Trip on Rails

TrustMRR

Trip on Rails is a full-featured trip planning web app that lets you organize every detail of your travels in one place. Plan activities with an interactive map, track costs and budgets, manage tasks

Trip on Rails is a full-featured trip planning web app that lets you organize every detail of your travels in one place. Plan activities with an interactive map, track costs and budgets, manage tasks and to-dos linked to your itinerary, attach files and documents, and gain insights into your trip at a glance — all powered by AI assistance and wrapped in a clean, collaborative interface built for modern travel planning.

Visit product View on TrustMRRMar 19, 2026Travel Software stripe

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Actual performance

Did not reach leaderboard

Launch Intel predictions

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

73%73% 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

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

43%43% 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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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