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Privacy Scout – understand privacy policies in minutes with LLMs

Hacker News

Privacy Scout – understand privacy policies in minutes with LLMs

At Atticus AI, we're on a mission to make legal understanding more accessible to everyone. After building our contract analysis tool and talking to folks, we noticed a common use-case: understanding privacy policies. We decided to build a standalone tool, Privacy Scout, to address this need. With Privacy Scout, all it takes is a URL to a privacy policy. From there, the tool takes over by: - Retrieving and analyzing the policy. - Grading the policy with an easy-to-understand and transparent scoring system. - Allowing users to compare privacy policies across companies in our public directory. Big learnings for us: - Updating our privacy policy was necessary due to the lack of key terms. - A significant portion of the work in effectively utilizing LLMs involves data engineering, such as data munging and developing robust pipelines. - Achieving standout performance with Retrieval Augmented Generation (RAG) requires extensive domain-specific experimentation. We're continuing to build a suite of tools to make legal understanding more accessible, and I would love your feedback!

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Actual performance

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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive
50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
48%48% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
41%41% 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
12%12% 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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