Tinder-style trip planning – Trip planning for the brainrot era

Hacker News

Hey there, I've been working on a project that helps you plan a trip by swiping through attractions like tinder. Features include: - Add places by swiping - Simple to use drag-and-drop itinerary planner - Add notes to each place - Get estimated travel and arrival times between places automatically - Automatically arrange places into your itinerary (This doesn't work that well tbh) - Share trips with friends Source code available at: https://github.com/Suu-ly/Outbound It's pretty good for planning day trips but frankly not the best for multi-day trips. Let me know what you think! Thanks

Visit product View on Hacker NewsJun 26, 2025

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

89%89% 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

54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

49%49% predicted probability of success on AppSumo, 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.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

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.

Incorrect prediction on native model

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