Nathan Lambert✓

Giotto AI @giotto_ai

Today we announce the launch of the Giotto family of models. We are unveiling Giotto Alto and Giotto Solo, the smartest European sovereign AI models on the market. Giotto Alto is our flagship model built for advanced reasoning capabilities, free for everyone and available on the App Store and Google Play. It is hosted on European cloud infrastructure, so your data stays in Europe. Giotto Solo is the smartest model that runs on a single GPU, so organisations can keep their AI on their own infrastructure. We are a Swiss lab with a European heart and we built this so that Europe can run advanced AI on its own terms. Use Giotto:

Cohere @cohere

While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report:

Jordan Schneider @jordanschneider

can we talk about nathan's background for a second

Nathan Lambert @natolambert

My first open model lecture, covering all the many pieces that build the ecosystem, and the open questions/debates determining where it goes next. How does distillation fit in? How do we manage risks? How do we see access to models changing in the future? I'm excited to revisit these questions about every 6 months, to give a state of the union on the field. Chapters 00:00 Introduction 01:36 What is an open model? 08:47 Why should we care about open models? 18:52 How competitive are the best open models today? 25:28 How are open models built? The role of distillation 34:52 Are people actually using open models? 41:47 What comes next with open models? 47:32 Conclusions What questions did I not answer that you have?

Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex

@natolambert right from the horse's mouth ah, right. That's "a single cluster", who knows how many they have

Martin Marek @mrtnm

Tinker's new StableReinforce objective is Reinforce + Score Centering. Here's how I reverse-engineered it 🧵