Robot Returns to Origin: Easy LeetCode Problem

LeetCode POTD | Day 29 2463. Minimum Total Distance Traveled Today’s problem was a bit more challenging compared to the last few days. I used a dynamic programming approach with recursion and memoization. First, I sorted both robots and factories, then tried assigning robots to factories in an optimal way while minimizing the total distance. The DP helped avoid recalculating overlapping subproblems. What I learned today: • When multiple decisions are involved, try thinking in terms of DP with states and transitions • Sorting beforehand can simplify the problem and make greedy or DP approaches easier to apply • Memoization is a lifesaver when recursion starts repeating work #LeetCode #DSA #POTD #CodingJourney #ProblemSolving #DynamicProgramming #DailyCodingChallenge

Today, I tackled a challenging DSA problem titled “Maximum Walls Destroyed by Robots.” This problem involved several key concepts: - Sorting - Binary Search (lower_bound / upper_bound) - Greedy + Dynamic Programming The core idea revolves around each robot having a specific range, with the objective of maximizing the number of walls covered while avoiding overlap conflicts between adjacent robots. Here’s what I implemented: - Utilized unordered_map to map each robot to its distance. - Applied binary search to efficiently determine reachable walls. - Maintained prefix decisions using Dynamic Programming: - subLeft: best result when the current robot contributes from the left - subRight: best result when the current robot contributes from the right A key learning from this experience was that optimizing overlapping ranges with constraints requires a combination of: - Local decisions for each robot's coverage - Global optimization through DP transitions This problem truly tested my ability to think in intervals, handle edge cases between adjacent elements, and optimize using the Standard Template Library effectively. Consistency and problem-solving lead to growth. #DSA #Coding #Cpp #LeetCode #ProblemSolving #SoftwareEngineering #FAANGPrep #POTD

🚀 Day 214 of #300DaysOfCoding Today I solved a Hard-level problem on LeetCode: 👉 Minimum Total Distance Traveled This problem really tested my understanding of Dynamic Programming + Greedy Thinking. 💡 Key Learnings: Sorting plays a crucial role in optimizing 1D distance problems Converting real-world constraints into DP states makes complex problems manageable Handling capacity constraints (factories limit) using iterative assignment 🧠 Approach: Sorted robots and factories based on position Used DP + Memoization to assign robots to factories efficiently Tried multiple assignments per factory within capacity limits ⚡ Why this problem is interesting? It combines: Greedy intuition (nearest assignment) Dynamic Programming (optimal substructure) Real-world modeling (capacity constraints) 📊 Complexity: Time: O(n × m × limit) Space: O(n × m) 🔥 Problems like this improve problem-solving depth and prepare you for top product-based companies. #LeetCode #DSA #DynamicProgramming #CodingJourney #ProblemSolving #SoftwareEngineering #MCA #GATEPreparation

Understanding how to implement nodes and edges in code is fundamental for representing complex systems and relationships. This demonstration showcases the practical application of these concepts, illustrating how to define agents (nodes) and their interconnections (edges) programmatically. This foundational knowledge is key for building sophisticated agent-based models and network simulations. Key takeaway: Visualizing and coding relationships allows for deeper analysis of system dynamics. #SoftwareDevelopment #DataStructures #Algorithms #Programming #AgentBasedModeling

🔥 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

Day 61/100 of #100DaysOfCode Today I worked on a basic string problem and also explored a higher-level problem related to optimization. First, I solved a string problem: removing spaces from a given string. Initially, it felt simple, but while coding I understood the importance of clear thinking. Key learning: Focus on characters we want to keep Traverse the string using a loop Build a new result string without spaces Example: Input: "geeks for geeks" Output: "geeksforgeeks" This strengthened my understanding of how to convert logic into code step-by-step. Along with this, I also looked at a more advanced problem involving robots and factories. Even though I didn’t fully implement it, I understood the core idea: Robots are placed on a line and need to reach factories Each factory has a limit on how many robots it can repair The goal is to assign robots to factories such that the total distance traveled is minimum The approach involves: Thinking in terms of assigning closest possible robots Considering constraints like factory limits Understanding that sometimes simple greedy logic is not enough Learning that advanced techniques like sorting and dynamic programming are used to find the optimal solution This made me realize how problems can evolve from simple logic to complex optimization, and how important it is to build a strong foundation before jumping into advanced topics. Still learning, still improving — one concept at a time. #coding #dsa #learning #100daysofcode

