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      <title>Java News Roundup: TornadoVM 6, JReleaser, LangChain4j, Java Operator SDK, JHipster, Yupiik Fusion</title>
      <link>https://www.infoq.com/news/2026/09/java-news-roundup-aug31-2026/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=LangChain4j+</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/java-news-roundup-aug31-2026/en/headerimage/java-news-roundup-image-1788715275955.jpg"/&gt;&lt;p&gt;This week's Java roundup for August 31st, 2026, features news highlighting: the GA release of TornadoVM 6.0; point releases of JReleaser, LangChain4j, Java Operator SDK, JHipster, Kotlin Toolchain and Yupiik Fusion; and maintenance releases of Micronaut and GraalVM Development Kit.&lt;/p&gt; &lt;i&gt;By Michael Redlich&lt;/i&gt;</description>
      <category>Yupiik</category>
      <category>JDK 28</category>
      <category>GraalVM</category>
      <category>TornadoVM</category>
      <category>LangChain4j</category>
      <category>Kotlin</category>
      <category>JReleaser</category>
      <category>JDK 27</category>
      <category>Java Operator SDK</category>
      <category>Java</category>
      <category>JHipster</category>
      <category>Micronaut</category>
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      <pubDate>Mon, 07 Sep 2026 02:30:00 GMT</pubDate>
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      <dc:creator>Michael Redlich</dc:creator>
      <dc:date>2026-09-07T02:30:00Z</dc:date>
      <dc:identifier>/news/2026/09/java-news-roundup-aug31-2026/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: Beyond Prompting: Context Engineering for Production-Grade AI</title>
      <link>https://www.infoq.com/presentations/context-engineering-redis-llm-architecture/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=LangChain4j+</link>
      <description>&lt;img src="https://res.infoq.com/presentations/context-engineering-redis-llm-architecture/en/mediumimage/ricardo-ferreira-medium-1787820318843.jpg"/&gt;&lt;p&gt;Ricardo Ferreira discusses moving beyond simple prompt engineering to build production-grade AI applications. He shares practical architectural strategies for integrating long-term and short-term memory using Redis, managing LLM token limits via summarization, mitigating context rot with reranking and semantic caching, and controlling exponential API costs under strict latency constraints.&lt;/p&gt; &lt;i&gt;By Ricardo Ferreira&lt;/i&gt;</description>
      <category>Retrieval-Augmented Generation</category>
      <category>Transcripts</category>
      <category>Architecture</category>
      <category>Redis</category>
      <category>Caching</category>
      <category>Generative AI</category>
      <category>QCon AI Boston 2026</category>
      <category>LangChain4j</category>
      <category>Large language models</category>
      <category>Agents</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>presentation</category>
      <pubDate>Wed, 02 Sep 2026 11:00:00 GMT</pubDate>
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      <dc:creator>Ricardo Ferreira</dc:creator>
      <dc:date>2026-09-02T11:00:00Z</dc:date>
      <dc:identifier>/presentations/context-engineering-redis-llm-architecture/en</dc:identifier>
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