Mastering Dynamic Programming with Recursion Memoization Tabulation

LeetCode 41 – First Missing Positive. 🧩 When I first read this problem, I thought it would be messy. But once I found the pattern, it turned out to be surprisingly clean. The problem in simple terms: Find the smallest positive integer missing from an unsorted array. The real challenge: O(n) time and O(1) extra space. No sorting, no hash maps, no extra arrays. At first, that feels impossible. But here's the trick that clicked for me: The answer has to be between 1 and n+1 (where n = length of array). Why? Because in the best case, if the array contains 1,2,3,...,n, then the missing number is n+1. Otherwise, there's a gap somewhere between 1 and n. Once I realized that, the solution became straightforward: Place each number in its correct position (number x should be at index x-1) | After rearranging, scan to find the first spot where the number doesn't match the index + 1 That index + 1 is our answer What I liked about this problem: It teaches you to work within tight constraints. Instead of adding more memory, you use the array itself as your workspace. If you're preparing for coding interviews, this is one of those problems worth understanding deeply, not for memorizing the solution but for learning how to think under constraints. Have you run into a problem that looked impossible but turned out to have a simple pattern? Curious to hear your experience. #LeetCode #CodingInterview #ProblemSolving #Java #Algorithms

Leveling up my DSA skills with Stacks! 🥞 Just finished writing a custom, array-based Stack implementation in Java. Instead of relying on built-in collections, I wanted to manually map out the exact LIFO (Last-In, First-Out) behavior from scratch. Here’s a breakdown of how I structured the code: 🔹 The Core State: Initialized a fixed-size integer array (size 6) along with a top pointer set to -1 to act as the index tracker for the current topmost element. 🔹 The push() Logic: Built to check boundaries first. If top < size - 1, it safely increments the pointer and adds the new value (array[++top] = value). If full, it catches the error and alerts that the stack is overflowing. 🔹 The pop() Logic: Verifies the stack isn't empty (top > -1), then dynamically retrieves the topmost value while decrementing the pointer (array[top--]), effectively "removing" the element. Testing edge cases is the best part. Purposefully pushing a 7th element (nums.push(22)) into my size-6 stack and seeing my custom "the stack is overflowing..." text pop up in the terminal proves the boundary validations are rock solid! Nothing beats the feeling of a bug-free terminal run! Onward to the next coding challenge. 🚀 #Java #Programming #DataStructures #Algorithms #ComputerScience #StudentDeveloper #LIFO

I’ve been getting back into solving algorithm problems recently, and I worked through a pretty challenging one: “Delete Columns to Make Sorted III.” At first glance, it looks like a simple string problem. But once you dig in, it becomes a lot more interesting — it’s really about finding a pattern across multiple sequences. 💡 Key idea I learned: Instead of thinking about deleting columns directly, you can reframe the problem as: ➡️ “What is the longest sequence of columns that keeps all strings in sorted order?” Once you see it that way, it turns into a dynamic programming problem — similar to finding a longest increasing subsequence, but across multiple strings. 🧠 What this reinforced for me: Many “hard” problems become easier when you reframe the question Patterns like DP and subsequences show up everywhere Explaining the solution clearly is just as important as solving it I wrote a full breakdown of my approach here: 👉 https://lnkd.in/giN69zAn I’m currently focused on sharpening my problem-solving and backend thinking again. If you’re working through similar problems or preparing for interviews, I’d be interested to hear how you approach these kinds of challenges. #Java #LeetCode #Algorithms #SoftwareEngineering #ProblemSolving

