Perfect weekend learning idea 🔥 FreeCodeCamp just released a solid course on AI-assisted coding — covering tools like OpenClaw, GitHub Copilot, Claude Code, and CodeRabbit. It’s a great way to see how developers are actually combining AI tools in real workflows 👀 Definitely worth checking out! Source 🔗: https://lnkd.in/e9q_NPNH #AI #ArtificialIntelligence #Coding #Programming #WebDevelopment #JavaScript #HTML #Developers #SoftwareDevelopment #LearnToCode #Tech #CodingLife #DevTools #MachineLearning #AItools #Productivity #100DaysOfCode #CodeNewbie #DevCommunity #TechEducation

💻 Today’s episode of Developer Life: Debugging This confused me a lot when I started… Coding is easy Finding bugs is hard 😅 Sometimes you spend hours just to fix one small issue But that’s how real learning happens 🚀 #DeveloperLife #Debugging

Debugging is where real learning happens. Every error teaches you something new and helps you become a better programmer. Don’t avoid bugs—understand them, fix them, and grow your coding skills. #CSEPathshala #Debugging #CodingSkills #LearnProgramming #DeveloperLife #EngineeringStudents #TechLearning #ProblemSolving 

Learning from Upsolving a Hard Problem on LeetCode Recently, I upsolved a Hard problem from Biweekly Contest 179 on LeetCode, and it gave me a really valuable insight into Dynamic Programming (DP). Key Takeaway: Sometimes, Tabulation fails while Memoization works better. Here’s why 1. In Tabulation (Bottom-Up), we often compute all possible states of the DP table — even those that are never needed to reach the final answer. 2. This can lead to unnecessary computations and, in some cases, TLE (Time Limit Exceeded). 3. In contrast, Memoization (Top-Down) only computes the states that are actually required, thanks to recursion + caching. 4. This makes it more efficient in problems where the state space is large but only a subset is relevant. My Experience: I initially implemented a Tabulation approach, but it resulted in TLE due to redundant state computations. Switching to Memoization helped me compute only the necessary states — and the solution passed efficiently! Lesson Learned: 1. Don’t blindly choose Tabulation over Memoization. 2. Always analyze the state space and transitions. 3. If many states are irrelevant, Memoization might be the better choice. This was a great reminder that choosing the right approach matters just as much as solving the problem itself. #LeetCode #DynamicProgramming #Memoization #Tabulation #Coding #ProblemSolving #TechLearning #SoftwareEngineering #DSA #Upskilling

Gonna be blunt here But this is the dumbest sh*t I've heard in a while. Software engineering has quite literally got to be one of the *least* gate-kept disciplines in the world. → You can run code on even the most basic machines. → Need a tutorial? Go to YouTube - there's a billion lovely folks openly sharing knowledge there. → Hundreds of thousands of open-source GitHub repos for anything from web scrapers to literal operating systems. Which btw is where your beloved Claude Code gets its training data from. The Odin Project - where I spent a whole year learning and building my foundations in programming - is quite literally the BEST programming curriculum I've gone through. Bar none. Kept going and up-to-date because of hundreds of professional developers who LITERALLY do not ask you for a dollar. So remind me again - who's doing the gatekeeping here? Is it the 20M developers? Or **you**, who decided that investing time & effort into learning and getting good at a difficult skill was just too much trouble?

