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Create your own prediction market in two minutes

Hacker News

Create your own prediction market in two minutes

Hi HN! Excited to show off the project we've been working on for the last couple months. We started with an idea for a crazy twist on prediction markets: You come up with a question for traders to predict, and then decide the outcome yourself. For example, you could create a market on “Will my date with [X] go well?” Anyone can bet on it, and the bets create a forecast on the chance your date goes well. After the date is over, you get to judge the result and reward the traders who picked the correct side. There are so many ways for this mechanism to go wrong: the creator of the market can be dishonest in deciding the outcome, or you may just disagree with their resolution, with no recourse. Nevertheless, we pitched user-created prediction markets in a grant proposal to the blog Astral Codex Ten, and somehow we won! Since then, it’s been an exciting two months of us hacking away to realize this idea, and now we’re happy to announce the official launch of Manifold Markets! We’ve gotten so much support from early users who are just as passionate about prediction markets as we are. You might ask: Is there a reason to create a prediction market, and not a Twitter poll? Are outcomes really that different from a simple person-by-person vote? We believe so. The magic driving prediction markets is accountability. When wagering a scarce currency, those proven right live on to make future bets, whereas those with less-savvy bets have their influence diminished. Prediction markets succeed because they reward accuracy, and that makes all the difference! Our goal is to make prediction markets an order of magnitude easier for you to create and share. It should be as frictionless as a Twitter poll. As part of that philosophy, we’re launching with a play money currency, which we believe is just as fun and predictive. We’ve already built up a passionate community of predictors and market creators, including writers like Richard Hanania and James Medlock, who have predicted everything from CDC recommendations to newsletter subscriptions to fatal shark attacks. There’s so much unexplored space to ask questions and get valuable forecasts. For example, with conditional markets, you can create several related markets that help you make a choice based on which has the highest likelihood of success! Let us know if you have any thoughts or suggestions; otherwise, looking forward to what markets you create!

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, including · Missing: supports, reddit linkedin, podcasting
93%93% 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, codex, new · Missing: mac, agents, macos
59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, ide · Missing: https docs, just released, exist
38%38% 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 · Strong signals: subscription · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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