// algorithms
Coding patterns with templates and walkthroughs: two pointers, sliding window, DP, graphs — and the problems each one unlocks.
6 min read A decision procedure for recognizing which algorithm pattern a problem wants, with the trigger phrase, data structure, and canonical problems to drill for each. Read article
7 min read A reference to the main dynamic programming families, what the dp array means in each, and the canonical problems that drill each one. Read article
7 min read A reference for the common sorting algorithms, their time and space costs, whether each is stable, and which one to name when an interview constraint changes. Read article
7 min read The 23 Gang of Four patterns in one table, plus the handful that actually show up in low-level design rounds and how interviewers phrase them. Read article
6 min read What the standard containers in Python, Java, and C++ actually cost per operation, and the ones that quietly turn an O(n) loop into O(n squared). Read article
8 min read The wording of a graph problem tells you whether to reach for BFS, DFS, topological sort, union-find, or Dijkstra. Read article
7 min read The per-operation cost of every common data structure, the amortized-versus-worst-case traps, and how to state complexity out loud without getting corrected. Read article
7 min read The seven recurrences that cover most interview dynamic programming, with the tells that let you recognize each one before you write code. Read article
7 min read A working reference for the concurrency primitives, deadlock, and lock-free tricks that come up in mid-to-senior interviews. Read article
5 min read Big-O answers one question, what the running time does when you double the input, and the gap between O(n log n) and O(n squared) at scale is where coding interviews are won or lost. Read article
5 min read Why Interval Problems Matter in Interviews Interval problems appear in scheduling, calendar, and range queries — and in coding interviews Read article
6 min read Why String Algorithms Matter in Interviews String problems are ubiquitous in technical interviews at Google, Meta, Amazon, and Microsoft. Key Read article
6 min read Why Graph Algorithms Matter in Interviews Graph problems appear constantly in technical interviews because they model real systems: social networks Read article
6 min read The Two Heaps Pattern Two heaps maintain the lower and upper halves of a data stream, allowing O(1) access to Read article
7 min read Grid Traversal Fundamentals Most matrix problems involve: (1) BFS from one or multiple source cells, (2) DFS to explore connected Read article
What's missing, wrong, or worth adding? One line helps.
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