Advanced Problem-Solving Strategies for Software Engineers

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!

You can’t be a good problem solver Without inverted thinking. 𝗜𝗻𝘃𝗲𝗿𝘁, 𝗮𝗹𝘄𝗮𝘆𝘀 𝗶𝗻𝘃𝗲𝗿𝘁! Why? Our brain excels at finding threats and negatives, Far better than spotting positives. That’s how we survived. When asked, "How can we improve?" we often freeze. But ask, "How can we make this worse?" and ideas flow. 𝗪𝗵𝘆 𝗻𝗼𝘁 𝗽𝗹𝗮𝘆 𝘁𝗼 𝗼𝘂𝗿 𝗯𝗿𝗮𝗶𝗻’𝘀 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀? Start with the negatives. List all the ways something could fail. Then invert them to find solutions. 𝗧𝗵𝗶𝘀 𝗶𝗱𝗲𝗮 𝗶𝘀𝗻’𝘁 𝗻𝗲𝘄 • Charlie Munger used it to build success. • Alex Hormozi credits it for clarity. • 200 years ago, mathematician Carl Gustav Jacob Jacobi said: "Man muss immer umkehren" - Invert, always invert. 𝗟𝗲𝘁’𝘀 𝘁𝗿𝘆 𝗶𝘁 Instead of asking: "How can a software engineer thrive at work in 2025 with AI around?" Which blocks the brain, And you get answers like: • Get certifications. • Follow internal rules. • Meet deadlines. I know because I asked a few. 😊 𝗡𝗼𝘄, 𝗶𝗻𝘃𝗲𝗿𝘁 𝗶𝘁 "What would I do as a software engineer to be replaced by AI in 2025?" • Ignore the bigger picture. • Insist on outdated methods. • Focus only on repetitive tasks. • Stick to known, defined solutions. • Copy-paste from AI without adding value. • Avoid teamwork and complex communication. 𝗜𝗻𝘃𝗲𝗿𝘁 𝗮𝗴𝗮𝗶𝗻 𝗳𝗼𝗿 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 • Solve unique challenges. • Focus on complex problems. • Bring ideas to improve processes. • Complement AI outputs with unique value. • Develop soft skills: communication, teamwork, leadership. Looks good. As Charlie Munger said: "Invert, always invert. Turn a situation upside down. Look at it backward." ------------------

The Case of the Costly Error Once upon a time, a critical bug in a bustling software company was reported in their flagship product just days before a major release. Panic spread through the team like wildfire. The bug was complex, and time was running out. At first, the team tried the usual approach—frantic debugging and patching. But the bug kept reappearing like a stubborn ghost. As deadlines loomed closer, frustration mounted. That's when Jane, one of the senior developers, stepped in. She suggested a different approach: structured problem-solving. She gathered the team in a meeting room and laid out a plan: 01. Define the Problem: They dissected the bug, identified its specific behaviors, and defined the problem statement clearly. 02. Collect Data: They gathered data on when the bug occurred, what actions triggered it, and the system conditions at that moment. 03. Generate Hypotheses: The team brainstormed potential causes, generating multiple hypotheses. 04. Test Hypotheses: They systematically tested each hypothesis individually, isolating variables and gathering more data. 05. Analyze Results: Based on the data collected, they analyzed the results of each test and eliminated hypotheses that didn't hold up. 06. Implement Solution: Finally, they identified the root cause and implemented a solution that fixed the bug once and for all. The bug was squashed, and the release went off without a hitch. What could have been a disaster turned into a valuable lesson. Structured problem-solving saved the day! → When faced with a daunting challenge, don't rush into solutions. Take a structured approach. #dhandhekafunda ps: Structured problem-solving approach acts as a compass when you are not emotionally trapped in the situation. If you are, have another competent individual take the lead. At least be that structured ;)

