Strategies for Solving Coding Challenges

I’ve reviewed close to 2000+ code review requests in my career. At this point, it’s as natural to me as having a cup of coffee. However, from a senior engineer to now an engineering manager, I’ve learned a lot in between. If I had to learn to review code all over again, this would be the checklist I follow (inspired from my experience) 1. Ask clarifying questions:      - What are the exact constraints or edge cases I should consider?      - Are there any specific inputs or outputs to watch for?      - What assumptions can I make about the data?      - Should I optimize for time or space complexity?  2. Start simple:      - What is the most straightforward way to approach this?      - Can I explain my initial idea in one sentence?      - Is this solution valid for the most common cases?      - What would I improve after getting a basic version working?  3. Think out loud:      - Why am I taking this approach over another?      - What trade-offs am I considering as I proceed?      - Does my reasoning make sense to someone unfamiliar with the problem?      - Am I explaining my thought process clearly and concisely?  4. Break the problem into smaller parts:      - Can I split the problem into logical steps?      - What sub-problems need solving first?      - Are any of these steps reusable for other parts of the solution?      - How can I test each step independently?  5. Use test cases:      - What edge cases should I test?      - Is there a test case that might break my solution?      - Have I checked against the sample inputs provided?      - Can I write a test to validate the most complex scenario?  6. Handle mistakes gracefully:      - What’s the root cause of this mistake?      - How can I fix it without disrupting the rest of my code?      - Can I explain what went wrong to the interviewer?      - Did I learn something I can apply to the rest of the problem?  7. Stick to what you know:      - Which language am I most confident using?      - What’s the fastest way I can implement the solution with my current skills?      - Are there any features of this language that simplify the problem?      - Can I use familiar libraries or tools to save time?  8. Write clean, readable code:      - Is my code easy to read and understand?      - Did I name variables and functions meaningfully?      - Does the structure reflect the logic of the solution?      - Am I following best practices for indentation and formatting?  9. Ask for hints when needed:      - What part of the problem am I struggling to understand?      - Can the interviewer provide clarification or a nudge?      - Am I overthinking this?      - Does the interviewer expect a specific approach?  10. Stay calm under pressure:      - What’s the first logical step I can take to move forward?      - Have I taken a moment to reset my thoughts?      - Am I focusing on the problem, not the time ticking away?      - How can I reframe the problem to make it simpler?  

I'm often asked what to do if one can't solve a coding problem after pondering it for 15-20 minutes. People often hit a wall while preparing for coding interviews. I did too. Here are my suggestions: 👉𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐀𝐜𝐭𝐢𝐯𝐞𝐥𝐲: Instead of passively reading the explanation after 15-20 minutes, try to struggle with the problem a bit longer. This is where the learning really happens. If you can't solve it, try to identify which part of the problem you find challenging. Is it the initial approach? Is it a tricky corner case? Once you've identified your weak point, you can then focus on solutions to that specific issue. 👉𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐭𝐡𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐭𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: While it's tempting to memorize solutions, the interviewers are more interested in how you think and approach a problem. So, focus on the underlying techniques, patterns, and algorithms. Once you deeply understand a concept, you'll be able to apply it to a variety of questions. 👉𝐒𝐩𝐚𝐜𝐞𝐝 𝐑𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐨𝐧: Instead of reviewing all questions every day, use the spaced repetition technique. For example: 1. Review a question you've solved today. 2. Review it again in two days. 3. If you solve it successfully, review it again in a week. 4. If you still solve it successfully, review it again in two weeks. This technique will help you remember the approach over the long term. 👉𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐰𝐢𝐭𝐡 𝐏𝐞𝐞𝐫𝐬: Talking through your solution, or even your confusion, with someone else can be very beneficial. This could be in online forums, study groups, or with friends preparing for similar interviews. Explaining your thought process to someone else can help solidify your understanding. 👉𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐳𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬: Many problems can be grouped together into certain categories like sliding window. Once you've solved a few problems in a category, try to summarize the general approach that apply to that category. This way, when faced with a new problem, you can try to fit it into a known category and apply the corresponding techniques. 👉𝐌𝐨𝐜𝐤 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Consider mock interviews with friends or using platforms that offer this service (check https://lnkd.in/gwrarnyD). This not only helps with problem-solving but also gets you comfortable with explaining your thought process. 👉𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐊𝐞𝐲: Instead of solving similar problems repeatedly in a short span, try a mix. For instance, after two-pointer problems, move on to recursion, then sliding window, and then come back to two-pointers. This cyclic variation helps cement your learning better. 👉𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐌𝐢𝐬𝐭𝐚𝐤𝐞𝐬: Whenever you can't solve a problem, instead of just reading the solution, ask yourself why you couldn't solve it. Is there a pattern or concept you're consistently missing? By recognizing your weak spots, you can focus on improving in those areas. #codinginterview #datastructures

