WebDefense – Block malicious websites using AI/ML-based detection

Hacker News

We have developed a web security browser extension i.e. WebDefense to block harmful or malicious websites that users might access by clicking links in email, social media/messaging applications or entering manually on browsers. Unlike existing products, it leverages "cloud-native" AI/ML-based detection methodology to quickly analyze any website in real-time and provide a response within a few seconds. Here's a quick demo on how it works: https://youtu.be/a6tvjq3Fz10 Please try it out and provide feedback. You can use the credentials below:- Username: finlocktest@yopmail.com Password: Testing@1234

Visit product View on Hacker NewsAug 1, 2022

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

70%70% 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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

60%60% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

29%29% 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

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

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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