Parst – A cross-language common regex interface

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https://github.com/bussierem/parst I made this as a little project for myself - I've been a fan of regexes, and found that I'm often the only person around me who has taken the time to learn them, so I find myself writing regexes for other people. I wanted to make this as a way for anyone to harness the power of regex for a lot of common operations, without needed to understand regex, blindly copy-paste regexes from the internet, or implement far less efficient string parsing methods to do what a quick regex could. The idea is for the entire thing to be language agnostic, and to have an ever-growing list of supported regexes AND supported regexes, expandable by the community, and deployed to any package manager people are willing to support. Tell me what you think! Side note: This is my first really "open source" project, so if you see any faux pas for allowing contributions, or license, etc, please please let me know so I can learn and update things.

Visit product View on Hacker NewsSep 24, 2018

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Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: open · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

59%59% 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, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient · Missing: plus, platform, intuitive

27%27% 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

16%16% 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

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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