Sharpen Your Algorithm Skills
This site is designed to help you explore different algorithmic approaches and problem-solving techniques, whether you're preparing for algorithm interviews or studying on your own to gain a deeper understanding of algorithms and data structures. Go step-by-step: read description > check solution > study examples > dig into the code.
Why do you need to solve LeetCode problems in the AI era?
This is a very easy and very hard question simultaneously. The short answer is - you do not need to do it. AI can do it very quickly and right for you. But the reality is much harder than we think.
Read more →We're used to evaluating an algorithm's Time complexity, but we often overlook its Space complexity. However, often the best solution for Time complexity is a brute-force solution for Space complexity and vice versa.
Heap problems often look different, but most of them are built around a small set of recurring patterns. Top K Elements, K-way Merge, Two Heaps, and Repeated Min/Max cover the most common ways Heap and Priority Queue are used in LeetCode and coding interview problems.
The Fast & Slow pointers pattern uses two pointers moving at different speeds to detect cycles, find middle elements, and solve Linked List and sequence problems efficiently.
The Binary Search pattern can be divided into four main variations. Finding a target in a sorted array is Classic Binary Search. The other three - Boundary Search, Binary Search on Answer, and Modified Sorted Data - represent more advanced ways of applying the Binary Search pattern.
The Sliding Window pattern is one of the most common techniques for solving array and string problems involving contiguous ranges. This section covers fixed-size and variable-size windows, common variations, recognition rules, and practical examples from LeetCode.
