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TheHypeBridge – AI-Powered Influencer Vetting Reports for Brands

Hacker News

TheHypeBridge – AI-Powered Influencer Vetting Reports for Brands

Hi HN, I built HypeBridge to fix influencer evaluation for brands. Our system creates “Carfax-like” vetting reports to pick authentic influencers fast. Why? Bad influencers with fake followers waste 70% of brand budgets. Manual vetting is a nightmare. We use AI to analyze content, engagement and audience fit, saving time and money. Delivers instant reports (authenticity, engagement, red flags) Tech: Next.js, Gemini, Firebase Check it out, let me know what you think: thehypebridge.com.

Visit product View on Hacker NewsJul 31, 2025

Actual performance

1points

2comments

Did not reach leaderboard

Launch Intel predictions

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

71%71% 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: gemini · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

32%32% 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

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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