🚀 Solving the "Two Sum" Problem in Java: 3 Essential Approaches for Interviews and Beyond 🚀 The Two Sum problem is a classic in Data Structures & Algorithms and one of the most frequently asked questions in tech interviews. It’s simple in concept—but powerful in teaching how to think about efficiency and trade-offs. Here are 3 essential approaches every developer should know 👇 🔹 1. HashMap Optimization (Best Choice) Leverages a HashMap to store values and their indices while traversing the array. ✔ Time Complexity: O(n) ✔ Space Complexity: O(n) 👉 Fastest and most commonly expected solution in interviews. 🔹 2. Two-Pointer Technique (Sorted Array) Sort the array and use two pointers (left & right) to find the target sum. ✔ Time Complexity: O(n log n) (due to sorting) ✔ Space Complexity: O(1) 👉 Great when space optimization matters and modifying input is allowed. 🔹 3. Brute Force Approach (Basic Understanding) Check every pair using nested loops. ✔ Time Complexity: O(n²) ✔ Space Complexity: O(1) 👉 Useful for learning, but not suitable for real-world performance. 💡 Key Takeaways: ✔ HashMap is usually the preferred approach for its speed (O(n)). ✔ Two-Pointer is a smart alternative when space is limited. ✔ Brute Force is a starting point, not a final solution. Mastering problems like this builds strong problem-solving intuition and prepares you for real-world system design challenges. Happy coding! 💻✨ #Java #Programming #DataStructures #Algorithms #InterviewPreparation #TwoSum #Coding #TechInterview #SoftwareDevelopment

I was asked to find an element in an array. I said binary search. The architect said it wasn't the most optimized solution. He was right. Binary search is O(log n). Fast. Clean. Textbook answer. But if you're searching the same array multiple times, build a HashMap once and every query after that is O(1). One operation. Every time. The question I should have asked before answering: "How many times will we search this array?" That one question changes the entire answer. I wrote a full breakdown of when each approach is actually correct, linear, binary, HashMap, with real production scenarios where the wrong choice brought systems down. Link in comments. What's the most humbling technical correction you've received? #Java #DataStructures #SoftwareEngineering #Algorithms #Programming #CodingInterview #BackendDevelopment #TechPakistan #FreshGrad #LearningInPublic

🚀 LeetCode Problem of the Day 📌 Problem: Minimum Distance to Target Element Given an integer array nums (0-indexed) and two integers target and start, we need to find an index i such that: nums[i] == target |i - start| is minimized 👉 Return the minimum absolute distance. 💡 Key Idea: We simply scan the array and track the minimum distance whenever we find the target. 🧠 Approach Traverse the array Whenever nums[i] == target, compute abs(i - start) Keep updating the minimum result 💻 Java Solution class Solution { public int getMinDistance(int[] nums, int target, int start) { int res = Integer.MAX_VALUE; for (int i = 0; i < nums.length; i++) { if (nums[i] == target) { res = Math.min(res, Math.abs(i - start)); } } return res; } } 📊 Complexity Analysis ⏱ Time Complexity: O(n) → single pass through the array 🧠 Space Complexity: O(1) → no extra space used ⚡ Simple linear scan, optimal solution — sometimes brute force is already the best solution! #LeetCode #Coding #Java #ProblemSolving #DataStructures #Algorithms #InterviewPrep