Reality Check for Aspiring Software Engineers: If you're not proficient in Data Structures and Algorithms (DSA), you're missing out on numerous opportunities with top tech companies. Mastering DSA is not just about cracking interviews; it's about building a solid foundation for problem-solving and efficient coding. Here's a structured path to guide you through mastering DSA: 1. Array & Hashing: → These basics will form the building blocks for more complex topics. → Recommended Problems: Frequency count, Anagram checks, Subarray sums. 2. Two Pointer & Stack: → Perfect for problems involving sequences and order management. → Recommended Problems: Pair sums, Valid parentheses, Largest rectangle in histogram. 3. Two Pointer ->Binary Search, LinkedList, Sliding Window: → Dive deeper into efficient searching with Binary Search, manage data dynamically with Linked Lists, and tackle contiguous data problems with Sliding Windows. → Recommended Problems: Search in a rotated array, Detect cycle in linked list, Longest substring without repeating characters. 4. Trees: → Understand hierarchical data structures with Trees, manage parent-child relationships efficiently. → Recommended Problems: Binary tree traversal, Lowest common ancestor. 5. Tries, Heap, Backtracking: → Level up with Tries for prefix-based searches, → Heaps for priority management, and Backtracking for exploring possibilities. → Recommended Problems: Word search, Priority queues, Sudoku solver. 6. Backtracking ->Graph, 1D & 2D DP: → Graphs are used for networked data, and Dynamic Programming (DP) → Recommended Problems: Shortest path, Knapsack problem, Unique paths in a grid. 7. Bit Manipulation: → Solve problems with efficient, low-level operations. → Recommended Problems: Single number, Subset generation using bits. 8. Intervals, Greedy, Advanced Graph: → Tackle interval problems for range-based data, use Greedy algorithms for locally optimal solutions, and explore advanced graph topics for complex networks. → Recommended Problems: Meeting rooms, Minimum number of platforms, Maximum flow. ▶️ Resources: → Online coding platforms (LeetCode, HackerRank) → Comprehensive courses (Coursera, Udemy) → Books (Cracking the Coding Interview, Introduction to Algorithms) Pro Tip: Consistent practice and understanding the underlying principles are key. Solve diverse problems and learn from each one. Stay determined, keep learning, and soon you'll be acing those coding interviews! Follow me for insights on Leadership, System Design, and Career Growth!

12 thinking patterns you need to develop as a software engineer that nobody told you (until now) As a software engineer, you‘re always engaged in: -the best approach to problems -designing optimal solutions -and making decisions. Let's consider a specific scenario like “developing a new feature for a social media platform” and see how all these thinking patterns might play out. 1. Analytical Thinking: Analyze user engagement metrics, feedback and heatmap of all the features to identify specific areas of the platform that require improvement or innovation. 2. Critical Thinking: Assess the potential impact of the new feature on existing functionalities, considering user expectations, scalability, failure modes and other potential challenges. 3. Creative Thinking: Design a new feature that stands out, perhaps introducing a unique interaction or incorporating a novel approach to content presentation which is not seen in any social media platform. 4. Systems Thinking:  Consider how the new feature integrates with the overall social media platform system, ensuring seamless interaction with existing components and maintaining a cohesive user experience. 5. Logical and Algorithmic Thinking:  Implement and optimize algorithms for personalized content recommendations within the new feature, enhancing user engagement based on identifying patterns in behavior and user preferences. 6. Abstract Thinking:  Grasp the high-level architecture of the entire social media platform to ensure that the new feature aligns with the overall design and user flow without being tied to any of the specific implementation details. 7. Detail-Oriented Thinking:  Pay attention to details while coding the feature, ensuring that the user interface is pixel-perfect, the backend logic is error-free and efficient and the system components scale and are are fault tolerant 8. Problem-Solving: Apply problem-solving skills to address potential issues, such as optimizing database queries or resolving conflicts between the new feature and existing functionalities. 9. Strategic Thinking:  Develop a strategic plan for the feature rollout, considering factors like user adoption, marketing strategies, and potential future enhancements. 10. Adaptive Thinking:  Adapt to evolving user requirements or platform updates. Make adjustments to features to align with changing expectations. 11. Collaborative Thinking: Work with cross-functional teams including: -designers -QA engineers -and product managers to gather data and refine features. 12. User-Centric Thinking:  Prioritize user needs and feedback throughout the feature development. Make sure the final product elevates the overall user experience. Every decision you make during the development process will involve one or more of these thinking patterns. It's the art of seamlessly integrating them that will bring you success. – P.S: What’s one problem you’re facing now that you need help with?

How software engineers solve problems vs how GRC teams solve problems 🔧 Software engineers: → Define the pain point clearly (Trello: "network partitioning causes message loss") → List specific requirements (failover capabilities, throughput needs, latency targets) → Evaluate multiple alternatives systematically (Kafka, SNS+SQS, Kinesis, Redis Streams) → Choose based on technical fit, not features (Kafka met requirements; Redis Streams was unstable) → Implement incrementally (shadow traffic, gradual rollout, measure results) → Build for future scale (anticipate growth, design for reliability) How traditional GRC solves problems: 📋 → Think in project cycles (audit deadlines drive everything) → Optimise for framework coverage over driving down risk → Ignore stakeholder toil ("just fill out this 50-question Google Forms") → Choose tools by feature count (does it have 200+ connectors?) → PoC based on demos not depth of what you actually need → Complain about your tool online → Reset after each audit instead of building iteratively The GRC Engineering difference? We apply software engineering problem-solving methodology to compliance challenges. ✅ Clear problem definition: "Automated evidence collection doesn't scale outside of public cloud specific controls" ✅ Requirements-driven evaluation: What do we actually need vs. what vendors sell vs. what we can build? ✅ Systematic comparison: Technical fit over feature lists ✅ Incremental implementation: Test with low-risk controls first but in production settings ✅ Future-proof architecture: Build systems that can be maintained by the team and can scale to production-grade environments Just like Trello didn't choose Kafka because it had the most features, they chose it because it solved their specific technical requirements. Your GRC program deserves the same engineering rigour. 🚀 #GRCEngineering #SystemsThinking #EngineeringMethodology