Day 68/150 🚀 LeetCode 657: Robot Return to Origin 🧠 Problem There is a robot starting at position (0,0). Given a sequence of moves, determine if the robot returns to the origin after completing all moves. 📌 Example Input → moves = "UD" Output → true Input → moves = "LL" Output → false 💡 Approach (Simulation) • Initialize x and y coordinates • Traverse the string of moves • Update position based on direction • Check if robot returns to origin (0,0) ⚡ Example Walkthrough Moves → "UDLR" U → y + 1 D → y - 1 L → x - 1 R → x + 1 Final Position → (0,0) ✅ ⏱ Time Complexity O(n) 📦 Space Complexity O(1) ✅ Result: Accepted ⚡ Runtime: 0 ms (Beats 100%) #Day68 #LeetCode #DSA #CodingJourney #ProblemSolving #Programming #SoftwareEngineering #DailyLeetCode

🚀 Day 36 of 100 Days LeetCode Challenge Problem: Robot Return to Origin Day 36 is a simple but important simulation + counting problem 🔥 💡 Key Insight: To return to origin (0,0): Total moves Right = Left Total moves Up = Down 👉 If both conditions satisfy → ✅ robot returns to origin 🔍 Core Approach: 1️⃣ Initialize Counters x = 0, y = 0 2️⃣ Process Each Move 'R' → x++ 'L' → x-- 'U' → y++ 'D' → y-- 3️⃣ Final Check If (x == 0 && y == 0) → true Else → false 💡 Alternative Approach: Count occurrences: count('R') == count('L') count('U') == count('D') 🔥 What I Learned Today: Simple problems test basic logic clarity Simulation problems are about correct tracking Don’t overcomplicate—keep it clean 📈 Challenge Progress: Day 36/100 ✅ Consistency still going strong! LeetCode, Simulation, Strings, Counting, Coordinates, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #Strings #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

🚀 Day 37 of 100 Days LeetCode Challenge Problem: Walking Robot Simulation Day 37 builds on previous robot problems with a full simulation + direction handling + hashing challenge 🔥 💡 Key Insight: We simulate movement step-by-step, but: 👉 Need to handle: Direction changes Obstacles Track maximum distance 🔍 Core Approach: 1️⃣ Track Direction Use directions array: North → (0,1) East → (1,0) South → (0,-1) West → (-1,0) 👉 Rotate index: Left → (dir + 3) % 4 Right → (dir + 1) % 4 2️⃣ Store Obstacles Efficiently Use a HashSet for O(1) lookup 3️⃣ Simulate Movement For each step: Move 1 unit at a time If next cell is obstacle → stop movement 4️⃣ Track Maximum Distance At every step: Compute x² + y² Keep max value 🔥 What I Learned Today: Simulation problems require step-by-step precision Hashing is essential for fast obstacle checks Direction handling patterns are reusable 📈 Challenge Progress: Day 37/100 ✅ Consistency unstoppable! LeetCode, Simulation, Hashing, Matrix, Direction Handling, Arrays, DSA Practice, Coding Challenge, Problem Solving #100DaysOfCode #LeetCode #DSA #CodingChallenge #Simulation #Hashing #ProblemSolving #TechJourney #ProgrammerLife #SoftwareDeveloper #CodingLife #LearnToCode #Developers #Consistency #GrowthMindset #InterviewPrep

I'm happy to share this, hope it helps someone. During my time in the automotive industry, I built a real-time quality control tool using computer vision, designed to detect wire harness components directly on the production line. The app worked great, but completely tied to proprietary data, so it stayed private. I've now cleaned it up, stripped all company specific elements, swapped the custom model for a publicly available YOLOv8 one, and restructured the codebase into a proper modular template that anyone can use. What it does: - Runs real-time object detection from a webcam or image file. - Displays detected objects with class labels and confidence scores. - Exports results as a text report or PDF label. Seneca once wrote in his Letters to Lucilius: “If wisdom were given me under the express condition that it must be kept hidden and not uttered, I should refuse it.” I’m no Seneca, but I think this may be useful for someone; feel free to use it, fork it, or build on it. For more details, check the README on GitHub:  https://lnkd.in/dMdKb4Xe #Python #ComputerVision #YOLO #OpenSource #MachineLearning