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Calendar and Reminders –> Keyword Filter –> Nice Widget on iOS and Mac

Hacker News

Calendar and Reminders –> Keyword Filter –> Nice Widget on iOS and Mac

On iOS you can only filter by Calendar, but I wanted more control to only show my important events / deadlines to keep an overview. Thats why I made Deadlines: A widget app that syncs with Apple Calendar & Reminders and allows you to filter them by keyword (supports Regex). Staying always up to date without any extra work from your side. Nowadays I just add #track or similar as a note to my important events and know that I won't overlook them. Recently I just added the option to open events & reminders in any app of your choosing. Also supports custom URL Schemes and Shortcuts. Let me know what you think :)

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Actual performance

2points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apple, open · Missing: agents, macos, agent
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, way · Missing: mobile apps, personal, entrepreneurs
50%50% predicted probability of success on TrustMRR, 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
30%30% 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
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
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.

Correct prediction on native model

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