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Search

Learn Search as a connected topic across chapters, concepts, simulations, and interview reasoning.

10 Concepts18 Articles4h 46m

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Learn Search as a connected topic across chapters, concepts, simulations, and interview reasoning.

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Algorithms
Data Structures
Coding Interview
Java

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Article 1A Beginner's Guide to Vector Database PrinciplesTLDR: A vector database stores meaning as numbers so you can search by intent, not exact keywords. That is why "reset my password" can find "account recovery steps" even if the words are different. 14 minArticle 2DFS — Depth-First Search: Go Deep Before Going WideTLDR: DFS explores a graph by diving as deep as possible along each path before backtracking, using a call stack (recursion) or an explicit stack. It is the go-to algorithm for cycle detection, path f15 minArticle 3Binary Search Patterns: Five Variants Every Senior Engineer KnowsTLDR: Binary search has five patterns beyond the classic "find the target": leftmost position, rightmost position, rotated array search, minimum in rotated array, and 2D matrix search. The root of eve17 minArticle 4BFS — Breadth-First Search: Level-by-Level Graph ExplorationTLDR: BFS explores a graph level by level using a FIFO queue, guaranteeing the shortest path in unweighted graphs. Recognize BFS problems by keywords: "shortest path," "minimum steps," or "level order16 minArticle 5System Design HLD Example: Search Autocomplete (Google/Amazon)TLDR: Search autocomplete must respond in sub-10ms to feel "instant." The core trade-off is Latency vs. Data Freshness: we use an offline pipeline (Spark) to pre-calculate prefix-to-suggestion mapping15 minArticle 6Text Decoding Strategies: Greedy, Beam Search, and SamplingTLDR: An LLM doesn't "write" text — it generates a probability distribution over all possible next tokens and then uses a decoding strategy to pick one. Greedy, Beam Search, and Sampling are different16 min

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