# Wikipedia

Published articles for Wikipedia.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Introducing TIN: full-text search for Postgres

DevFeed: [Introducing TIN: full-text search for Postgres](<https://devfeed.tech/articles/introducing-tin-full-text-search-for-postgres-31551.md>)

Original publisher: [Read original article](<https://planetscale.com/blog/introducing-tin>)

Author: Patrick Reynolds

Published: 2026-09-16T00:00:00Z

Content type: release

Language: en

Sources: [Blog -- PlanetScale](<https://devfeed.tech/sources/blog-planetscale.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>)

Tags: [backups](<https://devfeed.tech/tags/backups.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [bm25](<https://devfeed.tech/tags/bm25.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [full-text-search](<https://devfeed.tech/tags/full-text-search.md>), [index](<https://devfeed.tech/tags/index.md>), [performance](<https://devfeed.tech/tags/performance.md>), [postgres](<https://devfeed.tech/tags/postgres.md>), [reddit](<https://devfeed.tech/tags/reddit.md>), [replication](<https://devfeed.tech/tags/replication.md>), [search](<https://devfeed.tech/tags/search.md>), [text](<https://devfeed.tech/tags/text.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

### AI overview

PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance.

### Source excerpt

TIN is a fast, full-featured, full-text search index for Postgres

## Perplexity CEO Aravind Srinivas on building an answer engine for curiosity

DevFeed: [Perplexity CEO Aravind Srinivas on building an answer engine for curiosity](<https://devfeed.tech/articles/what-would-you-ask-if-no-one-could-judge-you-10002.md>)

Original publisher: [Read original article](<https://www.figma.com/blog/perplexity-ai-interview/>)

Author: Alia Fite

Published: 2024-06-26T00:00:00Z

Content type: article

Language: en

Sources: [Figma Blog](<https://devfeed.tech/sources/figma-blog.md>)

Topics: [Conversational AI](<https://devfeed.tech/topics/conversational-ai.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Natural language processing](<https://devfeed.tech/topics/nlp.md>), [App](<https://devfeed.tech/topics/app.md>), [Wiki](<https://devfeed.tech/topics/wiki.md>), [Google](<https://devfeed.tech/topics/google.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [app](<https://devfeed.tech/tags/app.md>), [ceo](<https://devfeed.tech/tags/ceo.md>), [conversational-ai](<https://devfeed.tech/tags/conversational-ai.md>), [learning](<https://devfeed.tech/tags/learning.md>), [llms](<https://devfeed.tech/tags/llms.md>), [natural-language-processing](<https://devfeed.tech/tags/natural-language-processing.md>), [perplexity](<https://devfeed.tech/tags/perplexity.md>), [search](<https://devfeed.tech/tags/search.md>), [slackbot](<https://devfeed.tech/tags/slackbot.md>), [web](<https://devfeed.tech/tags/web.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

### AI overview

Figma interviews Perplexity co-founder and CEO Aravind Srinivas about the company's vision for an answer engine that synthesizes information from the web. He discusses conversational search, sourcing, curiosity, and the origins of Perplexity as an internal Slackbot.

### Source excerpt

Co-founder and CEO Aravind Srinivas wants Perplexity to be not only a resource, but an engine for curiosity.

## Fuzzy string matching + simplest possible spellchecking + hunting for typos and misspellings in Wikipedia

DevFeed: [Fuzzy string matching + simplest possible spellchecking + hunting for typos and misspellings in Wikipedia](<https://devfeed.tech/articles/fuzzy-string-matching-simplest-possible-spellchecking-hunting-for-typos-and-misspellings-in-wikipedia-20561.md>)

Original publisher: [Read original article](<https://yurichev.com/blog/fuzzy_string/>)

Published: 2015-07-22T22:00:00Z

Content type: tutorial

Language: en

Sources: [Dennis Yurichev](<https://devfeed.tech/sources/dennis-yurichev.md>)

Topics: [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Code](<https://devfeed.tech/topics/code.md>), [Programming](<https://devfeed.tech/topics/programming.md>), [Python](<https://devfeed.tech/topics/python.md>), [Wiki](<https://devfeed.tech/topics/wiki.md>), [Linux](<https://devfeed.tech/topics/linux.md>)

Tags: [algorithms](<https://devfeed.tech/tags/algorithms.md>), [article](<https://devfeed.tech/tags/article.md>), [code](<https://devfeed.tech/tags/code.md>), [linux](<https://devfeed.tech/tags/linux.md>), [opensource](<https://devfeed.tech/tags/opensource.md>), [python](<https://devfeed.tech/tags/python.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>), [xml](<https://devfeed.tech/tags/xml.md>)

### AI overview

This tutorial explains fuzzy string matching through Levenshtein distance, which counts insertions, deletions, and replacements needed to transform one string into another. It demonstrates approximate matching with agrep and describes a Python script that uses a Levenshtein distance module to find spelling errors in a Wikipedia dump.

### Source excerpt

Fuzzy string matching + simplest possible spellchecking + hunting for typos and misspellings in Wikipedia

## Regaining Lost Knowledge

DevFeed: [Regaining Lost Knowledge](<https://devfeed.tech/articles/regaining-lost-knowledge-21695.md>)

Original publisher: [Read original article](<https://rhettinger.wordpress.com/2010/02/06/lost-knowledge/>)

Author: rhettinger

Published: 2010-02-06T23:28:30Z

Content type: article

Language: en

Sources: [Raymond Hettinger](<https://devfeed.tech/sources/raymond-hettinger.md>)

Topics: [Data structures](<https://devfeed.tech/topics/data-structures.md>), [Python](<https://devfeed.tech/topics/python.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [algorithms](<https://devfeed.tech/tags/algorithms.md>), [data-structures](<https://devfeed.tech/tags/data-structures.md>), [implementation](<https://devfeed.tech/tags/implementation.md>), [lost-knowledge](<https://devfeed.tech/tags/lost-knowledge.md>), [python](<https://devfeed.tech/tags/python.md>), [research](<https://devfeed.tech/tags/research.md>), [running-median](<https://devfeed.tech/tags/running-median.md>), [skiplists](<https://devfeed.tech/tags/skiplists.md>), [wikipedia](<https://devfeed.tech/tags/wikipedia.md>)

### AI overview

The article examines solutions for computing a running median over a large sliding window in a data stream. It describes an indexable skiplist with link widths that supports O(log n) updates and median lookup, offering a portable solution compared with earlier approaches.

### Source excerpt

Rediscovery of Indexable Skiplists in the solution of the Running Median problem.