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Aurca AI – Find Mispriced Event Contracts on Prediction Markets

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Aurca AI – Find Mispriced Event Contracts on Prediction Markets

I’ve built Aurca.ai, a web app to help people betting on prediction markets spot mispricing opportunities. It analyzes contracts using statistical models and machine learning by evaluating odds (from data) and uncover where the market might be off. I’ve been tinkering with this for a while, combining my love for data science and financial markets, and it’s finally ready for others to try. You can jump in and check it out at aurca.ai/dashboard—no signup needed to play around with the core features. Just pick a prediction market contract (it supports Kalshi for now, with more to be added later), and it’ll show you the model results. My approach uses historical data, probabilistic models, and machine learning to predict events and evaluate their occurrence probability and compare that with the market price. It’s early days, so expect rough edges—I’m here for feedback! What works? What’s confusing? Any features you’d want? I’ll be around to answer questions and tweak things based on what you think. P.S. All Models are trained using Bayesian methods, and the tool used to train is Numpyro on JAX.

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Actual performance

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
81%81% 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: mac, model, models · Missing: agents, macos, agent
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
39%39% 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 · Missing: plus, platform, intuitive
37%37% 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
12%12% 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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