Intellectually Curious
Intellectually Curious is a podcast by Mike Breault featuring over 1,800 AI-powered explorations across science, mathematics, philosophy, and personal growth. Each short-form episode is generated, refined, and published with the help of large language models—turning curiosity into an ongoing audio encyclopedia. Designed for anyone who loves learning, it offers quick dives into everything from combinatorics and cryptography to systems thinking and psychology.
Inspiration for this podcast:
"Muad'Dib learned rapidly because his first training was in how to learn. And the first lesson of all was the basic trust that he could learn. It's shocking to find how many people do not believe they can learn, and how many more believe learning to be difficult. Muad'Dib knew that every experience carries its lesson."
― Frank Herbert, Dune
Note: These podcasts were made with NotebookLM. AI can make mistakes. Please double-check any critical information.
Episodes
1955 episodes
Research Reimagined: Papers You Can Talk To
Justin Ross, a professor of public finance and economics, co-authored a new empirical working paper (alongside Whitney Afonso and Denvil Duncan) and built a local Model Context Protocol (MCP) server to accompany it. This MCP provides a structur...
AlphaProof Nexus: AI Meets Verified Mathematics
DeepMind’s AlphaProof Nexus pairs language models with Lean to convert creative proof sketches into formally verified mathematics. We dive into how an evolutionary loop of AI sub‑agents and the AlphaProof component tackle hard sub‑goals, automa...
Information Content of the Cosmic Web
Delve into how gravity shapes not just galaxies but information itself. We explain why density alone misses most of the universe's data, introduce the shear tensor and anisotropic deformation, and reveal how the cosmic web's filaments and walls...
Gbrain: The Self-Updating Memory Engine Powering AI Agents
We dive into Garry Tan's open-source project gbrain—a hybrid, self-labeling memory system that auto-builds a knowledge graph, timestamps facts, and maintains itself with cron jobs and a self-healing gbrain doctor. Discover how this design avoid...
MOSS and the Engine Under the Hood: Self-Editing AI and the Future of Core Code
Explore MOSS, the groundbreaking AI that can rewrite its own core logic via source-level adaptation. We unpack how it drafts fixes in a sandbox, runs a seven-stage pipeline to validate changes, performs an in-place container swap while preservi...
AI Solves The 80-Year Planar Unit Distance Puzzle
We discuss a significant mathematical breakthrough in which an OpenAI reasoning model autonomously disproved a famous 80-year-old conjecture in discrete geometry. Originally posed by Paul Erdős, the unit distance problem th...
Gemini Omni and the World-Model Revolution: AI That Simulates Reality
We break down Google's Gemini Omni—the shift from pixel-predicting video generators to world-model AI that fuses language reasoning with physical simulation. Learn how OmniFlash optimizes for fast, physics-consistent clips, how conversational e...
Scaling Claude Code: Best Practices for Large Codebases
We examine Claude’s agentic search that traverses live codebases in real time, using grep and LSP, anchored by a harness of per-directory rules and plugins. We contrast this with traditional RAG, explore memory-efficient 'skills' via progressiv...
Hermes Unleashed: Open-Source Self-Improving AI Assistants
A deep dive into Hermes Agent, an open-source, self-improving AI assistant developed by Nous Research that is designed to grow more capable through a continuous learning loop. Unlike static chatbots, this agent creates reusab...
Building AlphaGo from Scratch
A deep dive on Dwarkesh Patel interview with Eric Jang into how AlphaGo conquered Go by combining a value network, a policy network, and Monte Carlo tree search. We unpack how these two nets shrink the game’s vast space, how self-play trains be...
Revealing AI Reasoning with Log Analysis
Log analysis lets us see AI thinking behind the pass/fail, tracing inputs, each step, and outputs to uncover hidden reasoning that tests miss. We discuss what this means for building reliable AI systems, designing better benchmarks, and the fut...
Negative Time for Photons: A Quantum Tour Through a Rubidium Cloud
We explore recent experiments showing that single photons can arrive earlier than expected after passing through a chilled rubidium atom cloud. By probing the atoms with weak measurements and analyzing the residual energy left behind in the med...
Google DeepMind is Reimagining the Mouse Pointer for AI Interaction
We explore Google's DeepMind Gemini-powered mouse pointer, which uses real-time visual context around the cursor to perform multimodal inference at the OS level—turning pixels into actions, charts, and live suggestions without endless typing. W...
Black holes slingshot two billion stars
JWST infrared imagery reveals a pair of merging supermassive black holes in Abell 402 BCG, totaling about 60 billion solar masses, hardening and flinging billions of stars from the galaxy's center. We unpack how binary hardening works, the tens...
The USSR Olympiad Problem Book
Dive into the USSR Olympiad problem book by Shklarsky, Chensov, and Yaglom—320 unconventional puzzles designed for seventh- to tenth-graders that still stump PhD mathematicians. Learn how these problems force new mental models, not brute-force ...
Interaction Models: Scalable Real-Time Human-AI Collaboration
We dive into Thinking Machines Lab’s breakthrough that shatters the typing bottleneck by streaming real-time microturns and decoupling quick conversation from deep reasoning. Learn how a fast-front interaction model handles live dialogue, while...
The AI Co-Mathematician: Agentic Workflows for Mathematical Discovery
Google DeepMind has introduced the AI co-mathematician, a specialized agentic workbench designed to support the multifaceted and iterative nature of mathematical research. Unlike standard chatbots, this system utilizes a statef...
Natural Language Autoencoders for Unsupervised LLM Interpretability
Introducing Natural Language Autoencoders (NLAs), an unsupervised method developed by researchers at Anthropic to translate the complex internal activations of large language models into human-readable text. By utilizing an <...
Mollifier Layers for Efficient High-Order Inverse PDE Learning
This paper introduces Mollifier Layers, a novel, lightweight module designed to enhance Physics-Informed Machine Learning (PhiML) by replacing recursive automatic differentiation with convolutional operations. While traditional me...
The Rise of Point Absorbers
From the staggering potential of 29,500 TWh of wave energy to the nuts and bolts of point absorber wave energy converters, this episode shows how buoys that ride the surf can generate electricity, desalinate water, and power remote islands. We ...
Autocompleting Reality: The Rise of Large Event Models
This episode unpacks large event models—AI that can understand, represent, and forecast real-world event sequences over time, not just generate text. We explore how LEMs extract underlying rules with schema induction, marry neural nets with sym...
Agentic Commerce 2026: AI Shoppers Do the Shopping
A deep dive into how AI agents move from answering questions to taking real buying actions on your behalf. We break down the surge of agentic commerce, the infrastructure that makes it possible (and the ‘invisibility’ problem), real-world wins ...
Autodata Unleashed: How AI Learns to Learn
We dive into Meta AI's Autodata framework—an autonomous system that designs, tests, and iterates its own training data. From challenger models and weak/strong solvers to meta-optimization that removes negative grading, we explore how AI becomes...
Ineffable Intelligence: The Superlearner Manifesto
A radical exploration of a zero-data, self-learning AI that discovers physics and math from first principles. We unpack the ‘superlearner’ idea—an agent trained purely by reinforcement in a digital sandbox, rewarded for uncovering truths and so...
Stanford Future of Mathematics Symposium 2026
At Stanford's Future of Mathematics Symposium (May 1–2, 2026), AI shifts from calculator to collaborator while formal methods guard every step of the proof. This episode unpacks frontier reasoning, human–AI partnerships, and the visions of lead...
