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    <title>DEV Community: Maia Salti</title>
    <description>The latest articles on DEV Community by Maia Salti (@maiasalti).</description>
    <link>https://dev.to/maiasalti</link>
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      <title>DEV Community: Maia Salti</title>
      <link>https://dev.to/maiasalti</link>
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    <item>
      <title>The Idiot Index of Tokens</title>
      <dc:creator>Maia Salti</dc:creator>
      <pubDate>Wed, 12 Aug 2026 09:55:19 +0000</pubDate>
      <link>https://dev.to/maiasalti/the-idiot-index-of-tokens-1a4i</link>
      <guid>https://dev.to/maiasalti/the-idiot-index-of-tokens-1a4i</guid>
      <description>&lt;h2&gt;
  
  
  The Idiot Index
&lt;/h2&gt;

&lt;p&gt;In 2001, Elon Musk flew to Russia to buy refurbished ICBMs. He wanted to send a small greenhouse to Mars, cheaply, as a publicity stunt to reignite public interest in space travel. The Russians quoted him a price that he thought was absurd, and he flew home without a rocket.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F0xj8z7qivh48dw00fhce.jpg" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F0xj8z7qivh48dw00fhce.jpg" alt="A young Elon Musk at SpaceX" width="800" height="604"&gt;&lt;/a&gt;&lt;br&gt;
&lt;em&gt;A young Elon Musk at SpaceX&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;On the flight back, he did the math and thought about what a rocket is actually made of: aluminium, titanium, copper, and carbon fiber. He realised that the raw materials that go into a rocket cost around 2% of what SpaceX customers were being charged for a finished one. If materials were 2% of the price, the other 98% was everyone else's margin, decades of an industry that had never been forced to get cheaper.&lt;/p&gt;

&lt;p&gt;That calculation is the reason SpaceX exists. And, according to Walter Isaacson's 2023 biography, Musk started using a phrase to run the company: the &lt;strong&gt;Idiot Index&lt;/strong&gt;.&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/idiot-index-of-tokens" 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>startup</category>
      <category>business</category>
    </item>
    <item>
      <title>The AI Scaling Law and DeepSeek</title>
      <dc:creator>Maia Salti</dc:creator>
      <pubDate>Wed, 05 Aug 2026 04:15:00 +0000</pubDate>
      <link>https://dev.to/maiasalti/the-ai-scaling-law-and-deepseek-4cfj</link>
      <guid>https://dev.to/maiasalti/the-ai-scaling-law-and-deepseek-4cfj</guid>
      <description>&lt;h2&gt;
  
  
  NVIDIA's Crash
&lt;/h2&gt;

&lt;p&gt;On January 27th, 2025, NVIDIA lost 589 billion US dollars in market cap in one day, the largest one-day loss for any company in US history, beating its own prior record of 279 billion in September 2024. The broader tech/AI market lost about 1 trillion USD that day.&lt;/p&gt;

&lt;h2&gt;
  
  
  Compute
&lt;/h2&gt;

&lt;p&gt;Initially a company that sold computer chips for game rendering, NVIDIA now sells GPUs to the companies that create the frontier models and provide them with the compute they need to function. Compute has been a scarce asset for many of these competing companies (Anthropic, OpenAI, Google, Meta).&lt;/p&gt;

&lt;h2&gt;
  
  
  The Scaling Law
&lt;/h2&gt;

&lt;p&gt;Model capability has been known to scale predictably with compute.&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/deepseek-scaling-law" rel="noopener noreferrer"&gt;&lt;strong&gt;Read the full post with the interactive charts →&lt;/strong&gt;&lt;/a&gt;&lt;/p&gt;

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      <category>ai</category>
      <category>machinelearning</category>
      <category>news</category>
      <category>datascience</category>
    </item>
    <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;

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      <category>machinelearning</category>
      <category>opensource</category>
      <category>news</category>
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