Big O Notation Explained in 6 Simple Types

A lot of people are still learning coding like it’s 2020. Write code. Memorize syntax. Practice questions. But that’s not how it works anymore. Today, AI can write most of the code. That’s not the advantage. The real advantage is: knowing what to ask, what to fix, and what actually matters. That shift is exactly what we’re focusing on this Saturday. Not just tools. Not just SQL. But how the role of a software engineer is actually changing. And yeah, there’s something we’re revealing at the end too. If you’ve been feeling stuck or unsure about what to learn next, this might help more than another random tutorial. Join us this Saturday. To register, check the comments. #artificialintelligence #softwareengineering #coding #webdevelopment #careergrowth #technology

Sometimes learning doesn’t come from assigned tasks. It comes from *just sitting and building something for yourself.* Recently, I was playing around with **rate limiting** — not as a requirement, just out of curiosity. Tried implementing algorithms like: • Token Bucket • Leaky Bucket But more than the algorithms, I focused on **how I design the code.** My approach was simple: → Keep it modular → Keep it extensible → Keep it plug-and-play So instead of hardcoding logic, I designed it using: • Strategy Pattern → to switch algorithms dynamically • Builder Pattern → to configure and create limiter cleanly Each algorithm is isolated. No tight coupling. Easy to extend, easy to test. You can literally change the behavior of the system just by switching the algorithm — no code rewrite. No production pressure. No deadlines. Just experimenting, breaking things, and observing behavior. And that’s where the real insight came: Rate limiting is not just about restricting requests. It’s about understanding **how systems behave under different traffic patterns.** • Burst traffic behaves differently • Steady traffic behaves differently • Each algorithm has its own trade-offs Sometimes the best learning happens when: You’re not told *what to build* But you explore *how to design it right* That’s where engineering thinking evolves. #BackendEngineering #SystemDesign #RateLimiting #Java #DesignPatterns #SoftwareArchitecture #LearningByDoing

Most engineers see "LeetCode Hard" and immediately skip to the next problem. But the legendary N-Queens problem isn't about being a math genius. It’s about knowing how to fail gracefully. If you want to master Backtracking, this is the only problem you need to understand. Here is how I break it down: 👑 The Problem: Place $N$ queens on an $N \times N$ chessboard so that no two queens can attack each other. (No sharing the same row, column, or diagonal). The Mindset (Backtracking): You don't need a magic formula. You just need a systematic way to guess, fail, undo, and try again. 1️⃣ Make a Choice: Place a queen in the first available safe spot in Column 1. 2️⃣ Move Forward: Jump to Column 2 and repeat. 3️⃣ Hit a Wall? (The Magic Step): If you reach a column where EVERY square is under attack, you made a mistake earlier. So, you step back, pick up the previous queen, and move her to the next available spot. You exhaust every possibility until you build a valid board. The Technical Breakdown: Time Complexity: O(N!*N). You have N choices for the first column, N-1 for the second, and so on. The extra N comes from validating the board at each step. Space Complexity: O(N^2) to maintain the board state and recursion stack. Optimization: Most developers write a while loop to check the rows and diagonals for attacks. This takes O(N) time per placement. Want to impress your interviewer? Trade a tiny bit of space for a massive speed boost. Use Hashing Arrays to track attacked rows and diagonals. Instead of scanning the board, you do an O(1) lookup: if (leftRow[row] == 0 && lowerDiagonal[row + col] == 0). Boom. You just optimized a LeetCode Hard. Backtracking isn’t just an algorithm; it’s a problem-solving mindset for software engineering. Try a path, hit a dead end, roll back your state, and try the next one. Have you tackled N-Queens yet? What is your favorite Backtracking problem? 👇 #SoftwareEngineering #Algorithms #LeetCode #DataStructures #CodingInterviews #C++ #TechCareers

