Gradually slow the infinite scroll of Reddit/Twitter

Hacker News

Many people try to limit their screen time and I'm sure I'm not the only one who found the existing tools lacking. Mostly, they consist of locking yourself out of accessing some websites after a certain amount of time or during certain hours of the day. The problem with these is that - as long as you have the unlock code - you still need to have the willpower to avoid turning them off. This led me down the path of making the content itself less interesting; to break down the carefully crafted feedback loop that leads to endless scrolling. Sleeping Policeman is my attempt to create a gentile nudge to lower screen time. It slowly adds a delay to each request over the course of the day until content takes long enough to load that you find something better to do. Would love to hear your feedback!

Visit product View on Hacker NewsOct 11, 2024

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4points

Did not reach leaderboard

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

82%82% 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: code · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

34%34% 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

23%23% 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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