Mastering Coding Challenges

In the last 15 years, I have interviewed 800+ Software Engineers across Google, Paytm, Amazon & various startups. Here are the most actionable tips I can give you on how to approach  solving coding problems in Interviews  (My DMs are always flooded with this particular question) 1. Use a Heap for K Elements      - When finding the top K largest or smallest elements, heaps are your best tool.      - They efficiently handle priority-based problems with O(log K) operations.      - Example: Find the 3 largest numbers in an array.   2. Binary Search or Two Pointers for Sorted Inputs      - Sorted arrays often point to Binary Search or Two Pointer techniques.      - These methods drastically reduce time complexity to O(log n) or O(n).      - Example: Find two numbers in a sorted array that add up to a target.   3. Backtracking    - Use Backtracking to explore all combinations or permutations.      - They’re great for generating subsets or solving puzzles.      - Example: Generate all possible subsets of a given set.   4. BFS or DFS for Trees and Graphs      - Trees and graphs are often solved using BFS for shortest paths or DFS for traversals.      - BFS is best for level-order traversal, while DFS is useful for exploring paths.      - Example: Find the shortest path in a graph.   5. Convert Recursion to Iteration with a Stack      - Recursive algorithms can be converted to iterative ones using a stack.      - This approach provides more control over memory and avoids stack overflow.      - Example: Iterative in-order traversal of a binary tree.   6. Optimize Arrays with HashMaps or Sorting      - Replace nested loops with HashMaps for O(n) solutions or sorting for O(n log n).      - HashMaps are perfect for lookups, while sorting simplifies comparisons.      - Example: Find duplicates in an array.   7. Use Dynamic Programming for Optimization Problems      - DP breaks problems into smaller overlapping sub-problems for optimization.      - It's often used for maximization, minimization, or counting paths.      - Example: Solve the 0/1 knapsack problem.   8. HashMap or Trie for Common Substrings      - Use HashMaps or Tries for substring searches and prefix matching.      - They efficiently handle string patterns and reduce redundant checks.      - Example: Find the longest common prefix among multiple strings.   9. Trie for String Search and Manipulation      - Tries store strings in a tree-like structure, enabling fast lookups.      - They’re ideal for autocomplete or spell-check features.      - Example: Implement an autocomplete system.   10. Fast and Slow Pointers for Linked Lists      - Use two pointers moving at different speeds to detect cycles or find midpoints.      - This approach avoids extra memory usage and works in O(n) time.      - Example: Detect if a linked list has a loop.   💡 Save this for your next interview prep!

Being good at DSA & CP ≠ Being good at real-world software engineering.  I’ve seen this happen so many times, someone crushes coding rounds but struggles once they’re building systems.  Why?  Because real-world engineering isn’t just about solving problems. It’s about handling scale, concurrency, memory, and reliability, all at once.  Take a basic API. Sounds easy, right?  Now add multithreading, async calls, memory leaks, and thousands of requests per second—and suddenly, it’s chaos.  This is where CS fundamentals make or break you.  Here are 25 topics to help you bridge the gap between DSA and real-world projects:  ➥Concurrency and Multithreading   1. Thread Safety – Keeping shared data safe.   2. Mutex and Locks – Controlling access to resources.   3. Semaphores – Managing resource limits.   4. Condition Variables – Synchronizing threads properly.   5. Deadlocks and Starvation – Spotting and fixing them.   6. Atomic Operations – Performing thread-safe updates.   7. Thread Pools – Efficiently managing tasks.   8. Producer-Consumer Problem – Solving real-world concurrency issues.  ➥Memory Management   9. Heap vs Stack – When to use what.   10. Memory Leaks – Finding and fixing them.   11. Garbage Collection – How it works and where it fails.   12. Object Pooling – Reusing objects to save memory.   13. Paging and Segmentation – OS-level memory handling.   14. Caching Strategies – LRU, LFU, and cache eviction.  ➥Networking and Security   15. TCP/IP Basics – How connections actually work.   16. DNS Resolution – What happens when you hit enter on a URL.   17. SSL/TLS Handshake – How secure connections are set up.   18. OAuth and Token-Based Auth – Securely handling user sessions.   19. Session Management – Preventing hijacks and managing state.   20. Firewalls and Proxies – Protecting your network.   21. Load Balancers – Distributing traffic without breaking systems.  ➥System Design and Architecture   22. Event-Driven Systems – Managing async workflows.   23. Microservices Architecture – Building distributed systems.   24. Database Indexing – Making queries faster at scale.   25. CAP Theorem – Understanding consistency, availability, and partitioning trade-offs.  DSA gets you interviews.  CS fundamentals help you build systems that work.  – P.S: I’ve been getting 10+ queries regarding DSA, HLD, and LLD daily So, to answer all, I’ve launched my One Stop Resource guide for aspiring software engineers. This guide help you with: - full roadmap of DSA, HLD, and LLD for interviews - good resources that I used included to save you time - lots of problems and case studies for DSA and system design Here’s the link: https://lnkd.in/e-detVTg (220+ students are already using it)

