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    <title>PyPI recent updates for evalfloor</title>
    <link>https://pypi.org/project/evalfloor/</link>
    <description>Recent updates to the Python Package Index for evalfloor</description>
    <language>en</language>    <item>
      <title>0.3.0</title>
      <link>https://pypi.org/project/evalfloor/0.3.0/</link>
      <description>EvalFloor: is your LLM eval improvement real? Trying k prompt variants and keeping the best scores points on noise alone -- this computes how many, so you can tell a real gain from a lucky sample.</description>
      <pubDate>Mon, 21 Sep 2026 03:47:12 GMT</pubDate>
    </item>    <item>
      <title>0.2.1</title>
      <link>https://pypi.org/project/evalfloor/0.2.1/</link>
      <description>EvalFloor: is your LLM eval improvement real? Trying k prompt variants and keeping the best scores points on noise alone -- this computes how many, so you can tell a real gain from a lucky sample.</description>
      <pubDate>Mon, 21 Sep 2026 03:41:00 GMT</pubDate>
    </item>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/evalfloor/0.2.0/</link>
      <description>EvalFloor: is your LLM eval improvement real? Trying k prompt variants and keeping the best scores points on noise alone -- this computes how many, so you can tell a real gain from a lucky sample.</description>
      <pubDate>Mon, 21 Sep 2026 03:29:50 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/evalfloor/0.1.1/</link>
      <description>EvalFloor: is your LLM eval improvement real? Trying k prompt variants and keeping the best scores points on noise alone -- this computes how many, so you can tell a real gain from a lucky sample.</description>
      <pubDate>Mon, 21 Sep 2026 03:12:26 GMT</pubDate>
    </item>    <item>
      <title>0.1.0</title>
      <link>https://pypi.org/project/evalfloor/0.1.0/</link>
      <description>EvalFloor: is your LLM eval improvement real? Trying k prompt variants and keeping the best scores points on noise alone -- this computes how many, so you can tell a real gain from a lucky sample.</description>
      <pubDate>Mon, 21 Sep 2026 03:10:16 GMT</pubDate>
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