Learning DSA for Efficient Coding

🚀 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 🚀

💻 If you want to become a better developer… start here. I just went through this powerful resource on Data Structures & Algorithms, and it breaks down complex concepts into something actually understandable. From: ✔️ Arrays & Linked Lists ✔️ Stacks & Queues ✔️ Trees & Graphs ✔️ Sorting & Searching Algorithms To advanced topics like: 🚀 Dynamic Programming 🚀 Greedy Algorithms 🚀 Time Complexity (Big-O) 💡 One thing is clear: You can’t master coding without mastering DSA. Whether you're preparing for interviews or trying to level up your problem-solving skills — this is a must-read. 📄 Sharing this PDF for anyone serious about tech. #DataStructures #Algorithms #SoftwareEngineering #Coding #TechCareers #LearnToCode #Developers All credit goes to: Narasimha Karumanchi for this amazing resource.

Headline: Which Programming Language Should You Learn in 2026? 🚀 The tech landscape is shifting faster than ever. For students and early-career developers, the "right" language isn't just about syntax—it’s about market relevance and staying power. If you’re looking to grab the market and build a future-proof career, here are the top 3 contenders to keep on your radar: 🐍 1. Python: The AI Powerhouse Python isn't going anywhere. It is the backbone of the AI revolution, Machine Learning, and Data Analytics. Its massive library ecosystem and simplicity make it the entry point for almost every major tech innovation today. 🦀 2. Rust: The Safety King Voted the "most loved" language for years, Rust is replacing C++ in systems where memory safety and performance are non-negotiable. Major players like Google, Microsoft, and Amazon are increasingly moving their infrastructure to Rust. Learning this now sets you apart as a high-tier systems engineer. 🟦 3. TypeScript: Scalable Web Dev As web applications become more complex, standard JavaScript often isn't enough. TypeScript provides the "guardrails" that big engineering teams need to build bug-free, scalable apps. It is currently a must-have for modern Front-end and Full-stack roles. My Advice for Students: Don't just learn the syntax. Pick one language and build a "proof of concept" project. Whether it’s a machine learning model in Python or a high-performance tool in Rust, demonstrable skills beat a long list of keywords every time. #Programming #TechTrends #SoftwareEngineering #CareerAdvice #Coding #AI #FutureOfWork

Growth in DSA is not just about solving problems, but about evolving the way you think. Day 29/100 — Data Structures & Algorithms Journey Today marks a shift in my learning. After practicing multiple problems across different topics, I’m now moving into a new phase — focusing on learning new patterns in DSA and understanding them deeply. Instead of just solving questions, I’ll be: - Identifying patterns behind problems - Learning when to apply each technique - Improving problem-solving intuition - Focusing on optimized approaches New Focus Areas: - Advanced Dynamic Programming patterns - Sliding Window & Prefix Sum techniques - Graph algorithms - Backtracking & recursion patterns Why this shift? Because solving problems is one part, but recognizing patterns quickly is what makes problem-solving efficient. Key Takeaways: DSA is about patterns, not isolated problems Understanding “why” is more important than “how” Learning structured approaches improves speed and accuracy Consistency + strategy = real growth Excited to explore deeper concepts and take my problem-solving skills to the next level 🚀 #DSA #LeetCode #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Been reviewing a bunch of CVs from fresh engineering grads lately, and one pattern stands out. Everyone’s doing Python. Everyone’s doing “AI projects.” It’s great to see the enthusiasm and adoption of current tech trends but are fundamentals of software engineering and system design getting less attention? My advice to students: AI is cool, but strong engineering foundations will allow you to adapt over long term. Pick a project complex enough for you to fail and learn. Passion projects naturally push you into system design. The curious ones always stand out. They tinker, connect hardware and software, and solve real problems. #curiousity #AI #

Totally agree with Ashok Shetty. What I am observing is not just at the junior developer level but in the mid tier ones also, is what I call the 'PoC Mindset' . In the rush to build AI PoC's , many teams are forgetting the basics of software engineering. Core engineering principles like quality, performance, scalability, and availability are not getting the attention they need. If we do not get the engineering right, we will not be able to move AI from PoC to production. Mahadevan Padmanabhan / Prasenjit Kundu I am sure you will agree with me.. #EnterpriseAI #AIEngineering #SoftwareEngineering #FromPoCtoProduction #AIinProduction #ScalableAI #AIOps #EngineeringExcellence

