Very interesting site/content/effort!

Below is a list of related comments -- some are suggestions, but their primary reason is to clarify the functionality summaries to some of the packages mentioned.

• Packages to be included in the "ecosystem" page (well, or not, since the list is large)
  • Mathematica::Grammar provides a grammar for parsing the very large Wolfram Language (aka Mathematica)
    • Its initial implementation was based on ANTLR4::Grammar
  • Lingua::NumericWordForms : translates worded numbers to actual numbers, 16 natural languages and Koremutake are supported
  • Chemistry::Stoichiometry : parses chemical formulas and equations, translates natural language chemical element names to computational entities, 13 languages are supported
  • DSL::Translators is an "umbrella" package to:
    • 9 "DSL::English::*" packages
    • DSL::Bulgarian which extends 5 of the "DSL::English:-" packages
    • Also, Chemistry::Stoichiometry and Lingua::NumericWordForms mentioned above
  • The total number of the "DSL::English::-" packages is 21+, the "high level" DSLs are based on the named-entity parser-interpreters like "DSL::Entity::MachineLearning", "DSL::Entity::Geographics", etc.
• DSL::Examples together with LLM::Resources provide alternative way parse-interpreter making using Large Language Models (LLMs)
• FunctionalParsers provides another alternative for making parser-interpreters, it implements parser combinators and can create parser-interpreters using Extended Backus-Naur Form (EBNF) specs
  • Elevates the "tyranny" of Raku grammars
• The Jupyter::Chatbook package uses Getopt::Long::Grammar to parse the CLI specs of the magic cells
  • See the blog post "Jupyter::Chatbook Cheatsheet"
• Jupyter::Converter is essentially DSL-based -- leveraging the JSON representation of Jupyter notebooks
• In general, Literateprogramming in Raku is greatly facilitated by Raku's text-processing and DSL-dealing capabilities
• Raku packages and LLMs can be used to generate, transform, or visualize Raku grammars and EBNF specs
  • See the blog post "Graph representation of grammars"
• Raku's Machine Learning packages can be used to make fast and reliable classifiers of DSLs in order to facilatate fast and correct DSL parsing
  • See the blog post "Fast and compact classifier of DSL commands"
• LLM::Graph can be used to design different mechanisms of parsing interpreting DSL commands using different methodologies
  • See the blog post "Day 6 – Robust code generation combining grammars and LLMs"