Committed to sharing original, high‑quality technical articles; no fluff or promotional content.
The article introduces DeepSeek's new V3-0324 model, highlights its performance gains and new features like function calling and standardized JSON output, demonstrates Chinese and frontend coding tests, provides Java code examples for AI integration, and concludes with a summary of its business impact.
This tutorial shows how to use Spring AI MCP to create a Java MCP server, covering environment setup, business logic implementation, service registration, and client configuration, enabling seamless AI service integration with minimal effort.
This article introduces Java’s emerging Vector API, explains its SIMD‑based design, provides practical code examples for array addition, dot product, and complex calculations, and details performance benchmarks, integration with vector databases, usage considerations, and future development prospects.
This article introduces Google’s open‑source Gemma 3 model, highlighting its multimodal capabilities, massive 128k token context window, multilingual support, and provides step‑by‑step instructions for installing Ollama, pulling the model, and running local tests with code examples.
This article explains how to integrate Gitee's Model Control Protocol (MCP) with Java using LangChain4j, covering download, Maven setup, configuration, both stdio and SSE transport modes, sample code, output examples, and a comparison of the two modes to create an AI‑powered repository assistant.
This article explains Spring Framework 7's new BeanRegistrar interface, showing how to programmatically register beans efficiently, compare it with traditional registration methods, discuss @Conditional limitations, and outline future advantages for Java backend developers.
AI Services, inspired by Spring Data JPA and Retrofit, offers a declarative Java API that abstracts LLM interactions, supporting input formatting, output parsing, chat memory, function calling, and RAG, with detailed examples using LangChain4j, custom Spring AI annotations, AOP aspects, and controller integration.
QwQ-32B, Alibaba’s new inference‑optimized large language model built on the Qwen2.5 architecture, outperforms DeepSeek‑R1 across math reasoning, code generation, and safety benchmarks while requiring only 24 GB vRAM, and the article provides detailed performance data, resource‑efficiency analysis, and step‑by‑step Java and Ollama integration instructions.
This article explains how Server‑Sent Events (SSE) work, outlines the limitations of the native EventSource API, and demonstrates how the fetch‑event‑source library enhances SSE with POST support, custom headers, retry strategies, and visibility handling, enabling efficient real‑time AI data streams in modern web front‑ends.
This tutorial shows how to harness Redis's dual role as a high‑performance cache and a vector database, guiding you through Docker setup, vector storage methods, and Java Lettuce integration to build a private large‑language‑model knowledge base with DeepSeek4J.
