RepNet – Bootstrapping Web-of-Trust with AI Estimates

Hacker News

Web-of-Trust could help to address many problems on the web - spam, sybil attacks, fake reviews, and for holding journalists, politicians and businesses to account. This small blockchain based database (about 5,000 users/20,000 AI ratings) helps to simulate what might be possible. It allows you to choose two users and see the Web-Of-Trust rating one might create for the other based on the AI estimates of trust in the web between them.

Visit product View on Hacker NewsJun 19, 2024

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Actual performance

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

66%66% 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: 000 · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

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

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

26%26% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

24%24% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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