#EngineeringFun #episode3 𝗛𝗢𝗪 𝗔 𝗖𝗢𝗠𝗣𝗜𝗟𝗘𝗥 𝗪𝗢𝗥𝗞𝗦 You write code every day. But what actually happens the second you hit RUN? Most engineers have no idea. That gap costs them in debugging, interviews and system design. Let me fix that right now 👇 —————————————— 𝗪𝗛𝗔𝗧 𝗜𝗦 𝗔 𝗖𝗢𝗠𝗣𝗜𝗟𝗘𝗥? Your computer speaks only binary — zeros and ones. You speak Python. Java. C++. A compiler is the translator sitting in between. It converts your human code into machine language. Think of it like a factory assembly line. Raw material goes in. Finished product comes out. Your code is the raw material. Machine code is the final product. —————————————— 𝗧𝗛𝗘 𝟳 𝗦𝗧𝗔𝗚𝗘𝗦 𝗢𝗙 𝗔 𝗖𝗢𝗠𝗣𝗜𝗟𝗘𝗥 ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟭 — Lexical Analysis → Reads your code character by character → Breaks it into small pieces called tokens → int age = 25 becomes 4 tokens — keyword, name, operator, value ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟮 — Syntax Analysis → Checks if your code follows language grammar rules → Missing bracket? Wrong structure? Caught here. → This is where your red underline errors come from ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟯 — Semantic Analysis → Grammar is correct. But does it make sense? → Storing a word inside a number variable? Blocked here. → Wrong data types and undefined variables caught at this stage ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟰 — Intermediate Code → Converts your code into a middle language → Not your code. Not machine code. Something in between. → Makes optimisation and portability much easier ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟱 — Optimisation → The compiler makes your code faster automatically → x = 2 × 10 gets replaced with just 20 directly → Removes dead code. Simplifies loops. Reuses values. ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟲 — Code Generation → Converts optimised code into actual machine binary → Output is your executable file → .exe on Windows. Binary on Linux. Mach-O on Mac. ⚙️ 𝗦𝘁𝗮𝗴𝗲 𝟳 — Linking → Your code uses external libraries and packages → The linker connects all the pieces together → Now it is one complete working program —————————————— 𝗧𝗛𝗘 𝗙𝗨𝗟𝗟 𝗣𝗜𝗣𝗘𝗟𝗜𝗡𝗘 𝗜𝗡 𝗢𝗡𝗘 𝗩𝗜𝗘𝗪 Your Code ↓ Lexical Analysis — tokenise ↓ Syntax Analysis — grammar check ↓ Semantic Analysis — meaning check ↓ Intermediate Code — middle layer ↓ Optimisation — make it fast ↓ Code Generation — machine binary ↓ Linking — connect all parts ↓ Program Runs 🚀 —————————————— 𝗕𝗢𝗧𝗧𝗢𝗠 𝗟𝗜𝗡𝗘 You do not need to build a compiler. You need to understand how one works. Because when your build fails at 2 AM with a cryptic error — this mental model tells you exactly where to look. That is real engineering. —————————————— Follow for one concept every day. Save this. Share it with one engineer who needs it. 🔖 Comment below — did your college ever teach this? 👇 #HowCompilersWork #SoftwareEngineering #ComputerScience #RealEngineering #TechCommunity #Programming #EngineeringCommunity #java #dsa

Most developers are not slowed down by bad code. They are slowed down by bad thinking. Not syntax. Not framework choice. Not whether the project uses Java, Go, or Python. The real damage usually comes earlier: - no clear boundaries - too many dependencies in one request path - retries added without thinking - APIs designed around convenience instead of failure - teams optimizing for feature speed over system clarity That’s why some codebases feel heavy even before they get big. The problem is not always technical debt. Sometimes it’s decision debt. And that is much harder to fix. Debate: What does more long-term damage to software teams? A) bad code B) bad architecture C) bad product decisions D) bad debugging habits My vote: B first, C second. What’s yours? #Java #SoftwareArchitecture #Microservices #DistributedSystems #BackendEngineering

Ever had your entire script fail because of a single space? 😅 I was solving a real-world problem: monitoring disk space usage using a Bash script 💻   Simple goal → trigger an alert when usage crosses a threshold 🚨 Everything looked correct.   Logic? Fine.   Commands? Fine.  Still… it failed ❌ The culprit? DISK_USAGE = $(command) That one extra space around "=" sign, broke everything 💥 In Bash, variable assignment is strict: NO spaces allowed. Correct: DISK_USAGE=$(command) ✅ Incorrect: DISK_USAGE = $(command) ❌ That tiny difference cost debugging time ⏳ This is where it gets interesting 👇 As Python developers 🐍, we are used to writing: x = 10 It improves readability. It’s clean. It’s encouraged. But carry that same habit into Bash… and it breaks your code. Why? Because in Bash: Spaces are not cosmetic   Spaces are syntax ⚙️ The shell interprets: DISK_USAGE = value as: Command: DISK_USAGE   Argument: =   Argument: value  Which results in an error 😵 Lesson for every developer 🚀 We often switch between languages: Python, Bash, JavaScript, C++ But each language has its own rules. A good habit in one language can become a bug in another. Key takeaways: Be language-aware, not habit-driven 🧠   Understand how your code is interpreted 🔍   Never assume syntax consistency across languages ❗  The smallest characters can cause the biggest failures 🐞 If you are preparing for interviews or working on real systems, these tiny details matter more than you think. Have you ever been stuck because of something this small? Share your experience 👇 #SoftwareEngineering #Python #Bash #DevTips #CodingMistakes #Debugging #LearnToCode #100DaysOfCode #TechCareers #Programming