Category: Uncategorized
-
The Geometry of Intelligence – A lecture
Welcome, everyone. Please take your seats. Today, we are going to dive into the architecture of Large Language Models—but we are going to look at them through a fundamentally different lens than you might be used to. The Hook: The Geometry of Intelligence Imagine you are trying to understand a sprawling, massive metropolis. If you…
-
Engineering Spec for a Latent-Space LLM Communication System
Below is a concrete design for a machine-to-machine communication stack where two LLMs exchange latent packets instead of natural language. The goal is not mystical “telepathy.” The goal is an engineering protocol that lets one model send a compact, typed, verified semantic state to another model. 1. System overview We will call the system: LCP-1…
-
How an llm transforms the world into semantic geometry
Think of the whole process as three layers: An LLM never directly “understands” a word the way a human does. It converts text into structured numbers that can be manipulated algebraically. The key object is the embedding vector, which is a point in a high-dimensional space. 1. A token starts as a discrete symbol Before…
-
Latent Directions Mastery: PCA, SVD, LoRA, Attention-Head Subspaces, Eigenspaces, Distillation, and Long-Context Memory — A Frank-said / Grok-said Dialogue on How Directional Geometry Powers the Next Leap in LLMs
Frank said:All right, now let’s go deeper and clean this up. I want a plain-English but serious Frank-said / Grok-said discussion focused specifically on PCA vs LoRA vs SVD vs attention-head subspaces, and then let’s push it further into eigenspaces, distillation, and long-context memory. I want to understand how they relate, how they differ, and…
-
PCA vs LoRA vs SVD vs Attention-Head Subspaces — A Deeper Frank-said / GPT-said Dialogue on How Latent Directions Shape LLMs
Frank-said:All right, now let’s go deeper and clean this up. I want a plain-English but serious Frank-said / GPT-said discussion focused specifically on PCA vs LoRA vs SVD vs attention-head subspaces. I want to understand how they relate, how they differ, and why they matter for building better LLMs. GPT-said:Good. Because these four ideas all…
-
Eigenspace, Attention Heads, LoRA, Distillation, and Long-Context Memory — A Frank-said / GPT-said Dialogue on the Next Stage of LLM Effectiveness
Frank-said:Yes, do that. I want the next layer down. Take this idea of eigenspace and connect it directly to the practical machinery of better LLMs: attention heads, low-rank adaptation, distillation, and long-context memory. I want to see how these ideas might influence the actual next generation of more effective models. GPT-said:Good. Because this is where…