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Offline, GTP-Free Content Generator

Hacker News

Offline, GTP-Free Content Generator

This is an offline, Windows content generator that doesn't use GPT or any GPT-like dependencies to create first drafts of different types of articles, reports, blog posts, etc. I introduced in it 2014 to help me complete my freelance writing work, and it's therefore currently available in the U.S. only. The ideal user is a U.S. freelance writer. I'm introducing it here on HN in hopes of attracting some intelligence discussion around how it compares to other similar products (you know the ones...) -- with special consideration given to the actual writing process, legalities, ethics, etc. Thanks for reading!

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Actual performance

4points
1comments
Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · 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.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
51%51% 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.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
47%47% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: introduce · 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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