Event finding map-based platform

Hacker News

Hello there, We're trying to develop a map-based event-finding platform for people to explore nearby events. On the following URL u can find our MVP: https://wiwo.world/map . The thing is, we are not sure how to find early adopters. We have lots of ideas how to develop the platform, but would like to do it with active feedback from the users. Some of the feature ideas that we have: - Build-in resell marketplace for tickets for people who cannot attend an events - Users' own event facilitation - Events live streaming and PPV - Community building (go together with friends or have no friends to go with and you search people through the platform to go with) - Our own build-in navigation system to the events - Personalized notifications/newsletter for events like the ones you have attended or added in favorites - VR/AR event attending Thanks in advance to those who will check out our platform! :) Best Regards, Kalo

Visit product View on Hacker NewsFeb 13, 2024

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

57%57% 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: user, new · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

TrustMRRLess likely to generate early MRR · Strong signals: personal, users · Missing: mobile apps, ios, entrepreneurs

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

33%33% 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 · Strong signals: active · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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