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TimeCheck compares availability to find times when everyone is free

Hacker News

TimeCheck compares availability to find times when everyone is free

Finding a time to get together can turn into a flood of texts back and forth as people throw out times and shoot them down over and over again. Frustrated by this, I started TimeCheck as a side project with the goal of making it easier to find a time to gather for drinks, a meeting, or to play games/sports/music. TimeCheck takes a snapshot of each person's availability for the selected dates, compares them, then presents the date/times when all are free. Everyone in the group can vote on a preferred time from the results and the person that created the TimeCheck confirms the final time. Then you one-tap add the confirmed time to your iCal. All via one app bubble inside an iMessage chat. In the future I'd like to expand the scope so that TimeCheck works for iOS and Android users in the same chat, and make a WhatsApp version. The app already supports timezones but it only works for iCal right now. TimeCheck is available for beta testing via Testflight. If you would like to try it out please visit: https://timecheck.us Feedback from early adopters will shape the next iteration so if this could possibly be useful to you please give it a try and let me know what I can do to make it better for your use case! If you have comments on the merits of the idea, your pain points when scheduling, the landing page, experience with other scheduling apps... please share them here. Thank you!

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87%87% 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: apps, user · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, users · Missing: mobile apps, personal, entrepreneurs
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
38%38% 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
36%36% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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