StaticSearch – a simple search engine for static sites

Hacker News

I'm Craig Buckler and StaticSearch is my simple client-side only search engine for any static site (such as those created by Publican - https://publican.dev/ ). An example can be seen on https://publican.dev/ - click the search icon or press Ctrl|Cmd + K. Static sites cannot easily provide search facilities because there's no back-end framework, language, or database... You can use third-party services such as Alogia, AddSearch, or Google's Programmable Search Engine. These provide a search API, but often have a cost and can take a while to index. JavaScript-only options such as Lunr require you to pass all content in a specific format. Every page then has a full index of your site, so payloads can become large as your site grows. Pagefind analyses your built site and creates WASM binary indexes. But it requires some HTML configuration, uses considerable JavaScript code, and causes Content Security Policy issues. StaticSearch is a simpler option. It: - can use npx so there's no installation - quickly indexes built pages and adds them to your static site (like Pagefind) - requires no special HTML markers - respects robots.txt and meta tag settings - generates pure JavaScript and JSON files - is easy to add to your site using a native web component - includes options for custom search facilities - is vanilla JS compatible with all frameworks - has a tiny payload (13Kb of JS and 4Kb of CSS at most) - incrementally loads index data on demand and caches in the browser - determines when new word indexes are available. npm: https://www.npmjs.com/package/staticsearch Github: https://github.com/craigbuckler/staticsearch Full documentation: https://publican.dev/staticsearch/ All feedback is appreciated!

Visit product View on Hacker NewsJul 31, 2025

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

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

33%33% 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

19%19% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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