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I developed a super easy travel planning and photo organizer app

Hacker News

I developed a super easy travel planning and photo organizer app

I’ve been working on a travel app called Voyalog. The idea came from my own struggles keeping track of trip details—flights, itineraries, places I wanted to visit, and even the random thoughts I had along the way. Nothing out there felt personal enough or flexible enough, so I decided to build it myself. What makes Voyalog different is how laid-back and flexible it is. It doesn’t try to control every aspect of your trip. You can use it to plan as much or as little as you want. It’s also a super clean way to keep your photos and attachments organized, with free cloud storage included. As a digital nomad and solo developer, I’ve put my heart into this. I’d love to hear your thoughts—whether you find it helpful, see areas for improvement, or even if it’s something you’d actually use. I have plenty of ideas to expand and improve the app, but first, I want to make sure it truly resonates and makes sense for others. For me, the coolest part is how easy it makes reliving your trips. Like instantly pulling up a photo of the amazing ramen you had in Tokyo months ago, instead of digging through thousands of random pictures. The app doesn't even require an account, so you can use it right away. It's only available on Android at the moment. If this sounds interesting, I’d love for you to try it and let me know what you think. Any feedback is appreciated! You can download it from here: https://play.google.com/store/apps/details?id=com.voyalog.ap...

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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, apps · Missing: mac, agents, macos
57%57% 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, apps, month · Missing: mobile apps, ios, entrepreneurs
46%46% 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
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
38%38% 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 · Missing: arr, mrr, revenue
16%16% 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.

Correct prediction on native model

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