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#algorithms

8 articles

01 ·Data Structures·★ MEMBER·PAPER·13 min read Probabilistic Data Structures — Counting Without Counting Bloom filters, HyperLogLog and the Count-Min sketch explained from zero — how giving up the right to always be correct buys you memory that never grows, and how large services actually operate these sketches. 02 ·Complexity·FREE·9 min read NP-Completeness from Scratch — Not Unsolvable, but Fast to Verify NP does not stand for Non-Polynomial. It is the class of problems where, if someone hands you an answer, you can check it quickly. We build up P vs NP, reductions and NP-completeness from zero, then look at how all of it shows up in shift rosters and delivery routes. 03 ·Search & Optimization·★ MEMBER·PAPER·8 min read Graph Algorithms from Scratch — Shortest Paths and Where They Lead From transit apps to vector search, the world runs on dots and lines. We build up BFS, Dijkstra, and A* assuming zero background, then follow one unbroken thread all the way to HNSW — the graph search powering retrieval in the LLM era. 04 ·Search & Optimization·★ MEMBER·PAPER·8 min read Graph Algorithms from Scratch — Shortest Paths and Where They Lead From transit apps to vector search, the world runs on dots and lines. We build up BFS, Dijkstra, and A* assuming zero background, then follow one unbroken thread all the way to HNSW — the graph search powering retrieval in the LLM era. 05 ·Search & Optimization·★ MEMBER·8 min read Dynamic Programming From Scratch — On Remembering Subproblems Why naive recursion explodes exponentially, what memoization and table-filling actually change, and Fibonacci, knapsack and edit distance taken apart in order — ending at the places edit distance shows up in real AI systems, from ASR word error rate to diffusion step schedules. 06 ·Search & Optimization·★ MEMBER·8 min read Dynamic Programming From Scratch — On Remembering Subproblems Why naive recursion explodes exponentially, what memoization and table-filling actually change, and Fibonacci, knapsack and edit distance taken apart in order — ending at the places edit distance shows up in real AI systems, from ASR word error rate to diffusion step schedules. 07 ·Data Structures·FREE·7 min read Choosing a Data Structure — Arrays, Hashes, Trees and Heaps What arrays, hash tables, trees and heaps each make fast, and what each one gives up in return. A pick-by-use-case table, plus which structures actually show up in tokenizers, vector search and KV caches. 08 ·Complexity·FREE·7 min read Complexity From Scratch — What Big-O Actually Measures What O(n), O(n log n) and O(n²) feel like as wall-clock time. Constant factors versus growth rate, trading time against space, and the three reasons your profiler disagrees with the textbook — assuming no prior knowledge.