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      <title>Dropbox Collaborates with GitHub to Reduce Monorepo Size from 87GB to 20GB</title>
      <link>https://www.infoq.com/news/2026/04/dropbox-reduces-git-optimization/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Compression-news</link>
      <description>&lt;img src="https://www.infoq.com/styles/static/images/logo/logo_bigger.jpg"/&gt;&lt;p&gt;Dropbox reduced its backend monorepo from 87GB to 20GB by optimizing Git delta compression in collaboration with GitHub. The changes improved clone times, CI performance, and developer velocity, highlighting how repository storage inefficiencies can impact large-scale engineering workflows.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>github</category>
      <category>Optimization</category>
      <category>Software Engineering</category>
      <category>Productivity</category>
      <category>Compression</category>
      <category>Mono</category>
      <category>Continuous Integration</category>
      <category>git</category>
      <category>Infrastructure</category>
      <category>Continuous Deployment</category>
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      <pubDate>Wed, 22 Apr 2026 14:14:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/04/dropbox-reduces-git-optimization/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Compression-news</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-04-22T14:14:00Z</dc:date>
      <dc:identifier>/news/2026/04/dropbox-reduces-git-optimization/en</dc:identifier>
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    <item>
      <title>Google’s TurboQuant Compression May Support Faster Inference, Same Accuracy on Less Capable Hardware</title>
      <link>https://www.infoq.com/news/2026/04/turboquant-compression-kv-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Compression-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/04/turboquant-compression-kv-cache/en/headerimage/generatedHeaderImage-1776265077411.jpg"/&gt;&lt;p&gt;Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches by up to 6x. With 3.5-bit compression, near-zero accuracy loss, and no retraining needed, it allows developers to run massive context windows on significantly more modest hardware than previously required. Early community benchmarks confirm significant efficiency gains.&lt;/p&gt; &lt;i&gt;By Bruno Couriol&lt;/i&gt;</description>
      <category>Optimization</category>
      <category>Compression</category>
      <category>Performance</category>
      <category>Large language models</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 15 Apr 2026 16:53:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/04/turboquant-compression-kv-cache/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Compression-news</guid>
      <dc:creator>Bruno Couriol</dc:creator>
      <dc:date>2026-04-15T16:53:00Z</dc:date>
      <dc:identifier>/news/2026/04/turboquant-compression-kv-cache/en</dc:identifier>
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