Peppa PEG – An Ultra Lightweight PEG Parser in ANSI C

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After reading the [PEG Parsers series] written by Guido van Rossum, I started thinking writing a PEG Parser in ANSI C. Here are the reasons: - It's FUN. I've made several parser libraries, such as JSON, Mustache, Markdown, and I think I can take the challenge now. - I haven't had any opportunity to work on an Open Source project written in ANSI C. - Having a PEG parser in ANSI C can benefit whoever is developing a parser, as adding C bindings for other programming languages are not too difficult. And after SIX months' development, my project is now kinda feature complete. It's named Peppa PEG and you can find it here: https://github.com/soasme/PeppaPEG I have learned quite a lot during the journey of creating it, such as gdb, valgrind, cmake, etc. And I wouldn't make it to the end without learning from some awesome projects, such as pest.rs, cJSON, etc. Appreciate any feedbacks! Thank you! [PEG Parsers series]: https://medium.com/@gvanrossum_83706/peg-parsers-7ed72462f97c

Visit product View on Hacker NewsFeb 21, 2021

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

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

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

63%63% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

40%40% 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

14%14% 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.

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