Create Calendar Events from Itineraries

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Hi! I recently booked a few flights with points on Chase. Since they were through Chase and not through the airline, Google didn't automatically add them to my calendar. This year, I've also been attended a few weddings with long itineraries and a music festival with a selection of artists I wanted to add to my calendar. These _should_ be trivial to add to a calendar. Instead you end up transcribing a lot of info and double checking your timezones manually. It was easy to find a lot of people who struggled with creating events for a variety of reasons, so I figured this might be worth spending a couple hours to make a tool for this. (here's one example of many: https://old.reddit.com/r/delta/comments/19fde5h/adding_itine... ) That's why I made invite.sh, a thin GPT wrapper that takes any description of an event as input (flight confirmation emails, wedding itineraries, etc) and outputs calendar invite links for Google, Yahoo, Office365, and Outlook so you can easily add any events to your calendar. The site is live at https://www.invite.sh/ It's also open source! I'd appreciate stars: https://github.com/jrhizor/invite It's pretty simple, and yet-another-gpt-wrapper, but I'm hoping the addition of a UI makes it a bit more user friendly than trying to get this output from ChatGPT directly. Please let me know what you think!

Visit product View on Hacker NewsAug 17, 2024

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

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly, user friendly · Missing: plus, platform, intuitive

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

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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

40%40% 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

14%14% 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.

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