𝐖𝐡𝐲 𝐜𝐚𝐧’𝐭 𝐈 𝐫𝐞𝐦𝐞𝐦𝐛𝐞𝐫 𝐚 𝐬𝐢𝐦𝐩𝐥𝐞 𝐃𝐒𝐀 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧? This happens to me every single time during coding sessions and interviews. I’ve solved the exact same problem multiple times, but when I saw it again, went completely blank. And now, I think it’s pretty natural. We’re human beings, not machines. We forget. We freeze. We fumble. And that’s okay. But what’s the way out? Here’s what’s been helping me: ✅ 1. Repetition It’s not about solving 500 problems. It’s about whether you’ve revisited the same 50 problems enough to feel confident solving them in any situation - even under pressure. Repetition builds familiarity. Familiarity builds confidence. ✅ 2. Pattern Recognition The goal isn’t just to solve problems - it’s to recognize patterns. Once you start seeing the same logic in different problems, your brain starts connecting the dots faster. That’s when real progress begins. ✅ 3. Practice Under Pressure Solving a problem while sipping coffee is easy.But doing it with a ticking clock and interview stress? That’s different. Mock interviews, contests, or even timed sessions train your brain to stay calm under pressure. If you feel like you’re forgetting problems you’ve done before - don’t worry. You’re in the process of getting better. And if you're looking for a structured way to build that consistency... Check out #NationSkillUp by GeeksforGeeks - a 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐟𝐫𝐞𝐞 𝐜𝐨𝐮𝐫𝐬𝐞 with 15+ guided roadmaps to help you build skills with clarity and direction. checkout the link: https://gfgcdn.com/tu/VO8/ #DSA #CodingJourney #NationSkillUp #LearningNeverStop #coding #dsa #mockinterview #GrowthMindset #SkillUpWithGFG

