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    <title>Kabir Murjani — Writing</title>
    <link>https://kabir.codes</link>
    <description>Technical essays on machine learning, optimization, combinatorial search, and intelligent systems by Kabir Murjani.</description>
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    <managingEditor>kabir.murjani@iimb.ac.in (Kabir Murjani)</managingEditor>
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      <title>Playing Chess in 57 Megabytes</title>
      <link>https://kabir.codes/writing/chessnano</link>
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      <pubDate>Wed, 01 Apr 2026 00:00:00 GMT</pubDate>
      <description>A transformer that sees only notation, compressed via polar-angle quantization into a file smaller than a photograph.</description>
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        <p><strong>Abstract:</strong> There is something philosophically uncomfortable about treating chess as a language modeling problem. Chess has geometry. It has material balance, pawn structure, king safety, tactical patterns that span a dozen moves. A language model has none of that built in. It operates on a flat sequence of tokens with no explicit notion of a board, a square, or a piece. And yet, if you train a transformer on the move sequences of strong players, it has no choice but to learn *something* about chess in order to predict those sequences well. The only interesting questions are how much, and whether that something is enough to make legal moves reliably.</p>
        <p><a href="https://kabir.codes/writing/chessnano">Read the full article →</a></p>
        <img src="https://kabir.codes/blog-1.jpg" alt="Playing Chess in 57 Megabytes" />
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      <author>kabir.murjani@iimb.ac.in (Kabir Murjani)</author>
      <category>Machine Learning</category>
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