Problem Solving Techniques for Developers

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!

The best advice I got as a junior engineer: 1. Make it work: In the initial stages, focus on creating a functional solution. Prioritise getting the core functionality up and running to establish a baseline. 2. Then make it right: Once the basic functionality is achieved, shift your focus to refining the code. Clean up your implementation, improve code structure, and adhere to best practices for better maintainability. 3. Then make it fast & pretty: After achieving functionality and code cleanliness, work on optimizing performance and enhancing the user interface. Ensure that the software runs efficiently and has a polished, user-friendly design. 4. Embrace Continuous Learning: Stay curious and committed to ongoing learning. Keep abreast of new technologies, tools, and methodologies to stay relevant and enhance your skills throughout your career. 5. Seek Feedback and Collaboration: Actively seek feedback from peers and experienced colleagues to improve your skills. Foster a collaborative environment that encourages open communication, leading to innovative solutions and a stronger team dynamic. 6. Prioritize Documentation: Document your code, processes, and decisions clearly. This not only aids in understanding your work later on but also helps team members comprehend and maintain the code, contributing to an efficient workflow. 7. Understand the Business Context: Go beyond technical skills and strive to understand the broader business context. Align your technical efforts with organizational goals to make your contributions more impactful and meaningful. 8. Practice Problem-Solving: Develop a problem-solving mindset by breaking down complex issues into manageable components. This approach not only makes problem-solving feasible but also helps in identifying root causes and fosters resilience in the face of technical challenges. 9. Prioritize Security and Reliability: Emphasize security and reliability in your work. Write secure code, ensure robustness in solutions, and prioritize testing to create software that not only functions well but is also resilient to potential vulnerabilities and failures. Remember, a well-rounded set of skills and attitudes will not only make you a proficient engineer but also contribute to a positive and productive work environment.

“What Do We Know To Be Absolutely True?”  This simple yet profound question is at the heart of first principles thinking—a problem-solving approach that challenges assumptions and rebuilds solutions from the ground up. 💡  In a world full of best practices, templates, and "this is how we've always done it," thinking from first principles forces us to strip problems down to their fundamental truths and reimagine possibilities.  Here’s how it works:   1️⃣ Identify the fundamental truths: What do we know for sure, without relying on assumptions?   2️⃣ Rebuild from scratch: Starting from those truths, what’s the most effective solution?   3️⃣ Challenge the norm: Let go of the status quo if it doesn’t align with these core truths.  Examples of First Principles Thinking: 🔹 Elon Musk and SpaceX   - Problem: Reducing the cost of space travel.   - First Principles Thinking: Musk analyzed the components of a rocket (materials, fuel, etc.) rather than accepting the high prices set by existing manufacturers. He researched the cost of raw materials and concluded that building rockets from scratch could be significantly cheaper than purchasing them. This approach led to breakthroughs in reusable rockets and affordable space exploration.  🔹 Software Development  - Problem: Building a new software application.   - First Principles Thinking: Rather than copying existing applications, a developer could identify the core functionalities needed by users, understand the underlying technologies (like databases and programming languages), and create a solution that meets those needs in the most efficient way.  Why does this matter? It’s a mindset that fosters creativity, innovation, and game-changing insights. Whether in technology, design, strategy, or personal growth, questioning assumptions opens doors to better solutions.  Have you ever applied first principles thinking in your work or life? Share your experiences below—I’d love to hear how you’ve challenged assumptions and innovated! 👇  #Leadership #Innovation #FirstPrinciples #ProblemSolving #GrowthMindset #Creativity #Strategy #Entrepreneurship #Technology #CriticalThinking #PersonalGrowth #BusinessTransformation

