LLMs infer identity from patterns repeated across platforms — not from single pieces of content. Consistency is now a visibility multi...

Semantic similarity goes beyond matching phrases. LLMs compare intentions, contexts, and conceptual roles. Here’s how models perceive...

LLMs don’t store knowledge — they rebuild it probabilistically at generation time. Understanding this changes how we create and...

LLMs don’t measure trust — they infer it from cross-platform consistency. Here’s how models identify credible entities in...

Gli embedding non sono dizionari: sono mappe di possibilità. Ecco come i modelli moderni ricostruiscono significato, intenzione e con...

AI models do not see authors as profiles — they see semantic patterns. Here’s how LLMs build author identity and why it determin...

A deep exploration of how embeddings represent meaning in modern AI models, and why semantic vectors redefine search, visibility and message...

LLMs track authors as entities. Discover how consistency across platforms strengthens your identity inside AI systems.

Embeddings convert text into meaning-rich vectors. Here’s how large language models encode relationships, context, and concepts.