An Intelligent Planner That Adapts to How You Work

Hacker News

I have FINALLY learned how to manage time. I know procrastination is still a huge problem at my school. Every morning I can still find people just coming home from pulling all nighters. I'm working to compile best known solutions into a smart planner. Other solutions focus more on showing what to do that helping you get stuff done. Here's what it does: Helps yoy break your work down into smaller pieces (30minutes -> 1.5hrs, our attention spans are short) Helps you estimate how long work will take (based on your past personal history) Adapts to your working habits ("John really gets a lot of work done at 11pm on Sundays, let's ask him to schedule a short task then to save him time later") Show you how much free time you have and how you can get more Help you eliminate distractions Syncs with Google Calendar, Evernote and Dropbox (Coming later) …and more We're currently here: http://www.peersapp.com I'm looking for feedback on whether these strategies have worked for people. Also, what would your biggest reservation in using this?

Visit product View on Hacker NewsNov 13, 2013

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6points

5comments

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · 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.

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

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google · Missing: mobile apps, ios, entrepreneurs

44%44% 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

27%27% 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

18%18% 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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