#interviewing
The Complete Technical Interview Guide for Software Engineers
Technical interviews can feel like a completely different skill from actual software engineering. You might be a great developer who builds production systems every day, but freeze up when asked to reverse a linked list on a whiteboard. The good news: technical interviewing is a learnable skill, and with the right preparation strategy, you can walk in confident. This guide covers the full picture — what to expect, how to prepare, and how to perform on the day. Read more →
May 18, 2026
Behavioral Interview Questions: The STAR Method and Beyond
You’ve nailed the coding rounds, but now you’re sitting across from someone asking “Tell me about a time you disagreed with a teammate.” Your mind goes blank. You ramble for five minutes. You leave thinking “I should have mentioned that project…” Behavioral interviews trip up even experienced engineers because they feel unstructured. But they follow predictable patterns, and with the right preparation, you can answer them clearly and confidently every time. Read more →
May 18, 2026
System Design Interview Guide: From Requirements to Architecture
System design interviews are where senior engineering candidates often struggle the most. Unlike coding problems with clear right answers, design questions are open-ended conversations where you need to demonstrate breadth of knowledge, structured thinking, and the ability to make and justify trade-offs. The good news: there’s a repeatable framework you can follow, and the set of building blocks you need to know is finite. What Interviewers Evaluate System design interviews assess: Read more →
May 18, 2026
Common Coding Patterns for Technical Interviews
Grinding hundreds of LeetCode problems isn’t the most efficient way to prepare for coding interviews. Most interview problems are variations of a small set of patterns. Learn the patterns, and you can solve new problems you’ve never seen before. Here are the most important patterns, when to recognize them, and how to apply them. Two Pointers When to use: Sorted arrays, finding pairs, removing duplicates, palindrome checks. The idea is simple: use two index variables that move toward each other (or in the same direction) to avoid nested loops. Read more →
May 18, 2026
Salary Negotiation for Software Engineers
Most software engineers leave tens of thousands of dollars on the table by not negotiating. The initial offer is almost never the best offer a company can make. Negotiation isn’t adversarial — it’s a normal part of the hiring process that recruiters expect and prepare for. This guide covers the mechanics of tech compensation, when and how to negotiate, and the specific language that works. Understanding Tech Compensation Total compensation (TC) in tech typically has four components: Read more →
May 18, 2026
Dijkstra's Algorithm
Introduction Dijkstra’s Algorithm finds the shortest path from a source vertex to all other vertices in a weighted graph with non-negative edge weights. It’s the algorithm behind GPS navigation, network routing, and any system that needs to find the cheapest path between two points. The core idea: greedily pick the unvisited vertex with the smallest known distance, then update its neighbors’ distances. This is essentially BFS with a priority queue instead of a regular queue — the priority queue ensures we always process the closest vertex next. Read more →
March 31, 2026
Tries (Prefix Trees)
Introduction A Trie (pronounced “try”) is a tree-like data structure used to store and search strings efficiently. Each node represents a single character, and paths from root to node spell out prefixes. It’s the data structure behind autocomplete, spell checkers, and IP routing tables. The name comes from “retrieval” — tries are optimized for looking up strings by prefix. Unlike a hash table which gives you O(1) lookup for exact keys, a trie gives you O(m) lookup (where m is the key length) plus the ability to find all keys that share a prefix. Read more →
March 31, 2026
Selection Sort
Introduction Selection Sort works by repeatedly finding the minimum element from the unsorted portion and placing it at the beginning. It’s like sorting a hand of cards by scanning for the smallest card each time and moving it to the front. Selection Sort is simple to understand and implement, but it’s always O(n²) — even on sorted input. It makes the minimum number of swaps (at most n-1), which can matter when writes are expensive. Read more →
March 31, 2026
Linear Search
Introduction Linear Search is the simplest searching algorithm — it checks every element in a collection one by one until it finds the target or reaches the end. It’s like looking for a specific book on an unsorted shelf by scanning from left to right. It’s not the fastest search, but it works on any collection — sorted or unsorted, arrays or linked lists. Understanding Linear Search also helps you appreciate why Binary Search is so much faster on sorted data. Read more →
March 31, 2026
Insertion Sort
