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Caff.ai a Caffeine Manager for better sleep and more energy

Hacker News

Caff.ai a Caffeine Manager for better sleep and more energy

Hey HN, I often don't know if I can have a coffee at 2pm and started logging my caffeine intake so I wouldn't be up until 2am. For me it was never as easy as a cut off time (ie stop coffee at 1pm) because I vary the amount and timing of my coffee day to day. I made an app that helps you stay in your custom 'daytime target range' and under your 'bedtime target' caffeine level. It can predict how much caffeine you'll have at bedtime and tell you what time your caffeine will be below a configurable sleepable level. I also added a feature that gives you a notification 30 minutes before you can have your last cup of coffee and still be under your bedtime target at bedtime. This helps you stay maximally caffeinated while minimizing the negative effects. I have over 600 drinks in my drinks list, and I'd appreciate any feature suggestions or tangential app ideas as well.

Visit product View on Hacker NewsJan 15, 2023

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6points

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

70%70% 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 · Missing: mac, agents, macos

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

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

53%53% 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

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

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% 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.

Correct prediction on native model

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