Construct String with Distinct Characters in Windows

🚀 Day 14 of my programming journey LeetCode #20 (Valid Parentheses) 💡 Understand: Given a string containing brackets () [] {}, we need to check whether the input string is valid or not. 🔗 Match: I used this problem by using Stack method 🧠 Stack Concept: Stack works on LIFO 👉 Last In, First Out stack.pop() → used to remove and return a value stack.append() → add value in stack after creating stack.push() → add value in stack during creating 📝 Plan: I used the Stack Data Structure to solve this problem: Push opening brackets into the stack When a closing bracket appears: Check the top of the stack If it matches → continue If not → return False 💻 Implement: Shown in image 🖼️ 🔍 Review: I always use Python Tutor to see each step Learned how stack helps in tracking order Improved understanding of condition handling Practiced clean and readable logic 📊 Evaluate: Now we submit our code ✔️ #Python #CodingJourney #LeetCode #Programming #Developer #CodingLife #100DaysOfCode #LearnToCode #ProblemSolving #DataStructures #Stack #PythonDeveloper #SoftwareEngineering #Consistency #DailyCoding #ProgrammerLife #LearningJourney #ComputerScience #FutureDeveloper #CodingSkills #Growth #KeepLearning #leetcode20

I got tired of online document converters charging for basic features, so I built my own. Meet Radius a simple, secure tool for Word and PDF conversions. How I built it: 🔹 Backend: Python + FastAPI 🔹 Frontend: HTML/CSS 🔹 Goal: Speed, simplicity, and 100% free. Sometimes the best way to learn a new framework is to solve a problem you actually have. The site: https: https://lnkd.in/eGwV45xA #PythonDeveloper #FastAPI #WebDev #ProjectShowcase #Programming #FullStack

🔥 Day 76 of #100DaysOfCode Solved Climbing Stairs (LeetCode 70) today — a classic Dynamic Programming problem 🚀 At first, it looks simple… but the real learning is in identifying the pattern behind it. 💡 Key Insight: Each step depends on the previous two steps. That’s when it clicked — this is basically a Fibonacci pattern in disguise. Instead of using recursion (which is slow), I used an optimized iterative approach to achieve: ⚡ O(n) time complexity ⚡ O(1) space complexity 📈 Result: ✅ 100% runtime ✅ 90%+ memory efficiency This problem taught me: 👉 Always look for patterns before jumping into coding 👉 Optimization matters as much as correctness Small problems → Big concepts #Day76 #LeetCode #DynamicProgramming #CodingJourney #ProblemSolving #LearnInPublic

I built a BitTorrent client from scratch. Not using libraries. Not wrapping existing tools. Just raw sockets, async programming, and a lot of protocol reading. Here’s what I learned: • How peer-to-peer systems actually work beyond theory • Why trackers are only a small part of the system • How peers discover, connect, and exchange pieces • The challenges of handling multiple connections asynchronously Building this gave me a much deeper appreciation for distributed systems, especially how resilient and efficient BitTorrent is even today. Still improving it, but this has been one of the most fun and challenging projects I’ve worked on. I have decided to open source the project so others can learn from it GitHub Link: https://lnkd.in/dUMXdq5s Would love feedback or ideas on what to build next 👇 #programming #python #distributedsystems #networking #opensource

Understanding a codebase is usually much harder than running it. A lot of the real context lives in scattered docstrings, dependency chains, comments, and team memory, which makes unboarding and handoffs slower than they should be. I’ve been build an interactive DocFlow to make that easier for Python teams. The idea is simple: Load a repo, automatically map symbols and dependencies, and explore the structure visually while reading generated docs and source code in the same workspace. What I like about this approach is that it turns codebase understanding into something you can actually inspect instead of reconstructing manually from files, folders, and guesswork. Right now, the project supports: - scanning Python repos - auto-building a flow from symbols and relationships. - exploring code in spread or sectioned views - reading docs, markdown, and source side by side - importing from local folders, demo projects, or public GitHub repos Still early, but it’s been a useful direction for thinking about onboarding, documentation drift, and navigating unfamiliar systems faster. If you’ve run into this problem in your own team, I’d be interested in how you handle codebase understanding. #documentation #python #teamwork

🚀 Day 18 of My Coding Journey Today, I built a Browser History Simulation 🌐 using Python and Visual Studio Code. The goal was to mimic how a real browser handles navigation using commands like Back, Forward, and visiting new URLs. 🔍 Key highlights: • Implemented dynamic history tracking using arrays • Managed current page index efficiently • Handled Back and Forward navigation logic • Cleared forward history when visiting a new page 💡 Key learnings: Gained deeper understanding of state management Improved logical thinking with real-world scenarios Practiced handling edge cases effectively Building real-world logic step by step and improving consistency every day 🚀 🔗 GitHub Repository:https://lnkd.in/dRygaZbk #Python #freecodecamp #CodingJourney #ProblemSolving #Developers #LearningByDoing

