Travel Easy – A Simple Tool for Planning Road Trips

Hacker News

Hi everyone, Osito here! I love road trips and take them often, but I’ve always found the initial planning stage to be a bit time-consuming—it can take hours of browsing to get everything sorted. Over the weekend, I decided to tackle this problem and built a tool called Travel Easy. The app is currently in its MVP (Minimum Viable Product) stage. I’ve shared it with my inner circle, received some encouraging feedback, and now I want to share it with you all on Hacker News. Travel Easy aims to simplify the planning process, saving you time and effort. I’d love for you to try it out and share your thoughts, issues and feedback. This is just the beginning—I have plenty of ideas and features I’d like to add to make it an even more useful tool. If people find it valuable, I’d be excited to evolve Travel Easy from an MVP into a fully-fledged product. Check it out and let me know what you think.

Visit product View on Hacker NewsJan 24, 2025

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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.

best fitHighest predicted score across all platforms for this description.

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

59%59% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, lua, hacker news · Missing: https docs, just released, exist

54%54% 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 · Missing: plus, platform, intuitive

48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

25%25% predicted probability of success on TrustMRR, 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 · 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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