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: ...

Claude is an AI assistant built by Anthropic. It can write code, analyze documents, reason through complex problems, and use tools. Many developers now use Claude as their primary AI coding tool. But what exactly is Claude? How is it different from ChatGPT or Gemini? And why should you care as a developer? This article answers those questions. It is the first article in our Claude AI — From Zero to Power User tutorial series. By the end of this series, you will know how to use Claude’s API, build agents, and work with advanced features like tool use and the Model Context Protocol (MCP). ...

Two pointers and sliding window are two of the most frequently tested techniques in coding interviews. They turn brute force O(n^2) solutions into elegant O(n) solutions. In this article, you will learn converging pointers, same-direction pointers, fast/slow pointers, and both fixed-size and variable-size sliding windows. We show core examples in Kotlin, Python, and Go. Two Pointers: Converging (Opposite Direction) Two pointers start at opposite ends of the array and move toward each other. ...

Binary search is one of the most powerful techniques in algorithm design. It reduces O(n) searches to O(log n) by cutting the search space in half at every step. In this article, you will learn the classic binary search, common templates, search on answer, and rotated array problems. We show every example in Kotlin, Python, and Go. Classic Binary Search Given a sorted array and a target value, find the target’s index or return -1. ...

Sorting is one of the most fundamental operations in computer science. Many problems become easier once the data is sorted — binary search, duplicate detection, and merge operations all require sorted input. In this article, you will learn the most important sorting algorithms, their trade-offs, and when to use each one. We show every example in Kotlin, Python, and Go. Why Sorting Matters Sorting unlocks other algorithms: Binary search requires sorted data — O(log n) instead of O(n) Two pointers on sorted arrays solve many problems efficiently Duplicate detection becomes O(n) on sorted data (just compare neighbors) Merge operations are faster on sorted inputs Interviewers often ask: “Can you sort the input first to simplify the problem?” ...

Every coding interview expects you to analyze your solution’s efficiency. When the interviewer asks “What is the time complexity?” they want you to use Big O notation. In this article, you will learn what Big O is, how to calculate it from code, and how to compare different complexities. We show every example in Kotlin, Python, and Go. What is Big O Notation? Big O notation describes how an algorithm’s running time or space usage grows as the input size increases. It gives you the upper bound — the worst case scenario. ...

The Vercel AI SDK is the most popular way to use Claude in web applications. It handles streaming, tool use, and UI state — all with a few lines of code. In this article, you will build a complete chat application with Claude, Next.js, and the Vercel AI SDK. This is Article 27 in the Claude AI — From Zero to Power User series. This is the final article in the series. You should know the Messages API and Tool Use before this article. ...

Choosing the right data structure is half the solution to any coding problem. In interviews, the first question is always: “What data structure should I use?” A wrong choice leads to slow solutions or unnecessary complexity. In this article, you will learn a decision framework for choosing the right data structure, see head-to-head comparisons, and get a complete cheat sheet you can reference any time. This article covers everything from our first 9 articles in the series. ...

Union-Find (also called Disjoint Set Union or DSU) is a data structure that tracks elements split into non-overlapping groups. It answers one question very fast: “Are these two elements in the same group?” It is the best tool for connected components problems and appears in many graph-related interview questions. In this article, you will learn how Union-Find works, why path compression makes it nearly O(1), and how to use it in coding interviews. ...