Practice bitwise tricks by drag and drop

Hacker News

Inspired by sunday post of EABitTricks [0]. Biggest limitation right now is that app supports only signed 32-bit ints, since javascript converts numbers to this type before bitwise operations. So lots of tricks cannot be represented. Luckily some interesting tricks like snoob [1] can still be represented. Source code: https://gitlab.com/sirkmatija/bitr1ck [0] - https://news.ycombinator.com/item?id=34043314 [1] - https://github.com/electronicarts/EAStdC/blob/fbcc34e89c6363...

Visit product View on Hacker NewsDec 22, 2022

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1points

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

74%74% 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: io · Missing: https docs, excited, just released

57%57% 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.

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

42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

29%29% 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

18%18% 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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