Flappy Gopher with Online Ranking – A Go/WebAssembly Browser Game

Hacker News

I implemented a Flappy Bird style game using Go and the Ebitengine (2D Game Engine), compiled to WebAssembly to run in the browser. Tech stack: - Frontend: Go + Ebitengine -> WebAssembly - Backend: Cloudflare Pages Functions - Database: Cloudflare D1 Features: - Daily/weekly/monthly online leaderboards - Jump history recording and verification to prevent cheating - Fully serverless architecture to minimize operational costs Source code: https://github.com/ponyo877/flappy-ranking This started as an experimental project to create a game in Go and compile it to WebAssembly for browser play. By leveraging Cloudflare's edge computing, I've been able to deploy it globally with low latency. Feedback and suggestions for improvement are welcome!

Visit product View on Hacker NewsMar 23, 2025

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Actual performance

3points

4comments

Did not reach leaderboard

Launch Intel predictions

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

85%85% 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.

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

70%70% 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 HuntOn track for Day 1 leaderboard · Strong signals: using, code · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, monthly · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, 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.

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

16%16% 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.

Incorrect prediction on native model

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