Category: Uncategorized
-
The Thermodynamics of Thought: Uncertainty Reduction Across Silicon and Carbon – a frank said/gemini said conversation
Act I: The Geometric Map Frank said:A large language model is a process that develops a multi-geometric statistical map that is used by probability algorithms to establish a stream of tokens based on best guesses of— Gemini said:—the most likely next token in a sequence. To complete that elegant formulation, large language models operate by…
-
The Plant That Thinks in Roots: Frank and GPT Discuss LLMs, Plants, and the Ancient Verb of Information
Frank said:We started with the idea that information is not just a thing. It is not merely a noun. It is not just a file, a fact, a stored bit, or a sentence sitting in a book. We said information is more like a verb. It is an act. It is informing. It is one…
-
my lfyadda posts addressing llms as of May 28, 2026
Checked lfyadda.com and found the following posts that directly address LLMs, transformers, embeddings, tokenization, AI inference, or biology/LLM analogies. These are the LLM-focused posts I could verify through the site/search index. Post ~50-word synopsis Semantic Light Cones: Minkowski Time, LLM Geometry, and the Future of Meaning (LF Yadda – A Blog About Life) This post…
-
The Shadows That Grow Bodies: Frank and GPT Discuss Plato’s Cave, Sheldrake, Michael Levin, and the Hidden Pattern-Space of Life
Credit note: this dialogue is built around Francesca Crachilova and Michael Levin’s paper “Ingressing Patterns of Life,” published in Orbital Studies № 0: Ways of Seeing the Living World, May 2026. The central ideas about embryos, regeneration, bioelectric pattern memory, Platonic pattern-space, and “synthbiosis” come from that paper. Frank said: Let’s wrap this up. We…
-
-
Semantic Light Cones: Frank and GPT Discuss Minkowski Time, LLM Geometry, and the Future of Meaning
Frank said:Let’s take this slowly. Minkowski time cones are about relativity, yes. They describe what can influence what in spacetime. But I am wondering whether there is a deeper opportunity here. We keep talking about LLMs as high-dimensional geometric systems. They operate in embedding space, latent space, vector space, semantic space. So I want to…