Introduction Insertion Sort builds a sorted array one element at a time by picking each element and inserting it into its correct position among the already-sorted elements. It’s exactly how most people sort a hand of playing cards — you pick up one card at a time and slide it into the right spot. Despite being O(n²) in the worst case, Insertion Sort is genuinely useful. It’s the fastest algorithm for small arrays and nearly-sorted data, which is why many standard library sort implementations (like TimSort) use it as a building block. Read more →
March 31, 2026
Bubble Sort
Introduction Bubble Sort is the simplest sorting algorithm. It repeatedly steps through the array, compares adjacent elements, and swaps them if they’re in the wrong order. The largest unsorted element “bubbles up” to its correct position on each pass — like air bubbles rising to the surface of water. Nobody uses Bubble Sort in production (it’s too slow), but it’s worth understanding because it’s often the first sorting algorithm taught, it appears in interviews as a baseline comparison, and optimizing it teaches useful concepts. Read more →
March 31, 2026
Graph Representation
Introduction Before you can run BFS, DFS, or any other graph algorithm, you need to decide how to store the graph in memory. That choice affects the time and space complexity of every operation you perform on it. There are two standard representations: the adjacency list and the adjacency matrix. This tutorial covers both, when to use each, and how to handle directed, undirected, and weighted graphs. You should be familiar with Graphs Introduction and Arrays before reading this. Read more →
March 31, 2026
Merge Sort
Introduction Merge Sort is a divide-and-conquer sorting algorithm that splits an array in half, recursively sorts each half, then merges the two sorted halves back together. It’s like sorting a deck of cards by splitting it into smaller and smaller piles until each pile has one card, then merging piles together in order. Unlike Quick Sort, which can degrade to O(n²) with bad pivot choices, Merge Sort guarantees O(n log n) in every case. Read more →
March 31, 2026
Quick Sort
Introduction Quick Sort is a divide-and-conquer sorting algorithm that works by picking a “pivot” element and partitioning the array so that everything smaller goes to the left and everything larger goes to the right. Then it recursively sorts each side. It’s like organizing a messy bookshelf by picking a book in the middle, putting everything shorter to the left and taller to the right, then repeating for each side. Quick Sort is one of the most widely used sorting algorithms in practice — it’s the default in many standard library implementations because of its excellent average-case performance and low overhead. Read more →
March 31, 2026
Dynamic Programming
Introduction Dynamic Programming (DP) is an optimization technique that solves complex problems by breaking them into overlapping subproblems and storing their results so you never solve the same subproblem twice. Think of it like taking notes during a long calculation — instead of redoing work, you look up answers you’ve already figured out. If you’ve worked through Recursion, you’ve already seen the problem DP solves. Remember the naive recursive Fibonacci? It recalculates the same values over and over. Read more →
March 31, 2026
Recursion
Introduction Recursion is a programming technique where a function calls itself to solve a problem by breaking it down into smaller, similar subproblems. Think of Russian nesting dolls—each doll contains a smaller version of itself until you reach the smallest one that doesn’t open. Recursion is fundamental to computer science and appears in tree traversals, sorting algorithms, mathematical computations, and backtracking problems. While it can seem magical at first, understanding recursion unlocks powerful problem-solving techniques. Read more →
November 19, 2025
Depth-First Search (DFS)
Introduction Depth-First Search (DFS) is a fundamental graph traversal algorithm that explores as far as possible along each branch before backtracking. Think of it like exploring a maze by always taking the first path you see, going as deep as possible, then backtracking when you hit a dead end. DFS is essential for detecting cycles, topological sorting, finding connected components, and solving maze/puzzle problems. Unlike BFS which uses a queue, DFS uses a stack (or recursion, which implicitly uses the call stack). Read more →
November 19, 2025
Breadth-First Search (BFS)
Introduction Breadth-First Search (BFS) is a fundamental graph traversal algorithm that explores vertices level by level, visiting all neighbors of a vertex before moving to the next level. Think of it like ripples spreading out in water—you explore all vertices at distance 1, then distance 2, and so on. BFS is essential for finding shortest paths in unweighted graphs, level-order traversal of trees, and solving maze/puzzle problems. It uses a queue to maintain the order of exploration. Read more →
