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