Ho
Home Game Poker Settler (Open Source)
Hacker NewsHome Game Poker Settler (Open Source)
I built this for my home game. It’s super simple. But sharing because I wanted to show how fast you can build things these days. The tooling is incredible. From start to deployment this took ~1 hour. Tools used: ChatGPT, npx create-next-app, tailwind, Vercel, GitHub Here is the repo: https://github.com/zlwaterfield/poker-settler
Share cardActual performance
2points
3comments
Did not reach leaderboard
Launch Intel predictions
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting63%63% 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: chatgpt, open · Missing: mac, agents, macos58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: open source · Missing: https docs, excited, just released52%52% 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, intuitive39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal38%38% 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, revenue17%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: chat · Missing: web3, crypto, cryptocurrency2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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