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How the web could thrive with adblock and user support

Hacker News

How the web could thrive with adblock and user support

Ads suck, but what else is there? We think the answer is us--as in all of us reading the web. We are using adblockers as a way to start a conversation with readers: support us somehow, either via ads, or with a "fair pass" system, such as http://www.Atri.me/. To show how this could work on websites, we created *.demo.fairtread.co ( for instance: http://www.mit.edu.demo.fairtread.co/ ... try with your favorite site.). When you visit the demo url with an adblocker enabled, you'll see a "payment required" message. If you inspect the network tab, you'll also see that it's issuing a "402" http status code. If you install a fair pass (such as atri) you'll see that you can see the content again. The full system is now available for use at http://www.fairtread.com/

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Actual performance

10points
4comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, 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: io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting
46%46% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% 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
39%39% 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
13%13% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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