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TravelStreetview an app for locating your photos on Streetview

Hacker News

TravelStreetview an app for locating your photos on Streetview

https://travelstreetview.com This is a side project I've been working on for the last 2 years. It lets you select images on your phone and shows you the location where these images were taken on Google Maps and on Streetview. I had the idea for this app after going through the pictures I took after my first trip to the U.S west coast. I had several pictures where I didn't really remember were they were taken so I manually extracted the exif data and typed the coordinates into Streetview. I found it very interesting to be able to kind of revisit the locations where these pictures were taken (especially via Streetview) and thought I should turn this into an app. All your photos will stay offline on your phone unless you specifically decide to publish them. Since I had no past experience with app development I decided to use React Native. I liked the experience very much and I was able to share about 95 percent of the logic between iOS and android so you really only have to implement your app once. The downside however were all the breaking changes between version 0.37.0 (when I started) and 0.57.1 (now). In the beginning you could almost be certain that your app won't work after an update. Sometimes there were breaking changes which also affected other third-party dependencies and you had to wait for them to catch up too. These problems seem to have improved with recent versions though.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · Missing: supports, reddit linkedin, podcasting
75%75% 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 · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
50%50% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios, google · Missing: mobile apps, personal, entrepreneurs
49%49% 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
28%28% 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
19%19% 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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