Programming Fundamentals

Algorithms Behind AI

Connect complexity, search, sorting, recursion, graphs, and sampling to the mechanisms behind modern AI systems. Use the connections to estimate trade-offs and review code, even when an assistant wrote the first draft.

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Programming Fundamentals15notes

THE QUESTION THIS PAGE ANSWERS

ANSWER FIRST

What will the “Algorithms Behind AI” AI learning path help you do?

DECISION RULE

Core themes include Complexity: Is This Code Worth It?, Search and Sorting, Recursion and Divide-and-Conquer, Search and Decision.

TRY NEXT

Begin with “Big-O: See at a Glance How Long Code Will Run,” then choose the next note by the task in front of you.

WATCH FOR

Do not optimize for finishing the list. Explaining one trade-off with your own example matters more than opening more titles.

What this route helps you practice

Open the first note

Each chapter follows a class of real decisions. Follow the sequence, or enter at the problem you are solving today.

15notes

01Big-O: See at a Glance How Long Code Will RunDrag the data-size slider and watch O(1), O(log n), O(n), and O(n²) diverge; 10× the data—who barely flinches, who blows up on the spotInteractive4 min↗02Why Longer Context Costs More: The O(n²) BillThe attention mechanism makes every token look at every token: drag context length and watch compute and the bill climb with the square—why long chats get slow and expensiveInteractive5 min↗03Binary Search: The Optimal Number-Guessing GamePlay a 1-to-100 number-guessing round and feel the range cut in half each guess; a billion items found in 30 tries—how fast log n really isInteractive5 min↗04Sorting: Bubble Sort vs Quicksort RaceA head-to-head visual animation of two sorts: watch bubble sort nudge step by step and quicksort leap by partitions; see how lopsided the gap gets once the data growsAnimation5 min↗05Sorting's Real Face in AI: RerankPassages RAG retrieves can't go in as-is: coarse ranking first, then fine ranking. Tweak the weights yourself and watch candidates reshuffle—feeds and search results work the same wayInteractive5 min↗06Recursion: Break a Big Job into the Same Smaller JobWalk a directory tree, break down tasks, draw a fractal tree—same playbook: call yourself. Watch an Agent recursively split “build a site” into actionable small jobsInteractive5 min↗07Divide and Conquer: The Algorithm Behind Context CompressionCompaction in the AI Practicum is really divide and conquer: split a long chat, summarize each chunk, then merge. Run recursive summarization yourself and watch information compress layer by layerInteractive5 min↗08BFS and DFS: How an Agent Finds Files in a CodebaseWatch two search personalities in a maze animation: sweep layer by layer vs go all the way down one path; Coding Agent grep and web crawlers are variants of bothInteractive5 min↗09Greedy and Sampling: Two Personalities When AI Picks WordsAlways picking the biggest is greedy decoding; rolling dice by probability is sampling—the algorithms behind Temperature. Compare sentences from both strategies yourselfInteractive4 min↗10Beam Search: Look a Few Steps Ahead Before ChoosingOne greedy misstep cascades; Beam Search keeps several candidate paths and explores ahead. Interactively compare the sentences each strategy walks, and feel where “think first, then answer” comes fromInteractive6 min↗11AI Can Solve the Problems—Do You Still Need LeetCode?Honest talk on interviews today: which roles still test algorithms, how hard, and which stopped caring. Tap your target role and find your seatInteractive5 min↗12The Right Way to Learn Algorithms with AILet it set problems, play examiner, and walk complexity line by line—three ready-to-use prompt templates that turn AI from doing problems for you into practicing with youHands-on3 min↗13Summary · Five Algorithm Ideas at a GlanceComplexity / search & sorting / recursion & divide-and-conquer / graph search / greedy & sampling—each maps to a real mechanism in AI; take the whole chapter home on one tableRecap4 min↗14Run a Complexity Checkup on AI-Written CodeThree tiers: have AI self-report complexity, demand one-tier optimization and spell out the cost, then benchmark on large data to verify it isn't bluffingHands-on6 min↗15Algorithms · 30 Must-Answer QuestionsEach question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do they still test algorithms in the AI eraQuiz3 min↗