# Topics

> The subject index for the work and the notes.

- [Information retrieval](https://oppority.com/topics/information-retrieval.md): Finding a recorded item that answers a need. Search engines, relevance, and the structure of the data sit under this subject. It is the field I study.
- [Information retrieval / Search engines](https://oppority.com/topics/search-engines.md): The libraries and servers that keep an index and answer a query. The names under this subject are systems I have read and worked with.
- [Information retrieval / Search engines / Enterprise search](https://oppority.com/topics/enterprise-search.md): Search run for the people of one organization: schema, analyzers, ranking, and the daily work of keeping results trustworthy. About ten years of practice sit in this subject.
- [Information retrieval / Search engines / Apache Lucene](https://oppority.com/topics/apache-lucene.md): The Java library under many search servers. Inverted indexes, analysis chains, and similarity scoring. Solr and Elasticsearch are built on it.
- [Information retrieval / Search engines / Apache Solr](https://oppority.com/topics/apache-solr.md): A search server on Apache Lucene: schema, request handlers, and the operation of a cluster. It is the engine I have spent the most years with.
- [Information retrieval / Search engines / Elasticsearch](https://oppority.com/topics/elasticsearch.md): A distributed search and analytics engine on Apache Lucene. I have used it in the same years as Solr.
- [Information retrieval / Search engines / OpenSearch](https://oppority.com/topics/opensearch.md): A search engine in the Lucene family, in the same line of study as Solr, including what has to remain true in the top results when an index moves.
- [Information retrieval / Search engines / Luxir](https://oppority.com/topics/luxir.md): An open-source hybrid search engine by Yonik Seeley, the original author of Apache Solr. Text, vectors, facets, and geo over HTTP. My note is a laptop guide for the official Linux binary.
- [Information retrieval / Relevance](https://oppority.com/topics/relevance.md): Whether the result a person receives is one they can act on. Hybrid ranking, similarity, and the structure of the data sit under this subject.
- [Information retrieval / Relevance / Hybrid search](https://oppority.com/topics/hybrid-search.md): Lexical ranking such as BM25 combined with vector similarity in one result. The useful question is when each signal should win, and how to check that the blend did not quietly get worse.
- [Information retrieval / Relevance / Similarity](https://oppority.com/topics/similarity.md): How a ranker decides that two items are close: a lexical score such as BM25, or a vector score such as cosine. I study the choice, and how to tell that a new score did not quietly get worse.
- [Information retrieval / Relevance / Data structuring](https://oppority.com/topics/data-structuring.md): The shape of the data before a ranker runs. Vector search and lexical tagging sit under this subject. The structure decides what close is allowed to mean.
- [Information retrieval / Relevance / Data structuring / Vector search](https://oppority.com/topics/vector-search.md): Search by nearest neighbor in an embedding space. I am studying when a dense signal helps, and when a precise lexical handle should outrank it.
- [Information retrieval / Relevance / Data structuring / Lexical tagging](https://oppority.com/topics/lexical-tagging.md): Tokens, entities, and tags taken from the text itself, including tags extracted with classical tools or with a language model. A label a person can read, used as a handle for lexical retrieval.
- [Quantum computing](https://oppority.com/topics/quantum-computing.md): A subject I am reading, outside the systems I operate. The question that interests me is which computations change on a quantum machine, and which stay classical.
- [Quantum computing / Quantum search](https://oppority.com/topics/quantum-search.md): A reading of Grover's search beside classical search. Grover marks items in an unstructured list. Indexes, trees, and neighbor graphs already add structure, so a quantum search needs its own representation. This is a study note.
- [AI visibility](https://oppority.com/topics/ai-visibility.md): How an assistant finds a page and names it in an answer. Answer-engine optimization and generative-engine optimization are names for that question. Citing a page is a different job from retrieving a document.
- [AI visibility / Answer engines](https://oppority.com/topics/answer-engines.md): How assistants such as ChatGPT, Gemini, and Perplexity choose a source and name it. I study this because I work on retrieval.
- [Leadership](https://oppority.com/topics/leadership.md): How a team's output becomes something a person can trust. The published book and the notes on ownership sit here.
- [Leadership / Engineering management](https://oppority.com/topics/engineering-management.md): The work after the promotion: the team's output, the first 90 days, and the conversations nobody scripts for you. The Kindle book is the long version.
- [Leadership / Software engineering](https://oppority.com/topics/software-engineering.md): How software gets designed, shipped, and owned after the tutorial ends. Notes here are about delivery and trust.
- [Practice](https://oppority.com/topics/practice.md): Notes that are procedures: a guide you can follow, and the shell details inside one.
- [Practice / Guides](https://oppority.com/topics/guides.md): Step-by-step pages. A guide on this site is something you can follow, with the limits written down, including when a setup is only for a laptop.
- [Practice / Shell](https://oppority.com/topics/shell.md): Notes on the Unix shell, including Bash parameters used in real scripts. The 2020 note lives on Medium and is indexed here.