Coordinating Trips Between Friends and Family

Hacker News

I've been working on this project to simplify the organization done by one or two people to get a group of people to agree on a place to go that works with everyone's schedule. I've found in the past that email/text makes it tricky to get everyone to get a sense of the details of a trip, and also puts one person in the position to be persistent and spend a lot of time ensuring everyone's needs are met. This simple app attempts to ease that process and keep everyone in sync. It's similar to Troupe but aiming to focus on the basics of the upfront coordination rather than the activities/itinerary (at this point) Would love feedback and thoughts on the current state and where the best places to take it next are. It's a bit barebones at this point but more coming soon! Behind the scenes it uses flutter/firebase

Visit product View on Hacker NewsFeb 19, 2024

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

78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 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.

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

34%34% 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

14%14% 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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