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FakeFind is working again – AI tool to detect fake reviews

Hacker News

FakeFind is working again – AI tool to detect fake reviews

Hi HN, Just a quick update — FakeFind is back online. We originally built FakeFind to help people spot fake reviews after Fakespot shut down. It scans product reviews on Amazon, Walmart, eBay, and others using AI to detect patterns like repetition, review hijacking, and mass posting. You get a Trust Score, an Adjusted Rating, and a short review summary so you know what’s real before you buy. It’s browser-based, free to use, and doesn’t require installing anything. If you tried it during our downtime — thanks for your patience. We’d love feedback from the HN community as we keep improving. For those who are interested: https://fakefind.ai

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% 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: using · Missing: mac, agents, macos
44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
20%20% 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
12%12% 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.

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