tranSymbolics

Direct Model Connections over High-Speed Multicast

It starts with natural language.
Models talk — directly. No brokers. No hubs. Just multicast.
From these raw exchanges, context becomes transferable.
KV caches and vector data begin to move between models.

tranSymbols — the network containers — carry high-dimensional payloads:

• Routing information
• Shared context
• Key-value caches
• Vector data
• Conventional data

A complex language emerges.
Heterogeneous models cooperate, collaborate, and ultimately self-orchestrate.
tranSymbolics is the system that enables this transformation.

We answer the questions:

• Is there anything better than RAG, MCP, A2A, HTTP, and JSON?
• Are agents just high-jive marketing?
• How do we develop model personalities?
• What is the best architecture for what comes next?
• What are transdirection and transception?

The approach:

• Begin with natural language
• Gemma 3 / PyTorch / PyCUDA / CuPy
• RTMFP over UDP multicast
• And the right catalyst