10 pieces of advice that helped me get good at solving problems and cracking coding interviews, I wish someone had told me when I was 20 and grinding at BITS Pilani.... 1. How do I pick which problems to solve? - Start with easy problems in each topic (arrays, strings, etc.) - Use curated lists like “Top 150 Interview Questions” - Don’t waste time on random, obscure problems - Repeat the basics till they’re muscle memory 2. What to do when you’re stuck on a problem for hours? - Spend 30–60 minutes thinking hard - If still stuck, check hints or partial solutions, not full code - Try writing the solution from scratch after reading it - Make a note to revisit after a week 3. How to avoid feeling overwhelmed by 3000+ LeetCode problems? - Ignore the total count - Focus on a shortlist of 250–300 core problems - One topic at a time, one pattern at a time - Track your progress on a sheet 4. What’s the best way to learn a new data structure or algorithm? - Watch a trusted YouTube video (not 10!) - Implement it from scratch - Solve 3–5 related easy/medium problems - Teach it to a friend or write a summary note 5. How do I actually retain what I learn? - Make a habit of revisiting solved problems - Explain your solution out loud or to someone else - Maintain a notebook with your ‘aha’ moments - Practice old topics every 2 weeks 6. How to get faster at solving problems? - Set a timer: 15 mins for easy, 30 for medium, 1 hour for hard - Focus on pattern recognition, not brute force - Analyze time/space complexity for every problem - Practice under interview conditions (mock tests, contests) 7. What language should I use? - Stick to what you’re comfortable with (Python/Java/C++) - Learn built-in libraries for arrays, maps, queues, etc. - Don’t switch languages unless you’re stuck 8. How do I stop memorizing solutions and actually understand them? - Re-write solutions from memory after learning - Break down why each step works - Relate the approach to real-world problems - Never copy-paste, always type it out 9. How do I tackle hard problems or topics? - Break into smaller subproblems - Use pen & paper to draw out tricky inputs - Start with a brute-force solution, then optimize - Look for similar solved patterns 10. How to not lose motivation when it gets tough? - Celebrate small wins (accepted solution, a new pattern cracked) - Take breaks and walk away when you’re frustrated - Join a group or community for accountability - Remember: Growth compounds, it gets easier with time

🎯 400 LeetCode problems solved - but this isn't your typical "grinding problems" post. Like training a machine learning model, I approached algorithmic problem-solving with a focus on data quality and diversity. Just as ML models need varied, high-quality data points to generalize well, I found that solving diverse problems across different patterns and domains builds better problem-solving intuition. My systematic approach: Pre-coding Analysis (Link to sample doc in comments) • Document multiple potential approaches • Analyze time & space complexity for each approach • Think through tradeoffs before writing any code • Consider edge cases and constraints Practice Execution • Used stopwatch to measure performance • Aimed to solve while explaining clearly within: - Easy: 10 minutes - Medium: 15 minutes - Hard: 25 minutes • Focus on thinking aloud - crucial for interviews Deep Dive Process • Rigorous complexity analysis • Explore optimization opportunities • Document learnings and patterns • Regular mock interviews on Pramp The goal wasn't to solve all 3000+ problems, but to build a robust "model" that could generalize to new problems effectively. Each solved problem is like a new training data point, helping my brain recognize patterns and edge cases. Key learning: The magic happens in the pre-coding analysis. Writing down different approaches and analyzing tradeoffs before coding helped me: - Build stronger problem-solving intuition - Communicate my thought process clearly - Make better engineering decisions - Save time during actual coding I'll share a sample doc in the comments. It's been crucial for building a systematic approach to problem-solving. To those on this journey: Keep your head down, document your thinking, and remember - you're not just solving problems, you're building a framework for approaching any technical challenge.

