42-True is the first public concept of the Large Meaning Model (LMM)
described in paper/42-True_LMM_paper.pdf. It is a
schema and concept seed, not a training pipeline. The corpus it is defined
against does not exist yet and, by design, cannot be scraped — it must be
produced. Keep that framing intact in any docs or code you write.
forty_two_true/intent_pair.py— the primitive: the frozen four-tuple( Id, C, M, O ).forty_two_true/schema.py—ClassificationTier, orderedOutcomeGrade,TaxonomyNode.forty_two_true/recipes/— conceptualdeclare → classify → match → verifystages. These are reference demonstrations, not real services.data/example_intent_pairs.jsonl— synthetic, clearly-labelled examples.tests/— schema invariants and example-data validation.
- Immutability. All Pydantic models are
frozen=True. Construct new records; never mutate in place. - No real declared data. Any example records must be synthetic and labelled as such. Genuine declarations are gathered only under the consented, unobservable protocol — never committed here.
- Outcomes are earned, not asserted. A
VERIFIED_RESOLUTIONoutcome must carry aproof_ref;observe_outcomeenforces this. - Honesty about status. Don't describe the recipes as a working production system. They run on the example data; the network described in the paper is future work.
pip install -e ".[dev]"
pytest
ruff check .