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A more ergonomic sed using JavaScript regexes

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A more ergonomic sed using JavaScript regexes

I love `sed`, but specifying in-place replacement across linux/mac is challenging, and I find both the regular expression options hard to remember the exact syntax for. So I built a small wrapper around String.prototype.replace to give the syntax and power of javascript regular expressions (and, optionally, javascript functions): https://github.com/ariabuckles/jsed Or to install: npm install -g @ariabuckles/jsed Some examples: You can use it pretty similarly to `sed`: echo "this is a test" | jsed '\w+' 'word:$&' > word:this word:is word:a word:test Or you can use it more like javascript when needed: echo "1 2 3 4" | jsed '/\d+/g' '(num) => num%2 == 0 ? "even" : "odd"'

Visit productView on Hacker NewsAug 12, 2019
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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
49%49% 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 HuntUnlikely to reach the leaderboard · Strong signals: mac, using · Missing: agents, macos, agent
46%46% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
29%29% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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