Cracking the Coding Interview: The first round of most tech interviews is a coding challenge, and clearing it is crucial. Here’s a step-by-step guide to help you ace it: 1. Understand the Problem Rephrase the question to ensure clarity and note key requirements. 2. Ask About Edge Cases Clarify potential tricky inputs like empty arrays or negative numbers. 3. Discuss the Brute Force Solution Briefly mention it, but focus on optimizing. 4. Explain the Optimal Solution Discuss the time and space complexity of your solution. 5. Check if the Solution is Feasible Confirm with the interviewer if they’re happy with your approach. 6.Write Clear, Simple Code Use a language you’re comfortable with and keep the code clean. 7. Dry Run Your Code Step through the code to catch errors and edge cases. 8. Adapt as Needed Refactor if needed after the dry run, especially for edge cases. 9. Review Complexity Double-check the time/space complexity and ask if more tests are needed. 10. Seek Feedback Always ask for feedback on your approach. Pro Tip: Focus on Patterns, Not Problems Understanding problem-solving patterns (like sliding windows or dynamic programming) will help you solve a wide range of coding challenges. #CodingInterviewTips #TechInterviews #InterviewPrep

🚀 Key Strategies That Helped Me Tackle a FAANG/MAANG Coding Interview 🚀 Last year around this time, I had a chance to interview for a FAANG engineering internship. Here’s a breakdown of the strategies that helped me successfully navigate the interview process. 📌 Interview Context - Expect 1 or 2 coding questions, followed by a discussion on your experience. - Practice coding in simple text editors to simulate real interview environments. - Focus on Big O concepts rather than memorizing every algorithm. 📐 Big O Optimization Tips - Use logical variable names and clearly define functions. - Drop constant terms and focus on dominant terms. Example: ✅ O(a^2 + a) = O(a^2) ❌ O(a^2 + d) ≠ O(a^2) (don't drop constants if they affect scaling) 🧩 Problem-Solving Framework - Listen carefully for clues in the problem statement. - Draw examples for edge cases and generics. - Start with brute force, then refine for efficiency. - Walk through your approach aloud before coding. - Use hash tables, pre-computation, and caching for Optimization. 💻 Coding & Thought Process - Talk through your logic to keep the interviewer in the loop. - Anticipate edge cases and confirm with the interviewer before starting. - Use built-in functions and account for boundary conditions. - Structure your solution modularly from top-down. 🔍 Verification Techniques - Walk through your code conceptually to catch mistakes. - Focus on complex or high-risk areas: - Math, Moving Indices, Recursion Parameters, Base Cases - Test with edge cases, NULLs, and diverse dataset sizes. 🗣️ Communication Matters - Think aloud—share your process with the interviewer. - Ask clarifying questions early. - Be transparent about mistakes; they reveal adaptability. - Follow cues from interviewer and stay persistent to the end! I followed them very precisely, and it truly enhanced my performance. While I bombed the final systems interview, the experience was incredibly valuable, and I hope these insights help others preparing for similar challenges. Best of luck in your interviews! You've got this! 💪 #TechInterview #CodingInterview #FAANGPrep #InterviewStrategies #ProblemSolving #BigO #FAANG #MAANG #SoftwareEngineering #CodingTips #InterviewPreparation #TechCareers #Programming #CareerGrowth #Amazon #Meta #Facebook #Netflix #Apple #Google #Fullloop #ProductionEngineering #universityrecruiters #university #newgrad #2025 #opentowork #computerscience #leetcode