LeetCode Day 14: Binary Complement Problem Solution

🚀 Day 1 — Building Problem Solving Skills I’m continuing my journey to improve problem-solving skills by staying consistent, disciplined, and accountable — one problem at a time. 🧩 Problem: • Two Sum (LeetCode #1) 📚 Topic: Arrays (Pair Traversal) 💡 Key Insight: Checking all possible pairs works, but it highlights the need for more efficient approaches as input size grows. ⚡ Approach: • Pick an element using index i • Traverse remaining elements using index j • Check if nums[i] + nums[j] == target • Return indices when condition is satisfied 🎯 Takeaway: Even simple problems help strengthen logic and introduce optimization thinking. ⏱ Complexity: Time → O(n²) Space → O(1) 💻 Sample Input: nums = [2, 7, 11, 15] target = 9 ✅ Output: [0, 1] Consistency > Perfection 💪 #DSA #LeetCode #ProblemSolving #Python #CodingJourney #LearningInPublic #Arrays #TechGrowth

🚀 Day 13/60 – List Comprehension (Write Powerful Code in One Line ⚡) Why write 5 lines… When you can do it in 1 clean line? Welcome to List Comprehension 👇 🧠 What is List Comprehension? A shorter way to create lists using a single line of code. ❌ Traditional Way numbers = [1, 2, 3, 4, 5] squares = [] for num in numbers: squares.append(num * num) print(squares) ✅ List Comprehension Way numbers = [1, 2, 3, 4, 5] squares = [num * num for num in numbers] print(squares) 👉 Same result. Cleaner code. 🔍 With Condition numbers = [1, 2, 3, 4, 5, 6] even_numbers = [num for num in numbers if num % 2 == 0] print(even_numbers) ⚡ Real Example names = ["adeel", "ali", "ahmed"] upper_names = [name.upper() for name in names] print(upper_names) ❌ Common Mistake [num * num for num numbers] # ❌ Missing 'in' Correct: [num * num for num in numbers] # ✅ 🔥 Pro Tip Use list comprehension when: ✅ You want clean, readable code ❌ Avoid if logic becomes too complex 🔥 Challenge for today 👉 Create a list from 1 to 10 👉 Use list comprehension to get squares Comment “DONE” when finished ✅ Follow Adeel Sajjad to stay consistent for 60 days 🚀 #Python #LearnPython #PythonProgramming #Coding #Programming

Stop memorizing syntax. Start building systems. In 2026, the world doesn't need more people who just "know Python." It needs engineers who understand how components talk to each other. Syntax is the alphabet. Systems are the architecture. Learning to code isn't about passing a quiz. It’s about: 🛠️ Designing scalable logic. ⛓️ Managing data flows. 🚀 Deploying production-ready code. Tutorial hell happens when you stay at the surface. Break out of the loop. At KodeMaster AI, we don’t do passive watching. You code in your own editor. You push to Git. You build real-world systems that prove you’re interview-ready. Our career paths are designed for the shift from "coder" to "system builder." Stop watching. Start engineering. BUILD WITH KODEMASTER.

Just wrapped up Anthropic's Introduction to Subagents course and I'd like to highlight a few takeaways: Subagents shine when you need a result without cluttering your main thread with the journey of getting there. Why does that matter? Because context is everything in agentic workflows - every file read, every dead end explored, every intermediate step takes up space in your main thread's context window. A subagent does all that messy exploration in its own sandbox and hands back just the answer. Your main thread stays focused, clean, and ready to act. Three use cases that stood out: 🔍 Research - Let a subagent trace through an unfamiliar codebase and hand back a clean summary instead of flooding your context with every file it searched. 🔎 Code reviews - A separate context means fresh eyes. Claude actually reviews code better when it wasn't involved in writing it. 🎯 The decision rule - If the intermediate work matters, keep it in your main thread. If you just need the answer, delegate it. Equally valuable was learning what NOT to do: ❌ "Expert" personas - Prompting a subagent with "you are a Python expert" doesn't unlock hidden knowledge. Claude already knows Python. You're just adding overhead for zero benefit. ❌ Sequential pipelines - One agent reproduces the bug, another debugs it, another fixes it. Sounds clean, but each handoff loses context. When step 2 depends on what step 1 discovered, information gets compressed and lost between agents. ❌ Test runner subagents - When tests fail, you need the full output to understand why. A subagent that returns "3 tests failed" hides the details you actually need to debug, forcing extra work to get information that would've been right there in your main thread. Simple framework, but it's already changing how I think about agent architectures!

🔥 Day 54 of #100DaysOfCode 📌 Solved: Sort an Array (LeetCode 912) Today’s problem was all about mastering sorting fundamentals without using built-in functions — a real test of understanding core algorithms. 💡 Problem Insight: We are given an array of integers and need to sort it in ascending order with O(n log n) time complexity. 👉 This rules out simple approaches like Bubble Sort or Selection Sort ❌ 👉 Forces us to use efficient algorithms like: ✔️ Merge Sort ✔️ Quick Sort ⚙️ Approach I Used: Merge Sort 🔹 Divide the array into halves 🔹 Recursively sort both halves 🔹 Merge them in sorted order 🚀 Key Learnings: ✅ Divide & Conquer strategy ✅ Importance of time complexity ✅ Writing sorting logic from scratch (no shortcuts!) ✅ Handling edge cases while merging arrays 📊 Result: ✔️ Accepted ✅ ⏱️ Runtime: 999 ms 💾 Memory: Optimized #Day54 #LeetCode #DSA #Python #CodingJourney #100DaysOfCode #ProblemSolving #Tech #Developers #Learning

