The Neuron: AI Explained

What happens when AI models get powerful enough that the bottleneck stops being the model, and starts becoming the computer, the power grid, or the safeguards around it?

Corey Noles and Grant Harvey break down a packed week in AI, starting with OpenAI’s expected Astra model and the controversy around recurrent-depth reasoning, chain-of-thought monitoring, and critical cybersecurity capabilities. They also dig into Google’s Gemini 3.8 Flash and Flash Cyber, and why benchmark charts increasingly matter less than what a model can actually do in real work.

Then the conversation moves from models to machines: a $399 open-source robot duck, Dyson’s wildly overengineered CameraJet toothbrush, the return of the CPU as AI agents increasingly operate computers directly, and Apple’s increasingly data-center-like Mac Studio hardware for local AI workloads.

Finally, Corey and Grant unpack the fight over AI data centers in Texas, what communities should demand in exchange for hosting them, and why ChatGPT’s advertising business may become a major new piece of OpenAI’s economics.

Subscribe to The Neuron at theneuron.ai for a daily briefing on the AI stories that actually matter.

The Neuron: https://www.theneuron.ai/
The Neuron Academy: https://www.neuronacademy.com/

Anthropic just released Claude Fable 5.1, its newest frontier model for long-running coding, research, and agentic work.

So naturally, we’re putting it to the test LIVE. 🤖

Early testing suggests Fable 5.1 is faster, easier to work with, and capable of handling massive coding projects, long-form writing, and autonomous agent tasks more efficiently. Every even found that it used less than half as many tokens as Opus 5 in one internal test.

But it has some quirks too. It can blow past word counts, create unnecessary subagents, and occasionally keep working when you tell it to stop.

Today, Grant and Corey from The Neuron are testing those claims for themselves.

We’re putting Fable 5.1 through:

🧠 Complex research and knowledge work
💻 Coding and long-running agent workflows
✍️ Writing, editing, and instruction following
🤖 Autonomous tasks where Claude runs on its own
🛑 Constraint, budget, and STOP tests
🆚 Whatever else the live chat wants us to try

Anthropic also cut cached-input pricing by 75%, potentially making long-running Claude agents significantly cheaper to operate.

Can Fable 5.1 actually become the model you hand a giant task and walk away from?

Let’s find out live.

UPDATE: Our write-up on this livestream, with time-stamps: https://theneuron.ai/news/claude-fable-5-1-can-do-the-work-the-hard-part-is-managing-it/

🔗 Anthropic announcement:https://www.anthropic.com/claude-fable-and-mythos-5-1

🔗 Every’s Fable 5.1 Vibe Check:https://every.to/vibe-check/fable-5-1-vibe-check

📩 Subscribe to The Neuron for AI news, tools, and practical experiments without the hype:https://theneuron.ai/

AI companies spend enormous effort making models safer, but the model tested in the lab isn't necessarily the system a company eventually deploys.

Alice CEO and co-founder Noam Schwartz joins Corey and Grant to explain why prompts, tools, memory, permissions, data, and agent-to-agent interactions create an entirely new security surface.

They dig into the growing “trust gap” around enterprise AI, why prompt injection may become a permanent cat-and-mouse game, what open-weight models change for defenders, and why Schwartz believes the real security boundary has to exist at every layer of an AI system.

The bigger takeaway: as AI moves from answering questions to taking actions, traditional cybersecurity may need to merge with fraud prevention, threat intelligence, and trust and safety.

OpenAI security incident: https://openai.com/index/hugging-face-model-evaluation-security-incident/
Alice: https://alice.io/
Alice on agentic AI security: https://alice.io/blog/key-security-risks-posed-by-agentic-ai-and-how-to-mitigate-them
Alice open-weight research: https://alice.io/blog/okay-here-is-how-to-build-a-bomb-millions-download-dangerous-llms

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

Sponsored by Dell Technologies and NVIDIA. Learn more at https://www.techrepublic.com/hubs/the-enterprise-guide-to-scalable-ai/

AI tools are shipping faster than most normal humans can figure out what half of them actually do.

So Thursday, August 20 at 10 AM PT / 1 PM ET, we’re going LIVE to translate this week’s biggest AI launches into plain English. 😸

The goal: understand what these tools actually are, who they’re for, what’s useful vs. hype, and which ones are worth trying.

No three developers yelling model benchmarks at each other for an hour. Instead, we’re covering:

🤖 Qwen 3.8: What a powerful open model is, why you might use one instead of ChatGPT or Claude, and when that makes sense.

💻 Unsloth Studio: Run and experiment with AI models on your own computer, even if you’ve never touched a terminal.

⌨️ Cursor Origin: Cursor wants to host your code too. Here’s what that could mean for people building websites, apps, and internal tools with AI.

🛠️ DeepSeek Harness: What an “agent harness” is, why everyone keeps talking about them, and whether they matter outside hardcore coding circles.

💬 Buzz: Imagine Slack or Discord, except AI agents can join the workspace, collaborate with humans, and do work.

🐦 Berd: One desktop home for your AI agents, projects, skills, tools, and models. You can also give your agents little animated bodies. Please emotionally prepare yourself.

Plus, we’ll cover the other notable models and tools that dropped this week.

And if OpenAI drops Astra before we go live? We’ll cover that too. If it’s incredible, great. If it belongs in the “cool, another model” bucket, that’s part of the roundup too.

📅 August 20 @ 10AM PT / 1PM ET

Bring your questions. No PhD required. 😸

TOOLS / LINKS:
Qwen 3.8: https://qwen.ai/blog?id=qwen3.8
Unsloth: https://unsloth.ai/
Cursor: https://cursor.com/changelog/origin-code-hosting
DeepSeek: https://github.com/deepseek-ai/deepseek-harness
Buzz: https://buzz.xyz/
Berd: https://berd.xyz/
OpenAI: https://openai.com/index/pacing-model-development-cyber-capabilities/

AI can write code, summarize documents, and hold a conversation. But it still struggles with the structured data businesses rely on to predict churn, fraud, demand, pricing, and risk.

Alexandre Pasquiou explains why language models flatten the relationships inside spreadsheets, how tabular foundation models learn from rows and columns, and why Neuralk believes one general model could replace hundreds of custom predictive systems.

The conversation also covers Seldon, AI agents, enterprise adoption, and Alexandre’s prediction that tabular foundation models will power every predictive workload by 2030.

Learn more about Neuralk: https://www.neuralk.ai/
Subscribe to The Neuron newsletter: https://theneuron.ai