Can someone please help me find the promised recordings of the 3-day AI Advantage Summit?

📝 TL;DR 📝 Anthropic published new interpretability research on July 6 describing a small, privileged space inside Claude's internal activations, which they call "J-space," that appears to hold concepts the model can hold in mind and reason with before ever writing them down. It behaves functionally like global workspace theory, an influential neuroscience framework for how the brain filters what becomes consciously reportable. This is not a consumer product. It is research with real safety implications: catching hidden goals and detecting when Claude privately recognizes a staged test. 🧠 Overview 🧠 This is a genuinely significant piece of AI interpretability research, and it is worth understanding on its own terms rather than through either "AI is conscious" or "this is nothing" framing, because it is neither. Anthropic's interpretability team, the group that has spent recent years trying to open the black box of how large language models actually work internally, found a small subspace of Claude's neural activations that functions differently from the rest of the model's computation. Most of what happens inside a language model as it processes a prompt is not directly reportable, the model cannot describe or act on most of its own internal computation. Anthropic found that a small, sparse portion of that internal activity behaves differently: it is verbalizable, it can be held onto and reused across a reasoning process, and it appears to function as a kind of internal staging area for concepts the model is actively working with, separate from both the model's raw computation and its final output text. 📜 The Announcement 📜 The research, titled "A global workspace in language models," was published on the Transformer Circuits Thread on July 6, 2026, credited to eighteen researchers on Anthropic's interpretability team. The core discovery is what they call J-space, identified using a new technique called the Jacobian lens, or J-lens. The method works by calculating, for each word in the model's vocabulary, the average mathematical effect a given internal activation pattern has on making the model eventually produce that word, whether immediately or later in its response.