Ever tried thinking backwards to move forward? It's not just a brain teaser - it's a powerful problem-solving tool called inversion. 19th-century mathematician Carl Gustav Jacobi stumbled upon a profound idea: "Invert, always invert." Fast forward to modern times, where Charlie Munger, Warren Buffett's straight-faced sidekick at Berkshire Hathaway, discovers this concept and thinks, "Ah yes, the key to business success—contemplating failure." Munger popularizes this reverse psychology of inversion in business and investing, forever changing how people overthink their decisions. Let's talk about how you can use this mental judo to tackle tech challenges. 1. Prevent Crashes Before They Happen 🛠️ Instead of asking "How do we keep things running?", try "What could make everything fall apart?" List all the ways your system could fail Find the weak spots Build safeguards against those weak spots 2. Boost Security by Thinking Like a Bad Guy 🕵️ Forget "How do we protect ourselves?" Ask "If I were a hacker, how would I break in?" Put on your black hat Spot the holes in your defenses Patch them before the real bad guys find them Now you're playing cyber-chess, not cyber-checkers! 3. Make Users Happy by Fixing What Bugs Them 😊 Don't ask "What do users like?" Flip it: "What drives users crazy?" Find the things that make users want to throw their devices Map out where users get stuck or confused Smooth out those rough spots Result? Happy users who actually enjoy your product! 4. Speed Up Development by Squashing Time-Wasters ⏩ Forget "How do we code faster?" Ask "What's slowing us down?" Spot the bottlenecks in your process Figure out why things get stuck Fix those problem areas 5. Innovate by Imagining Your Own Downfall 💡 Don't just ask "How can we improve?" Try "What could make our product totally useless?" Think about what might make your tech outdated Spot potential game-changers in your industry Create solutions before problems even show up Now you're not just keeping up - you're leading innovation! Why This Matters: You'll catch problems early Your systems will be fortress-strong Users will love your stuff, and bring gifts Your team will work like a Swiss timepiece You'll define the tip of the spear Next time you're stuck, try flipping your thinking upside down. You might be surprised at the solutions you find! What do you think? Have you ever used inversion to solve a tough problem? Share your stories in the comments!

Stuck on a coding problem? Here’s how top engineers actually solve them. Whether you’re prepping for interviews or building real-world systems, it’s not just about writing code — it’s about solving problems intelligently. Here’s a 10-step mindset that transforms debugging into breakthroughs: 1. Understand the problem Restate it in your own words. Clarity first, code later. 2. Work through examples by hand Manual tracing helps uncover hidden logic. 3. Break it down Small steps → Simple code → Fewer bugs. 4. Pick the right approach Map it to known algorithms or problem patterns (greedy, sliding window, recursion, etc.) 5. Write pseudocode first Your thinking should be clear before your syntax is. 6. Code in chunks Build incrementally and test as you go. It’s okay, the random print statements are always going to help (just comment them out after ;)) 7. Test edge cases Empty inputs, large datasets, invalid values — test for chaos. 8. Optimize after it works First, get it working. Then, make it elegant and efficient. 9. Stay calm when stuck Take a break. Talk it out LOUD. Google concepts, not answers. Still doesn’t work? Try to get at least one test case. 10. Reflect after solving Ask: What did I learn? What pattern was this? Could I solve it faster next time? ⸻ 💬 Real talk: Being a good coder isn’t about avoiding bugs but about knowing how to find your way out of them.

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.

Why-Why Analysis Why-Why Analysis is a problem-solving technique used to identify the root cause of a problem by repeatedly asking "Why?" until the underlying issue is uncovered. Here's a step-by-step approach: 📌Identify the Problem: Clearly define the problem or symptom you are experiencing. 📌Ask "Why?": Ask why the problem is occurring. Write down the answer. 📌Ask "Why?" Again: For each answer given, ask why it is happening. Write down this answer as well. 📌Repeat: Continue asking "Why?" for each successive answer until you reach a root cause. This usually involves 5 to 7 iterations. 📌Analyze and Address: Once you identify the root cause, analyze it to find potential solutions. Implement corrective actions to address the root cause and prevent recurrence. 📌Verify: After implementing solutions, monitor the situation to ensure that the problem has been effectively resolved and does not reoccur. This iterative process helps in drilling down to the core issue rather than addressing only the symptoms.

