Finding Middle Node in Linked List with Two-Pointer Technique

🚀 Day 6 – Functions in Python Today I learned one of the most important concepts in programming — Functions. Instead of writing the same code again and again, functions allow us to reuse logic efficiently. 🔹 A function is a reusable block of code 🔹 Defined using the def keyword 🔹 Can take inputs and return outputs 💡 Example: def add(a, b): return a + b print(add(10, 20)) # 30 🧠 Key Learning: Functions make code cleaner, scalable, and easy to maintain. This is where programming starts feeling powerful. Ajay Miryala 10000 Coders #Python #CodingJourney #Functions #100DaysOfCode #Developer

🚀 Just solved the “Valid Number” problem on LeetCode! This problem looks simple at first glance—but handling edge cases like decimals, signs, and exponents makes it a great test of attention to detail and logical thinking. ✅ Key takeaways: Careful handling of edge cases is crucial Validating input step-by-step can simplify complex parsing problems Writing clean, readable logic beats overcomplicated solutions 💡 Performance: ⚡ Runtime: 3 ms 🧠 Efficient space usage ✅ All test cases passed Problems like this remind me that consistency in practice is what builds strong problem-solving skills. On to the next one! 🔥 #LeetCode #Coding #Python #ProblemSolving #SoftwareEngineering #AIEngineerJourney link of #Solution :- https://lnkd.in/ga9b5pVb

🚀 Day 83 of #100DaysOfCode 📌 Problem Solved: Letter Combinations of a Phone Number (LeetCode 17) Today’s challenge was all about exploring backtracking and how recursive decision-making builds combinations efficiently. 🔍 Given a string of digits (2–9), the goal is to generate all possible letter combinations based on the classic phone keypad mapping. 💡 Key Learning: Instead of trying every possible string blindly, backtracking helps us build combinations step-by-step and explore only valid paths — making the solution clean and efficient. ⚡ Highlights: ✔️ Used recursion to explore all possibilities ✔️ Implemented a digit-to-letter mapping ✔️ Built combinations incrementally ✔️ Achieved optimal runtime performance 📈 Result: ✅ All test cases passed ⚡ 0 ms runtime (Beats 100%) Consistency is starting to compound — one problem at a time. 💪 #LeetCode #DSA #Python #CodingJourney #Backtracking #100DaysOfCode

In my previous post, i talked about breaking code with one small change. This time? It gets worse. You write the code. 1 error. You fix the error. 12 new errors. And your screen is basically on fire. Every programmer has been here. Every single one. Here is the truth about errors in coding: Errors are not failure. They are feedback. Python does not hate you. It is telling you exactly what is wrong. Fixing one error exposing others means you are making progress. The code was always broken. Now you can finally see it. How to handle cascading errors like a professional: Fix from the top. The first error often causes all the others. Read the full error message. The answer is always in there. Do not fix everything at once. One error at a time. Take a break. Fresh eyes fix bugs faster than tired ones. Note: Debugging is not the obstacle. It is the job. #Python #Debugging #DataScience #LearnToCode #BeginnerCoder #Coding #StudentLife

🚀 Week 2 — DSA + System Design This week I focused on making my basics stronger. 📚 DSA (C++ & Python): • Practiced Arrays again • Worked more on Time & Space Complexity • Learned Binary Search and solved different types of problems • Solved around 30–35 questions 🏗️ System Design (LLD): • Revised OOP concepts • Practiced writing cleaner and better structured code ❌ Challenges: Binary Search was confusing in different problems Applying time complexity in real questions was not easy 💡 Realization: Now I’m starting to understand when to use which approach 🎯 Week 3 Goal: Start Linked List and Recursion Staying consistent. Chandan Kumar #DSA #SystemDesign #BuildInPublic #LearningInPublic

