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Helishopter - real social shopping

Hacker News

Helishopter - real social shopping

I've posted a few times regarding www.helishopter.com[1] , but I wasn't prepared to really post until we fixed up a few of the styling issues. Now, there are still a lot of bugs and UI issues, but you get the idea.<p>Our mission is:<p>"To be the premier destination for shopping inspiration and product research, by providing intelligent recommendations and tapping into the advice of our users network of social connections"<p>Of course, it will work a lot better once people start building follower/following lists, as one of the central features is to find inspiration by looking into other peoples "shopping bags".<p>Another issue is that the majority of our content is Swedish (we're based in Stockholm), but we're working on it now.<p>As always, we're looking for as much feedback as possible, so please let me know what's good, what's bad and how we can improve.<p>Note: 1. Mobile-version is non-existent (yet), please ignore.

Visit productView on Hacker NewsJan 16, 2013
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5points
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Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user · Missing: mac, agents, macos
68%68% 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
56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, ide, io · Missing: https docs, excited, just released
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
39%39% 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
16%16% 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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