Learn how to solve the Longest Common Subsequence coding problem to prepare for your next technical interview! Longest Common Subsequence is a classic test of whether you can define a two-dimensional DP state and reason about choices across two inputs simultaneously. Get the state definition right and the solution writes itself. Get it wrong and you'll spin in circles.

Learn how to solve the N-Queens coding problem to prepare for your next technical interview! Interviewers love the N-Queens problem because it reveals whether you can reason about constraints and prune aggressively, or whether you reach for brute force and hope for the best.

Learn how to solve the Letter Combinations of a Phone Number coding problem to prepare for your next technical interview! This question is a a classic interviewers test for whether you can systematically explore combinations without losing control of the recursion.

Learn how to solve the Subsets coding problem to prepare for your next technical interview! The Subsets problem tests whether you understand how to explore a decision tree without missing cases or duplicating work. It's a classic interview question because the same thinking shows up across backtracking, bit manipulation, and combinatorics problems.

Learn how to solve the Permutations coding problem to prepare for your next technical interview! Interviewers use it to see whether you can reason about recursion, backtracking, and state management, and whether you can do it without losing track of where you are.

Learn how to solve the Unique Paths coding problem to prepare for your next technical interview! Unique Paths tests whether you can recognize a counting dynamic programming pattern and model state correctly. It's a classic interview question because the logic scales cleanly to harder grid problems. Once you've internalized the thinking here, a whole family of harder problems opens up.

Learn how to solve the Target Sum coding problem to prepare for your next technical interview!

Learn how to solve the Coin Change coding problem to prepare for your next technical interview!

Learn how to solve the House Robber coding problem to prepare for your next technical interview!

Learn how to solve the Fibonacci Number coding problem to prepare for your next technical interview!

Learn how to solve the "Search in a Rotated Sorted Array" coding problem to prepare for your next technical interview!

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...

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 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.

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.