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A complete dynamic programming interview prep hub: what DP is, the core DP patterns interviewers test, a day-by-day curriculum covering memoization, tabulation, knapsack, LCS, LIS, edit distance, and coin change, plus a complexity quick reference.
A complete 60 day algorithm study plan for FAANG interviews: a week-by-week roadmap, 60-minute daily session structure, and the pitfalls to avoid.
An arrays interview prep hub: why arrays dominate FAANG interviews, the core techniques (two pointers, sliding window, prefix sums, in-place operations), the day lessons that teach them, and a complexity quick reference for array operations and sorting.
A complete FAANG interview prep plan: what interviews actually test, a 4-stage roadmap, timelines by experience level, and how Google, Meta, and Amazon differ.
The greedy approach: when local choices give a global optimum, complexity, a Python example, common pitfalls, and the days that cover greedy algorithms.
Backtracking explained: the choose-explore-unchoose template, complexity, a Python example, common pitfalls, and the days that cover backtracking.
String algorithms for interviews: operations and Big-O, pattern matching with KMP and Rabin-Karp, a Python example, pitfalls, and the days that cover them.
A pattern-first guide to binary tree interview questions. Organizes tree problems into four families: traversal (DFS and BFS), recursive tree properties (depth, balance, sameness), BST-invariant problems (validation, kth smallest), and path problems (lowest common ancestor, max path sum), with full Python solutions, the recursive 'trust the subtree' mental model, complexity analysis, and links to the tree days of the 60-day curriculum.
How the Meta coding interview actually works in 2026: two problems per 45-minute round with no compiler to lean on, why speed and communication matter more here than anywhere else in FAANG, and the DSA topics that dominate: arrays and strings, hash maps, trees and BFS/DFS, intervals, and binary search. Includes a worked merge-intervals Python solution, a brief on the Pirate (system design) round for E4+, what the behavioral round screens for at each level, and a realistic preparation timeline.
A practical 2026 guide to Google coding interview prep: the full loop from recruiter screen to hiring committee, the four things Google actually grades (algorithms, data structures, communication, and 'Googleyness'), realistic prep timelines for L3 through L5, the topics that show up most, a worked example of interview-style problem solving in Python, and the five mistakes that sink otherwise-strong software engineer candidates.
The 10 dynamic programming patterns FAANG interviews actually test: 1D DP, knapsack, unbounded knapsack, LCS, LIS, edit distance, interval DP, palindrome DP, grid paths, and state machines, each with a Python example, a canonical LeetCode problem, a complexity table, and a recommended study order so dp patterns stop feeling like fifty unrelated problems.
A practical guide to the Amazon coding interview in 2026: the full loop structure (online assessment, phone screen, onsite with Bar Raiser), the DSA topics that actually appear most (hash maps, trees, graphs/BFS, heaps and top-K problems), and why Leadership Principles carry as much weight as code. Includes a worked top-K Python solution, a STAR-format story bank template for LP questions, the traps that sink otherwise-strong candidates, and how to map the whole syllabus onto a 60-day preparation plan.
A practical guide to the bitwise techniques that show up in coding interviews, starting with why interviewers love bit manipulation and a refresher on the six core operators (AND, OR, XOR, NOT, shifts). Covers six essential tricks with ASCII bit diagrams: checking and setting the k-th bit, clearing the lowest set bit with n & (n-1), XOR self-cancellation, power-of-two detection, swapping without a temp variable, and multiplying or dividing by powers of two with shifts. Then walks through five classic interview problems (single number, counting bits, missing number, power of two, and reversing bits), each with a worked Python solution and complexity analysis.
An honest, tiered guide to coding interview prep resources in 2026. The free tier covers LeetCode's free set, NeetCode 150, Tech Interview Handbook, the Blind 75, structured challenges like Algorithms in 60 Days, and CS50 for fundamentals. The paid tier reviews LeetCode Premium, AlgoExpert, Grokking-style pattern courses, interviewing.io mock interviews, and the classic books (CTCI, EPI), with candid notes on what each is actually worth. The community tier covers Discord servers, Blind, and peer mock-interview platforms. Ends with recommended stacks by timeline and budget.
