Us

Using AI to Summarize Terms and Conditions

Hacker News

Using AI to Summarize Terms and Conditions

88% of people never read the terms and conditions of websites or services they use. However, most people want to know what they are agreeing to in those terms. That is why we created Legal Leaf. We strongly believe that everyone should have easy access to those agreements, in language they can understand. Legal Leaf works behind the scenes, in your browser, to read and summarize these terms using powerful AI. We're constantly working to improve the accuracy of these summaries. The results are displayed in the top right corner without affecting web speeds. Legal Leaf is a beta product still going through development, but it's improving rapidly and we would love a group of willing beta testers. http://leaf.legal

Visit product View on Hacker NewsMar 12, 2018

Actual performance

91points

41comments

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Launch Intel predictions

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

71%71% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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