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Event Scheduling for Groups

Hacker News

Event Scheduling for Groups

A while ago a friend asked for a way to replace pen and paper to track who’s attending lunch at their co-living community; people come and go and they regularly go from 10 to 50 people, with guests occasionally joining. Initially I suggested a shared Google Sheet or Calendar, but as neither didn’t really work for them, I thought it’d be easy enough to make an app just for them, in the spirit of this article that made the front page a while back[^1]. It took a couple of weeks, was nearly free to host on Firebase, and they just loved it and kept using it daily for 2 years, with just the occasional fix from me. At some point though, some of them started asking about using it for other groups and to manage more events. That app was really not designed with that in mind, I didn’t even think about a multi tenancy of any sorts! So I took all the learnings from what was now the MVP, and with some friends we now launched this new version, built on Flutter and Supabase. Hopefully more people will find it useful! I have some ideas to extend it with CalDAV and Calendar integration for “power users”; but with the idea of keeping things clean and simple for most users. [^1]: https://www.robinsloan.com/notes/home-cooked-app/

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
81%81% 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, user, new · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
53%53% 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 · Strong signals: host, occasional, users · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: google, users, way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
21%21% 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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