Development

Many developers spend months grinding LeetCode and still struggle in interviews. The problem is not how much you practice — it is how you practice. In this article, you will learn the most effective study strategies, a proven study plan, and how to avoid the most common mistakes. The Wrong Way: Random LeetCode Grinding Random grinding means: open LeetCode, pick a problem, spend 2 hours, fail, look at the answer, move to the next problem. Repeat 300 times. ...

Coding interviews have two main components: data structures and algorithms (DSA) and system design. Many developers wonder: which should I study? The answer depends on your experience level and the role you are targeting. In this article, you will learn what each tests, how to allocate study time, and how DSA and system design connect. What DSA Interviews Test DSA interviews test your ability to: Solve a coding problem in 30-45 minutes Write clean, correct code Analyze time and space complexity Think through edge cases Communicate your thought process Typical DSA question: “Given an array of integers, find two numbers that add up to a target.” ...

Dynamic programming is the hardest interview topic, but most DP problems follow a few common patterns. If you learn these patterns, you can solve new DP problems by recognizing which pattern applies. In this article, you will learn six DP patterns with full examples. We show core examples in Kotlin, Python, and Go. The 4-Step Approach to Any DP Problem Start with brute force recursion. Write the recursive solution without worrying about efficiency. Add memoization. Cache results of subproblems. Convert to bottom-up. Fill a table iteratively. Optimize space. Reduce memory if possible. Pattern 1: Linear DP Each state depends on one or two previous states. This is the simplest DP pattern. ...

Tree and graph problems make up a large portion of coding interviews at top tech companies. The key is recognizing patterns. In this article, you will learn the five most common tree patterns and four most common graph patterns, with full code examples. We show core examples in Kotlin, Python, and Go. Tree Pattern 1: DFS Traversal Variants Different traversals solve different problems: Inorder (left, root, right): BST gives sorted order Preorder (root, left, right): Serialize/copy a tree Postorder (left, right, root): Delete a tree, calculate subtree values Inorder Traversal (Iterative) Python: ...

String problems are among the most common in coding interviews. They combine multiple patterns — two pointers, sliding window, hash maps, and tries. In this article, you will learn the most important string patterns and how to recognize which one to use. We show every example in Kotlin, Python, and Go. String Basics Before diving into patterns, remember these important facts: Python: Strings are immutable. Concatenation in a loop is O(n^2). Use "".join() or a list. Kotlin: Strings are immutable. Use StringBuilder for building strings. Go: Strings are immutable byte slices. Use strings.Builder for efficient concatenation. Pattern 1: Two Pointers on Strings Use two pointers to check palindromes, reverse strings, and compare characters from both ends. ...

Most coding interview questions at FAANG and top tech companies follow a small number of patterns. Analyses of real interview question sets consistently find that the vast majority — often cited as 70-90% — map to 10-12 core patterns. If you learn these patterns, you can solve most interview problems — even ones you have never seen before. In this article, you will learn all 10 patterns with examples. We show code in Python, with key examples in Kotlin and Go. ...

BFS and DFS are the most important graph algorithms for coding interviews. In the earlier graphs article, we covered the basics. Now we go deeper: Dijkstra’s algorithm for weighted shortest paths, multi-source BFS, matrix-as-graph problems, and the most common patterns you will see in interviews. We show every example in Kotlin, Python, and Go. BFS Patterns Pattern 1: Shortest Path in Unweighted Graph BFS finds the shortest path in unweighted graphs because it explores nodes level by level. Every node at distance d is visited before any node at distance d+1. ...

Greedy algorithms make the locally optimal choice at each step, hoping it leads to a globally optimal solution. They are simpler than dynamic programming and often faster. But they only work when the greedy choice is provably correct. In this article, you will learn when greedy works, classic greedy problems, and how to tell greedy apart from DP. We show core examples in Kotlin, Python, and Go. What is a Greedy Algorithm? A greedy algorithm makes the best possible choice at each step without looking ahead. It never reconsiders previous choices. ...

Dynamic programming (DP) is the most feared interview topic. But it does not have to be. DP is just recursion with memory — you save the results of subproblems so you do not solve them again. In this article, you will learn the two DP approaches (top-down and bottom-up), how to identify DP problems, and how to solve the most common ones. We show every example in Kotlin, Python, and Go. ...

Recursion and backtracking are essential for solving problems where you need to explore all possibilities. Permutations, combinations, subsets, sudoku, and N-Queens all use backtracking. In this article, you will learn how recursion works, the backtracking template, and how to solve common interview problems. We show core examples in Kotlin, Python, and Go. What is Recursion? Recursion is when a function calls itself. Every recursive function has two parts: Base case: the condition that stops recursion Recursive case: the function calls itself with a smaller problem Python: ...