Automatically copy events between your calendars

Hacker News

I initially built this one for myself. When working from home and with multiple clients, I had to manually add my appointments so that my colleagues wouldn't accidentally book that time. After a while, it just became tedious and I decided to build this out. I think this will be useful for anyone working with multiple calendars and also service providers who manage appointments through calendars. Looking forward for your feedback! Also, appreciate any inputs on the pricing, ideally I would prefer this to have it as a buy once software, but I'm afraid the APIs would evolve and that would need continued work on my side.

Visit product View on Hacker NewsJan 11, 2024

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63%63% 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: apis · Missing: mac, agents, macos

50%50% 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

34%34% predicted probability of success on TrustMRR, 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

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

25%25% 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 · 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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