Jinx – a link shortener you host free on GitHub Pages

Hacker News

Jinx is just links, a small static website on GitHub to share short links to your stuff. Add your links to one file, turn on GitHub Pages, and point your own domain at it. I built it because the options all seemed wrong for a small team: paid shorteners charge monthly for what's essentially a lookup table; free self-hosted ones (YOURLS, Shlink, etc.) still need a server and a database to maintain; and free tiers of hosted services like bitly or tinyurl cap your links or paywall basic features. It’s working well for my team at work. What it does: • Short links on your own domain (yourname.com/thing) that redirect instantly. • A searchable page listing all your links. Typos are redirected to the search page. • QR codes for each link, generated in js in the browser, no external API calls. • Click counts via GoatCounter (free, privacy-preserving, no cookies) • Nested paths if you want them (e.g. contact/email, contact/whatsapp, etc.) • Access control for who can edit links is done through github repo access. Why another link shortener? Paid link shorteners cost money every month for something that's really just a lookup table. Free open-source link shorteners require you to host the app somewhere and pay monthly for that instead. Freemium services like bitly paywall you with limits on the number of links, edit ability, sometimes redirect your QR code to an ad . So I built a link shortening tool focused on simplicity as a static website and open-sourced it. Setup: fork the repo, edit the config and the links, turn on GitHub Pages. Takes about five minutes. MIT licensed, free, no ads, no account required beyond GitHub itself. Live example: jinx.fyi Code: github.com/gdmcdonald/jinx Would love to hear feedback, what do you like or not like, what would make it more useful for you?

Visit product View on Hacker NewsSep 18, 2026

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

73%73% 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.

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

65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

AppSumoMay struggle as an AppSumo deal · Strong signals: host, calls · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% 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 · Strong signals: paid · Missing: web3, chat, crypto

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

Correct prediction on native model

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