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Gameplan – Play poker against a solver

Hacker News

Gameplan – Play poker against a solver

I wanted to learn game theory optimal poker, but the existing tools were kind of clunky and unintuitive for semi-beginners like me. I wanted an app where I could play poker against an opponent that’s grounded with GTO frequencies, and get feedback instantly in order to learn an intuition to hand strengths and optimal actions in any scenario (also the GTO frequencies so that I could learn to rotate styles to stay unreadable). So I ran a solver and solved thousands of game trees from flop to river with preset stacks, that can be matched to the current stacks in the game, to get as accurate frequencies as possible. I’d be especially interested in feedback from people who are familiar with GTO poker, to point out if I have misunderstood something. My contact details are in my bio. Thanks for checking it out! https://gameplan.poker

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Actual performance

2points
Did not reach leaderboard

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TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive · Missing: plus, platform, reviews
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
35%35% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, io · Missing: https docs, excited, just released
24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
19%19% 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
17%17% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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