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FakeFind – An AI-powered Fakespot alternative that detects fake reviews

Hacker News

FakeFind – An AI-powered Fakespot alternative that detects fake reviews

Mozilla recently shut down Fakespot, leaving millions of online shoppers without a tool to verify review authenticity. So we built FakeFind. FakeFind is a free, browser-based tool that lets you paste in a product link from Amazon, Walmart, or eBay. It uses NLP to analyze the reviews and detect signs of fake or coordinated manipulation — like review hijacking, repetitive phrasing, or bulk ratings. In seconds, it returns a Trust Score, an Adjusted Rating, and a quick summary to help shoppers avoid scams. We built it to be lightweight, fast, and transparent — no extension, no account required. Would love thoughts from the community: https://fakefind.ai

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Actual performance

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
52%52% 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.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive
37%37% 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
36%36% 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
21%21% 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
14%14% 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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