Automate LeetCode Submissions with Python

#100DaysOfLeetcode journey 🚀 🚀 Day 72/100 — Hamming Distance & Early-Exit Optimization! Today’s Problem: 2452. Words Within Two Edits of Dictionary 🔹 The Goal: Given a set of query words and a dictionary, identify which queries can be transformed into any dictionary word using a maximum of two character replacements. 🔹 The Insight: This is a classic "String Similarity" problem. Since all words are guaranteed to have the same length, the number of edits required is simply the Hamming Distance—the count of positions where characters differ. 🔹 The Logic: Exhaustive Comparison: For each query, we check it against the dictionary entries until we find a match within the 2-edit threshold. The Early Break (Pruning): This is the key optimization. While comparing two words character by character, if the difference count (diff) exceeds 2, we immediately stop and move to the next dictionary word. Short-Circuiting: Once a query word is validated against any dictionary word, we add it to our results and immediately move to the next query, avoiding unnecessary comparisons. ✨ Achievement: Day 72! Moving into the final 30 days of the challenge. Today’s solution highlights how simple logic—like breaking a loop early when a threshold is breached—can significantly improve the performance of $O(N \cdot M)$ algorithms without the complexity of more advanced data structures like Tries. 🔍 Steps followed: ✔ Hamming Distance Logic: Calculated positional differences for equal-length strings. ✔ Threshold Gating: Implemented a diff > 2 check to prune redundant character comparisons. ✔ Order Preservation: Ensured the results followed the original query sequence. 🔧 The Stats: Time Complexity: $O(Q \cdot D \cdot L)$ (where $Q$ is queries, $D$ is dictionary size, and $L$ is word length) Space Complexity: $O(1)$ auxiliary space. 72 days down! The logic is getting sharper, and the finish line is coming into focus. Let’s keep pushing! 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #StringAlgorithms #Optimization #Algorithms #Day72

#100DaysOfLeetcode journey 🚀 Day 66/100 — Optimizing Circular Proximity! Today’s Problem: Minimum Circular Distance to Matching Elements 🔹 The Goal: Given an array that "wraps around," find the shortest path from a specific index to any other occurrence of the same value. 🔹 The Insight: A naive search for every query would be $O(Q \times N)$, which is catastrophic for large datasets. The breakthrough here is Spatial Indexing. By pre-grouping the indices of every value and keeping them sorted, we transform a global search into a localized neighbor check. 🔹 The Logic: Value-to-Index Mapping: I used a HashMap to store sorted lists of indices for each unique number. Logarithmic Search: Using binarySearch, I instantly located the query index within its value-group. The "Circular Sandwich": Because the indices are sorted, the closest matching elements are always the immediate neighbors in the list. By checking the left, the right, and the wrap-around (first/last) elements, we guarantee the global minimum in $O(\log N)$ time. ✨ Achievement: Day 66! Successfully applied pre-computation and binary search to optimize a spatial query problem. Understanding that the "closest" element in a circular topology is always an adjacent neighbor in the sorted index space is a powerful shortcut for range-based problems. 🔍 Steps followed: ✔ Map Pre-processing: Clustered indices for constant-time value lookups. ✔ Binary Search Integration: Optimized neighbor identification to logarithmic time. ✔ Modular Distance Logic: Correctly handled the $N - \text{linearDist}$ calculation for circular boundaries. 🔧 Complexity Analysis: Time Complexity: $O(N + Q \log N)$ Space Complexity: $O(N)$ 67 days down! The logic is getting faster, and the problems are getting more interesting. Let's keep the streak alive! 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #ArrayAlgorithms #BinarySearch #Optimization #Day66

🚀 Day 14 of My LeetCode Journey — Linked List Confidence Growing Today’s problems: 🔹 Add Two Numbers (LeetCode 2) 🔹 Odd Even Linked List (LeetCode 328) 💡 Problem 1: Add Two Numbers This problem simulates addition like we do manually: 👉 Traverse both linked lists 👉 Add corresponding digits + carry 👉 Create new nodes for the result 👉 Handle remaining carry at the end 🔥 A great mix of linked list traversal + math logic 💡 Problem 2: Odd Even Linked List This one is all about rearranging nodes: 👉 Separate nodes into odd index and even index 👉 Maintain two pointers (odd & even) 👉 Finally connect odd list with even list ⚡ No extra space needed — done in-place! 🧠 What I Learned: - Handling carry properly is crucial in problems like addition - Rearranging pointers without losing references is key - Linked List problems are getting more intuitive with practice 🔥 Key Takeaways: - Break problems into smaller steps (traverse, compute, connect) - Always track pointers carefully to avoid losing nodes - Practice is making complex problems feel simpler Grateful for the learning journey with Namaste DSA and Akshay Saini 🚀 Day 15 loading… 💪 --- #LeetCode #DataStructures #Algorithms #CodingJourney #100DaysOfCode #SoftwareEngineering #Programming #InterviewPrep #JavaScript #CodingLife #TechGrowth #ProblemSolving #Developers #LearnToCode #LinkedList #DSA #NamasteDSA #AkshaySaini