🚀 30 Days of LeetCode Challenge – Day 11 Today’s focus: Number manipulation & edge-case handling ✅ Problem Solved 🔹 9. Palindrome Number Concept: Reverse Comparison / String Approach A number is a palindrome if it reads the same forward and backward. 🔹 Approach 1️⃣ If the number is negative → return False (negative numbers can’t be palindromes). 2️⃣ Convert the number to a string. 3️⃣ Reverse the string using slicing [::-1]. 4️⃣ Compare original and reversed values. ⏱ Complexity • Time Complexity: O(n) • Space Complexity: O(n) 💡 Key Learning This solution works, but let’s be honest — this is not the best interview solution. Why? It uses extra space (string conversion). Interviewers often expect a pure mathematical approach (reverse half of the number). 👉 Better approach (optimal thinking): Reverse only half of the digits using modulo and division. Compare halves → O(1) space. 📈 What Improved Today: Thinking beyond just “working code” Understanding difference between acceptable vs optimal solution Awareness of interviewer expectations Grateful for the mentorship of Vinay Sharma at REGex Software Services for pushing me to not just solve problems, but solve them the right way. 📌 Day 11 completed — improving not just speed, but quality. #LeetCode #DSA #Python #Algorithms #CodingJourney #ProblemSolving #RegexSoftwareServices

Just solved Longest Common Prefix on LeetCode — but more importantly, I strengthened a mindset 👇 Most people focus on what the solution is. I’m learning to focus on how to think. 💡 Today’s takeaway: Instead of overcomplicating, I used a simple idea — Start with the first string, and keep shrinking it until it fits all others. Sounds basic? That’s the point. Great problem-solving is often about clarity over complexity. 📈 Progress update: 126/126 test cases passed Clean, optimized logic But beyond stats, what matters is this: I’m getting better at breaking problems down, step by step. 🚀 Goal: 80% problem solving, 20% theory — building real DSA intuition. If you’re also on the grind, remember: Consistency > Intelligence. #DSA #LeetCode #ProblemSolving #CodingJourney #Python #LearningInPublic

Spent the last few weeks refining how we run Claude for complex projects, and this visual nails the exact process. Inputs come in two parts, the persistent project context from CLAUDE.md and the immediate question. Because that md file reloads before every interaction, the agent never starts from zero. The Claude Code Agent processes it all, runs workflows to build the strategy, then activates tools ranging from Python execution to targeted searches. You end up with generated code and actions that stay aligned with the bigger picture instead of drifting off course. It’s the difference between occasional useful responses and consistent, production-ready output. At SkillAgence we’ve seen this level of structure change how fast teams can iterate. If you’re working with LLMs for development, have you locked in a similar context system yet?

🚀 Debugging Journey: Largest BST in a Binary Tree (with Solution) Today I worked on a classic DSA problem — finding the largest BST inside a Binary Tree — and it really tested my debugging + recursion skills. 💡 Key Learnings: 🔹 Use correct boundary values → float('inf'), float('-inf') 🔹 Always track multiple things in recursion (size, min, max, BST status) 🔹 Correct condition is: max(left) < root < min(right) 🔹 Small syntax mistakes can break the whole logic ✅ Approach: For every node, return: Size of BST Minimum value Maximum value Whether it's a BST If subtree is BST → combine left + right Else → take max of left/right subtree 💻 Python Solution: class Solution: def largestBst(self, root): def helper(node): if not node: return 0, float('inf'), float('-inf'), True N1, min1, max1, isBST1 = helper(node.left) N2, min2, max2, isBST2 = helper(node.right) if isBST1 and isBST2 and max1 < node.data < min2: return (N1 + N2 + 1, min(min1, node.data), max(max2, node.data), True) return max(N1, N2), 0, 0, False ans, _, _, _ = helper(root) return ans 📌 Key Takeaway: “Debugging isn’t just fixing code — it’s understanding your logic deeply.” This problem helped me improve: ✔️ Tree Traversal ✔️ Recursion Thinking ✔️ Debugging Mindset #DSA #Python #BinaryTree #CodingJourney #Debugging #LearnInPublic #TechSkills

📌 Sunday is for Revision Revisiting the problems I’ve already solved to strengthen my understanding and retain concepts better. 🚀 Day 4 – Leveling Up Problem-Solving Same pattern. Slight twist. Better thinking. Showing up daily is the real win. 📌 Today’s Problem: Sum of Squares of N Numbers Looks similar to yesterday’s problem… But the approach needs a sharper mindset. 🔹 Approaches Explored 1️⃣ Iterative Approach (For Loop) → Adding squares one by one 2️⃣ Formula-Based Approach → Direct mathematical solution for better efficiency 💡 Key Takeaway Even small variations in problems can change how you think. ✔️ Learned how similar problems require different perspectives ✔️ Understood that formulas can optimize performance 📈 Progress Update: From understanding patterns → to applying smarter solutions Consistency is the real growth engine 🔑 #Python #ProblemSolving #100DaysOfCode #Consistency #LearningJourney #DeveloperMindset