Minimal Viable social sharing

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I've came up with a social sharing app that has only 2 screens, does not require sign up or sign in. The app can be found on the stores: http://itunes.apple.com/us/app/wisaw/id1299949122 http://play.google.com/store/apps/details?id=com.echowaves.w... Here are some guiding principles that are reflected in our Privacy Manifesto: Extreme simplicity: The "WiSaw" app has only 2 screens. The more complex the app gets, the easier it becomes for the business to manipulate the users behavior and the data they provide. We believe that "Less is More". Absolute privacy: It's achieved by never asking a user for any personal information (not even login). There is no data -- there is nothing to leak. All data we collect is short lived -- everything disappears from the system after short while -- again, this makes it difficult to leak the data which we do not have. Complete transparency (product): To make a software product useful and interesting for the users, it does need to have access to some data. All data we collect is public. Everyone can see and do everything with it -- there are no special roles and permissions. We do not moderate any of the content the users upload -- we believe in the power of the community to do the right thing. If anyone finds some content to be offensive, they can simply delete it, which will also improve the quality of the data for everyone else. Complete transparency (company): How do you know, we are not lying to you? Simple -- every line of code we write is published to the OpenSource repository in GitHub. Check it out: https://github.com/echowaves At this point I'm looking for some constructive feedback. Am I totally wrong with my assumptions? Is this something that can become big?

Visit product View on Hacker NewsFeb 18, 2019

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

80%80% 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: apple, google, apps · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, 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

49%49% 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: personal, apps, google · Missing: mobile apps, ios, entrepreneurs

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

38%38% 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

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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