Tool to check website for external scripts for GDPR

Hacker News

I worte a small tool to check for external script usage on your website for GDPR compliance. When I was CTO we used WGET to check if there were external scripts that made it somehow through review or introduced by marketing. With more and more European countries finding E.g. Google Fonts problematic, I thought there should be an easier way and created FuguFugu mainly as a weekend project. FuguFugu will scan your website and check for external scripts, images and CSS imports. Be kind, it has some rough edges, bugs and I'm still learning Go. https://github.com/FuguFuguHq/fugufugu What do you think? Open for feedback and ideas.

Visit product View on Hacker NewsSep 27, 2022

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3points

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56%56% 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.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

48%48% 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 · Missing: https docs, excited, just released

46%46% 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: google, way · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, open · Missing: mac, agents, macos

40%40% predicted probability of success on Product Hunt, 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 · Strong signals: introduce · Missing: web3, chat, crypto

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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