 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 ✅ Anagrams share the same sorted form ✅ Hashmaps are powerful for grouping ✅ Always think about time complexity ✅ Start with brute → then optimize 💬 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗧𝗶𝗽 If interviewer asks: 👉 “Can you optimize further?” → Use character count 👉 “Can you simplify?” → Use sorting approach 🔥 Practicing these patterns daily helps you crack coding interviews faster! #DSA #Python #CodingInterview #SoftwareEngineering #LeetCode #100DaysOfCode #Developers #Programming

I recently went through two strong interview rounds for a position, only to find out the role was being canceled entirely. It wasn't a rejection of my skill; it was a business pivot. In many ways, that's more frustrating than a "no," because there's no feedback to take back to the drawing board. So, I decided to build my own drawing board. Instead of just waiting for the next opportunity, I’m building a "Job Hunter's Toolkit," a suite of local tools written in Python to help me (and others) navigate this landscape with data, not just opinions. Phase 1: The Application Tracker * Hands-on Python: It’s a perfect way to keep my skills sharp during my final month at WGU. 1️⃣Data-Driven Insights: It exports to Excel perfectly, allowing me to track my Applied -> Interview -> Outcome percentages. 2️⃣Objective Perspective: I want to see exactly where the success is happening, rather than relying on market theory. This isn't about gaming the system; it's about applying Architectural Rigor to the job search. As my Python knowledge grows, so will the toolkit. You can follow the progress and grab the tools here: 👉 https://lnkd.in/eWRV8nYX For the job seekers: How are you using your "downtime" to sharpen your tools? For the hiring managers: How much do you value seeing a candidate's "Internal Projects" during the interview process? #Python #JobSearch #DataAnalytics #CloudEngineering #BuildInPublic #WGU #SolutionsArchitect #Automation #OpenSource #Resilience

🚀 When Model Performance Drops in Production In one of my interviews, I was asked: 👉 “What would you do if your model performance degrades over time?” 🧠 My approach I start by checking Data Drift. https://lnkd.in/d7PezmUS This means: 👉 the data in production is different from training data. And when that happens, even a good model starts failing. ⚙️ Simple first step I don’t jump into complex methods. I start with: Compare mean of training data Compare mean of new data Measure the difference Use a threshold to detect drift 🎯 Final thought Start simple. Detect the change early. Then improve the system. #MachineLearning #MLOps #DataDrift #AIEngineering #Python