iT

iTypedMyPaper creates evidence that you wrote something without AI

Hacker News

iTypedMyPaper creates evidence that you wrote something without AI

Hi HN! I’m a writer whose other passion is programming. From 2015 to now, I’ve earned my living as a ghostwriter. Due to recent developments, the job market for ghostwriters has dropped precipitously, and over the last two years, I’ve been building tools for my own use, to create evidence of my manual work process in case anyone ever falsely accuses me of using AI to do my writing. iTypedMyPaper is the first public version of these tools, intended for professional writers as well as students and schools. The code for the desktop app (used to create keystroke evidence locally and then send it to the iTypedMyPaper server) is free open source software under the MIT license. The iTypedMyPaper service will be paid on either a monthly or per-report basis, but it’s completely free throughout spring semester 2025. The website: https://itypedmypaper.com A post about the launch on my personal blog: https://gardnermcintyre.com/post/writing-as-something-humans... Example report: https://itypedmypaper.com/download-report-example Code for the desktop app on GitHub: https://github.com/humthentic/itypedmypaper-v1 My email is billy {at} humthentic and then the domain extension which is com. Thank you for taking a look!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: email, using, code · Missing: mac, agents, macos
52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, monthly · Missing: mobile apps, ios, entrepreneurs
40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
38%38% 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 · Strong signals: host · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, 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 · Strong signals: paid · 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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