Insert Coin: An iOS / Android / web app to locate arcade locations

Hacker News

Hello everyone! Six months ago, I took over the the Pinmyballs app, which is mainly known in the French-speaking world for locating public places with pinball machines. Initially, I wanted to shut it down and transfer the data to Pinballmap, but I felt like doing something different. I love arcade games as much as pinball, and there was no app to track arcade locations. So I decided to continue developing Pinmyballs by adding arcade games, as well as darts, billiards, and foosball. The app was rewritten from the ground up, using Ionic framework (will be rewritten in React Native by the end of the year), and Symfony as back-end. And for obvious reasons, I also renamed it to Insert Coin. Everyone can contribute and add locations and their game to the app. As the app launched recently, it misses a lot of locations, so don't hesitate to contribute and share the app around you if you want to! - The app is available in six languages (English, Dutch, French, German, Italian, Spanish) and is completely free (no ads, no subscriptions) - You can easily add locations you know about, games and comment about their state / condition - You can find places around you for a particular game - The games database may not be complete, so if you notice any games missing, feel free to let me know, and I’ll add them! - You can save your favorite locations and receive notifications when games are added or removed from them! - And more features to come! The app is available on: - Web: https://www.insert-coin.app - iOS: https://apps.apple.com/app/insert-coin/id6664072981 - Android: https://play.google.com/store/apps/details?id=com.insertcoin... Currently, there are 2,700 locations and 12,000 games listed.

Visit product View on Hacker NewsJan 29, 2025

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

75%75% 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, apple, google · Missing: agents, macos, agent

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: ios, apps, month · Missing: mobile apps, personal, entrepreneurs

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, 000, io · Missing: https docs, excited, just released

55%55% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

28%28% 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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