🚀 Day 28/30 – 30 Days of Python Project Challenge Consistency builds skill. Skill builds confidence. 🚀 As part of my 30-day challenge, I’m focused on solving real-world problems while strengthening core development concepts. 🧠 Today’s Project: Video to Audio (MP3) Converter I built a Python-based media utility that allows users to quickly extract audio tracks from video files through a simple graphical interface. ✨ Why this project matters: This project bridges the gap between file management and media processing. It demonstrates how Python can automate tedious tasks—like converting formats—with just a few lines of code and a user-friendly file picker. ⚙️ Key Features: Native File Explorer: Uses Tkinter's filedialog to browse and select videos easily 📂 High-Quality Extraction: Preserves audio fidelity during the MP4 to MP3 transition 🎵 Resource Efficient: Automatically closes file streams to save memory 🔋 Instant Feedback: Console confirmation once the conversion is complete ✅ 💡 Concepts Applied: Media Processing with MoviePy OS Interactivity through Tkinter's GUI components Stream Management (opening/closing file handles) Error Handling for file selection and module dependencies Environment Configuration for Linux-based development 🔗 GitHub: https://lnkd.in/djsKiuE7 📌 Takeaway: Automation is at its best when it removes friction. Turning a multi-step manual conversion into a single-click script is what makes coding so rewarding. On to Day 29. 🔥 #Python #BuildInPublic #DeveloperJourney #30DaysOfCode #MoviePy #Tkinter #Automation #SoftwareDevelopment #Coding #Learning #OpenSource #Projects

House Robber - LeetCode 198 - Medium I solved the House Robber problem, which is a classic dynamic programming challenge. The goal is to determine the maximum amount of money that can be robbed from a series of houses without alerting the police, which happens if two adjacent houses are broken into. The problem is essentially about making an optimal decision at each step: whether to rob the current house or skip it to maintain the non-adjacent constraint. By breaking this down into smaller sub-problems, I built a solution that ensures the maximum possible profit by the time we reach the final house. Key Learnings 1) Decision Logic: At each house, we compare two options: robbing the current house (adding its value to the profit from two houses back) or skipping it (taking the profit from the previous house). 2) State Transition: Using the formula dp[i] = max(dp[i-1], nums[i] + dp[i-2]) to store the maximum profit at each stage in a DP array. 3) Memoization: By storing the results in a table, we avoid redundant recursive calculations, resulting in a highly efficient linear time solution. Time and Space Complexity Time Complexity: O(N) — We traverse the list of houses exactly once. Space Complexity: O(N) — We use a DP array of size N to store the maximum profit at each stage. #LeetCode #DynamicProgramming #Blind75 #SDEPrep #DataStructures #Python #ProblemSolving #CodingJourney #Freshers

Most people don’t struggle with Dynamic Programming because it’s hard. They struggle because they were never taught how to think in DP. If DP still feels random to you on LeetCode or CodeForces , this is exactly what’s missing. Dynamic Programming is not about memorizing patterns. It’s about understanding states, transitions, and building solutions step by step. This structured series is designed for anyone serious about DSA and problem solving. What you’ll master: • How to think in states and transitions • From basic recursion to advanced DP patterns • DP on arrays, strings, grids, and trees • Optimization techniques (space and time) • Combining DP with Greedy and Binary Search • Solving hard problems with clarity, not guesswork Whether you're preparing for interviews or aiming to get better at contests, this covers both. No random questions. No gaps. Just a clear roadmap to mastering DP the right way. If DP has ever felt confusing, this will change how you approach problems. Playlist: https://lnkd.in/gaBaDQZN Channel: https://lnkd.in/gTRG5v9M #dsa #dynamicprogramming #leetcode #codeforces #coding #interviewprep

Day 15/100: Stepping into Intermediate Python - The Coffee Machine Project! Today marks the start of the "Intermediate" phase in my #100DaysOfCode journey. I moved away from simple games to building a functional simulation of a real-world machine. Why Day 15 was different: Instead of just "input/output," I had to manage a System State. The program needs to remember how much water, milk, and coffee is left after every transaction. Key Features I Implemented: Resource Management: Checking if ingredients are sufficient before taking an order. Coin Processing: Calculating totals from Quarters, Dimes, Nickels, and Pennies (precise decimal math!). Transaction Logic: Handling payments, providing change, and updating the machine's "profit" ledger. Report Generation: A special command to see the current status of all resources. I'm now building logic that mirrors how real hardware software works. Onward to Object-Oriented Programming (OOP) tomorrow! Check out my Day 15 code here: https://lnkd.in/gAG6a6qU #Python #VSCode #100DaysOfCode #SystemDesign #SoftwareDevelopment #ProgrammingJourney