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Intellectually Curious
Intellectually Curious
Mike Breault

Intellectually Curious

ScienceTechnologyMathematicsEnglish

Five minutes of wonder. Discover something fascinating about AI, space, science, or mathematics—with an optimistic view of what comes next.

Intellectually Curious is a daily podcast for people who love learning. Explore surprising ideas, ingenious inventions, and discoveries that reveal how our universe works and what people can achieve. Most episodes run 5–7 minutes: a little curiosity to brighten your morning, commute, or coffee break.

Follow for a new discovery every day.

Created and curated by Mike Breault. Episodes are generated with Google NotebookLM. Mike listens to and reviews every episode before publication. AI can make mistakes; please double-check critical information.

Sponsored by Embersilk — embersilk.com


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Episodes

SKY-001: Teaching AI to Think in 3D
25 October 2026

SKY-001: Teaching AI to Think in 3D

Designing an engine or a building takes more than describing it in words. Meet SKY-001, a spatial world model that breaks complex designs into parts and plans the visual steps needed to construct them. We explore how its Hierarchical Composer and Construction World Model work together across computer-aided design and building layouts. From turning an image into a tree of mechanical components to...
Agent S4: How AI Agents Build Their Own Tools
24 October 2026

Agent S4: How AI Agents Build Their Own Tools

What if an AI agent could improve the tools it uses, instead of waiting for a human to redesign them? We explore Agent S4, a research system that evolves the software interfaces used by computer-operating agents. A meta-agent studies task experience, modifies tool repositories, and combines promising ideas across generations. We unpack the difference between improving an agent’s tools and...
Crystals Meet AI: Connecting Atomic Structures and Language
23 October 2026

Crystals Meet AI: Connecting Atomic Structures and Language

What if describing the material you want could help you find promising candidates? We explore CLaC, an AI model that connects three-dimensional crystal structures with language. Researchers trained it using descriptions generated from computed material properties, helping it relate words and numbers to the arrangement of atoms.The study shows how this approach can search and rank existing crystal...
Inside Prometheus: How Meta Builds an AI Supercluster
22 October 2026

Inside Prometheus: How Meta Builds an AI Supercluster

What does it take to make hundreds of thousands of GPUs work together? We step inside Prometheus, Meta's gigawatt-scale AI supercluster in New Albany, Ohio, whose first phase helped train the Muse family of models. Meta says the system already spans multiple connected buildings, with plans to reach roughly one million GPUs at full scale.We explore the fast networks that keep training coordinated,...
Four Gemini Agents Beat Minecraft: From Code to Cooperation
21 October 2026

Four Gemini Agents Beat Minecraft: From Code to Cooperation

Four AI agents, one shared world, and an Ender Dragon to defeat. We explore how Gemini 4 Argon coding agents completed a single Minecraft survival run on Easy difficulty, writing JavaScript programs using Mineflayer and reading text feedback. Over 27 hours and 14 minutes, they gathered resources, built tools, and worked toward a common goal.The story echoes Voyager's earlier work on reusable...
Inside MAI-Base-2: Microsoft's Approach to Efficient AI Training
20 October 2026

Inside MAI-Base-2: Microsoft's Approach to Efficient AI Training

How can a huge AI model put only part of its network to work at a time? Drawing on Microsoft's pre-training report, we explore MAI-Base-2 and its mixture-of-experts design, where the total parameter count is much larger than the portion used for each token. We also explain how smaller experiments arranged in "scaling ladders" helped guide the full training run.Then we unpack "dropless"...
FDE-Gym: Can AI Turn Requests into Results?
19 October 2026

FDE-Gym: Can AI Turn Requests into Results?

What does it take for an AI agent to turn a customer's request into a useful working system? We explore FDE-Gym, a benchmark inspired by forward-deployed engineers—the people who work with customers to clarify needs, uncover constraints, and integrate software into existing operations. Its simulated environments are adapted from real projects, giving agents a place to tackle the details of...
JevAgentHarness: Faster AI Through Smarter Delegation
18 October 2026

JevAgentHarness: Faster AI Through Smarter Delegation

Does every step of an AI task need a large language model? We explore JevAgentHarness, a research framework that lets smaller decision models handle routine multiple-choice decisions, such as choosing the next tool. Confidence thresholds fitted to an agent's own runs determine when to delegate and when the larger model should take over.The key is testing how choices play out across a whole task,...
The Agentic Scientist: Sakana AI's Research Teammate
17 October 2026

The Agentic Scientist: Sakana AI's Research Teammate

What changes when an AI research assistant can coordinate a team of coding agents? We explore Sakana AI's Agentic Scientist, a system designed to turn research ideas into experiments by dividing work among agents, comparing results with baselines, and testing which parts of an approach make a difference. Researchers can steer the process as the work develops.We follow the path from setting up a...
Finding the Fastest Route by Doing Less Work
16 October 2026

Finding the Fastest Route by Doing Less Work

Can a computer find the shortest route to every destination without sorting those destinations by distance? We explore the insight behind the 2025 and 2026 advances in shortest-path algorithms, using a simple city-map analogy to make a classic computer science problem feel familiar.Dijkstra's algorithm provides the starting point for a story about grouping work, choosing useful representatives,...