🗓 7 April 2026 🚀 LeetCode Problem #973 — K Closest Points to Origin Solved this problem using a Heap (Priority Queue) approach in Python. 💡 Approach: - Calculated the distance of each point from origin using: 👉 x² + y² (no need for square root) - Used a max heap (by pushing negative distance) - Maintained heap size = k - If size exceeds k → removed the farthest point ⚡ Time Complexity: O(n log k) ⚡ Space Complexity: O(k) 🔥 This is a great example of optimizing from brute force (sorting O(n log n)) to a more efficient heap-based solution. Consistency update: solving problems daily and improving problem-solving skills step by step. #leetcode #dsa #python #coding #100DaysOfCode #heaps #learning

🚀 *Day 6 / 500 – Coding Journey* Today’s problem: Remove Duplicates from Sorted Array 🔁 At first glance, it looks simple—but it really tests your understanding of *two-pointer techniques* and in-place operations. 💡 *Key Learnings:* * Efficient use of *left & right pointers* * Importance of *in-place modification* (no extra space) * Writing clean logic with optimal time complexity *O(n)* Consistency > Motivation. Every day, every problem, one step closer to mastery 💻🔥 #Day6 #500DaysOfCode #DSA #CodingJourney #Python #ProblemSolving #GrowthMindset

Day 26/100: Writing Cleaner, Faster, and Pythonic Code! Today was all about efficiency and elegance. I dived deep into Comprehensions—one of Python’s most powerful features for creating new sequences from existing ones. Key Technical Takeaways: List Comprehension: Reducing multi-line for loops into a single, readable line. [new_item for item in list if test] Dictionary Comprehension: Creating complex mappings and filtering data on the fly. Conditionals in Comprehensions: Mastering how to use if and if-else inside a single line of code. Project: NATO Alphabet Converter: Developed a tool that takes any word and converts it into its NATO phonetic code (Alpha, Bravo, Charlie...) using dictionary comprehension. My code is becoming significantly shorter, more readable, and much more efficient. It’s amazing how a simple shift in syntax can make such a big difference in software quality! Check out my NATO Alphabet project here: https://lnkd.in/gZ2bdXua #Python #CleanCode #100DaysOfCode #Pythonic #SoftwareDevelopment #CodingEfficiency #VSCode

Day 58 of my #100DaysOfCode challenge 🚀 Today I implemented a Python program to generate prime numbers within a given range. This is a practical extension of prime checking and useful in many DSA and real-world problems. What the program does: • Takes a range (start, end) as input • Checks each number in the range • Identifies whether it is prime or not • Returns a list of all prime numbers in that range Example Output: Prime numbers between 1 and 50: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47] How the logic works: Start from max(2, start) For each number: • Assume it is prime • Check divisibility from 2 → √n If divisible → not prime If not divisible → add to result list 👉 Uses square root optimization for better performance Why this is important: – Builds on prime number fundamentals – Useful in: Competitive programming Number theory problems Range-based queries – Helps understand optimization using √n Time Complexity: O(n√n) Space Complexity: O(k) (number of primes) Key Takeaways: – Applying optimized prime checking – Working with ranges and loops – Improving efficiency using √n – Writing clean and scalable code #100DaysOfCode #Day58 #Python #Programming #DSA #Algorithms #PrimeNumbers #NumberTheory #CodingPractice #ProblemSolving #InterviewPrep #Optimization #DeveloperJourney #Consistency #BTech #CSE #AIandML #VITBhopal #TechJourney

🚀 Solved today’s LeetCode challenge: Longest Common Prefix 💻🔥 Problem: Given an array of strings, find the longest common starting substring among all strings. ✅ Example: ["flower", "flow", "flight"] → "fl" ["dog", "racecar", "car"] → "" 🧠 My Approach: Started with the first word as a prefix and compared it with the remaining strings one by one. Whenever a mismatch happened, I kept shrinking the prefix from the end until it matched. This helped me understand: 🔹 String slicing in Python 🔹 Prefix matching logic 🔹 Edge case handling 🔹 Writing optimized clean code 💡 Time Complexity: O(n * m) (n = number of strings, m = prefix length) Consistency > Motivation. One problem every day builds strong problem-solving skills 📈 Check Out : https://lnkd.in/dtumdVUQ #LeetCode #Python #DSA #CodingJourney #ProblemSolving #100DaysOfCode #SoftwareEngineer #Programming #Tech #Learning