From Code Generation to System Integration: Why AI Coding Tools and Agentic IDEs Must Evolve to Solve Real Software Development Challenges Since GPT-3 went mainstream, AI coding tools have sprinted through three waves. 1. First came smart autocomplete. 2. Then came cloud companions tuned to specific stacks. 3. Now we’re in the agent wave – tools that read whole repos, open terminals, run tests and raise pull requests on their own. Every cycle starts the same way: Wow. Impressive. Look at how much this can do for me. But the uncomfortable truth is this: most of what these tools automate is commodity knowledge. Framework boilerplate, CRUD patterns, standard integration glue, typical test shapes – once a pattern exists in public code, a model can learn it and repeat it very well. That used to feel like expertise. Now it’s autocomplete on steroids. The real problems have barely moved: • Design and architecture. Not just file-by-file edits, but coherent system design: boundaries, contracts, data flows, failure modes, performance budgets – a holistic solution, not local patchwork. •  End-to-end SDLC integration. How change actually flows from idea to production: design, review, CI, approvals, environments, rollout strategies and on-call ownership. • Change management and legacy transformation. How to evolve decade-old systems, untangle hidden dependencies, migrate behaviour safely and avoid breaking everything that still quietly depends on “that old module”. • Traceability. Knowing who or what changed what, why, and what else was impacted – across code, configs, data pipelines and policies. • How strongly workflows enforce the top 10 principles like reliability, security, cost and maintainability that were outlined in the earlier post – not as posters on a wall, but as gates every change must pass through. This is where vibe-coding tools become dangerous. The model writes the feature, generates the tests, explains the diff. Everything looks green. It feels safe enough to ship on vibe. Without deep expertise and a solid workflow around it, that is not productivity. It is an efficient way to inject new risk into a live system. If code patterns are now cheap, differentiation shifts somewhere else: • To how clearly an organisation defines how systems should be built and evolved • To how tightly AI tools are integrated with that SDLC, not just with the editor • To how well workflows embody design principles, change discipline and traceability by default Writing code is becoming a commodity. However, writing holistic, thoughtful systems, and continuously evolving and governing them safely, is where the true value lies AI coding copilots and agentic IDEs now need to evolve from “look what I can generate” to “look how I help you integrate, operate and transform”. That’s when it stops being “wow, impressive demo” and becomes “yes – this is finally solving the real problem.”

Code reviews are essential, and here's how I review code written in an unfamiliar language or an unfamiliar codebase 1. Check for code readability 2. Check for uniformity in code patterns 3. Check for basic non-redundancy of logic Once, these were pointed out, I started asking many questions to 1. Gather context for the changes 2. Ensure the correctness of the task it was supposed to accomplish During the discussion, I try to probe enough to understand things myself (unfamiliar codebase) and challenge the understanding of the engineer. This helps me build the context and familiarity for future code reviews happening in the same codebase. Once the heavy lifting is done, I take an opinion from someone who knows the language (if possible) to ensure that language-specific features are used correctly and standards are followed diligently. These pointers seem quite stretched, but given we are unfamiliar with the codebase and language, spending time during initial code reviews means I would be more productive while reviewing other changes happening in the same codebase. PS: I am not an expert, and these are things I follow, so please take this advice with a pinch of salt. âš¡ I keep writing and sharing my practical experience and learnings every day, so if you resonate then follow along. I keep it no fluff. youtube.com/c/ArpitBhayani #AsliEngineering

What if every time you asked AI to 'improve' your code, you were actually making it less secure? Our research, presented as 'Security Degradation in Iterative AI Code Generation: A Systematic Analysis of the Paradox' at IEEE ISTAS25-IEEE International Symposium on Technology and Society 2025, revealed a counterintuitive finding:- iterative AI-based code 'improvement' can introduce more security vulnerabilities, not fewer. Analyzing 400 code samples across 40 rounds of iterations, we discovered a 37.6% increase in critical vulnerabilities after just five iterations. Key findings every developer should know include:- 1. Efficiency-focused prompts showed the most severe security issues. 2. Even security-focused prompts introduced new vulnerabilities while fixing obvious ones. 3. Code complexity strongly correlates with vulnerability introduction. 4. Later iterations consistently produced more vulnerabilities than early ones. As builders working with agentic AI solutions, this research challenges the assumption that iterative refinement always improves code quality. The reality is that AI autonomy in code iteration can create a dangerous illusion of improvement while systematically degrading security. The bottom line is that human expertise isn't just helpful in AI-assisted development, it's absolutely essential. The future of secure coding lies in human-AI collaboration, not AI autonomy. My co-authors Profs Shivani Shukla and Romilla Syed and I are grateful for the engaging discussions at IEEE ISTAS and excited to see how this research shapes safer AI-assisted development practices. Ready to discuss the security implications of your AI development workflow? Drop a comment or DM, let's explore how to build more secure AI-assisted systems together. Check out the paper - https://lnkd.in/d7EYwnaR #AISecurity #CodeGeneration #CyberSecurity #ISTAS2025 #SecurityResearch #HumanAICollaboration #ResponsibleAI #SoftwareSecurity

