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Plan Harmony – Collaborative Trip Planning Tool

Hacker News

Plan Harmony – Collaborative Trip Planning Tool

A few months ago I took the nerdiest approach to planning a trip to Italy. I built a web app to plan with friends and now it's in a beta release. All with the goal of making planning travel with friends easier. You can create itineraries through an intuitive calendar interface. Saving all the important information about each destination/event in you trip you can access while planning and while on the trip. Trips cost money, unfortunately, but you can track and maintain your trip budget. Track who's paid and who hasn't. Make settling the tab super easy. One of the hardest parts of trip planning is figuring out what to do or where to stay. Not everyone is always up for the same thing. Use the vote/like button on events to help make group decisions about the trip. And so much more to come! Thanks for checking out Plan Harmony. I'm really excited to hear what you think!

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
60%60% 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 · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, way · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
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: intuitive, interface · Missing: plus, platform, reviews
40%40% 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
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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