InfoQ Homepage Articles
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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/empowering-decentralizing-architectural-decision-making/en/smallimage/empowering-decentralizing-thumbnail-1761736119696.jpg)
Empowering Teams: Decentralizing Architectural Decision-Making
In today’s rapidly evolving tech landscape, centralized architectural decision-making can become a bottleneck to delivery performance and innovation. Through stories from our own journey, we’ll share how decentralizing decisions improved alignment across teams, empowered faster decision-making, and fostered a culture of ownership.
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Beyond Accidental Quality: Finding Hidden Bugs with Generative Testing
Generative testing uncovers hidden software bugs by exploring the input space and verifying system invariants. This surpasses example-based tests that rely on known scenarios and can miss edge cases.
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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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InfoQ Cloud and DevOps Trends Report - 2025
This InfoQ Trends Report offers readers a comprehensive overview of emerging trends and technologies in the areas of Cloud and DevOps. This report summarizes the InfoQ editorial team’s and external guests' view on the current trends in Cloud and DevOps technologies and what to look out for in the next 12 months.
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Three Questions That Help You Build a Better Software Architecture
To architect effectively for an MVP, teams must answer three questions in order: Is the business idea worth pursuing? What performance and scalability are needed? How much maintainability and supportability are required? These guide Minimum Viable Architecture decisions. Empirical testing helps reject costly assumptions early and adapt architecture as the MVP evolves.
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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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If Architectures Could Talk, They’d Quote Your Boss
Software architecture reflects how organizations communicate and make decisions. Failures stem from misaligned incentives, unclear ownership, and structural gaps—not technical flaws. Architects must design not just systems, but the conditions for systems to thrive, using platform thinking to reduce friction and foster autonomy.
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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.