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linkies.com - organization by using hashtags

Hacker News

linkies.com - organization by using hashtags

I have been working on linkies.com as side project for a while now and as we get closer to our private beta launch I could really use some 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 on linkies or while using other social based networks. You can also discover and subscribe to your friends or just the things that you are interested in. There is a video that explains the service in more detail on the landing page if your interested. If I can get any traction or interest on HN then I will setup a private beta invite just for readers of HN. I would really appreciate any early feedback as we get this site ready to launch. Thank you for your time.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos
69%69% 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
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% 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
39%39% 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
22%22% 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
13%13% 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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