Ubimage an iPhone app that draws your geotagged photos on the horizon

Hacker News

As soon as Ubimage has the correct permissions it starts showing your pictures and videos on the horizon (provided you have any in your current location). If you have many you can filter by distance, altitude, years or favorites. Pics matching an area are grouped in a cluster and once you open it you have a carousel to select from. You can filter pics from a cluster by showing only those that point in a certain direction. This is because pics usually also store the direction the camera was facing. Once you select a picture you can see its statistics relative to you, copy coordinates, share it and open in the Maps app. I've been building Ubimage on and off for a while, at the beginning I just wanted an app to help me find again spots I've already rode with my enduro bike, the idea was simple, read the gps and draw the thumbnails of the pics from Photos app on the screen, sort of augmented reality. I even gave it a nice name, Panoramixer, then I thought that I didn't want to fight Obelix's lawyer but I digress. After one year I started working on it again and when I finally found how to pin thumbs to the horizon I decided that Ubimage maybe could be published. It's a free app no ads, no IAP, no account and no analytics, if you're paranoid you can use it offline. I'm not planning on becoming millionaire with it, I just want to share it with like minded people. you can download it from the App Store the link is on the support site: https://ubimage.app I will not make an Android version, but maybe I could share ideas with someone willing to clone Ubimage. I'd like to know what you think about this app and if pictures land correctly on your part of the world. I've written this post as a human, the code of the app not so much. ciao Sathia

Visit product View on Hacker NewsAug 29, 2026

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

84%84% 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: code, open · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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 · Strong signals: soon · 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 · 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 · 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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