Reimplementation of Lazy Tetris

Hacker News

Demo video: https://youtu.be/ZoQ6w73rbkY Someone first implemented something like this and shared it on HN https://news.ycombinator.com/item?id=44103839 ...and I love it :D Unfortunately, they removed it later (no idea why). So I decided to make my own implementation. Key features: - The pieces are selected at random, unlike the true Tetris, which uses Multi bag sampling. VERY interesting dynamic. Try it! - The pieces don't fall on their own. They await my instruction - The line clears when I decide it's time for it to clear :D Enjoy ;-)

Visit product View on Hacker NewsJun 28, 2025

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Actual performance

2points

Did not reach leaderboard

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

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, 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

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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