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Throw – A space for asking and answering questions anonymously

Hacker News

Throw – A space for asking and answering questions anonymously

Hello HN community, For the past two years we’ve been working on this disruptive new thing. It’s about people, community, communication and truth. Throw is the new space for asking and answering questions anonymously. We believe that in today’s world (both online and offline) content in communication exchanges is strongly influenced by the personas, profiles and façades people maintain/upkeep/safeguard socially, ideologically and on relationships. From the way people post on Instagram the life they want others to believe they have, or the way people behave on thanksgiving with family, or at work, or with friends; on every social setting and interaction to some degree acting and behaving according to that setting and the people they interact with. These dynamics influence the content itself, as people don’t just respond to a question like computers do. What ends up happening is that the responder comes up with the answer by blending the possibly objective answer with feelings, setting, desires, commitments, ideologies, fears, insecurities, etc (social pressure or social agenda). Something is missing between social media and the traditional Q&A… Throw addresses this by creating a space free from this social agenda. Thus focusing strictly on the content exchanged and providing a safe, comfortable and unbiased space where people can ask and answer anything freely with no bias, fears or strings attached. We've put key components of social media, Q&A and surveying industries into one platform. Not only may Throw be used for personal and private questions and answers. But the power of crowdsourcing allows for a great variety of use cases like market research, validation of content and ideas, trivia, and much more. Serious matters and also just for fun… It’s a query marketplace which means that “throwers” (people who ask questions) pay a fee proportional to the answers they need and in turn “catchers” (who catch them and respond) get compensated. This way we guarantee every user gets as many responses as he/she needs. As for dealing with anonymity, we have built a sophisticated moderation protocol to neutralize and quickly ban people that contribute negatively as it is a priority people feel safe and comfortable in this community. We have worked very hard to create a delightful product and are basically launching today! If this is something that may be of your interest or you’d like to be one of the first to test it out download our app and play around with it. https://www.trythrow.com Also, if you have questions there is additional information in the FAQs section on our web page that could be of help. Finally, we really appreciate any feedback we can get (of any kind). So if there's anything you like, don't like, or any other thought about Throw, we'd love to hear about it! You may post a comment below or through the contact section on the web page. Be curious and dare to know! Thank you!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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, computer, new · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, answers, way · Missing: mobile apps, ios, entrepreneurs
46%46% 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
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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