Creating a viral pre-launch platform for collecting email addresses

Hacker News

Hey HN, Just wanted to share this cool idea I whipped up in a day for my new project. How it works is: -When a user enters their email address, they must verify -Upon verification, they are given a unique referral code they can share -Sharing the unique referral code will reward them for verified emails The idea is, rather than just collecting email addresses for launch, others can help you advertise and be rewarded for it. I got this idea from Tim Ferriss’s ‘How to collect 100,000 email addresses in 1 week’ blog post. Thinking of turning this into a product on it’s own that people can plug into their apps for pre-launch email collecting. You'll have to enter your email and verify to see the whole experience. What do you think?

Visit product View on Hacker NewsMay 30, 2024

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

85%85% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% 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, 000, 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.

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

42%42% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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