41 articles
Master LeetCode 39 — Combination Sum by understanding the backtracking decision tree, why unlimited reuse is handled by staying at the same index, and how sorting enables early pruning. Includes Python and JavaScript solutions with line-by-line comments, a full visual dry run, common mistakes, and follow-up questions on LC 40, LC 216, and LC 377.
LeetCode 40 is a FAANG backtracking favorite that tests duplicate handling. Sort the candidates and skip same-level repeats to enumerate unique sum combinations.
LeetCode 45 asks for the minimum number of jumps to reach the end. A greedy BFS-layer approach gives O(n) time and O(1) space — a FAANG favorite.
LeetCode 324 — asked at Google and Amazon. Rearrange an array so nums[0] < nums[1] > nums[2] < nums[3]... The key insight: find the median, then use a virtual index mapping to interleave smaller and larger halves without adjacent equal elements. O(n) time with nth_element.
LeetCode 565 — asked at Amazon and Google. Find the longest cycle in a functional graph where each index maps to nums[index]. Mark visited nodes in-place (set to n) to avoid revisiting. O(n) time, O(1) extra space — classic cycle detection pattern.
LeetCode 870 — asked at Google and Amazon. Rearrange array A to maximize the count of positions where A[i] > B[i]. Apply Sun Tzu greedy: against each of B's strongest, send your weakest if you cannot win; otherwise send your smallest winning element. O(n log n) time.
LeetCode 90 — asked at Amazon, Apple, and Google. Generate all unique subsets from an array with duplicates. Sort first, then in backtracking skip duplicate elements at the same recursion depth. O(2^n) time — the cleanest duplicate-handling pattern in all backtracking problems.
Compute the integer square root (floor) of x using right-boundary binary search. Find the largest k where k*k <= x, understand the upper-mid trick, and learn why this is the mirror image of the left-boundary template.
LC 367 asks if a positive integer is a perfect square without using built-in sqrt. Binary search on [1, num] for a value k where k*k == num. Also learn the elegant O(sqrt(n)) odd-number identity. A classic easy binary search problem at Google and Apple.
LeetCode 191 Number of 1 Bits: count set bits using Brian Kernighan trick n & (n-1). Foundational popcount technique that every FAANG interviewer expects you to know cold.
LeetCode 338 Counting Bits: compute popcount for every integer 0..n in O(n). Master the elegant DP recurrence dp[i] = dp[i >> 1] + (i & 1) that FAANG interviewers love.
LeetCode 190 Reverse Bits: reverse the binary representation of a 32-bit unsigned integer. Master the shift loop and the elegant divide-and-conquer mask reversal used in real-world DSP and crypto code.
LeetCode 371 Sum of Two Integers: add integers using only XOR and AND. Master the half-adder, carry propagation, and two's complement trick that reveals how CPUs actually compute sums.
LeetCode 89 Gray Code — generate an n-bit sequence where consecutive numbers differ by exactly one bit. The one-line XOR formula gray(i) = i XOR (i shifted right by 1) cracks it. FAANG-favorite bit manipulation interview problem.
LeetCode 318 Maximum Product of Word Lengths — find two words sharing no letters with maximum length product. Encode each word as a 26-bit set, then check disjointness with one bitwise AND. The textbook FAANG bitmask interview problem.
The complete 1D Dynamic Programming roadmap for FAANG interviews — Fibonacci, House Robber, Kadane, Coin Change, LIS, Jump Game, Decode Ways, and Palindrome patterns with Python and JavaScript templates.
The full 1D Dynamic Programming cheatsheet for FAANG interviews — eight pattern transitions, knapsack loop directions, LIS patience sort, and the complete problem index in one place.
The complete 2D Dynamic Programming roadmap for FAANG interviews — LCS, Edit Distance, grid path counting, interval DP, stock state machines, and 2D knapsack with Python and JavaScript templates.
The full 2D Dynamic Programming cheatsheet for FAANG interviews — seven pattern transitions, stock state machine, interval DP template, LCS reconstruction, and the complete problem index.
