Tool for event with friends scheduling (We Should Do It)

Hacker News

Hey guys, I'd like to ask you about your feedback on my new personal project. It's a small tool to help scheduling events with friends (how often you pretend to make plans with them and it ends up with one of you saying "yeah, we should do it!"). Application is made with Meteor, I wonder what do you think, I made all of the stuff there (music comes from "music stock library" :D) — do you think it would be a good idea to also include public events from all over the world?

Visit product View on Hacker NewsJun 6, 2013

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

70%70% 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.

TrustMRRFits verified-revenue profile · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs

62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

58%58% 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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

8%8% 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.

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