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A curated repository of solved LeetCode and DSA problems mapped across core algorithmic patterns, optimized for top-tier software engineering interviews.
Master stack data structure — Monotonic stack, NGE, stock span, expression evaluation, min-stack. 30+ interview problems with O(n) solutions.
A comprehensive collection of code solutions for LeetCode and Codeforces problems, designed to enhance your competitive programming skills and help you tackle coding challenges with confidence.
Some Important Data Structure and Algorithm Questions while preparing for Interviews. These questions are very important and most popular one so you should revise them when appearing for interviews
Collection of problems solved with the help of Stack Data Structure.
Repo contain organized solutions to LeetCode problems in languages like Python or C++. It’s a useful resource for learning algorithms, coding practice, and interview preparation.
Implementation of Data Structures and Algorithms in C++. I also upload my leetcode solutions here.
Solutions to Leetcode questions
Competitive programming problem designed to expose failure modes in frontier LLMs. Features a 3-layer trap invalidating greedy, O(N²) DP, and prefix-sum optimizations. Correct O(N log V) solution via monotonic stack + DP. Built for Turing's Python TopCoder Problemsetter Assessment.
Collection of LeetCode solutions in Java covering Data Structures, Algorithms, Dynamic Programming, Trees, Graphs, Binary Search, and Interview Preparation
Are you in love with PHP? So do I! I enjoy solving problems and implementing data structures and algorithms in PHP.
A slim and efficient in-memory Deque (doubly-ended queue) for Node.js projects, designed with Data-Oriented Design principles and implemented using a cyclic buffer to optimize memory layout through sequential item allocation. A notable real-world use case is maintaining the minimum or maximum item in a data stream.
Worked LeetCode problems in C++, kept as a study log rather than a submission archive. One standalone program per problem, each opening with the reasoning the code cannot show: the invariant it maintains, why a prune is sound, what it costs in time and space.
Optimized Java implementations of fundamental algorithms with a focus on Big O complexity and performance analysis.
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