Java
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Intelligent Document Processing with Apache Camel, Docling & LangChain4j
Enterprise applications often need to process large numbers of documents such as PDFs, invoices, manuals, reports, and policy documents. Extracting…
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LLM Tool Call Reasoning with Embabel AI
Large language models become significantly more useful when they can interact with external tools instead of only generating text. A…
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Introduction to HyDE: Hypothetical Document Embeddings for RAG
Retrieval-Augmented Generation (RAG) has become one of the most common approaches for building AI applications that need to work with…
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Haskell’s Monads Outside Academia: A Plain-Language Guide
Ask ten developers what a monad is, and you will likely get ten different answers, most of them nervous. The…
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G1’s Own Throughput Fix: JEP 522 and the Quiet Death of “G1 Trades Throughput for Latency”
For a decade and a half, “G1 gives up some throughput for lower pause times” has been treated as a…
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Null Safety and the Billion-Dollar Mistake
Tony Hoare spent one sentence apologizing for the null reference and the software industry has spent six decades cleaning up…
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LLM-as-a-Judge with Spring AI Recursive Advisors
Large Language Models (LLMs) are increasingly used not only to generate content, but also to evaluate the output produced by…
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Introduction to Apache Causeway
Building an enterprise application often involves implementing the same infrastructure repeatedly: domain objects, user interfaces, validation, persistence, navigation, and business…
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LLM Integration: Apache Camel & OpenAI Component
Large Language Models (LLMs) are increasingly becoming part of enterprise applications. However, integrating an LLM into an existing application is…
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