AI, Accessibility, and Learning
Large language models sit in an unusually double-edged position for disabled learners. The same technology that can turn a dense PDF into plain language on request, or generate live text suggestions for someone who…
Artificial Intelligence
Technical foundations, capabilities, and limitations of modern AI systems.
Running Agentic LLM Workflows Locally: Ollama vs a VS Code Agent Extension
Two quite different decisions get bundled together whenever someone asks "should I run my AI coding agent locally": which model runs the reasoning, and which harness turns that reasoning into edited files, executed…
Explaining What Language Models Do: Methods and Their Limits
"Explainable AI" for large language models covers two different problems that get run together in casual usage. One is using an LLM as an explanation-generation tool for some other system — asking a language model to…
Jev and "System One" Models: What Is Claimed and What Has Been Tested
28 September 2026
Meaning, Ontology, and the Limits of Fixed Definitions
"What does this word mean?" sounds like the simplest question in the world, and often it is answered by looking the word up. Sometimes that answer settles the matter. Sometimes it starts an argument. Someone says a…
Words as Tools: Meaning, Mediation, and Activity
The companion page on meaning and ontology treats meaning as a three-way relation between a sign, an object, and an interpretant, shaped by context and by the practices in which a word is used. That account explains a…
When Meaning Becomes Architecture: Knowledge Bases, Self-Models, and Conceptual Mediation in Agentic Systems
This page builds on: Words as Tools: Meaning, Mediation, and Activity , which treats words as cultural tools, and Modelling the Self: Object-Oriented Cognition Applied Reflexively , which introduces the self-model as an…
The Compression of Knowledge: From Explicit Graphs to Latent Representations
Concept maps, language models, distributed agents, and the problem of trust