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Linkwarden – An open source collaborative bookmark manager

Hacker News

Linkwarden – An open source collaborative bookmark manager

Hey there HN, a major problem we're dealing these days is content on the internet can be taken down for any reason. On the other hand, we have lots of bookmarks and links that we want to keep for future reference. That's why we've built Linkwarden, a fully self-hostable, open-source collaborative bookmark manager to collect, organize and archive your webpages and bookmarks. One thing we're planning to do in the near future is to add a free plan to make it more accessible to everyone, especially if you’re just starting out without a commitment. This plan is also intended to bring users closer together with the goal to have a social aspect. As of now you could either use our Cloud offering [0] to directly support this project and experience Linkwarden, or you could self-host it on your own machine [1]. Almost a year ago we introduced Linkwarden (the new version) for the first time on Hacker News [2]. We've announced tons of new features since then [3]. Feel free to ask any questions, and we’ll do our best to answer! [0]: https://linkwarden.app/#pricing [1]: https://github.com/linkwarden/linkwarden [2]: https://news.ycombinator.com/item?id=36942308 [3]: https://blog.linkwarden.app

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, user, new · Missing: agents, macos, agent
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source, hacker news, io · Missing: https docs, excited, just released
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive
47%47% 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
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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