Or

Organization by using hashtags

Hacker News

Organization by using hashtags

I have been working on linkies.com as side project for a while and really hoping to get your thoughts and feedback on the overall idea. The idea: Linkies is an innovative social way to save/collect all the things you love and discover by utilizing the power of hashtags. You can automatically create albums or collections of links, pictures or posts based on the hashtags you use while signed into linkies.com or even while using other social based networks. Linkies also allows you to discover and subscribe to your friends or more importantly just the things that you are interested in. Check out the video on our landing page. It explains the services Linkies will provide in more detail. If I get traction or interest on HN then I will then setup a private beta invite just for the HN readers. Again, I would really appreciate any early feedback as we get this site ready to launch. Thanks for your time!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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: using, plain · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, 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
25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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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