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Future Products (hypejar.com)

Hacker News

Future Products (hypejar.com)

Hello HN, we are hypejar.com. In a few weeks, we will be launching a site that makes it easy for you to find information about products soon releasing. We will be the largest and most extensive source of information on upcoming products on the internet. Also, we believe that it would be useful for all of us to know what the levels of anticipation are for those upcoming products. As such, we hope that our concept of “hype” will reveal to all of you the level of demand in the market for various products. In anticipation of our impending launch, we invite you to follow us on Twitter (@hypejar) and like us on Facebook (facebook.com/hypejar). Before we launch, we hope to provide you with tidbits of cool information about future products.

Visit product View on Hacker NewsAug 14, 2012

Actual performance

5points

2comments

Did not reach leaderboard

Launch Intel predictions

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

86%86% 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

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

nativeThis product was originally launched on this platform.

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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.

Correct prediction on native model

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