I

I made a scam detector and build a public database for it

Hacker News

I made a scam detector and build a public database for it

We think this is helpful because scams are getting more out of hand than ever, so we wanted to build a tool that can help people to identify scams instantly and ALSO creates a large dataset to "train" everyone's brain on scam detection patterns. After browsing 10-20 scams, anyone can quickly learn what indicators to look for in subtle (or not so subtle) scams. 1. Upload screenshot of anything you think is suspicious 2. GPT-vision goes to work, extract text and analyze the screentho and give you an instant result 3. Each upload adds to a growing public database, letting everyone to see and learn from real-world scam patterns. Try it out, contribute a recent scam that you've received lately, and hopefully this could help more people.

Visit product View on Hacker NewsNov 14, 2024

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Indie HackersFits the IH revenue-focused audience · 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 · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

60%60% 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 · 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 · Missing: plus, platform, intuitive

27%27% 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

16%16% 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.

Correct prediction on native model

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