Built a spaceship endless runner in 7 days using AI tools

Hacker News

I wanted to push myself to build something fun, fast, and playable entirely in the browser. I’m not a traditional game dev, but I’ve been experimenting with AI tools like Cursor (AI-powered IDE) and figured it might be possible to go from zero to playable in a week. Ended up creating a 3D endless spaceship runner - you fly through space dodging UFOs and their laser beams. I used Three.js for the environment and leaned heavily on AI (mostly Cursor + ChatGPT) for scripting logic, fixing weird bugs, and optimizing gameplay feel. I didn’t plan much. Just opened the IDE, started describing what I wanted to build to the AI, and kept iterating on what felt fun. The UFO obstacle behavior was surprisingly hard to tune without it getting too unfair. AI helped a ton with tweaking that. The game runs in-browser with no downloads. I know it's rough around the edges, but shipping it in 7 days taught me more about building games (and about AI dev tooling) than anything else I’ve done recently.

Visit product View on Hacker NewsJul 23, 2025

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

90%90% 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: cursor, chatgpt, using · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

63%63% 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

42%42% 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

17%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, cryptocurrency

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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