Turas.app for Collaborative Trip Planning

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Over the New Year weekend, I was planning a trip with my wife on Google Docs and it became really frustrating trying to move days and activities around and figuring out which days we needed which hotels and trying to see which order of days/activities worked best for what we wanted to do. So I did what any weekend hacker does and ended up coding something. Turas lets you: - Collaboratively organize and plan your trips and easily reorganize days and places on your itinerary - Quickly visualize how changing the start date changes the structure of your trip - Visualize routes and paths between places on your itinerary and quickly load into Google Maps on the go - Organize your checklists - packing list, pre-departure, and more - Generate a text trip summary - perfect for sending in emails and text messages - Create an inspiration board to capture your notes and ideas as you plan your adventure If you'd like to see a short intro: https://www.youtube.com/watch?v=qFx5fJUR53I I admit, I ended up "rage building" Turas.app over that weekend out of frustration with Google Docs so I did not look into other options like Wanderlog; I just knew what my wife and I needed based on how we organize our multi-day trips. Would love to get HN's feedback!

Visit product View on Hacker NewsJan 16, 2023

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

84%84% 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, new, email · Missing: mac, agents, macos

80%80% 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

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

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

19%19% 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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