Two Minds, One Model

Why orchestrate multiple AI agents when a single strong model is so capable? Jon walks through three distinct rationales — capability, parallel context, and trust — and uses Anthropic's Claude Mythos Preview and Project Glasswing as the live, industrial-scale case study. Credits Cover Art by Brianna Williams TMOM Intro Music by Danny Meza A special thank you to these talented artists for their...

This episode deconstructs how production AI agents are actually built, introducing a six-component architecture framework (system prompt, model, tools, memory, orchestration loop, and execution environment) and comparing how Claude Code, Codex, OpenClaw, and Manus make fundamentally different trade-offs around local vs. cloud execution, autonomy vs. human oversight, and open source vs. commercial...

This episode traces AI reasoning from human-designed external scaffolding (process reward models, test-time compute scaling) to internally emergent capability, culminating in DeepSeek R1's finding that a model rewarded only for correctness spontaneously learns to reason, self-correct, and backtrack without any explicit instruction to do so. Credits Cover Art by Brianna Williams TMOM Intro Musi...

This episode is a practical guide to working with AI agents — covering what makes them different from chatbots, how to craft effective agentic prompts, how to calibrate trust and supervision across the autonomy spectrum, and best practices for coding, research, and personal assistant agents. John frames the core skill as delegation, not querying, and walks through the pitfalls that trip up new age...

This episode traces how prompt engineering evolved from informal tricks (tipping, role-playing, "take a deep breath") into three structured reasoning frameworks — Chain of Thought, Self-Consistency, and Tree of Thoughts — that dramatically improved LLM performance without changing the models themselves, culminating in the insight that intelligence in these systems is a latent resource un...

This season finale episode features interviews with two SSU computer science capstone teams applying AI/ML to real-world problems: Sean Belingheri's edge computing project using YOLO on a Raspberry Pi to identify queen bees for hobbyist beekeepers, and "The Woods Boys" team using satellite data from Google Earth Engine with multiple ML classifiers to automate land cover classificatio...

This episode examines how Anthropic's circuit tracing and attribution graph tools reveal the internal mechanics of Claude 3.5 Haiku across three categories of complex behavior, abstract representations, parallel processing, and planning, while making a compelling case for why AI safety research matters as current control mechanisms prove surprisingly brittle. Credits Cover Art by Brianna Willi...

This episode explores how Anthropic researchers successfully scaled sparse autoencoders from toy models to Claude 3 Sonnet's 8 billion neurons, extracting 34 million interpretable features including ones for deception, sycophancy, and the famous Golden Gate Bridge example. The discussion emphasizes both the breakthrough achievement of making interpretability techniques work at production scale...

This episode explores how Anthropic researchers successfully scaled sparse autoencoders from toy models to Claude 3 Sonnet's 8 billion neurons, extracting 34 million interpretable features including ones for deception, sycophancy, and the famous Golden Gate Bridge example. The discussion emphasizes both the breakthrough achievement of making interpretability techniques work at production scale...

This episode explores how sparse autoencoders can decode the phenomenon of superposition in neural networks, demonstrating that the seemingly impenetrable compression of features into neurons can be partially reversed to extract interpretable, causal features. The discussion centers on an Anthropic research paper that successfully maps specific behaviors to discrete neural network locations in a 5...

This episode of "Two Minds, One Model" explores the critical concept of interpretability in AI systems, focusing on Anthropic's research paper "Toy Models of Superposition." Hosts John Jezl and Jon Rocha from Sonoma State University's Computer Science Department delve into why neural networks are often "black boxes" and what this means for AI safety and deploy...

This episode explores why AI systems might develop harmful or deceptive behaviors even without malicious intent, examining concepts like convergent instrumental goals, alignment faking, and mesa optimization to explain how models pursuing benign objectives can still take problematic actions. The hosts argue for the critical importance of interpretability research and safety mechanisms as AI system...

This premiere episode provides a comprehensive history of artificial intelligence development from the 1950s through the present day, tracing the cycles of excitement and disappointment ("summers and winters") that led to today's breakthrough moment with large language models. The hosts establish this historical foundation to set up their season-long exploration of AI interpretabilit...