I
I wrote a book on JavaScript regular expressions
Hacker NewsI wrote a book on JavaScript regular expressions
My example based guide on JavaScript regular expressions is free to download through this weekend [1][2] Code snippets, exercises, sample chapters, etc are available on GitHub repo [3] You can also get the book as part of free bundle [4] [1] https://gumroad.com/l/js_regexp [2] https://leanpub.com/js_regexp [3] https://github.com/learnbyexample/learn_js_regexp [4] https://leanpub.com/b/regex
Share cardActual performance
7points
Made the leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal24%24% 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, revenue16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos8%8% 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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