DailyDo.it - a better way to manage your day

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This is my first web app, so there are bound be to be a few kinks that need to be worked out. That's why it's here, in hopes that some of the awesome people I read about on HN can take a minute to offer some criticism. http://dailydo.it/ DailyDo.it is meant to replace the canonical to-do list with a do-it list – a list of things that you will actually do. My monetization plan is based on team do-it lists, where you can see your teammates' do-it lists and assign them tasks, while keeping the individual do-it lists totally free. Please let me know if you find any bugs, what the pain points are, what you like/don't like, and anything else that comes to mind. Any and all feedback is greatly appreciated!

Visit product View on Hacker NewsMay 15, 2012

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

65%65% 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.

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

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

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

51%51% predicted probability of success on Indie Hackers, 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.

Correct prediction on native model

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