StoryGrill – Your personalized newspaper

Hacker News

Hi everyone, we are a startup with a new concept for delivering the best news to you. The site is called StoryGrill, and it keeps you updated with the most recent news from your favorite newspapers. You need to choose your favorite newspapers/magazine/sites, and then relax: the site will update in real-time whenever there are updated news from your favourite newspapers. We have many ideas how to improve this(personalization, notification, etc..) but we prefer to stay lean and get feedback from actual users. We would love to hear what you think about that. Be brutally honest, we can handle it ;) Our MVP can be found at http://www.storygrill.com We are also inviting users to try our mobile version of the app. If you like to try it, get an invitation here: bit.ly/1LcGf3c Looking forward to hear all your feedback! The StoryGrill Team

Visit product View on Hacker NewsMar 9, 2016

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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 · Missing: supports, reddit linkedin, podcasting

66%66% 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 HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

14%14% 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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