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      <title>Google Mantis: an Agentic Vulnerability Scanning Harness for Reducing False Positives</title>
      <link>https://www.infoq.com/news/2026/09/google-mantis-vulnerability-scan/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Google</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/google-mantis-vulnerability-scan/en/headerimage/google-mantis-scanner-1788693601725.jpeg"/&gt;&lt;p&gt;Google has open-sourced Mantis, an AI-agent framework designed to automate the software vulnerability lifecycle, from identifying and validating vulnerabilities to reproducing and fixing them. Google says it developed Mantis to address the high rate of false positives and hallucinated vulnerabilities produced by conventional AI-powered code scanning.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Agents</category>
      <category>Open Source</category>
      <category>Large language models</category>
      <category>Security Vulnerabilities</category>
      <category>Google</category>
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      <pubDate>Sun, 06 Sep 2026 12:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/google-mantis-vulnerability-scan/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Google</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-09-06T12:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/google-mantis-vulnerability-scan/en</dc:identifier>
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    <item>
      <title>Swiggy Uses 350+ Features and Multi-Task MLP to Predict Customer Lifetime Value</title>
      <link>https://www.infoq.com/news/2026/09/swiggy-pltv-multitask-mlp/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Google</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/swiggy-pltv-multitask-mlp/en/headerimage/generatedHeaderImage-1787507980584.jpg"/&gt;&lt;p&gt;Swiggy developed an in-house predicted lifetime value model using more than 350 pre order features and a multi task MLP for Food and Instamart. Adding order count as an auxiliary task reduced model parameters by 63% while improving predictive performance. The pLTV signal is used with Google Target ROAS bidding to optimize customer acquisition.&lt;/p&gt; &lt;i&gt;By Leela Kumili&lt;/i&gt;</description>
      <category>Machine Learning</category>
      <category>Online Learning</category>
      <category>Modeling</category>
      <category>Software Engineering</category>
      <category>Customers &amp; Requirements</category>
      <category>Acquisition</category>
      <category>Neural Networks</category>
      <category>Advertising</category>
      <category>Continuous Delivery</category>
      <category>Google</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Wed, 02 Sep 2026 13:55:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/swiggy-pltv-multitask-mlp/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Google</guid>
      <dc:creator>Leela Kumili</dc:creator>
      <dc:date>2026-09-02T13:55:00Z</dc:date>
      <dc:identifier>/news/2026/09/swiggy-pltv-multitask-mlp/en</dc:identifier>
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