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Hi HN! My name's Josh and I'm the sole founder of DocDecoder. I had the idea for this project earlier in 2023. It seemed like an obvious use-case for GPT, and I was shocked that it didn't already exist. Around August, I started watching black mirror (the TV series), and there's one episode called 'Joan is Awful'. The premise of the episode is a woman who signs up for Netflix (or Streamberry, as it's called in the show), accepts the terms of use, and goes about her life. When she gets home from work, she notices a new show on Streamberry called 'Joan is Awful'. Long story short, Streamberry's terms of use included a clause permitting them to digitally clone the user, and use their likeness in any way they want (in this case, in a defamatory TV show). I remember finishing the episode with a huuuge drive to start the project because it really clicked how big the issue is - so big, that black mirror made an episode about it. It effects everyone, everywhere, and we're all helplessly aware of it. To view existing summaries, you can search for a site via this page: https://docdecoder.app/summaries I really believe in this project. Please, if you have any feedback or suggestions I would love to hear them :)

Visit product View on Hacker NewsAug 29, 2024

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

77%77% 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: started · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

54%54% 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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · 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

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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