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CasePlum – Custom-printed iPhone 6 Cases (Passive Income Idea)

Hacker News

CasePlum – Custom-printed iPhone 6 Cases (Passive Income Idea)

Hey guys, made this site in 2-3 weeks, just in time for the iPhone 6 launch. Thought it'd be an interesting passive/side income idea, which I know is a popular topic on HN. Apologies if its buggy or doesn't support certain browsers/screen sizes. Trying to be lean develop it Also, since the community here is great and has provided excellent feedback before, here's a $10 off coupon just for you guys: HNthirty. Any tips on how to market it? I'll happily share numbers with the community if it goes anywhere.

Visit product View on Hacker NewsSep 19, 2014

Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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

79%79% 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 HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released

35%35% 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

34%34% 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

25%25% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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