Writing code that works is only the beginning. The real difference comes from writing code that works efficiently. The right data structures and algorithms help you build software that is faster, more reliable, and easier to maintain. They influence how applications handle large amounts of data, how websites respond under heavy traffic, and how AI models process information effectively. When you understand which structure to use; arrays, linked lists, trees, hash maps, queues, or graphs, your solutions become more predictable and scalable. Debugging becomes easier because your code is organized with intention and built to perform consistently. This is what separates simply writing code from thinking like an engineer. Strong foundations in data structures and algorithms improve every project you build and every technical problem you solve. Develop the skill that powers efficient software and professional-level problem-solving. Master data structures and algorithms with Learn Programming Academy and start building smarter code today. #programming #java #python #coding #LearnToCode

𝐎𝐟𝐟𝐢𝐜𝐢𝐚𝐥𝐥𝐲 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬 & 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬 𝐒𝐭𝐮𝐝𝐞𝐧𝐭: 𝐓𝐡𝐞 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 𝐭𝐨 𝐁𝐞𝐜𝐨𝐦𝐢𝐧𝐠 𝐚 𝐒𝐞𝐧𝐢𝐨𝐫 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 🚀 It's been about five months since my last post here. I've been heads-down on a contract project and it took up most of my bandwidth. During that time, something became very clear to me. I noticed it's not just about building a product. Anyone can build something that works. The real challenge is building something that's fast, efficient, and scales especially when that product starts serving millions then billions of users. As a Softwrar engineer, understanding the deep technical side of what you build is very important and honestly one of the things that truly qualifies you as one. I want to fully understand what's happening under the hood of everything I build, the trade-offs, the decisions, the why behind every approach. That depth is what separates engineers who build things from engineers who build things that actually last under real pressure. That's what led me here. 𝗜'𝘃𝗲 𝗼𝗳𝗳𝗶𝗰𝗶𝗮𝗹𝗹𝘆 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗦𝗔 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝘃𝗲𝗿𝗶𝗻𝗴: 🔸 Problem-solving techniques, greedy algorithms, divide and conquer, dynamic programming and time complexity analysis 🔹 Core data structures including arrays, linked lists, stacks, queues, trees and heaps 🔺 Graph algorithms covering BFS, DFS and path and network problems 🔸 String algorithms focused on efficient processing and pattern matching 🔹 Advanced algorithms and complexity around optimization and computational limits 🔺Genome Assembly challenge for practical problem-driven application Alongside the coursework, I will be practicing consistently on LeetCode to sharpen not just how I code but how I think through problems. As a Python/Django backend developer, this is one of the most intentional investments I've making in my growth. The engineers I look up to don't just build. They understand deeply, think efficiently and write code that scales. Let's go. 🚀 #DSA #DataStructures #Algorithms #BackendDevelopment #Python #SoftwareEngineering #LeetCode #ContinuousLearning #SeniorEngineer

Your Complete Roadmap to Master Data Structures & Algorithms If you're serious about cracking top tech roles, DSA isn’t optional — it’s your foundation. Here’s a step-by-step roadmap to go from beginner to advanced: - Build strong fundamentals - Master core data structures - Learn algorithms that actually matter - Practice consistently (LeetCode mindset) - Build real-world projects and showcase your skills Most people stay stuck because they don’t follow a structured path. Clarity is greater than random learning. Start small, stay consistent, and win big. Learn, build, and iterate. Ready to master DSA the right way? Join Coder Pathshala and start building real skills. #DSA #DataStructures #Algorithms #Coding #SoftwareEngineering #TechCareers #LeetCode #Programming #Developers #CodingJourney #CoderPathshala

Why Understanding Fundamentals Is More Important Than Tools In tech, new tools and frameworks appear almost every day. But one thing stays constant: Fundamentals. Programming languages may change. Frameworks may evolve. But core concepts remain the same. Important fundamentals include: • Data structures and algorithms • How APIs work • System design basics • Databases and data flow • Problem-solving skills Developers who focus only on tools often struggle when technology changes. Developers who understand fundamentals can adapt quickly to anything. One thing I’m focusing on: Learn the basics deeply, then apply them with tools. Strong fundamentals = long-term growth. #Programming #SoftwareEngineering #TechLearning #DeveloperSkills #Growth

