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your very own horse_ebooks generator
Hacker Newsyour very own horse_ebooks generator
As Buster Benson (of Twitter) mentioned on his blog (http://wayoftheduck.com/diy-horse-ebooks), @horse_ebooks was killed off yesterday. In response, he created a simple Ruby script that uses Markov Chains to generate @buster_ebooks, a version of @horse_ebooks made from his Tweet archive (https://gist.github.com/busterbenson/6695350). A script is cool, but what if you could have a gem? SAY NO MORE!
Share cardActual performance
2points
Did not reach leaderboard
Launch Intel predictions
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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting39%39% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released28%28% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos9%9% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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