Frontend Fuzzy Search

Hacker News

I have spent several years working on search engines in the backend and have now utilized that experience to develop a fuzzy search library for the frontend. It's fast, accurate and can be used for all languages. It should be easy to integrate into your Javascript / Typescript projects. If you test it and find any edge cases that did not work for you please let me know. The implementation is based on 3-grams by the book, augmented with a novel trick of sorting the characters within the 3-grams for enhanced accuracy. For a detailed explanation you may refer to my related blog post at https://www.m31coding.com/blog/fuzzy-search.html . Happy Coding!

Visit product View on Hacker NewsFeb 14, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: coding · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

24%24% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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