Pushpin, an iOS client for Pinboard, is now open source

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Just wanted to share that I just open-sourced Pushpin today under the GPLv3 license. I've been building it since 2012 and it's become one of the most popular iOS clients for Pinboard [0] [1] [2]. It hasn't gotten a large update in quite a while, but it still runs perfectly on the latest iOS and iPad OS devices. Enjoy! [0]: https://pinboard.in/ [1]: https://thesweetsetup.com/apps/best-pinboard-app-ios/ [2]: https://toolsandtoys.net/pushpin-for-pinboard/

Visit product View on Hacker NewsFeb 17, 2025

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

84%84% 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: apps, open · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs

68%68% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

57%57% 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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

23%23% 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.

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