Matcheroni, a tiny C++20 header library for building lexers/parsers

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Howdy HN, as part of my ongoing programming language experiments I've ended up creating my own C++20 lexing and parsing library of sorts. Matcheroni is an alternative to parser generators and regular expressions that uses trees of C++ templates to implement highly customizable lexers and parsers that have minimal impact on build times or binary sizes, while still remaining comparable in performance to Boost regular expressions. The repo includes two example projects - a simple regex parser in ~300 heavily documented lines of code, and a much larger but not quite finished C99 lexer and parser. All feedback appreciated!

Visit product View on Hacker NewsJul 6, 2023

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

76%76% 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.

Hacker NewsStrong engagement from HN community · Strong signals: 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: tiny, code · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

50%50% 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

41%41% 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

20%20% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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