The Neuron: AI Explained

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

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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

Want to make better AI videos but have absolutely no idea what you’re doing?

Perfect. Neither did most of us at first. 😹

Join The Neuron LIVE this Thursday at 10 AM PT for a total beginner’s guide to AI video generation and video prompting, featuring the team behind the newly announced LTX-2.5.

We’re going hands-on with the new model while learning the fundamentals of actually prompting video models effectively.

What we’ll cover:
🎬 How video prompting is different from image prompting
✍️ How to write your first good AI video prompt
🎥 How to control shots, motion, subjects, and scenes
🧠 What video models actually understand when you describe a scene
🛠️ A live demo of the brand-new LTX-2.5
❓ Live Q&A with members of the LTX team

No previous AI video experience required.

If you can describe the video you want to make, we’ll show you how to turn that idea into a much better prompt.

And because this is live, bring your weird prompts, difficult questions, and video ideas. We’ll put the model through its paces together.

📅 Thursday, August 13
⏰ 10 AM PT

Learn more about LTX-2.5

🔗 Explore LTX-2.5: https://ltx.io/model/ltx-2-5

📰 Read the announcement: http://ltx.io/newsroom/introducing-ltx-2-5

🤗 LTX-2.5 on Hugging Face: https://huggingface.co/Lightricks/LTX-2.5

AI can now read DNA like language, generate working biological sequences, and help scientists identify disease-related mutations. Radical Numerics CEO Eric Nguyen explains how genome language models work, why his earlier model Evo helped scientists design CRISPR systems and bacteriophage genomes, and how the company’s newer model, Omnii, combines DNA with RNA, proteins, and other biological signals. The conversation explores personalized medicine, gene therapy, AI-designed biological threats, “deepfake viruses,” and whether the same technology that creates new biology can also defend against it.
Learn more about Radical Numerics: https://www.radicalnumerics.ai/