LeetCode 135 Candy is an Amazon, Google, and Apple Hard that turns into a 10-line problem when you spot the two-pass greedy. Sweep left then right and take the max to satisfy both rating constraints.
LC 763 Partition Labels partitions a string into maximum parts where each letter appears in at most one part. Track the last occurrence of each character and greedily extend the current partition boundary — an elegant O(n) greedy interval merge.
Master the hashmap interview patterns that power 87 percent of FAANG O(1) lookup questions: complement maps, frequency counting, prefix-sum hashing, two-way bijections, and cache design across 45 LeetCode problems.
LeetCode 1 Two Sum is the most asked FAANG hashmap interview question. Master the one-pass complement HashMap that turns the brute-force O(n^2) into O(n).
LC 234 Palindrome Linked List is a popular interview problem at Facebook, Amazon, and Apple that combines three core techniques: fast/slow pointer midpoint finding, in-place list reversal, and two-pointer comparison. Master the O(1) space solution with Python and JavaScript code and a step-by-step dry run.
LC 17 Letter Combinations of a Phone Number is the cleanest cartesian-product backtracking template ever written. Master the digit-to-letter mapping, the per-position branching, and why the iterative BFS variant is asked at Amazon.
Master Partition Equal Subset Sum (LC 416) and Partition to K Equal Sum Subsets (LC 698) with bucket backtracking, sorting tricks, and FAANG-grade pruning.
Solve LeetCode 238 Product of Array Except Self in O(n) time and O(1) extra space using two passes of prefix and suffix products, with the segment tree and Fenwick tree extensions for the mutable variant.
LeetCode 146 LRU Cache is the most-asked design problem at FAANG. Build O(1) get and put using a doubly linked list and hashmap with full Python and JavaScript code.
LeetCode 460 LFU Cache asked at Google and Apple for staff-level roles. Achieve O(1) get and put using frequency buckets with doubly linked lists.
LeetCode 104 — Maximum Depth of Binary Tree, asked by Amazon, Google, Meta and Apple as a phone-screen warmup. Solve it in one line of recursive DFS or with iterative BFS level counting in O(n) time.
LeetCode 112 — Path Sum, asked at Amazon, Microsoft, Apple and Meta. Use DFS with a running remainder to detect any root-to-leaf path that sums to a target value in O(n) time.
LeetCode 100 — Same Tree, asked at Amazon, Meta, Google and Apple. Walk both trees simultaneously and return false on the first structural or value mismatch in O(n) time.
LeetCode 617 — Merge Two Binary Trees, asked at Amazon, Apple, Meta and Microsoft. Walk both trees in parallel, sum overlapping nodes, and reuse existing pointers in O(n) time.
LeetCode 938 — Range Sum of BST, asked at Amazon, Facebook (Meta), Google and Apple. Use the BST property to prune entire out-of-range subtrees and run in O(h + k) time.
LeetCode 700 — Search in a BST, asked at Amazon, Microsoft, Apple and Meta. Eliminate half the tree at each step using the BST property and finish in O(h) time, O(1) iterative space.
Trim a BST so all values lie within [low, high] using recursive subtree pruning. LeetCode 669 is a Medium FAANG question asked at Amazon, Google, and Apple.
LeetCode 814 Binary Tree Pruning removes every subtree that contains no 1. Solve it in O(n) using post-order recursion — a classic Amazon and Google interview question.
LeetCode 988 Smallest String Starting From Leaf returns the lexicographically smallest leaf-to-root string. Solve in O(n * h) using DFS with path strings — popular at Amazon and Google.
LeetCode 173 BST Iterator implements a controlled inorder traversal with O(h) memory and amortized O(1) next. Asked at Amazon, Google, Meta, and Apple as a class-design tree question.
LeetCode 606 (Easy) asked at Amazon and Apple. Serialize a binary tree in preorder with parentheses, omitting empty parens only when they do not affect the one-to-one mapping, in O(n) time.
LeetCode 344 Reverse String is the simplest two pointer swap problem and a daily warm-up at Amazon, Apple, and Meta. Solve it in O(n) time and O(1) space using opposite-end pointers without allocating a new array.