Welcome to a unique storytelling experiment. What you're about to hear wasn't written in the traditional sense. It was generated entirely by SAM—the Small Agent Model—an AI trained on technical documents, research papers, and patterns pulled from a vast archive of open-access knowledge, including thousands of PDFs from arXiv.org. This story began with a single prompt and evolved entirely within SAM’s internal reasoning. No plot outline. No human editing. Just raw output shaped by the model’s logic, curiosity, and sense of narrative. Our goal? To test whether a small, locally-running AI could hold focus across a long-form story—maintaining character development, tension, and thematic consistency. The result is a digital hallucination… but one grounded in real science, speculative fiction, and machine-learned creativity. Let’s begin.

In this episode, we unpack a cutting-edge approach to building lean, high-performance AI models tailored for cybersecurity. Based on our latest white paper, we explore a multi-stage knowledge distillation pipeline that transfers expertise from large teacher models to smaller, more efficient student models like Phi-3 Mini. Topics include structured data enrichment, virtual machine-based learning, test-time reinforcement learning (TTRL), and curiosity-driven exploration powered by Information Theory. Whether you're an AI researcher, cybersecurity professional, or tech strategist, this episode offers a deep yet accessible guide to making specialized AI practical for real-world, resource-constrained environments.

​The paper titled "MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits" by Brandon Radosevich and John Halloran investigates security vulnerabilities introduced by the Model Context Protocol (MCP), an open standard designed to streamline integration between large language models (LLMs), data sources, and agentic tools. While MCP aims to facilitate seamless AI workflows, the authors identify significant security risks associated with its current design.​