#100DaysOfLeetcode journey 🚀 Day 67/100 — Optimizing Circular Proximity! Today’s Problem: Minimum Circular Distance to Matching Elements 🔹 The Goal: Given an array that "wraps around," find the shortest path from a specific index to any other occurrence of the same value. 🔹 The Insight: A naive search for every query would be $O(Q \times N)$, which is catastrophic for large datasets. The breakthrough here is Spatial Indexing. By pre-grouping the indices of every value and keeping them sorted, we transform a global search into a localized neighbor check. 🔹 The Logic: Value-to-Index Mapping: I used a HashMap to store sorted lists of indices for each unique number. Logarithmic Search: Using binarySearch, I instantly located the query index within its value-group. The "Circular Sandwich": Because the indices are sorted, the closest matching elements are always the immediate neighbors in the list. By checking the left, the right, and the wrap-around (first/last) elements, we guarantee the global minimum in $O(\log N)$ time. ✨ Achievement: Day 67! Successfully applied pre-computation and binary search to optimize a spatial query problem. Understanding that the "closest" element in a circular topology is always an adjacent neighbor in the sorted index space is a powerful shortcut for range-based problems. 🔍 Steps followed: ✔ Map Pre-processing: Clustered indices for constant-time value lookups. ✔ Binary Search Integration: Optimized neighbor identification to logarithmic time. ✔ Modular Distance Logic: Correctly handled the $N - \text{linearDist}$ calculation for circular boundaries. 🔧 Complexity Analysis: Time Complexity: $O(N + Q \log N)$ Space Complexity: $O(N)$ 67 days down! The logic is getting faster, and the problems are getting more interesting. Let's keep the streak alive! 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #ArrayAlgorithms #BinarySearch #Optimization #Day67

🚀 Day 44/60 — LeetCode Discipline Problem Solved: Regular Expression Matching Difficulty: Hard Today’s problem was one of the toughest so far. It involved implementing regex matching with support for: • '.' → matches any single character • '*' → matches zero or more of the previous element 💡 Key Insight: This problem is solved using Dynamic Programming + Recursion (Memoization). Instead of checking blindly, we store intermediate results to avoid recomputation. 💡 Key Learnings: • Understanding recursion deeply • Using memoization to optimize overlapping subproblems • Handling multiple cases carefully (especially '*') • Thinking in terms of states → dp(i, j) ⚡ Performance: Runtime: 6 ms Like solving a maze… every wrong turn teaches something— and every remembered path saves time. This wasn’t just coding— this was strategy. #leetcode #coding #programming #developer #softwareengineer #codinglife #coders #code #python #java #cpp #javascript #webdevelopment #machinelearning #ai #artificialintelligence #datascience #100daysofcode #60daysofcode #dsa #datastructures #algorithms #dynamicprogramming #dp #recursion #memoization #backtracking #hardproblem #codingchallenge #dailycoding #problem-solving #codingjourney #tech #technology #developerlife #programmerlife #learncoding #codingcommunity #codersofinstagram #instacoder #techlife #devcommunity #softwaredeveloper #engineerlife #codingmotivation #growthmindset #consistency #nevergiveup #studentlife #btech #computerscience #aiml #futureengineer #placements #internship #careergoals #studygram #learning #keepgoing #successmindset #hustle #grind #explorepage #trending #viral #reels #linkedinpost #github #opensource #100xdev #faangprep #interviewprep

🚀 Day 46 of My Coding Journey Today’s challenge was: Anagram Groups 🔤🧩 The task was to group words that are anagrams of each other — meaning they contain the same letters, just in a different order. 💡 Example: ["listen", "silent", "enlist"] → All belong to the same group 💡 Approach I used: • Sorted each word alphabetically to create a unique key • Used a dictionary to group words with the same sorted key • Stored all matching words together as anagrams • Returned all grouped values as a list 🧠 Key Learnings: Using sorting as a powerful technique for pattern matching Efficient use of dictionaries (hash maps) Improved understanding of grouping problems Learned a common interview pattern for string manipulation This is a very popular interview problem and a great way to strengthen logic building 💻🔥 🔗 GitHub Code: https://lnkd.in/gECUw9iz @freeCodeCamp #freeCodeCamp #Python #100DaysOfCode #CodingJourney #ProblemSolving #Algorithms #LearnInPublic #Developers

