The Neuron: AI Explained

AI agents and automation sound complex, but they’re really about one simple idea: helping you spend less time on repetitive work and more time on the things that need your judgment.

In this beginner-friendly Neuron Live, we’ll break down what AI agents are, how automation actually works, and how to start using both without getting overwhelmed.

You’ll learn:
🤖 How AI agents are different from regular chatbots
⚙️ What actually happens inside an automation workflow
🧰 Where tools like ChatGPT, Claude, Make, ClickUp, and other AI assistants fit in
💼 Practical ways to use AI at work and in everyday life
🔁 How to spot tasks that are worth automating
⚠️ Common mistakes beginners make with AI workflows
✅ How to decide what should stay human and what AI can help with

No coding experience required. No jargon.

Just a clear, practical conversation about how to make AI more useful, more responsible, and less intimidating.

Join us live, bring your questions, and leave with a better understanding of how to make AI do more than just answer prompts.

Subscribe to our newsletter: https://www.theneuron.ai/

What if the next big AI breakthrough is not a bigger model, but a completely different kind of computer?

Jeff Shainline, co-founder and CEO of Great Sky, joins The Neuron to explain how his team is building brain-inspired AI hardware using superconductors, photonics, and analog computation. Great Sky’s architecture, called Superconducting Optoelectronic Networks, or SOENs, is designed to move beyond the traditional GPU roadmap by co-locating memory and processing, communicating with light, and mimicking some of the high-connectivity dynamics found in biological brains.

In this conversation, Jeff breaks down why today’s chips can struggle with fast, multimodal inference; why transformers may be powerful but inefficient for some future workloads; how Great Sky’s system differs from quantum computing; and why early applications could include fusion reactors, particle physics, video understanding, content moderation, and eventually new model architectures that do not map neatly onto today’s hardware.

Subscribe to The Neuron for grounded, practical conversations about where AI is going next—and what actually has to work before the hype becomes real.

Voice agents are moving from “cool demo” to real product infrastructure.

In this livestream, we’re joined by Ben Cherry of LiveKit to break down what it actually takes to build real-time AI agents that can listen, respond, interrupt, call tools, and work in production.

LiveKit is an open source framework and developer platform for building voice, video, and physical AI agents in production.

We’ll talk through the stack behind real-time AI experiences, then build and test a live demo together on The Neuron.

In this live demo, we’ll cover:

🎙️ How LiveKit helps developers build voice, video, and physical AI agents
⚡ What makes real-time agents different from normal chatbots
🧠 How voice agents handle latency, interruptions, speech, and tool calls
🛠️ Why production-ready AI agents are much harder than a weekend demo
🚀 What builders should know before shipping voice AI to real users

And yes, we’re doing a live demo, which means there is at least a small chance the agent talks back at exactly the wrong time. Perfect television.

Guest: Ben Cherry, LiveKit
LiveKit: https://livekit.com/
Ben on LinkedIn: https://www.linkedin.com/in/bcherry-product-engineer
Ben on GitHub: https://github.com/bcherry

Subscribe to The Neuron for clear, useful AI news, demos, and explainers for people trying to understand where this tech is actually going.

https://www.theneuron.ai/

What happens when AI stops simply giving answers and starts producing proofs a computer can verify?

In this episode of The Neuron, Corey Noles and Grant Harvey talk with Tudor Achim, Co-Founder and CEO of Harmonic, the company behind Aristotle — a formal reasoning system built to generate machine-checkable mathematical proofs. Tudor explains why math may be the clearest test case for moving AI from “trust me” to “check me,” and why formal verification could matter far beyond Olympiad benchmarks.

They discuss what “mathematical superintelligence” actually means, why Tudor thinks solving a Millennium Prize problem would be a meaningful threshold, and how Lean-based proofs could change the way mathematicians collaborate. They also explore Aristotle’s real-world use cases, from open math problems to verified software, chip design, scientific computing, and the future of AI-assisted discovery.

Plus: why Tudor thinks formal math has reached a “zero to one” moment, why specs may be the bottleneck in verified software, and why humans still need to direct the questions AI systems try to solve.

Subscribe to The Neuron and sign up for The Neuron Daily at theneuron.ai.

What happens when AI stops simply giving answers and starts producing proofs a computer can verify?

In this episode of The Neuron, Corey Noles and Grant Harvey talk with Tudor Achim, Co-Founder and CEO of Harmonic, the company behind Aristotle — a formal reasoning system built to generate machine-checkable mathematical proofs. Tudor explains why math may be the clearest test case for moving AI from “trust me” to “check me,” and why formal verification could matter far beyond Olympiad benchmarks.

They discuss what “mathematical superintelligence” actually means, why Tudor thinks solving a Millennium Prize problem would be a meaningful threshold, and how Lean-based proofs could change the way mathematicians collaborate. They also explore Aristotle’s real-world use cases, from open math problems to verified software, chip design, scientific computing, and the future of AI-assisted discovery.

Plus: why Tudor thinks formal math has reached a “zero to one” moment, why specs may be the bottleneck in verified software, and why humans still need to direct the questions AI systems try to solve.

Subscribe to The Neuron and sign up for The Neuron Daily at theneuron.ai.