We made a way to earn subscriptions by having your computer turned on

Hacker News

With the recent explosion of streaming services, paying for the variety of monthly subscriptions is becoming a costly burden. Because of this, we made a desktop app that passively earns users money towards digital subscriptions based on how much data they serve on our peer-to-peer video content delivery network. We created this because we realized large corporations are taking user data to run targeted ads without compensating them. With our project, we hope that users will be able to pay for internet content using their extra computer processing power instead of their personal data. Note that this is a pre-alpha version of our product but please check it out and let us know what you think! Also note, that in future versions we will provide a power user feature with more data on the amount of CPU and bandwidth our application is using in order to provide even more transparency and control to the user.

Visit product View on Hacker NewsJul 26, 2022

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3points

5comments

Did not reach leaderboard

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

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best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, computer, using · Missing: mac, agents, macos

74%74% 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

64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · Missing: arr, mrr, revenue

17%17% 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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