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HTime, a clock to find the best time to meet and eliminate email tennis

Hacker News

HTime, a clock to find the best time to meet and eliminate email tennis

hTime is a hobby project turned into a product after hearing what people think about it. It started as a clock combining UTC and all time zones in one clock face to eliminate time zone calculations - a pain I had to deal with when scheduling meetings across time zones. Here is an older discussion around hTime on HN ( https://news.ycombinator.com/item?id=28928550 ). The problem we're solving is when you schedule a meeting with 2+ attendees where you don't have access to one or all of their calendars. It gets worse when they are spread across different time zones. Here is where email tennis starts and time zone confusion kicks in. It takes up to 20 minutes to finally find a good time to meet. After listening to initial users' feedback, we turned hTime from a clock-only into a scheduling tool that helps you schedule with multiple users, multiple time zones, and multiple calendars per user in less than 10 seconds. No email or messaging tennis, calendar checking, or time zone math anymore. It's still early stage. I'd love to hear the community's thoughts. hTime works best for teams. So to try Schedule, ideally, you'd have 2+ users that you know on it.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · 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: ide, 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: user, new, email · 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 · Strong signals: users · Missing: mobile apps, ios, personal
40%40% 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
31%31% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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