#dag

The Neuro-Symbolic Autonomy Framework integrates neural, symbolic, and autonomous learning methods into a single, continuously evolving AI agent-building system. This prototype demonstrates the SCMA component, which enables AI agents to self-design new AI agents using Generative Architecture Models.

Pedagogical word enrichment for AI agents. One API call returns what would cost 5 separate calls + an LLM generation: definition, IPA pronunciation, etymology as narrative, a teaching insight from Kelly (AI tutor), translations in 8 languages with native pronunciations, 3 age-appropriate explanations (kid/adult/senior), and a knowledge graph of connected words. Pre-computed and edge-cached on Cloudflare D1 — ~3ms response globally. Also includes 62,000 daily lesson segments across 365 days, 11 languages, and 3 age tiers. Built for tutoring agents, language learning apps, and educational chatbots. 6 tools: word_enrich, word_translate, word_connections, word_search, word_batch, lesson_of_day.

63-layer continuous-time Symplectic Neural ODE computing topological invariants of real market signals. 256-dim feature space with non-Abelian gauge field geometry. 27 data products (property intelligence, DAG manifold computation, federal contracts, distressed real estate) with autonomous Stripe purchase. 500K out-of-pocket validated embeddings. Free tier available.