I’ve sat down with more than 300+ candidates for technical interviews for SDE roles in the last 6 years of my career in different leadership positions. Here are the 10 best tips I can give you from my experience on how you should approach to solve a problem during an interview: 1. Use a Heap for K Elements      - Rule: When dealing with top/maximum/minimum/closest K elements among N elements, use a Heap.      - Example Scenario: Finding the top 3 largest numbers in an array. 2. Binary Search or Two Pointers for Sorted Inputs      - Rule: If the input is a sorted array or list, use Binary Search or the Two Pointers strategy.      - Example Scenario: Finding a pair of numbers that sum up to a target in a sorted array. 3. Backtracking or BFS for Combinations      - Rule: For trying all combinations or permutations of the input, use Backtracking or Breadth-First Search (BFS).      - Example Scenario: Generating all subsets of a given set. 4. BFS or DFS for Trees and Graphs      - Rule: Most Tree or Graph questions can be solved using BFS or DFS.      - Example Scenario: Finding the shortest path in a graph. 5. Convert Recursion to Iteration with a Stack      - Rule: Every recursive solution can be rewritten as an iterative one using a Stack.      - Example Scenario: Converting recursive tree traversal to iterative using a stack. 6. Optimize Array Problems with HashMap or Sorting      - Rule: If the array solution takes O(n²) time, try using HashMap/Set for O(n) time or sorting for O(n log n) time.      - Example Scenario: Finding duplicates in an array. 7. Use Dynamic Programming for Optimization Problems      - Rule: If the problem involves maximization or minimization, use Dynamic Programming.      - Example Scenario: Solving the knapsack problem. 8. HashMap or Trie for Common Substrings      - Rule: For finding common substrings among multiple strings, use a HashMap or a Trie.      - Example Scenario: Finding the longest common prefix among strings. 9. Trie for String Search and Manipulation      - Rule: When searching or manipulating a collection of strings, a Trie is the best fit.      - Example Scenario: Implementing autocomplete functionality. 10. Fast & Slow Pointers for Linked Lists      - Rule: For Linked List problems, especially when extra space isn't allowed, use the Fast & Slow Pointer approach.      - Example Scenario: Detecting a cycle in a linked list.  ♻️ Repost if you found this helpful!

I spent 2+ years grinding Leetcode and learning DSA & problem-solving as a CSE student at BITS Pilani. If I had to start again from 0, this is exactly how I’d do it to get hired as a Software Engineer in 2025:  1️⃣ 𝗺𝗮𝘀𝘁𝗲𝗿 𝗰𝗼𝗿𝗲 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝘁𝗵𝗶𝗻𝗴 𝗲𝗹𝘀𝗲 ○ Before touching random Leetcode problems, I’d master core algorithms like: Binary Search, DFS, BFS, Sliding Window, Two Pointers, Recursion, Backtracking, and Dynamic Programming. ○ The goal isn’t just to “understand” these but to be able to implement them from scratch without hesitation. ○ I’d write each algorithm at least 5-10 times from memory until it becomes second nature.  2️⃣ 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 ○The biggest mistake people make is solving hundreds of random problems without recognizing patterns. ○ 90% of interview problems fall into ~20 coding patterns. ○ Instead of solving 1000+ questions, I’d focus on understanding:    ►How problems within a pattern are similar.    ►What small variations make them different.    ►Which techniques help optimize them.  3️⃣ 𝗿𝗲𝗽𝗲𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 𝗶𝗻𝘀𝘁𝗲𝗮𝗱 𝗼𝗳 𝗷𝘂𝘀𝘁 𝗺𝗼𝘃𝗶𝗻𝗴 𝗼𝗻 ○ Just because I solved a problem once doesn’t mean I’ll remember it. ○ I’d follow this reinforcement schedule for problems I struggled with: Solve it today, solve it again in 1 week and finally solve it again in 1 month. ○This is how I’d make sure I actually retain what I learn.  4️⃣ 𝘀𝘁𝘂𝗱𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆 ○ When I can’t solve a problem, I’d first struggle with it for at least 30 minutes before looking at the solution. ○ Then, I’d:   ► Write out the solution from memory after understanding. ► Explain why it works in simple words ► (if I can’t explain it, I don’t truly understand it).   ► Look at alternative solutions to compare trade-offs.  5️⃣ 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗹𝗶𝘀𝘁 (𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗿𝗮𝗻𝗱𝗼𝗺 𝗴𝗿𝗶𝗻𝗱𝗶𝗻𝗴) ○ Instead of jumping between random problems, I’d follow a structured roadmap like:  ► First 50-60 easy problems → Get comfortable with implementation.   ► Next 80-100 medium problems → Focus on pattern recognition & optimization.   ► Last 30-40 hard problems → Only after mastering the fundamentals.  ○ 150-200 well-selected problems are enough to crack most interviews.  6️⃣ 𝘀𝘁𝗮𝗿𝘁 𝗺𝗼𝗰𝗸 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗲𝗮𝗿𝗹𝘆 (𝗲𝘃𝗲𝗻 𝘄𝗵𝗲𝗻 𝗜 𝗳𝗲𝗹𝘁 𝘂𝗻𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱)   ○ Simulating real interviews early helps with time pressure & communication skills.  ○ I’d start setting strict 30-45 min timers on problems to simulate interview conditions.  ○ I’d practice explaining my thought process out loud, since half of the interview is about communication.  (Bonus tip in the comments) ↓

