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WeddingDeck, Online Whitelabel Wedding Planner

Hacker News

WeddingDeck, Online Whitelabel Wedding Planner

Hey guys, I’m launching my first night & weekends project and looking for some feedback, http://www.WeddingDeck.com. It’s a whitelabel online wedding planner. I think you get the idea but to be more precise, it’s an online wedding planner that help relieve stress from people getting married. At the same time, a wedding organizer can sign up and create accounts for his clients, with his logo and his domain name and offer it as his product. So the website is here : http://www.weddingdeck.com You got also a bit of the backstory here: http://www.position-absolute.com/articles/launching-my-first-product/

Visit product View on Hacker NewsFeb 6, 2012

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

75%75% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: 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.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

Incorrect prediction on native model

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