College: "Here's how to code." Production: "Here's how to suffer." 👇 What college taught me: → Time complexity is O(n log n) → Design patterns save lives → Clean code is a moral obligation → Always comment your code What production taught me: → If it works, don't touch it 🙏 → The only design pattern is panic → Clean code is a myth written by people without deadlines → Comments are lies your past self left for future you 😭 The stages of reading a codebase: Week 1 → "This is actually well structured" Week 2 → "Who wrote this monstrosity?" Week 3 → "Why does this even work?" Week 4 → "Oh no. It was me." 😰 Things college never taught me: → How to act calm during a prod incident 🧯 → How to say "works on my machine" with a straight face → How to fix a bug at 2am that you introduced at 9am → How to blame the intern professionally The most valuable skill in software? Not algorithms. Not system design. Knowing which legacy code to fix — and which one to walk away from pretending you never saw it. 🫡 What's the most brutal thing production taught you? ⬇️ #SoftwareEngineering #Developers #Programming #CodingHumor #BackendEngineering #Tech #AI

🚀 5,000 Views. Zero Shortcuts. Jumping straight into coding without a roadmap is the biggest mistake college students make. Blindly solving LeetCode problems is exactly why so many fail their technical interviews. I know this, because I was there. We just crossed 5,000+ views on my latest TechWithNinad video breaking down the exact framework I used to solve over 500 LeetCode problems. The response from FY, SY, TY, and even class 12 students has been phenomenal, and it proves that the community is ready to learn the right way. Here is the exact blueprint we discussed to survive tech and ace interviews: - Lock in one programming language and master its syntax. Stop jumping around. - Master core Data Structures and Algorithms instead of memorizing solutions. - Map the underlying patterns. A sliding window problem is the exact same whether asked by Amazon or a local startup. - Understand Object-Oriented Programming (OOP) to build custom Trees, Graphs, and Tries. - Practice brutal consistency. Solving 1-2 problems daily compounds much faster than random bursts of intensity. Thank you for 5,000 views. If you are struggling with your technical interview prep or just getting started in your first year, this is the only roadmap you need. Watch the full breakdown on my YouTube channel, TechWithNinad. Connect with me here: LinkedIn: https://lnkd.in/dCWD_sDN Instagram: https://lnkd.in/djYFBkaR GitHub: https://lnkd.in/dTjMcbXR LeetCode: https://lnkd.in/dkNBpnzz 💻 #LeetCode #SoftwareEngineering #CodingInterviews #TechWithNinad #DataStructures #Algorithms #ComputerScience #TechCareers #StudentDeveloper #ProgrammingTips 🚀

In Computer Science, a programmer is often perceived as someone who writes code. In practice, that is one of the least important parts of the role. Programming is a continuous sequence of decisions, many of which are not visible in the final code. Before a single line is written, you are already making trade-offs: What exactly is the problem we are solving? What constraints actually matter, latency, cost, reliability, time? What can be simplified or deferred? Two engineers can be given the same requirement and produce completely different systems. Not because of syntax. Because of judgment. Consider something as simple as building a login system. One approach might prioritize speed: implement email/password quickly and ship. Another might add OAuth, session management, and rate limiting from the start. A third might rethink the requirement entirely, do we even need authentication here yet? All three are valid. The difference lies in how decisions are made. As a programmer, you are constantly: Interpreting incomplete or ambiguous requirements. Deciding what not to build Structuring systems so they can evolve without breaking. Writing code that someone else can understand months later. Balancing clarity against performance, speed against robustness. And these decisions compound. A small shortcut today can become a system-wide limitation later. An unnecessary abstraction can slow down every future change. A poorly defined requirement can lead to weeks of rework. This is why “code that runs” is a very low bar. The real question is: Does the system hold when things change? Because they always do. Requirements shift mid-sprint. Traffic increases unexpectedly. New features collide with old assumptions. In those moments, the quality of earlier decisions becomes visible. This is the part of programming that is rarely taught. Most learning focuses on tools, languages, and frameworks. But tools don’t make decisions, people do. And there are no perfect tools, no universal “best practices,” and no one-shot answers. Only approaches that fit a specific context. At ICAMP, the focus is not just on writing code. It is on developing the ability to: Frame problems correctly Evaluate trade-offs explicitly Make decisions that hold over time Because in the end, programming is not about code. It is about judgment. Follow ICAMP → Personalized AI Coding Bootcamps for Computer Science. #ComputerScience #EdTech