6 things about learning DSA & problem solving I know now after spending 13+ years as a Software engineer, I wish I knew in my 20s:  [1] Problem-solving isn’t memorization. It’s pattern recognition. - You don’t need to invent solutions from scratch. - The best developers quickly recognize patterns by connecting new problems to old ones. - Tip: Train your mind to spot familiar structures in new questions.  [2] Develop Your "Fast Brain" - Our brain has two modes: Fast (intuitive) and Slow (analytical). - Your fast brain quickly points you in the right direction (e.g., "This feels like a binary search!"). - Tip: Solve varied problems repeatedly so your intuition instantly kicks in during interviews.  [3] Verify Solutions With Your "Slow Brain" - Your fast brain isn't always right. - Use your analytical brain to logically verify solutions step-by-step. - Tip: Always pause and double-check your intuition—don’t rush.  [3] Build Your Personal "Pattern Library" - Problems aren’t random, they follow common patterns. - Examples: Sliding Window, Two-Pointers, Backtracking. - Tip: After each problem, note down the core components and approach, building your own mental library.  [4] Just Solving Problems Isn’t Enough - Simply solving problems without analyzing them won’t build intuition. - Tip: Break problems down, identify core components, and deeply understand why certain solutions work.  [5] Intuition Comes from Deliberate Practice - Memorizing solutions won't help in new situations. - Repeated, intentional practice of problems, especially challenging ones, trains your intuition to "see" solutions quickly. - Tip: Practice intentionally, focusing on identifying patterns rather than just solving fast.  [6] Accept That Mastery Takes Time - You won’t build intuition overnight. Be patient. - Regular, thoughtful practice compounds, your skill improves gradually. - Tip: Trust the process and keep practicing consistently. If you feel stuck right now, remember:   Every great engineer you admire once struggled with the basics too. Keep practicing, keep growing, you'll get there. – P.S: If you're preparing for a SWE role, do check out my guide on behavioral interviews. If you want to break into big tech, startups, or MAANG companies, you must ace the behavioral round. This guide will help you do it → https://lnkd.in/drnsTNhU (230+ engineers are already using this!)

Are you interested in getting better at solving problems in production? Take a look at the OODA loop. By breaking the problem solving process into discrete steps, it can help you avoid rushing to the wrong conclusion. We used this heavily at The Last Pickle. Observe: Use your dashboards and tools to gather as much information as possible. Leverage the USE method to consider system resources, identify algorithmic inefficiencies, and be open to new information. Orient: Build a mental model based on the information you’ve observed. Challenge the assumptions you’ve made. Hold your convictions loosely and be open to changing your mind. Decide: This is where you finally figure out what you're going to do based on your mental model. Make sure you understand the consequences of your decision, are able to roll it back, and if it's changing production, you're able to do some sort of A/B test to determine if things have improved. Act: Time to finally do the thing. Who's responsible? Make sure it's clear who's doing what and how you'll evaluate success. Go back to step one, and observe. Repeat till the problem is solved.

Ideas can spark excitement, but they can also lead us astray. We often rush to build without understanding the problem. This can waste time and money. Too many customers jump in with their solutions. They think they know what’s best. But they miss the bigger picture. Here’s how to avoid that trap: 1. Identify the Issue: ↳ Understand what problem you are trying to solve. 2. Gather Input: ↳ Invite your team to share their thoughts and ideas. 3. Explore Solutions: ↳ Look for options that may cost less or work better. 4. Know Your Platform: ↳ Learn about features that can help solve your problem. 5. Focus on the User: ↳ Consider how your choices affect the end user. 6. Be Open-Minded: ↳ Stay flexible and ready to adapt your ideas. 7. Collaborate: ↳ Work together with your team for the best results. 8. Test Ideas: ↳ Try out different solutions before committing. 9. Keep Learning: ↳ Stay updated on new tools and features. 10. Embrace Feedback: ↳ Listen to input from your team and customers. 11. Document Everything: ↳ Keep track of ideas and solutions explored. 12. Reflect: ↳ Review what worked and what didn’t for future projects. Building the right solution starts with understanding the problem. Stay focused on the issue. Involve your team. Explore all options. This approach leads to better outcomes.