A web version of Pips game (NYT domino game)

Hacker News

Hi everyone, I’m an indie developer learning Next.js and a big fan of the NYT game Pips. Inspired by it, I built https://pipsgamer.com — a responsive web version of Pips with smooth gameplay on both desktop and mobile. What makes this project different from NYT’s version is that you can play it infinitely under three difficulty levels: Easy / Medium / Hard. This is the first time I’ve built a game. Along the way I ran into many difficulties: implementing the game logic, configuring the UI, matching layouts for small and large screens, etc. I spent many lonely nights and sometimes even doubted whether I could complete the whole project. After 24 days of persistent effort, the project is finally finished. No signup required — just go and play. If you try it out I’d really appreciate your feedback: what you like, what bugs you see, what could be improved. Thanks!

Visit product View on Hacker NewsSep 23, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

74%74% 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.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

68%68% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% 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 · Strong signals: way · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

37%37% 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.

Correct prediction on native model

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