Much has been said about LLM context. A language model without context produces generic responses, sometimes wrong, sometimes absurd. We call it hallucinating. The solution is known: give it context. Documents, history, fresh data. The model improves. But there is another type of agent. Not an assistant that answers. An agent that acts. It does not produce text. It executes transactions. It manages funds. It triggers protocols. And it too needs context. A different kind of context. The contex...

Much has been said about LLM context. A language model without context produces generic responses, sometimes wrong, sometimes absurd. We call it hallucinating. The solution is known: give it context. Documents, history, fresh data. The model improves. But there is another type of agent. Not an assistant that answers. An agent that acts. It does not produce text. It executes transactions. It manages funds. It triggers protocols. And it too needs context. A different kind of context. The contex...

Invarians is built on a discrete, minimal, and rigorous notation: (SxDx)_Lx + Bsx This notation is intentionally compact, as Invarians is designed for: AI agent economies · A2A · On-chain cross-layer · Toward decentralised certification But it relies on a critical rule: the symbol x does not have the same meaning depending on where it appears. Understanding this distinction is fundamental.(SxDx)_Lx — layer-level readingThis expression describes the state of a given layer. It captures, in a co...

Invarians is built on a discrete, minimal, and rigorous notation: (SxDx)_Lx + Bsx This notation is intentionally compact, as Invarians is designed for: AI agent economies · A2A · On-chain cross-layer · Toward decentralised certification But it relies on a critical rule: the symbol x does not have the same meaning depending on where it appears. Understanding this distinction is fundamental.(SxDx)_Lx — layer-level readingThis expression describes the state of a given layer. It captures, in a co...