Walking Robot Simulation II LeetCode Optimization

🔥 Day 554 of #750DaysofCode 🔥 🚀 Problem Solved: Walking Robot Simulation Today’s problem tested my simulation + hashing skills in a really interesting way! 🤖 What I implemented: I simulated the robot’s movement step-by-step on an infinite grid while handling obstacles efficiently. 💡 Instead of using complex structures, I used: 👉 HashSet to store blocked positions 👉 Direction array to manage movement (N, E, S, W) 🧠 Core Idea: Maintain current direction using an index Rotate: Right → (dir + 1) % 4 Left → (dir + 3) % 4 Move one step at a time (very important 🚨) Before every step: Check if next position is blocked If yes → stop moving for that command ⚡ Key Insight: 👉 You cannot jump directly k steps 👉 You MUST move step-by-step to correctly detect obstacles 💻 My Approach: ✔️ Stored obstacles as "x,y" in HashSet ✔️ Used direction vector: North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) ✔️ Tracked max distance using: 👉 x² + y² 📈 Complexity: Time: O(N + total steps) Space: O(M) for obstacles 🎯 What I learned: Simulation problems require careful step execution Hashing makes obstacle lookup super fast Small implementation details can make or break the solution 💬 Honestly, this problem looks easy at first, but handling directions + obstacles correctly makes it a great practice problem! #Day554 #750DaysOfCode #LeetCode #Java #DSA #CodingJourney #ProblemSolving #Developers #Tech

#Day359 of #1001DaysOfCode 📘 LeetCode Daily Challenge Problem: Walking Robot Simulation (LeetCode 874) 💡 Approach: Simulated the robot’s movement step-by-step while tracking direction changes. Used a HashSet to store obstacle positions for O(1) lookup, ensuring the robot stops correctly when encountering obstacles. Updated the maximum distance from origin after each move. ⏱ Time Complexity: O(n + k) 🧠 Space Complexity: O(m) Staying consistent and improving problem-solving skills every day 🚀 #DSA #Java #LeetCode #ProblemSolving #Coding

🚀 Day 553 of #750DaysOfCode 🚀 🤖 LeetCode 657: Robot Return to Origin Today’s problem was simple yet a great reminder of how powerful basic logic can be. 📌 Problem Summary: A robot starts at (0,0) and follows a sequence of moves: 'U' → Up 'D' → Down 'L' → Left 'R' → Right 👉 Goal: Check whether the robot returns back to the origin after all moves. 💡 Approach: Instead of overthinking, I focused on tracking movement on axes: Vertical → U cancels D Horizontal → L cancels R If both balances are zero → back to origin ✅ 🧠 Key Insight: This problem is not about simulation, but about balance. ⚡ Complexity: Time → O(n) Space → O(1) 🎯 Takeaway: Even easy problems strengthen your fundamentals. Consistency > Complexity 💯 🔥 553 days down, 197 to go! Let’s keep building 🚀 #LeetCode #Java #DSA #CodingJourney #Consistency #ProblemSolving #Developers #100DaysOfCode #Tech

Day 93: Optimization > Completion 📉 Problem 3661: Maximum Walls Destroyed by Robots Today was a massive lesson in efficiency. I initially cleared all test cases with a Memoization + Binary Search approach, but at O(N²), I knew it wasn't the most optimal way to handle the constraints. I decided to dig deeper and refactor the entire logic into a cleaner Dynamic Programming solution. The Strategy Shift: • The O(N²) Trap: My first pass worked, but nested recursion with memoization can get heavy. I wanted to see if I could solve it in a single linear pass. • State Transition: I moved to a DP approach using subLeft and subRight to track the maximum walls destroyed up to each robot. • Precision Boundary Logic: By pre-calculating the left and right firing ranges for each robot using Binary Search (lowerBound/upperBound), I could transition states in O(N) time. I’m honestly not proud of my initial approach today. It felt a bit like forcing a solution rather than finding the most elegant one. Pushing myself to find the O(N) path when I already had a working "Pass" was the real challenge, but it’s where the growth happens. We go again tomorrow. 🚀 #LeetCode #Java #DynamicProgramming #BinarySearch #Algorithms #DailyCode

