It

It is too easy to get your private data stolen if you are a traveler

Hacker News

It is too easy to get your private data stolen if you are a traveler

Having traveled with my older brother in Italy, I realized there is no good app for finding travelers who speak your language. For example, at Naples, we couldn't find many people who speak English at all. When I moved to the Netherlands I've got a new problem - it was hard to find people who speak my native language. So I decided to develop an app. But before doing it, I analysed market of existing products. I quickly found a bunch of competitors and started to research them. I was surprised that a lot of applications share private data of users, some of them share even passwords. Here are a few examples of iOS apps, and the pictures will say everything for themselves: backpackr https://www.dropbox.com/s/0sd5txstnwagzsb/res_back.png?dl=0 You can get email, facebook ID and password of any user over cleartext http travelmate https://www.dropbox.com/s/xopdo2rx0tem30r/res_travelmate.png?dl=0 http, email, facebook_id booola https://www.dropbox.com/s/u8y811yfupeuo1b/res_booola.png?dl=0 emai, facebook_id trippers https://www.dropbox.com/s/xkh62i5pg5rbe1v/res_trippers.png?dl=0 emai, facebook_id outbound https://www.dropbox.com/s/hjw1fpts5xaeons/res_outbound.png?dl=0 email party with a local https://www.dropbox.com/s/hunjtgvo46p24b7/res_party.png?dl=0 email Is it a normal practice?

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Indie HackersFits the IH revenue-focused audience · Strong signals: started, ios · 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.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, user, new · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, users · Missing: mobile apps, personal, entrepreneurs
50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% 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
14%14% 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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