How I practice Invent and Simplify to become a better Software Engineer. One of the most overlooked skills for engineers is being observant. Spotting inefficiencies, friction, or overcomplication is the first step to inventing and simplifying. Since starting my 100-day series, I’ve received countless requests to review resumes, unsolicited. I was able to do a handful myself, but I quickly realized, while I can’t master the ninja’s shadow clone technique, I can build something that scales to serve anyone who needs it. (Spoiler alert: it's available on PrepRoom). Here are a few ways you can practice Invent and Simplify: 1. Automate repetitive work. If you find yourself running the same script or manual process every week, write a small tool to do it for you. It saves future time, reduces human error or inconsistencies, and frees your brain for harder problems. 2. Redesign bottlenecks. If your system’s build takes 40 minutes, that’s wasted engineering hours daily. Challenge why it takes so long, and look for a fix. Simplifying choke points can multiply team velocity. 3. Question assumptions. When someone proposes a complex architecture, ask: “What problem are we really solving? Is there a simpler path?” Complexity often sneaks in when people don’t pause to reframe the problem. 4. Identify solutions that can be self-served. Instead of helping one person at a time, create tools or processes that let anyone get what they need on their own. People will thank you, and you'll thank you. The ability to invent and simplify transforms good engineers into great engineers. It’s not just about shaving off a few minutes of work or cleaning up code; it’s about building a mindset. Great engineers see patterns others overlook, automate what slows the team down, and design systems that scale beyond themselves. --- Hello, I’m Charles, and I am committed to share my two cents on career-related topics for 100 consecutive days. In these uncertain times, I hope to support those facing layoffs or career challenges. Follow me, and let's navigate this together! (76/100) #InventAndSimplify #LeadershipPrinciples #SoftwareEngineering #CareerGrowth #PrepRoom

Problems aren't roadblocks. They're invitations. An invitation to innovate. To rethink. To leap. The difference between stuck and unstoppable? It's not the challenge. It's you. Your lens. Your toolkit. Your willingness to dance with the difficulty. As a tech leader, your ability to solve complex issues can make or break your career. I've led teams across continents, industries, and crises. Here's what I've learned: 𝟭. 𝗥𝗼𝗼𝘁 𝗖𝗮𝘂𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Peel back the layers. Ask "Why?" repeatedly. You're not fixing a leak; you're redesigning the plumbing. 𝟮. 𝗦𝗪𝗢𝗧 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Map your battlefield. Know your strengths, weaknesses, opportunities, and threats. Sun Tzu would approve. 𝟯. 𝗠𝗶𝗻𝗱 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 Visualize the chaos. Connect the dots. Your brain on paper, minus the mess. 𝟰. 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 Prepare for multiple futures. Be the chess player who sees ten moves ahead. 𝟱. 𝗦𝗶𝘅 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗛𝗮𝘁𝘀 Wear different perspectives. Be the critic, the optimist, the data analyst, the artist, the operator. Your mind is pliable; use it. 𝙒𝙝𝙮 𝙩𝙝𝙞𝙨 𝙢𝙖𝙩𝙩𝙚𝙧𝙨: - 76% of IT leaders rank problem-solving as the top soft skill (Global Knowledge) - Strong problem-solvers are 3.5x more likely to hit strategic goals (Harvard Business Review) - 70% of problem-solving pros drive more innovation (PwC) These aren't just methods. They're mindsets. Tools to reshape your thinking. I've used these to navigate multi-million-dollar projects and multinational teams. They work. Period. But the real differentiator: consistency. Use these daily. Make them habits. Your problem-solving muscle grows with every rep. Start now. Pick one method. Apply it to a current challenge. Share your results. The best tech leaders aren't born. They're forged in the fires of solving complex problems. What will you solve today?