Most developers treat AI coding agents like magical refactoring engines, but few have a system, and that's wrong. Without structure, coding with tools like Cursor, Windsurf, and Claude Code often leads to files rearranged beyond recognition, subtle bugs, and endless debugging. In my new post, I share the frameworks and tactics I developed to move from chaotic vibe coding sessions to consistently building better, faster, and more securely with AI. Three key shifts I cover: -> Planning like a PM – starting every project with a PRD and modular project-docs folder radically improves AI output quality -> Choosing the right models – using reasoning-heavy models like Claude 3.7 Sonnet or o3 for planning, and faster models like Gemini 2.5 Pro for focused implementation -> Breaking work into atomic components – isolating tasks improves quality, speeds up debugging, and minimizes context drift Plus, I share under-the-radar tactics like: (1) Using .cursor/rules to programmatically guide your agent’s behavior (2) Quickly spinning up an MCP server for any Mintlify-powered API (3) Building a security-first mindset into your AI-assisted workflows This is the first post in my new AI Coding Series. Future posts will dive deeper into building secure apps with AI IDEs like Cursor and Windsurf, advanced rules engineering, and real-world examples from my projects. Post + NotebookLM-powered podcast https://lnkd.in/gTydCV9b

In the last few months, I have explored LLM-based code generation, comparing Zero-Shot to multiple types of Agentic approaches. The approach you choose can make all the difference in the quality of the generated code. Zero-Shot vs. Agentic Approaches: What's the Difference? ⭐ Zero-Shot Code Generation is straightforward: you provide a prompt, and the LLM generates code in a single pass. This can be useful for simple tasks but often results in basic code that may miss nuances, optimizations, or specific requirements. ⭐ Agentic Approach takes it further by leveraging LLMs in an iterative loop. Here, different agents are tasked with improving the code based on specific guidelines—like performance optimization, consistency, and error handling—ensuring a higher-quality, more robust output. Let’s look at a quick Zero-Shot example, a basic file management function. Below is a simple function that appends text to a file: def append_to_file(file_path, text_to_append): try: with open(file_path, 'a') as file: file.write(text_to_append + '\n') print("Text successfully appended to the file.") except Exception as e: print(f"An error occurred: {e}") This is an OK start, but it’s basic—it lacks validation, proper error handling, thread safety, and consistency across different use cases. Using an agentic approach, we have a Developer Lead Agent that coordinates a team of agents: The Developer Agent generates code, passes it to a Code Review Agent that checks for potential issues or missing best practices, and coordinates improvements with a Performance Agent to optimize it for speed. At the same time, a Security Agent ensures it’s safe from vulnerabilities. Finally, a Team Standards Agent can refine it to adhere to team standards. This process can be iterated any number of times until the Code Review Agent has no further suggestions. The resulting code will evolve to handle multiple threads, manage file locks across processes, batch writes to reduce I/O, and align with coding standards. Through this agentic process, we move from basic functionality to a more sophisticated, production-ready solution. An agentic approach reflects how we can harness the power of LLMs iteratively, bringing human-like collaboration and review processes to code generation. It’s not just about writing code; it's about continuously improving it to meet evolving requirements, ensuring consistency, quality, and performance. How are you using LLMs in your development workflows? Let's discuss!

