Filtroo, an app to promote responsible sharing

Hacker News

Hi everyone, Today I would like to tell you about an app - which we believe might have a great social impact on the global community - that me and my partner have been working on for some time (part-time). I am pretty sure that you, at some point in your life or perhaps repeatedly, disturbed by an image or a video you saw online (on social media or a news website) and you probably said ‘’you wish you didn’t see that’’ or thought at least you had some kind of a warning. How do we know? Because we did! We are against censorship in traditional and social media. However, we believe people should have the option not to see disturbing media contents. That is why we came up with filtroo, a free to use mobile application, enables anyone to lock, share and unlock disturbing media contents, promoting responsible sharing. Please go ahead and check our first build on the App Store https://appsto.re/tr/FBucjb.i You can also visit our website: www.filtroo.com We are looking forward to hearing your feedback (and contributions).

Visit product View on Hacker NewsJun 16, 2017

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

72%72% 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 HuntUnlikely to reach the leaderboard · Strong signals: apps, new · Missing: mac, agents, macos

48%48% 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

46%46% 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 · Missing: plus, platform, intuitive

40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

39%39% predicted probability of success on TrustMRR, 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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