MindGenie – Save 2 hours a day with smart scheduling

Hacker News

Hey folks! I've just revamped our landing page and added some cool features to help you be more productive: Assign points to tasks for better prioritization Crush text-based tasks efficiently Fit tasks into your schedule smoothly Get notifications to stay on track Split big tasks into smaller parts Boost focus with the Pomodoro Technique We'd love for you to check it out and show your support. Pay for $19 plan and unlock the power of MindGenie (using your own API) with 1-year license deal! Don't miss out on this fantastic offer! Visit our site and let us know what you think! Cheers!

Visit product View on Hacker NewsJun 16, 2023

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

79%79% 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

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: tasks, using · Missing: mac, agents, macos

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

39%39% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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