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I made a deck of GLITCH playing cards

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I made a deck of GLITCH playing cards

Sorry for the green username, long time lurker first time contributor :) It all started with a dream. I love playing cards and I've always wanted to make my own deck. I started the initial work years ago but took it seriously a few months ago. I launched the campaign on Kickstarter about a month ago. It took a lot of work to get it rolling. I found some amazing support on reddit and on twitter and it just kind of snowballed from there. It really took off after being featured on VICE and LaughingSquid. I'm doing an IAmA over on reddit right now, if you are interested: http://www.reddit.com/r/IAmA/comments/28t9zw/iama_playing_card_designer_amaa/ Here a some pictures of the project (* I had a lot of fun :) http://imgur.com/a/MKnES And (of course) the kickstarter: https://www.kickstarter.com/projects/457846685/glitch-playing-cards-printed-with-by-uspcc

Visit productView on Hacker NewsJun 22, 2014
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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
92%92% 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 NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, way · Missing: mobile apps, ios, personal
36%36% 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
18%18% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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