When colleagues can't find something, it usually isn't missing: of 50 zero-result queries at one university library, only one in ten was for something it didn't have. Sort the gaps in three to six months of search logs into can't be found, doesn't exist and different words, and fix titles and synonyms first; AGENTS.md files for AI need the same care, since one evaluation found they didn't raise success rates overall while raising costs by about a fifth.
Week 0 is about search: DFS and BFS differ only in whether the frontier is a stack or a queue; A* adds the steps already taken to a heuristic estimate and guarantees the shortest path when the heuristic never overestimates; Minimax has the two players take the maximum and the minimum in turn, and alpha-beta pruning cuts branches that cannot change the result. With time links for every section, plus the approach, common mistakes and ways to check your work for the Degrees and Tic-Tac-Toe projects; the reference implementations are folded at the end of each project.
07-280 is CMU's new Spring 2026 AI+ML core: 24 lectures and 12 main assignments move from heuristic search and CSPs to AlexNet, GPT-2, and AlphaZero. Its public material supports self-study, but the official schedule lists no complete recordings, and Canvas checkpoints, Gradescope, and staff feedback remain unavailable.
Lecture 2 decomposes search into a problem, frontier, and priority: UCS uses paid cost, Greedy uses estimated remaining cost, and A* combines them as `f=g+h`; tree and graph search require different optimality conditions.
Lecture 4 exposes structure through variables, domains, and constraints, then upgrades DFS with backtracking, forward checking, AC-3, MRV, and LCV; the goal is to prove failure earlier.
Lectures 1–12 form one decision pipeline: define states, moves, and objectives, then use heuristics, losses, regularization, and backpropagation to control an otherwise intractable search space.
The repository has a 20-query Traditional Chinese/English golden dataset, but no document-level qrels, retrieval runs, raw latency data, or executable benchmark script. Reporting Recall@k, MRR, or nDCG as measured results would therefore be dishonest; this article defines the contract needed to run them reproducibly.
Lectures 1–4 and Project 1 connect DFS, BFS, UCS, A*, state representation, and heuristic design. The goal is not memorizing algorithms but separating what the frontier, cost, and state each control.
Algolia packages indexing, search-as-you-type, facets, and UI components as a hosted service; it ships quickly, but data synchronization, relevance, and usage costs remain your responsibility.
Elasticsearch and OpenSearch both build text analysis, BM25, aggregations, and vector search on Lucene, but licensing, governance, hybrid-search APIs, and managed ecosystems have followed separate paths since the 2021 fork.
Pagefind scans static HTML after an Astro build and ships its index with a WebAssembly search runtime; the browser fetches only the index chunks required by a query, so no search server is needed.
Typesense is a search server centered on instant, typo-tolerant keyword search. Collection schemas, field weights, facets and sorting are enough to build site search, with a choice between self-hosting and Typesense Cloud; Chinese fields need locale: zh, and domain terminology may require custom segmentation.
CS221 is Stanford's course on AI principles and techniques. It lays AI out in four layers — reflex, states, variables and logic — with deep learning as only the lowest one. The executable Python lectures and all eight assignments, with code skeletons and a local grader, are public; lecture recordings, hidden test cases and assignment solutions are not.
When using AI agents like Claude Code or Cursor, built-in WebFetch / WebSearch often gets blocked by Cloudflare, geo-restrictions, or rate limits. Connecting a search MCP server is the most direct fix. This post compares the options actually available in 2026.