In high school I was deciding between fashion design and computer programming. Turns out neither. Except, kind of both, sideways, on my own terms. I've spent 15+ years deliberately code adjacent. Always technical, always in the room with engineers, occasionally doing things nobody told me I wasn't supposed to. Xbox, publishing the “Message of the Day” to console players worldwide. Adobe and Microsoft, testing software in localized versions for languages I couldn't speak (which teaches you a lot about how systems actually behave when nobody's watching). Expedia, somehow leading stored procedure code reviews with database developers from an ops and project management seat. Kubernetes, AWS, Docker, ML, neural nets, and Cursor AI. I've taken the training, spoken the language, and translated the complexity for any room. But building something myself? There was always a wall. Not a knowledge wall. A syntax wall. The kind that's particularly unforgiving for an ADHD brain that can hold the logic clearly but loses the thread somewhere in the semicolons. It started with a kid, too many free hours, and an Apple IIe in the early 80s, teaching herself AppleSoft Basic because the blinking cursor was more interesting than anything else in the house except books. Learning Logo at computer camp. Fortran and C in college. A half-finished Python course quietly abandoned somewhere between lists and loops. Always close. Never quite through the door. This weekend I decided to change that. Personal project, personal machine, personal curiosity. I installed Claude Code, described what I wanted in plain English, and shipped a working Python application. It caught its own bugs, asked permission before touching anything, and remembered my constraints. No syntax battles. No quiet defeats. No compilers failing because of transposed : or ; This is what it feels like when the wall comes down. And it matters, not just for me, but for every technical leader, PM, or ops person who thinks like a builder but hasn't been able to ship like one. The barrier between having an idea and making a thing has genuinely collapsed. Turns out five decades of code adjacent was just prep work. The grimoire is open. 🔮 #ClaudeCode #AI #AIEnablement #TechPM #EmergingTech #ADHD #WomenInTech #NeverTooLate

🚀 Mastering Dynamic Programming Patterns I’ve recently started a focused journey to strengthen my Data Structures & Algorithms (DSA) foundation. Instead of randomly solving problems, I’m approaching Dynamic Programming (DP) the way top engineers do — by mastering patterns first. 🔹 Patterns Covered So Far ✅ 0/1 Knapsack (Bounded) — pick each element at most once Solved: Subset Sum Partition Equal Subset Sum Count Partitions with Given Difference Minimum Subset Sum Difference ✅ Unbounded Knapsack — reuse elements multiple times Solved: Rod Cutting Problem Coin Change (Min Coins & Number of Ways) Unbounded Knapsack 💡 What I’m Focusing On Identifying patterns in under 30 seconds Converting recursion → memoization → tabulation → space optimization Writing clean, bug-free implementations with correct edge-case handling Understanding trade-offs (time vs space) — not just passing test cases ⚡ Why This Approach? From what I’ve learned, companies like Google, Amazon, and Microsoft don’t just evaluate whether you can solve a problem — they assess: How you approach patterns How you optimise solutions How clearly you communicate your thinking 📈 What’s Next in My DP Roadmap: Longest Increasing Subsequence (LIS) DP on Strings (LCS, Edit Distance) DP on Grids & Advanced Patterns 💬 If you’re also preparing for high-scale backend or product engineering roles, I’d love to connect, share insights, and grow together. #DynamicProgramming #FAANG #Google #Amazon #Microsoft #SoftwareEngineering #DataStructures #InterviewPrep #ProblemSolving #LeetCode #TechCareers #BackendEngineering #CodingJourney #systemdesign #softwaredevelopment #leetcode #gfg

I built a RAG engine from scratch. Here's what tutorials don't teach you.   After 3 years at Amazon building backend systems at scale, I wanted to bridge the gap between traditional software engineering and AI/LLM engineering. So I built DocQuery — a tool that lets you ask natural language questions about your documents and get cited answers.   The stack: Python, Claude API, ChromaDB, Sentence Transformers.   Building it was straightforward. Making it work well? That's where it got interesting:   → My first version returned "no relevant results" for questions that clearly had answers in the document. Why? The default similarity threshold (0.3) was too aggressive for short documents. I debugged it by inspecting raw cosine similarity scores and found valid matches scoring 0.27. Lowered the threshold to 0.15 — problem solved. Lesson: there's no magic number. You test and tune.   → Chunking strategy matters more than you'd think. I split documents into 500-word chunks with 50-word overlap. Without overlap, sentences at chunk boundaries get split — and neither chunk has the full context. A small design choice that directly affects answer quality.   → Hallucination prevention isn't one thing — it's layers. System prompt that restricts answers to provided context + low temperature (0.2) for precision + similarity threshold filtering before anything reaches the LLM.   None of this was in any tutorial I read. You only learn it by building, breaking, and fixing.   🔗 GitHub: https://lnkd.in/gSk8HgWq   Currently building more AI/LLM tools. Open to backend and full-stack roles where I can combine system design with AI engineering.   #RAG #LLM #AI #Python #SoftwareEngineering #BuildInPublic #OpenToWork

I can’t believe that: - Python is free - PyTorch, TensorFlow, and JAX are free - Hugging Face Transformers & Datasets are free - Google Colab (with free GPUs) is free - LangChain and LlamaIndex are free - DeepLearningAI courses are free - Jupyter and VSCode are free - Andrej Karpathy is free - GitHub is free You don’t need a powerful machine or paid courses to get started. You can train models, explore datasets, and build useful projects - all for free. Yes, deploying and scaling ML products will eventually cost something. But to learn, prototype, and even contribute to open-source projects - everything you need is already out there. The only thing missing is consistency and curiosity. And that part is on you 💪 -- P.S. We’re building the best AI English tutor in the world. Try it to improve your speaking - it’s 15× cheaper than a human one → GetFluently.app