Learn how to solve the Non-Overlapping Intervals coding problem to prepare for your next technical interview!

"Merge Intervals" is one of those classic algorithm problems that shows up frequently in technical interviews. It's a great test of your...

Jump Game II is a classic follow-up to the original Jump Game problem. It’s not just about whether you can reach the end... now you have to do it in the fewest jumps possible! That small change turns a simple reachability problem into one that tests how well you can optimize greedy strategies or dynamic programming under pressure.

The "Jump Game" question is a popular one for interviews because it tests your ability to think greedily and work with dynamic movement through an array. It's a great warm-up for range-based greedy logic and helps build intuition for reachability problems, concepts that show up often in competitive coding and systems design.

The "Gas Station" problem is one of those deceptively simple problems that really tests your understanding of greedy strategies and circular arrays. It shows up frequently in interviews because it blends math, algorithmic reasoning, and a need for linear-time optimization. Whether you're optimizing a delivery route or designing a resource allocation system, this kind of thinking comes in handy.

"Candy" is one of those deceptively simple problems that tests your ability to think through constraints and find a clean, efficient strategy. The "Candy" problem is a classic greedy algorithm challenge often asked in interviews. It tests whether you can distribute limited resources while meeting strict conditions on relative ranking — something that comes up surprisingly often in real-world systems.

"Assign Cookies" is a problem that is a simple yet elegant introduction to greedy algorithms. It asks us to match two lists - one representing the greed of children and the other representing cookie sizes - in a way that maximizes happiness. It’s a great warm-up to practice sorting and thinking about matching strategies in real-world-like scenarios.

The "Longest Repeating Character Replacement" question is one of those string manipulation problems that sneak up on you. At first glance, it seems like a counting or brute-force challenge, but the optimal solution leans heavily on sliding window and frequency analysis. It’s a fantastic warm-up for mastering problems involving substrings and character frequency tracking, common themes in many real-world parsing and formatting systems.

The Minimum Size Subarray Sum problem is a great example of using the sliding window technique to solve a real-world scenario - finding the smallest set of contiguous actions or values that meet a threshold. It helps build your skills with optimizing time and space over brute-force solutions and comes up frequently in interviews at all levels.

The Longest Substring Without Repeating Characters problem is one of those warm-up problems that seems deceptively simple but teaches you a ton about sliding window techniques and string manipulation. It shows up frequently in interviews and also mimics real-world cases like processing tokens or user input where uniqueness matters.

Maximum Subarray - Kadane's Algorithm is is a classic problem that introduces one of the most elegant uses of dynamic programming. It teaches you how to keep track of optimal sub-solutions and make decisions based on current and previous results. Once you’ve got this down, it opens the door to more complex dynamic programming challenges.

The KSum problem generalizes 2Sum, 3Sum, and 4Sum into a flexible recursive solution that can find all unique combinations of k numbers in an array that sum to a target. It’s a fantastic problem for practicing recursion, backtracking, and thinking about how to build modular solutions that scale with complexity. Once you’ve nailed KSum, you can solve any of the others as special cases.

This is a great follow-up to 2Sum and 3Sum. It challenges you to apply the same two-pointer strategy but adds complexity by requiring a quadruplet instead of a pair or triplet. This kind of problem comes up when you're working on systems that require combinatorial sum checks, like budgeting scenarios, fraud detection with transaction records, or even algorithmic trading strategies. The key here is optimization over brute-force enumeration.

This is a great warm-up for array and two-pointer problems. It’s similar to the classic 3Sum, but instead of finding a triplet that adds to a specific number, we need the closest possible sum. This introduces an optimization twist that requires careful comparison and pointer movement, making it an excellent real-world scenario—think of situations where you need to get as close as possible to a threshold value without hitting it exactly, such as budget planning or resource all

Finding triplets that sum to zero is a foundational exercise in two-pointer techniques, sorting, and careful duplicate handling. The “3Sum” problem tests your ability to combine sorting with pointer sweeps to achieve O(n²) performance.

Determining the maximum water a container can hold between vertical lines is foundational in graphics rendering, fluid simulations, and two-pointer optimizations. The “Container With Most Water” problem challenges you to squeeze the best area in O(n) time.

Combining multiple sorted streams into one sorted output is a classic challenge faced in database merge operations, log file aggregation, and external sorting. The “merge K sorted lists” problem tests your ability to coordinate multiple pointers, leverage priority data structures, and keep everything running in optimal time.

When you need to find the one missing entry in a sequence—say missing log IDs, seats, or data packets— Missing Number  is the classic...

Finding the most frequent items in a dataset is a common real-world task—think trending topics in social media, most visited pages on a...

Finding the intersection of two arrays - i.e. the common elements between two lists - is a real-world task you’ll do when reconciling...