Can You Beat the Machine?

Hacker News

Let me start by saying from where did the idea of this project, because I find it is one of the most interesting things about a project. The idea comes reading "Thinking, Fast and Slow". I created a simple game to give a simple demonstration of how we are often biased in making choices. If you are interested you can go into that reading this paper Choices, Values, and Frames: http://web.missouri.edu/~segerti/capstone/choicesvalues.pdf and this article: https://en.wikipedia.org/wiki/Loss_aversion The game consists of choosing between two different bets. If you want to see here is the link: https://thundershotgames.github.io/CYBTM/ Let me know what you think, thank you !

Visit product View on Hacker NewsOct 27, 2016

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2points

1comments

Did not reach leaderboard

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac · Missing: agents, macos, agent

22%22% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% 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.

Incorrect prediction on native model

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