Day 16 of My Coding Journey Today I explored two powerful programming concepts: ✔️ Factorial ✔️ Fibonacci Today’s focus was on writing smarter, not longer code. Recursion Got introduced to the concept where a function solves a problem by calling itself. At first it felt confusing, but once I understood the flow, it actually made complex problems simpler. Practiced: Realized how important it is to define a proper base case to avoid infinite loops! Functions with Arguments Started converting basic programs into functions. This helped me understand how to: ✅ Avoid repetition ✅ Improve code readability ✅ Make programs reusable Now instead of hardcoding values, I pass them as arguments — much cleaner! Small concepts like these are changing how I approach problems. It’s less about just writing code, and more about structuring it effectively. 🔥 One step closer to becoming a better developer. #PythonLearning #codegnan #Functions #CodingJourney #100DaysOfCode

Variables are the backbone of every program They store data, bring flexibility, and power problem-solving in coding. Mastering variables is the first step toward becoming a great developer. #Programming #CodingBasics #Variables #LearnToCode #SoftwareDevelopment #CodingLife #TechSkills #DeveloperJourney #ComputerScience #ProgrammingConcepts #OibreTechnologies

Daily Dose of Surviving in a Programming Contest - sometimes the real optimisation isn’t in the process, it’s in redefining what you track. In competitive programming contests especially on CodeForces, linear combination equations across multiple variables can quickly explode in complexity. But instead of tracking full values, a smarter approach is to manage remainders using modulo—shrinking the problem into something far more controllable. This matters in contests because time isn’t just about coding—it’s about how efficiently you think. This technique is itself called as Frobenius technique or problem. Here’s the thinking flow: • Problem Reading → Identify it as a linear combination / sum constraint problem • Pattern Recognition → Full values aren’t required, only divisibility or remainder matters • Constraints Analysis → Direct DP or brute force grows too large • Approach Selection → Track states using remainders modulo k • Implementation → Transition between states using (current + value) % k • Debugging → Handle cycles, duplicate states, and unreachable remainders What most people miss is this: • They try to compute exact values • When the problem only cares about equivalence classes (remainders) That shift reduces both time complexity and cognitive load under pressure. This maps directly to real systems: • Hashing, partitioning, load balancing → all rely on grouping by equivalence Efficient systems don’t track everything—they track what matters. If you’re plateauing in CP, it’s rarely about syntax—it’s about abstraction. Where I learned this trick? Try solving this question - https://lnkd.in/gpnmPQN4 Are you solving the exact problem—or the minimal version required to get the answer? Let's discuss in comments below. Follow Vishu Kalier for more such Competitive Programming insights. #CompetitiveProgramming #DSA #Algorithms #Modulo #ProblemSolving #Optimization #Coding #SystemDesign #dailydose #cfbr #datastructures #java #math #codeforces #eternal #zomato #VIT

DSA Journey Continues 🚀 Today I solved the “Product of Array Except Self” problem on LeetCode. This problem really helped me understand how to optimize without using division and extra space. Problem Goal: For each index, return the product of all elements except the current one. Step-by-Step Approach : 1. Left Product Pass Traverse from left to right Store product of all elements before current index Keep updating a variable left 2. Right Product Pass Traverse from right to left Multiply with product of all elements after current index Maintain another variable right Core Idea: First pass → store left products Second pass → multiply with right products Final array gives the result Why This is Efficient: No division used Time Complexity: O(n) Space optimized (no extra arrays, only output array) What I Learned Today: Prefix and suffix product concepts How to solve problems in two passes Writing optimized solutions without extra space Understanding patterns like this makes problem solving much easier over time. #Day4 #DSAJourney #Java #LeetCode #ProblemSolving #Coding #Learning #Consistency #100DaysOfCode 10000 Coders Pathulothu Navinder

Excited to share our latest video from BK's TechStack: a comprehensive programming refresher! This session is designed to solidify foundational coding knowledge for developers at all stages. We explore fundamental concepts, from the intricate relationship between software and hardware and various software distribution models, to a deep dive into programming paradigms like Procedural, Object-Oriented (OOP), and Functional Programming. We also cover essential program structure, syntax, core concepts, and the development tools that streamline our work. Boost your understanding and navigate the tech landscape with confidence. Join us to reinforce your programming essentials and expand your skillset! What programming concept do you find most crucial for new developers? #ProgrammingRefresher #CodingBasics #SoftwareDevelopment #TechEducation #BKsTechStack