Peta::NN is a neural network library written in Perl: models are defined, trained, measured and shipped from a Perl script, with no dependency beyond perl 5.36. It is not a binding to somebody else's framework and it does not try to be PyTorch. It trains micro models - a few thousand to a few hundred thousand weights, each answering one narrow question about a string - and composes them: in a pipeline, or fused into one model that runs a whole batch on the graphics card without coming back to Perl in between. It runs on perl5 and on pperl, on three backends, and under pperl one of them is the GPU. Two models of 18,761 and 10,002 weights form the plural of 98.3% of German nouns they were never shown, and of every single noun of the core vocabulary.

Use AI. Use it more and better. If you are not yet equipped to use it well - that is fine, learning takes time - but please do not inhibit those in the community who are.

That is the whole argument. The rest of this piece is why I think it is correct, and why I think the current register of the Perl community around this topic is costing us something specific and avoidable.

We took PetaMem's 13-year-old Lingua::* number conversion modules - dormant since 2013 with 17 languages - and brought them back to life. The suite now covers 61 languages across 7 writing systems (Latin, Cyrillic, Arabic, Devanagari, Armenian, Hebrew, CJK), including all 24 EU official languages plus Latin, Hindi, Yiddish, Mongolian, Uyghur, and more.

New in this release: cross-language numeral arithmetic with overloaded operators, ordinal support for 14 languages, capabilities introspection, and a Galois-field-based transitive test that walks the entire number space across all languages - 5000 steps, zero failures.

my $a = Lingua::Word2Num->new("zwanzig");      # German 20
my $b = Lingua::Word2Num->new("šestnáct");     # Czech 16
say ($a + $b)->as('fr');    # trente-six
say ($a + $b)->as('la');    # triginta sex

Everything on CPAN: cpanm Task::Lingua::PetaMem