Refind – The home for the best links on the web

Hacker News

We’re building Refind, a community of founders, hackers, and designers who collect and share the best links on the web. It’s super early but we’re on Product Hunt today and we don’t want to miss the opportunity to reach out to HN too! Founders, hackers, and designers are the audience we had in mind when building this so we’d really love to know what you think! https://refind.com HOW IT WORKS Save great links that will come in handy in the future. Discover what others save. And then find everything again when you really need it – for example when you later search for this topic on Google, Refind highlights links you or your friends saved (optional). DELICIOUS? Delicious pioneered social bookmarking in 2003. We’re trying to take up on where they left off. And here’s how we believe Refind fits into today’s landscape: https://refind.com/home#difference READ LATER? Refind is complementary to Instapaper or Pocket: Read Later is a reading list, Refind is an archive. Read Later is todo, Refind is fire and forget. Here’s how we see and use the two in combination (with the example of Pocket): https://medium.com/@refind/refind-pocket-a0ecb08de814 WHY? Here’s why we’re building this: https://medium.com/@refind/this-is-why-we-re-building-refind-7e7229bee370 We really hope you like it! Again, it’s super early but we’re going to work on this for a very long time so we’d love to know what you think! I’ll be around here and you can also reach me on Twitter (https://twitter.com/dominikg) or at dominik@refind.com. Thanks a lot, Dominik (Founder)

Visit product View on Hacker NewsDec 8, 2015

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

76%76% 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: google · Missing: mac, agents, macos

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

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

37%37% 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

14%14% 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.

Correct prediction on native model

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