November 19, 2025
Graphs - Introduction
Introduction A graph is a non-linear data structure consisting of vertices (nodes) connected by edges. Unlike trees, graphs can have cycles, multiple paths between nodes, and connections in any direction. Graphs model relationships and networks, making them fundamental to solving real-world problems in social networks, maps, computer networks, and more. Graphs are one of the most versatile and powerful data structures in computer science. By the end of this tutorial, you’ll understand graph terminology, types of graphs, representation methods, and when to use them. Read more →
November 19, 2025
Heaps and Priority Queues
Introduction A heap is a specialized tree-based data structure that satisfies the heap property: in a max-heap, parent nodes are always greater than or equal to their children; in a min-heap, parent nodes are always less than or equal to their children. Heaps are the foundation for priority queues and enable efficient algorithms like heap sort and finding the kth largest element. Unlike Binary Search Trees, heaps don’t maintain a fully sorted order—they only guarantee the min/max element is at the root. Read more →
November 19, 2025
Binary Search Trees (BST)
Introduction A Binary Search Tree (BST) is a specialized binary tree with an important ordering property: for every node, all values in the left subtree are less than the node’s value, and all values in the right subtree are greater. This simple property enables efficient searching, insertion, and deletion operations. BSTs are one of the most important data structures in computer science, forming the basis for efficient ordered collections, database indexing, and many algorithmic solutions. Read more →
November 19, 2025
Binary Trees
Introduction A binary tree is a hierarchical data structure where each node has at most two children, referred to as the left child and right child. Unlike linear data structures like arrays or linked lists, binary trees organize data in a hierarchical, branching structure that enables efficient searching, sorting, and hierarchical representation. Binary trees are fundamental to computer science and appear in file systems, databases, compilers, and countless algorithms. By the end of this tutorial, you’ll understand different types of binary trees, how to traverse them, and when to use them. Read more →
November 19, 2025
Queues
Introduction A queue is a linear data structure that follows the First-In-First-Out (FIFO) principle. Think of it like a line at a coffee shop: the first person to join the line is the first person to be served. This fair ordering makes queues perfect for managing tasks, requests, and data that needs to be processed in the order it arrives. Queues are everywhere in computing—from managing print jobs and handling network requests to implementing breadth-first search algorithms. Read more →
November 19, 2025
Stacks
Introduction A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle. Think of it like a stack of plates: you can only add or remove plates from the top. The last plate you place on the stack is the first one you’ll take off. Stacks are fundamental in computer science and appear everywhere—from the call stack that tracks function calls in your program to undo/redo functionality in text editors. Read more →
November 19, 2025
Linked Lists
Introduction A linked list is a linear data structure made of nodes where each node stores a value and a reference (a “link”) to the next node. Unlike arrays, linked lists do not store elements contiguously in memory. This gives them different performance characteristics and trade-offs. If you are new to time and space complexity, start with our guide to Big-O Notation. You may also want to review Arrays to understand how linked lists compare. Read more →
November 18, 2025
Binary Search
Introduction Binary search is a classic algorithm used to quickly find the position of a target value within a sorted collection. It repeatedly divides the search interval in half, discarding the half that cannot contain the target. Because of this halving behavior, binary search runs in logarithmic time: O(log n). If you are new to complexity analysis, review our guide to Big-O Notation. Also see our overview of Arrays, since binary search is most commonly applied to sorted arrays. Read more →
November 18, 2025
Big-O Notation
Introduction In the realm of computer science and software engineering, the performance of an algorithm plays a pivotal role. But how do we gauge this performance? That’s where the concept of Big-O notation comes in. This article provides an overview of Big-O notation, its importance, and some common time complexities you might come across. What is Big-O Notation? Big-O notation is a theoretical measure of the execution time of an algorithm or the space it uses in relation to its input size. Read more →
September 14, 2023
Data Structures
Master fundamental data structures and algorithms. From arrays and linked lists to trees and graphs. Read more →
September 11, 2023