Urligram – Social Bookmarking Platform

Hacker News

Hi all! We are a tiny team of two that enjoyed using del.icio.us back in the day. It was a simple and efficient tool for two things: saving urls for later, and browsing the web thanks to the tags and some other basic features. So we wondered about rebuilding it. Its been quite a ride, but we finally have a streamlined version of what we are looking for. This is www.urligram.com: a prototype for a new social bookmarking platform. We have been using it for some time, and while there is a lot of work to be done yet, we think it is stable enough to be useful. Currently you will be able to save urls ‑as public or private‑, tag them, create lists ‑public or private as well‑ add bookmarks to lists, share lists... we also prepared an extension for Firefox and Chrome, so you wont have to leave the current page to save it. Please feel free to sign up and have a look. If you have any advice, notice some bugs or find things that would be better done in a different way please let us know! Alex and Anton

Visit product View on Hacker NewsJun 13, 2023

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Actual performance

6points

Did not reach leaderboard

Launch Intel predictions

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

75%75% 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 · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: platform, efficient · Missing: plus, intuitive, reviews

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · 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

17%17% 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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