A subreddit dedicated to beta-reading

Hacker News

(it's naked and will stay naked for at least a week or two) There are stuff like Discord or Scribophile, which can provide good opportunites, but it's such a mess and sometimes it can be frustrating - for both parties. So, r/betareading is meant to become a platform for both parties to meet and exchange freely. I'll have more free time soon to work on the subreddit and promote it. Probably will add guidelines for both writers and beta-readers. Because a writer can name the genre of his text and his length, but beta-readers skills should be categorized and made public: from grammar to pacing or character development, including favorite genres, format, languages spoken fluently and time available. And maybe a wiki afterwards. This has always existed presumably, it's time to make it more formal. For writers I see two advantages: - easier to send your copy to your stranger than maybe to a buddy - a good, well-read and well-trained beta-reader will provide better feedback than your bud For beta-readers I see one advantage: - easier than to lurk Scribophile for a month wondering wth happens over there, or to join Discord and read thousands of pages of Sonic fanfic https://www.reddit.com/r/betareading/ In the meantime, feel free to join and stick to the one and only rule existing for now; tag your titles with [R] or [BR]!

Visit product View on Hacker NewsAug 20, 2019

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

68%68% 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: exist, existing, ide · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal

51%51% 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

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

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

33%33% 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

16%16% 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.

Correct prediction on native model

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