Consistency is a Myth. Most people flexing their consistency on GitHub and LeetCode are just aiming to keep their daily contribution graph green. It all comes down to one simple question: What are my learnings? If you have a solid answer to that, then you are on the right track. And to the people who say numbers of questions don't matter—isn't your salary in numbers? Everything matters. Numbers , Skills , Proficiency , Efficiency , Code of Conduct , Discipline , Luck and what not. Because in the end, growth isn’t one-dimensional. With this signing off and aiming to complete many more miles stones ahead. #LeetCode #SoftwareEngineering #ProblemSolving #TechCommunity #GrowthMindset #RealTalk #Learning #Consistency #DSA #Growth #Discipline #SoftwareDevelopment #Python #CodingJourney #LinkedInCommunity

#100DaysOfLeetcode journey 🚀 🚀 Day 68/100 — Numerical Symmetry & Mirror Distances! Today’s Problem: Mirror Distance of an Integer 🔹 The Goal: Define the "Mirror Distance" of a number $n$ as the absolute difference between the number and its digit-reversal. For example, if $n = 123$, the mirror is $321$, and the distance is $|123 - 321| = 198$. 🔹 The Insight: This is a fundamental exercise in Digit Manipulation. Reversing a number isn't just a string operation; it’s an arithmetic process. By using the modulo operator (% 10) to extract the last digit and integer division (/ 10) to shift the decimal place, we can reconstruct the mirror image in a single pass. 🔹 The Logic: Arithmetic Reversal: I used a while-loop to peel off digits and build the reversed integer. This naturally handles the "omit leading zeros" rule (e.g., reversing $120$ results in $21$ because $0 \times 10 + 2 \times 1 + 1$ arithmetic logic). Absolute Variance: The final result is simply the absolute magnitude of the gap between the two values. Negative Handling: I ensured the logic remains robust for negative integers by reversing the absolute value and reapplying the sign. ✨ Achievement: Day 68! We are well into the second half of the challenge. Today’s solution highlights how core arithmetic can replace heavy string parsing for better performance and lower memory overhead. 🔍 Steps followed: ✔ Digit Extraction: Peeling digits using remainder operations. ✔ Numerical Reconstruction: Building the reversed value through decimal shifting. ✔ Error Guarding: Managed potential sign issues to ensure a valid absolute distance. 🔧 The Stats: Time Complexity: $O(\log_{10} n)$ Space Complexity: $O(1)$ 68 days down! The journey into algorithmic mastery continues. Let's keep the momentum! 🛠️ #Java #DSA #LeetCode #CodingJourney #100DaysOfCode #SoftwareEngineer #InternshipBound #Programming #MathAlgorithms #Logic #Optimization #Day68

💯 Reached 300 LeetCode Problems — A Step Forward in My DSA JourneyI’m excited to share that I’ve solved 300 problems on LeetCode, building a strong foundation in Data Structures and Algorithms. Throughout this journey, I focused on: • Arrays & Strings • Linked Lists • Stacks & Queues • Recursion & Backtracking This helped me improve: ✅ Problem-solving skills ✅ Logical thinking ✅ Writing optimized code 💡 Advice for Beginners (based on my experience): If you’re just starting DSA, don’t try to learn everything at once. 👉 Start with: • Arrays & Strings (very important basics) • Then move to Linked Lists • Practice Stacks & Queues • Learn Recursion and Backtracking 👉 Important Tips: • Focus on understanding patterns, not just solutions • Solve problems consistently (even 2–3 per day) • Don’t jump into advanced topics like Trees, Graphs, or Dynamic Programming too early • Revisit problems you couldn’t solve Consistency matters more than speed. Small daily progress leads to big results. Still learning, still improving 💪 Let’s keep growing 🚀 #LeetCode #DSA #CodingJourney #ProblemSolving #Java #Beginners #Consistency 👍

One of the biggest mistakes beginners make when trying to enter tech? Trying to learn everything at once. Python. JavaScript. Data Science. UI Design… And months later, they’re still stuck in the learning phase, with no clear direction. This is the gap LEMP is designed to solve. We provide a structured pathway into tech, starting with QA (software testing), a role that allows beginners to: • Start without coding • Understand real systems • Build practical, job-ready experience More importantly, we guide learners beyond just “taking courses” → into actually becoming job-ready. Because the goal isn’t to keep learning. It’s to start working. If you’ve been trying to figure out where to start, you don’t need more information, you need a clear roadmap. 👉 Follow us and start your journey the right way #SoftwareTesting #QATesting #TechCareers #CareerTransition #LearnTech