InfoQ Homepage Programming Content on InfoQ
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/articles/artificial-intelligence-driven-phishing/en/smallimage/thumbnail-artificial-intelligence-driven-phishing-1780562847202.jpg)
Artificial Intelligence-Driven Phishing: How Phishing Technique Is Evolving and Implemented
In this article, the author examines how AI is transforming phishing from a manual, targeted activity into an automated and scalable attack model. The article breaks down each stage of the phishing lifecycle, showing how AI improves reconnaissance, profiling, content generation, delivery, and interaction, while outlining layered defenses that combine controls, processes, and user awareness.
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/articles/adoption-curve-twenty/en/smallimage/20-birthday-InfoQ-thumb-image-updated-1780931679034.jpg)
The Technology Adoption Curve, Twenty Years On
Today, June 8th, InfoQ celebrates 20 years. This is not a comprehensive history, but a deliberately selective look at the technologies and practices InfoQ identified early, where they sit on the adoption curve in 2026, and how that curve may evolve over the next five to ten years.
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/articles/secure-ai-stack-model-production-series/en/smallimage/Article-Series-Securing-the-AI-Stack-From-Model-to-Production-thumb-image-1780040531515.jpg)
Article Series: Securing the AI Stack: from Model to Production
This series provides your roadmap for the machine age, exploring how to move from vulnerable prototypes to resilient systems through layered defense, robust MLOps, and integrated governance.
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/articles/vector-search-hybrid-retrieval-rag/en/smallimage/vector-search-hybrid-retrieval-rag-thumbnail-1779972811121.jpg)
Why Vector Search Alone Isn't Enough: Hybrid Retrieval for RAG
In this article, author Aaditya Chauhan discusses the limitations of RAG pipelines based purely on vector search and how an internal omni-search application using Reciprocal Rank Fusion (RRF) that combines BM25 and vector results, can enhance the search solution.
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/articles/solving-ai-productivity-paradox-test-automation/en/smallimage/solving-ai-productivity-paradox-test-automation-thumbnail-1779953915743.jpg)
The AI Productivity Paradox in Test Automation: Moving beyond Structural Validation to Perception and Intent
The AI productivity paradox states that AI scales whatever abstraction it is built on. If that abstraction is structurally brittle, it scales structural brittleness. This article shows that to build a future of reliable, AI-driven test automation, we must stop scaling DOM-centric abstractions and build a new testing paradigm grounded in perception and intent.
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/articles/schema-proliferation-problem/en/smallimage/schema-proliferation-problem-thumbnail-1779270222602.jpg)
The Schema Proliferation Problem in Kafka and Flink Pipelines: How to Solve It
Schema proliferation builds slowly and gets expensive fast. One schema per event type feels right until there are ten tables, union queries spanning all of them, and a single field rename touching every schema. Discriminator-based schema consolidation collapses that to two tables, turning multi-table unions into a single query, while new variants are additive and don't break existing consumers.
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/articles/local-first-ai-inference-cloud/en/smallimage/Local-First-AI-Inference-A-Cloud-Architecture-Pattern-for-Cost-Effective-Document-Processing-thumb-image-1778141518292.jpg)
Local-First AI Inference: a Cloud Architecture Pattern for Cost-Effective Document Processing
The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
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/articles/asp-net-core-side-car/en/smallimage/Implementing-the-Sidecar-Pattern-in-Microservices-based-ASP-NET-Core-Applications-thumb-1778079284259.jpg)
Implementing the Sidecar Pattern in Microservices-Based ASP.NET Core Applications
Today's applications require monitoring, logging, configuration, etc. Each of these concerns can be implemented as a component or a service. These cross-cutting concerns can be tightly integrated into the application. While this tight coupling ensures effective use of shared resources, an outage in any of these components can take your application down. Enter the sidecar design pattern.
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/articles/mcp-java-architectural-strategy-llm-integrations/en/smallimage/mcp-java-architectural-strategy-llm-integrations-thumbnail-1776772947180.jpg)
MCP in the Java World: Bringing Architectural Strategy to LLM Integrations
Discover how the Model Context Protocol (MCP) Java SDK is establishing a new architectural discipline for enterprise LLM integrations. By defining explicit contracts and leveraging MCP servers as anti-corruption layers, it ensures governance, loose coupling, and security alignment with the JVM ecosystem and existing operational practices, moving integrations beyond fragility to resilience.
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/articles/orchestrating-agentic-multimodal-ai-pipelines-apache-camel/en/smallimage/orchestrating-agentic-multimodal-ai-pipelines-apache-camel-thumbnail-1776763980414.jpg)
Orchestrating Agentic and Multimodal AI Pipelines with Apache Camel
In this article, author Vignesh Durai discusses how agentic and multimodal AI systems can be engineered using Apache Camel and LangChain4j technologies. The key components in the solution include LLM-based reasoning, retrieval-augmented generation (RAG), and image classification.
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/articles/redesign-pdf-table-extraction/en/smallimage/redesign-pdf-table-extraction-thumbnail-1776414059821.jpg)
Redesigning Banking PDF Table Extraction: a Layered Approach with Java
PDF table extraction often looks easy until it fails in production. Real bank statements can be messy, with scanned pages, shifting layouts, merged cells, and wrapped rows that break standard Java parsers. This article shares how we redesigned the approach using stream parsing, lattice/OCR, validation, scoring, and selective ML to make extraction more reliable in real banking systems.
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/articles/building-trpc-api-typescript/en/smallimage/building-trpc-api-typescript-thumbnail-1776246612091.jpg)
Building Production-Ready tRPC APIs: the TypeScript Alternative to Apollo Federation
This article details our migration from Apollo Federation to a TypeScript-based tRPC stack, which resulted in an 89% reduction in bugs and 67% faster response times. It also covers the mistakes we made, the unexpected performance gains, and an overview of the production architecture we use today to handle 2.4 million daily requests with 99.97% uptime.