AlgorithmsDataStructures

Classic CS fundamentals — hash maps, trees, graphs, heaps, sorting/searching, Big-O reasoning. Mostly interview-prep/background, rarely hand-written in a CRUD app.

Why we need this / what value this brings

The right data structure/algorithm choice can be the difference between an operation that's instant and one that doesn't scale — though most CRUD code never needs anything beyond a hash map or array. Also the single highest-leverage topic for technical interview prep.

When to use this

When a specific operation's performance is a measured problem, or in interview/exam prep contexts.

How to use or implement this

Reach for the standard library's built-in structures first; only hand-roll something specialized once profiling shows a genuine need. For interview prep, work through the subtopics below roughly in order — each builds on the last (Big-O → sorting/searching → hashing/trees → graphs → DP/greedy/strings).

Subtopics

• BigOAndComplexityAnalysis
• SortingAlgorithms
• SearchingAlgorithms
• HashingAndCollisionHandling
• TreesAndHeaps
• GraphAlgorithms
• DynamicProgramming
• GreedyAlgorithms
• StringAlgorithms

Research questions

• Any place in Localz where an off-the-shelf data structure choice (e.g. a priority queue for booking dispatch) would actually matter? (see TreesAndHeaps)
• Text diff algorithms (Myers diff / LCS-based) are what actually compute a pre/post command-output diff before it ever reaches an LLM — worth doing this diff programmatically first (cheap, exact) rather than asking the LLM to spot differences in two 10MB blobs (expensive, error-prone, and it'll blow the context window anyway — see AI/ContextWindowManagement-ChunkingStitching). (see StringAlgorithms)

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Empty folder — drop notes, links, and findings here as you research.