Developer Builds Language-Independent Meaning Engine Without LLMs or Hard-Coded Rules

A developer has released an experimental semantic engine called the Ternary Semantic Brain Core, which learns word meanings without using large language models, embeddings, or any hard-coded linguistic knowledge. The system uses a ternary representation — values of -1, 0, and +1 — to denote inhibition, unknown, and excitation states, treating uncertainty as a valid first-class answer. It was trained and tested on English and Turkish monolingual dictionaries, producing over 288,000 concept neurons and 102 million synaptic connections while using approximately 1.3 GB of RAM. Without being explicitly told that 'water' equals the Turkish word 'su,' the engine was able to identify cross-language semantic similarity through emergent relationships. The project is available as a binary-only experimental release on GitHub, with full architecture documentation and a research paper included in the repository.