Continuum Game (68k Mac) Ported to JavaScript

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This was an interesting porting project for a few reasons (IMO): - The original game is/was awesome and, from a programming perspective, a wonder -- smooth scrolling arcade game on a 128kb Mac in 1984... - The port was done with a lot of help from AI (mostly Claude Code, but some Gemini CLI as well). I'm a programmer; it wasn't vibe-coded. But I couldn't have done the port of 68k assembly without it. FWIW, Claude seemed better at actually porting the 68k assembly but Gemini was better at finding bugs. YMMV. - I love Redux and Redux Toolkit for state management. For the port, I put the entire game state in Redux, including all the physics, movement, etc. Every thing that happens in the game is a little redux action. You can watch the whole game get played in the RTK debugger. For some reason that makes me happy. I've released all my code as MIT. Would love to make a "modern" version some day, but for now I've just tried to be faithful to the original. There are a few bugs, noted as issues in the github repo. Feel free to add more.

Visit product View on Hacker NewsSep 18, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, claude, gemini · Missing: agents, macos, agent

92%92% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

83%83% 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: io, including · Missing: https docs, excited, just released

48%48% 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 · Missing: mobile apps, ios, personal

36%36% 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

34%34% 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

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

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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