Top 5 Must-Know DSA Patterns👇🏻👇🏻 DSA problems often follow recurring patterns. Mastering these patterns can make problem-solving more efficient and help you ace coding interviews. Here’s a quick breakdown: 1. Sliding Window • Use Case: Solves problems involving contiguous subarrays or substrings. • Key Idea: Slide a window over the data to dynamically track subsets. • Examples: • Maximum sum of subarray of size k. • Longest substring without repeating characters. 2. Two Pointers • Use Case: Optimizes array problems involving pairs or triplets of elements. • Key Idea: Use two pointers to traverse from opposite ends or incrementally. • Examples: • Pair with target sum in a sorted array. • Trapping rainwater problem. 3. Binary Search • Use Case: Efficiently solves problems with sorted data or requiring optimization. • Key Idea: Repeatedly halve the search space to narrow down the solution. • Examples: • Find an element in a sorted array. • Search in a rotated sorted array. 4. Dynamic Programming (DP) • Use Case: Handles problems with overlapping subproblems and optimal substructure. • Key Idea: Build solutions iteratively using a table to store intermediate results. • Examples: • 0/1 Knapsack problem. • Longest common subsequence. 5. Backtracking • Use Case: Solves problems involving all possible combinations, subsets, or arrangements. • Key Idea: Incrementally build solutions and backtrack when a condition is not met. • Examples: • N-Queens problem. • Sudoku solver. Why These Patterns? By focusing on patterns, you can identify the right approach quickly, saving time and improving efficiency in problem-solving.

😱𝐀𝐈 𝐰𝐫𝐢𝐭𝐞𝐬 𝐜𝐨𝐝𝐞 𝐟𝐚𝐬𝐭, 𝐛𝐮𝐭 𝐢𝐬 𝐢𝐭 𝐜𝐫𝐞𝐚𝐭𝐢𝐧𝐠 𝐚 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐧𝐢𝐠𝐡𝐭𝐦𝐚𝐫𝐞? AI-powered coding tools like GitHub Copilot have revolutionized software development, helping developers write code faster than ever. But as AI adoption skyrocketed in 2024 (with 63% of developers using AI in their workflow), a crucial question emerged: 🤔 Is AI-generated code 𝐢𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐪𝐮𝐚𝐥𝐢𝐭𝐲, or are we setting ourselves up for long-term tech debt? A new study analyzing 211 million lines of code from 2020–2024 revealed some eye-opening trends: 𝐌𝐨𝐫𝐞 𝐂𝐨𝐝𝐞, 𝐋𝐞𝐬𝐬 𝐌𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 🛑 AI is great at producing code but bad at reusing it. 🔄 Instead of refactoring, AI often duplicates existing patterns, making maintenance harder over time. 𝐂𝐨𝐝𝐞 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐇𝐚𝐬 𝐄𝐱𝐩𝐥𝐨𝐝𝐞𝐝 📈 2024 saw an 8x increase in duplicated code blocks compared to 2020. 📋 Copy/pasted lines now outnumber moved lines, meaning less refactoring and more redundancy. 🐞 When a bug appears, it must be fixed in multiple places, increasing defects and wasted effort. 𝐇𝐢𝐠𝐡𝐞𝐫 𝐃𝐞𝐟𝐞𝐜𝐭 𝐑𝐚𝐭𝐞𝐬 & 𝐂𝐡𝐮𝐫𝐧 📉 Google's DORA 2024 Report found that for every 25% increase in AI adoption, delivery stability dropped by 7.2%. 🔄 AI-generated code is rewritten shortly after being added, suggesting lower quality outputs. 🏗️ Developers are spending more time fixing AI-generated issues than building new features. 𝐖𝐡𝐚𝐭 𝐧𝐨𝐰? 🔧 𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐂𝐨𝐝𝐞 𝐑𝐞𝐮𝐬𝐞: AI should help optimize and refactor, not just generate more code. 📊 𝐁𝐞𝐭𝐭𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: Lines of code ≠ progress. Focus on maintainability over volume. 🧠 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐀𝐈 𝐔𝐬𝐞: AI has no brain use your own! AI should assist thoughtful development, not replace good coding practices. 🔗 Link to the report can be found in the comments. #AI #SoftwareDevelopment #SoftwareEngineering #DevOps #CodeQuality