🚀 Day 32 of #LeetCode Journey ✅ Problem: Robot Return to Origin (LeetCode 657) Today’s problem was simple yet a great way to strengthen basic logic and coordinate tracking skills. 🔍 Problem Statement: Given a string of moves (U, D, L, R), determine if the robot returns to the origin after completing all moves. 💡 Approach: * Start from position (0,0) * Track horizontal (x) and vertical (y) movements * Update position based on each move * Finally, check if we are back at (0,0) 🧠 Key Insight: Equal number of opposite moves cancel each other out: * U cancels D * L cancels R ⏱️ Complexity: Time: O(n) Space: O(1) 📌 Takeaway: Even simple problems help build strong fundamentals in problem-solving and coding logic. #Java #LeetCode #CodingJourney #100DaysOfCode #Programming #Developer

#Day358 of #1001DaysOfCode 📘 LeetCode Daily Challenge Problem: Robot Return to Origin (LeetCode 657) 💡 Approach: Simulated the robot’s movement on a 2D plane by tracking vertical and horizontal positions. Each move updates the position, and if the robot returns to (0,0), it means all movements are balanced. ⏱ Time Complexity: O(n) 🧠 Space Complexity: O(1) Consistency is key — one problem every day 🚀 #DSA #Java #LeetCode #ProblemSolving #Coding

🚀 Solved LeetCode 2463 – Minimum Total Distance Traveled Today I worked on a challenging Hard-level Dynamic Programming problem that combines Greedy + Sorting + DP with Memoization — a great exercise for strengthening problem-solving skills. 🔍 Problem Insight: We are given positions of robots and factories (with limited capacity). The goal is to assign each robot to a factory such that the total distance traveled is minimized, while respecting capacity constraints. 💡 Key Learnings: Sorting plays a crucial role in optimizing assignments Greedy intuition helps in pairing closest robots with factories Dynamic Programming is essential to explore all valid assignments efficiently Memoization avoids recomputation and improves performance ⚙️ Approach Used: Sort robots and factories based on position Use DP to decide whether to skip or use a factory Try assigning multiple robots (within capacity) and track minimum cost 📈 Complexity: Efficient solution using DP reduces exponential possibilities to manageable computation. 🔥 Takeaway: This problem is a perfect example of how combining multiple concepts leads to an optimal solution. It really helped me improve my thinking around state transitions and optimization strategies. #LeetCode #DataStructures #Algorithms #DynamicProgramming #CodingJourney #ProblemSolving #Java #CompetitiveProgramming

Day 91 on LeetCode —Final Prices With a Special Discount 🛒💸✅ Today’s problem focused on array traversal and discount simulation logic. not apply stack because my array logic works equally fine 🔹 Approach Used in My Solution • Traverse each item in the prices array • Search on the right side for the first smaller or equal value • Apply that value as a discount • Store updated answers directly in the result array Simple, clean, and easy to understand. ⚡ Complexity: • Time Complexity: O(n²) • Space Complexity: O(n) 💡 Key Takeaways: • Sometimes a direct array traversal approach explains the logic more clearly • Focused on building the solution step-by-step instead of forcing optimization immediately • Even brute force approaches strengthen understanding of problem flow and conditions 🔥 Consistency and clarity in logic matter just as much as optimization. #LeetCode #DSA #Algorithms #DataStructures #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #Consistency #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

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

Claude Code is eating your wallet. One dev was spending $200+/day with zero visibility. So he built codeburn - a terminal dashboard that shows exactly where tokens go. The shocking finding: → 56% on "conversation" (Claude thinking) → Only 21% on actual coding More than half the money = Claude talking, not working. What it tracks: • Cost by task type (coding, debugging, exploring) • Cost by project, model, tool • Daily/weekly/monthly breakdown • Interactive terminal UI One line install: npx codeburn Free. Open source. No API keys. Save this before your next coding session 💸 #ClaudeCode #AI #DevTools #CodingTools #AITools #Cursor #Programming