InfoQ Homepage Programming Content on InfoQ
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Agentic Terminal - How Your Terminal Comes Alive with CLI Agents
In this article author Sachin Joglekar discusses the transformation of CLI terminals becoming agentic where developers can state goals while the AI agents plan, call tools, iterate, ask for approval where needed, and execute the requests. He also explains the planning styles for three different CLI tools: Gemini, Claude, and Auto-GPT.
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The Architect’s Dilemma: Choose a Proven Path or Pave Your Own Way?
Software platforms and frameworks act like paved roads: they accelerate MVP/MVA delivery but impose decisions teams may not accept. If the paved roads don't reach your destination, then you may have to take an exit ramp and build your own solution. Experiments are necessary to determine which path meets your specific needs.
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/articles/nextgen-search-ai-opensearch-mcp/en/smallimage/nextgen-search-ai-thumbnail-1765802308459.jpg)
NextGen Search - Where AI Meets OpenSearch through MCP
In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP).
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/articles/java-trends-report-2025/en/smallimage/the-infoq-trends-report-500x500-article-thumb-logo-1764844623172.jpeg)
InfoQ Java Trends Report 2025
This report summarizes how the InfoQ Java editorial team and several Java Champions currently see the adoption of technology and emerging trends within the Java and JVM space in 2025. We focus on Java the language, as well as related languages like Kotlin and Scala, the Java Virtual Machine (JVM), and Java-based frameworks and utilities.
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/articles/reducing-false-positives-retrieval-augmented-generation/en/smallimage/reducing-false-positives-retrieval-augmented-generation-thumbnail-1762786576150.jpg)
Reducing False Positives in Retrieval-Augmented Generation (RAG) Semantic Caching: a Banking Case Study
In this article, author Elakkiya Daivam discusses why Retrieval Augmented Generation (RAG) and semantic caching techniques are powerful levers for reducing false positives in AI powered applications. She shares the insights from a production-grade evaluation with 1,000 query variations tested across seven bi-encoder models.
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/articles/training-data-preprocessing-for-text-to-video-models/en/smallimage/training-data-preprocessing-for-text-to-video-models-thumbnail-1762244675850.jpg)
Training Data Preprocessing for Text-to-Video Models
In this article, author Aleksandr Rezanov discusses the data preparation for generative text-to-image models to accelerate work on video generation services to be used in TV series and films. He explains how data is prepared and can serve as a starting point for creating custom datasets to develop proprietary models.
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/articles/rag-with-spring-mongo-open-ai/en/smallimage/rag-with-spring-mongo-open-ai-thumbnail-1761292137478.jpg)
Building a RAG Application with Spring Boot, Spring AI, MongoDB Atlas Vector Search, and OpenAI
The RAG paradigm redefines AI: it combines generative models and business data for accurate, contextualised responses. The article shows how to integrate Spring Boot, Spring AI, MongoDB Atlas and OpenAI into a powerful and flexible pipeline capable of transforming the way businesses access and create value from data, with applications ranging from finance and healthcare to customer service.
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/articles/PDCA-AI-code-generation/en/smallimage/AI-code-gen-with-a-formal-and-repeatable-PDCA-cycle-thumb-image-1760692414560.jpg)
A Plan-Do-Check-Act Framework for AI Code Generation
AI code generation tools promise faster development but often create quality issues, integration problems, and delivery delays. A structured Plan-Do-Check-Act cycle can maintain code quality while leveraging AI capabilities. Through working agreements, structured prompts, and continuous retrospection, it asserts accountability over code while guiding AI to produce tested, maintainable software.
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/articles/go-channels-happens-before-concurrency/en/smallimage/Go-Channels-Understanding-Happens-Before-for-Safe-Concurrency-thumb-1759993668874.jpg)
Go Channels: Understanding Happens-Before for Safe Concurrency
This article dives into the happens-before semantics of Go channels, explaining how they relate to memory visibility, synchronization, and concurrency correctness. We'll examine subtle pitfalls, illustrate them with examples, and explore the architectural implications for system designers.
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/articles/unintended-consequences-automation-software/en/smallimage/unintended-consequences-automation-software-thumbnail-1759912534899.jpg)
Exploring the Unintended Consequences of Automation in Software
This article lays out some of the common assumptions and misconceptions about automation and its role in software (and software incidents), what our research has found regarding how automation shows up in software incidents, and some ideas around how people can better design automated tools to help people better handle software incidents.
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/articles/onnx-ai-inference-with-java/en/smallimage/Bringing-AI-Inference-to-Java-with-ONNX-thumb-image-1759229686552.jpg)
Bringing AI Inference to Java with ONNX: a Practical Guide for Enterprise Architects
Java applications can now run transformer-based AI models directly within the JVM—without Python, REST wrappers, or microservices. This guide shows how to integrate ONNX-powered inference with tokenizer support, GPU acceleration, modular deployment, and observability, enabling architects in regulated domains to adopt AI without disrupting compliance or CI/CD workflows.
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/articles/pipeline-language-migrations/en/smallimage/a-pipeline-approach-to-language-migrations-thumb-image-1759158012773.jpg)
A Pipeline Approach to Language Migrations
Automated language migrations can be made reliable and maintainable by structuring them as pipelines with clear, testable stages. This avoids the pitfalls of big-bang rewrites while providing transparency and modularity. The pipeline approach ensures idiomatic code, preserves legacy business logic, and supports large-scale transformations from outdated systems.