The Neuron: AI Explained

In this week's live-stream replay, we go live for a 2-hour, hands-on deep dive into GPT-5.1 Codex Max with Alexander Embiricos, product lead for OpenAI Codex. You’ll walk out feeling like an agentic-coding wizard, even if you’re starting from zero. GPT-5.1 Codex Max is OpenAI’s latest frontier agentic coding model. It’s built on an upgraded reasoning backbone and trained to handle real-world software engineering tasks end to end: PRs, refactors, frontend builds, and deep debugging. It can work independently for hours, compacting its own history so it can refactor entire projects and run multi-hour agent loops without losing context. In this live session, we’ll set it up together, build real agents, and push Codex Max to its limits.

Modern AI has been dominated by one idea: predict the next token. But what if intelligence doesn’t have to work that way?

In this episode of The Neuron, we’re joined by Eve Bodnia, Founder and CEO of Logical Intelligence, to explore energy-based models (EBMs)—a radically different approach to AI reasoning that doesn’t rely on language, tokens, or next-word prediction.

With a background in theoretical physics and quantum information, Eve explains how EBMs operate over an energy landscape, allowing models to reason about many possible solutions at once rather than guessing sequentially. We discuss why this matters for tasks like spatial reasoning, planning, robotics, and safety-critical systems—and where large language models begin to show their limits.

You’ll learn:
What energy-based models are (in plain English)

Why token-free architectures change how AI reasons

How EBMs reduce hallucinations through constraints and verification

Why EBMs and LLMs may work best together, not in competition

What this approach reveals about the future of AI systems

To learn more about Eve’s work, visit https://logicalintelligence.com.

For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

AI is moving fast — and 2026 is shaping up to be a turning point.
In this livestream, Corey and Grant from The Neuron break down our biggest AI predictions for 2026, including:

🏆 Which companies, tools, and model types are most likely to come out on top

📉 Who could lose ground (and what’s driving the shift)
🎲 The wildcards most people aren’t factoring in yet
👀 What to watch across AI policy, agents, open source, and consumer adoption

🧠 The skills and strategies that will matter most in 2026
Join us live for audience Q&A and a real-time debate on the hottest AI takes — then drop your prediction in the comments: what’s the biggest AI surprise coming in 2026? 🔮

Subscribe for weekly AI coverage from The Neuron and more livestreams like this.
🎙️ https://theneuron.ai

In this special episode, we go hands-on with three cutting-edge AI tools from Google Labs. First, Jaclyn Konzelman (Director of Product Management) demos Mixboard, an AI-powered concepting board that transforms ideas into visual presentations using Nano Banana Pro. Then, Thomas Iljic (Senior Director of Product Management) shows us Flow, Google's AI filmmaking tool that lets you create, edit, and animate video clips with unprecedented control. Finally, Megan Li (Senior Product Manager) walks us through Opal, a no-code AI app builder that lets anyone create custom AI workflows and mini-apps using natural language.

Subscribe to The Neuron newsletter: https://theneuron.ai

Links:
Mixboard: https://mixboard.google.com 

Flow: https://flow.google 

Opal: https://opal.google 

Google Labs: https://labs.google

Autonomous coding agents are moving from demos to real production workflows. In this episode, Factory AI co-founder and CTO Eno Reyes explains what "Droids" really are—fully autonomous agents that can take tickets, modify real codebases, run tests, and work inside existing dev workflows.

We dig into Factory's context compression research (which outperformed both OpenAI and Anthropic), what makes a codebase "agent-ready," and why Stanford research found that the ONLY predictor of AI success was codebase quality—not adoption rates or token usage.

Whether you're a developer curious about autonomous coding tools or just want to understand where AI engineering is headed, this episode is packed with practical insights.

🔗 Try Factory AI: https://factory.ai

📰 Subscribe to The Neuron newsletter: https://theneuron.ai

📖 Resources mentioned:
• Factory's compression research: https://factory.ai/news/evaluating-compression