<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Maia Salti</title>
    <description>The latest articles on DEV Community by Maia Salti (@maiasalti).</description>
    <link>https://dev.to/maiasalti</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4061522%2F780b0d75-45a6-4700-8261-69939f42edcc.png</url>
      <title>DEV Community: Maia Salti</title>
      <link>https://dev.to/maiasalti</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/maiasalti"/>
    <language>en</language>
    <item>
      <title>The J-Space: How I Learned To Read An LLM's Mind</title>
      <dc:creator>Maia Salti</dc:creator>
      <pubDate>Tue, 04 Aug 2026 02:35:09 +0000</pubDate>
      <link>https://dev.to/maiasalti/the-j-space-how-i-learned-to-read-an-llms-mind-4foj</link>
      <guid>https://dev.to/maiasalti/the-j-space-how-i-learned-to-read-an-llms-mind-4foj</guid>
      <description>&lt;p&gt;Last week, Anthropic published a paper on a discovery they made regarding Claude's internal reasoning they call the &lt;strong&gt;J-space&lt;/strong&gt;. It seems to be the steps that Claude works through before it commits to a final word: the closest thing we've come to seeing the inside of an LLM's "brain."&lt;/p&gt;

&lt;p&gt;Although it holds a median of only 6–7% of a concept's representation inside the model and never more than about a tenth of the model's activity at any layer, if you switch it off, Claude's multi-step reasoning collapses to almost nothing. Fluent speech and simple recall remain intact.&lt;/p&gt;

&lt;p&gt;I quite liked the &lt;a href="https://www.youtube.com/watch?v=rKV5JcALQoQ" rel="noopener noreferrer"&gt;video that Anthropic released&lt;/a&gt; with the &lt;a href="https://www.anthropic.com/research/global-workspace" rel="noopener noreferrer"&gt;research post&lt;/a&gt;. It's a long research post though, so I thought I'd write a summary of the parts I considered the coolest and how I interpreted the mathematics of the J-space.&lt;/p&gt;




&lt;p&gt;&lt;em&gt;This is a preview of a post from my blog.&lt;/em&gt; &lt;a href="https://www.maiatalksabout.ai/blog/j-space" rel="noopener noreferrer"&gt;&lt;strong&gt;Read the full post with the interactive charts →&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>ai</category>
      <category>llm</category>
      <category>anthropic</category>
      <category>modeltraining</category>
    </item>
    <item>
      <title>Attention Is Still All You Need</title>
      <dc:creator>Maia Salti</dc:creator>
      <pubDate>Tue, 04 Aug 2026 02:34:40 +0000</pubDate>
      <link>https://dev.to/maiasalti/attention-is-still-all-you-need-201d</link>
      <guid>https://dev.to/maiasalti/attention-is-still-all-you-need-201d</guid>
      <description>&lt;p&gt;You know it's a great day for AI when Peter Diamandis uploads an emergency episode on his &lt;em&gt;Moonshots&lt;/em&gt; podcast. If you haven't heard of it, go listen to a few of the most recent episodes. Those guys really fire me up and keep me thinking optimistically about where AI is heading.&lt;/p&gt;

&lt;p&gt;Diamandis pulled together his full panel for this episode: Salim Ismail, Dave Blundin, Emad Mostaque, and Alexander Wissner-Gross (sidenote: my father also highly recommends Alexander's Substack, &lt;a href="https://theinnermostloop.substack.com/" rel="noopener noreferrer"&gt;The Innermost Loop&lt;/a&gt;. He says it's the one place to know everything about AI developments). They half-jokingly called the model release an AI Sputnik moment, although at some point they argued it was even larger than that.&lt;/p&gt;

&lt;p&gt;A couple of articles ago, I wrote about &lt;a href="https://www.maiatalksabout.ai/blog/deepseek-scaling-law" rel="noopener noreferrer"&gt;DeepSeek&lt;/a&gt; and how they worked within the constraints of U.S. export controls and still managed to release a powerful model that was competitive with the U.S. frontier models of that time. This is that, but maybe 10x.&lt;/p&gt;




&lt;p&gt;&lt;em&gt;This is a preview of a post from my blog.&lt;/em&gt; &lt;a href="https://www.maiatalksabout.ai/blog/attention-is-still-all-you-need" rel="noopener noreferrer"&gt;&lt;strong&gt;Read the full post with the interactive charts →&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>ai</category>
      <category>machinelearning</category>
      <category>opensource</category>
      <category>news</category>
    </item>
  </channel>
</rss>
