I self-published “Law for Internet Entrepreneurs”

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Hey all - I recently self-published "Law for Internet Entrepreneurs: 10 important U.S. legal cases about web design every tech founder, software engineer, & web developer should know" Please email me at contact@xuova.com for a FREE digital copy of the book. You can purchase the book here in multiple formats: https://www.amazon.com/dp/B0B2HRPMKG/ I put together 10 very important U.S. legal cases that have shaped legal issues about Internet businesses. These topics include: - web scraping - online contracts - music streaming - software copyright and fair use - software patent - domain names and trademarks - copyright and fair use

Visit product View on Hacker NewsJun 10, 2022

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86%86% 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 · Missing: https docs, excited, just released

66%66% 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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

23%23% 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

13%13% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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