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I built a Chrome Extension to protect personal data from AI platforms

Hacker News

I built a Chrome Extension to protect personal data from AI platforms

Hello HN, I built Caviard.ai, a Chrome extension to help protect personal data when using AI platforms like ChatGPT and DeepSeek. The extension automatically detects sensitive information — such as names, addresses, and emails — and replaces them before text is sent to AI services. You can toggle between the original and replaced values to ensure privacy. All processing is done locally, ensuring that sensitive data never leaves your browser. Why did I build it? I wanted a simple solution to use AI services without compromising my privacy, as I noticed that many platforms freely access personal data for model training and other purposes. I couldn't find a tool that offered this functionality, so I decided to create one. Feel free to try it out and share any feedback or suggestions! I'd love to hear what you think. Thanks! Damien

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Actual performance

1points
Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: model, email, chatgpt · Missing: mac, agents, macos
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
41%41% 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 · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · 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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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