I Built an AI That Plans Your Perfect Trip in Seconds

Hacker News

I’m excited to introduce my new AI-powered SaaS that takes the hassle out of travel planning. With just a few inputs, my AI generates personalized day-to-day itineraries, including the best hotels, restaurants, and activities, all in seconds. Key features: Custom Itineraries: Tailored day-to-day plans based on your preferences. Top Hotels and Restaurants: Recommendations for the best places to stay and dine. Recommended Activities: Discover must-see sights and activities suited to your interests. Instant Results: Save time with quick and accurate travel plans. I built this tool to simplify travel planning and help you make the most of your trips. Give it a try and let me know what you think!

Visit product View on Hacker NewsJun 9, 2024

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Actual performance

2points

Did not reach leaderboard

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

71%71% 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

60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

33%33% 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, including · Missing: https docs, just released, exist

30%30% 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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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