s0rce - A New Approach to News

Hacker News

s0rce is a new kind of news site - one that focuses on factual information and data, breaks out of the standard article format, and seeks to better integrate users in the creation of content. We recommend starting with one of our more complete issue/event pages: http://s0rce.com/issue-event/republican-primary A more detailed introduction to s0rce can be found here: http://s0rce.com/blog/post/fixing-the-news We're a two person team, so some aspects of the site and content are incomplete - news is a lot of work. Feedback and contributions are welcome (try clicking the [!] links).

Visit product View on Hacker NewsMar 8, 2012

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Actual performance

13points

5comments

Made the leaderboard

Launch Intel predictions

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

71%71% 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

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

57%57% 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: users · 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: users · Missing: mobile apps, ios, personal

32%32% 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

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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