Reframes binary search as a general search-space reduction technique rather than a sorted-array lookup trick. Covers the classic template and the two bugs that break it, then extends the idea to 'search on the answer' problems like Koko Eating Bananas and Split Array Largest Sum, and to rotated sorted arrays. Works through six real interview problems with Python solutions, explains how to recognize a hidden binary search from monotonicity, and shows why the same 15-line template solves problems that look nothing like array lookup.
Why hash maps and sets deserve more interview prep time than trees or dynamic programming. Explains the O(1) lookup superpower that turns quadratic brute-force solutions into linear ones, then covers the three patterns behind most hash map interview questions: frequency counting (valid anagram, top-K elements with Counter), the two-sum complement lookup family (two sum, subarray sum equals K with prefix sums), and grouping by canonical key (group anagrams with defaultdict). Includes five fully-solved Python problems, a pattern recognition cheat sheet, complexity analysis, and the follow-up questions interviewers ask about collisions and worst-case behavior.
A complete topic hub for graph algorithms in coding interviews. Covers what graphs are and how to represent them (adjacency list vs. matrix), when to choose BFS vs. DFS with full Python implementations, the shortest-path ladder from plain BFS through Dijkstra, Bellman-Ford, and Floyd-Warshall, and the five interview patterns that account for most graph questions: connected components and island counting, cycle detection, topological sort with Kahn's algorithm, grid-as-graph BFS, and union-find. Includes a curated problem list ordered from easy to hard, complexity tables, and links to the day-by-day graph lessons in the 60-day curriculum.
A mental model for solving recursion interview problems without tracing every call. Introduces the 'leap of faith': trust the recursive call to solve the smaller problem, then define only the base case, the reduction step, and the combine step. Applies the three-question framework to five real interview problems in Python: reversing a linked list, validating a BST with min/max bounds, generating subsets by include/exclude, counting climbing-stairs paths (with memoization), and generating balanced parentheses with backtracking. Covers the recursion-to-DP pipeline, Python's recursion limit, and the follow-up questions interviewers actually ask.
A complete guide to the sliding window technique for coding interviews. Explains why windows beat recomputing every subarray from scratch, the crucial difference between fixed-size and variable-size windows, and the grow/shrink template that solves nearly every variable-window problem. Works through five interview problems with full Python solutions: Maximum Sum Subarray of Size K, Longest Substring Without Repeating Characters, Minimum Size Subarray Sum, Longest Repeating Character Replacement, and Minimum Window Substring. Covers the amortized O(n) argument, window-state bookkeeping with hash maps, and the signals that tell you a problem wants a sliding window.
A pattern-recognition guide to the two pointer technique for coding interviews. Explains the three flavors (converging pointers on sorted data, fast/slow same-direction pointers, and pointers across two sequences) and the problem-statement signals that tell you which one to reach for. Works through six interview problems from easy to hard with Python solutions: Two Sum II, Valid Palindrome, Remove Duplicates from Sorted Array, Container With Most Water, 3Sum, and Trapping Rain Water. Covers why the converging-pointer proof works, the deduplication details that break 3Sum, and a checklist for spotting two pointer problems in the wild.
A complete guide to backtracking for coding interviews. Explains backtracking as constrained depth-first search over a decision tree, then introduces the universal choose/explore/unchoose template that solves nearly every backtracking interview problem. Works through four problem archetypes with full Python solutions: subsets (include/exclude decisions), permutations (ordering with a used-set), combination sum (reuse with pruning), and N-Queens (constraint satisfaction with column and diagonal tracking). Covers time complexity for each archetype, the classic mutable-list copy bug, and how to recognize a backtracking problem from its problem statement.
Explains merge sort as a divide-and-conquer algorithm: recursively split an array into halves, sort each half, and merge the results in order. Includes a complete Python implementation with step-by-step commentary. Introduces Big-O notation to reason about algorithmic efficiency and explains why merge sort achieves O(n log n) in all cases by combining log n levels of recursion with O(n) merging work per level. Compares merge sort against bubble, selection, insertion, and quick sort across best, average, and worst-case scenarios.
Introduces two foundational algorithms every programmer needs to know: linear search, which scans a list element by element until the target is found and returns its index (or -1), and binary search, which requires a sorted list but cuts the search space in half with each comparison for dramatically faster lookups on large datasets. Provides Python implementations of both and explains when each is appropriate based on the size and order of the data being searched.
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