This tutorial explains the Model Context Protocol (MCP) for integrating large language models with external data sources, details its architecture, connection lifecycle, and core JSON‑RPC features, and provides a step‑by‑step Python implementation that fetches real‑time tech news and integrates with Claude Desktop.
This article outlines the evolution of BizDevOps standards in China, detailing recent policy mandates, the expansion of the DevOps maturity model to organization‑level practice, the AI‑driven SOMM operation assurance framework, and the integration of large‑model AI into R&D and operational workflows, highlighting their impact on enterprise efficiency and governance.
Nacos 3.0 introduces major upgrades—including JDK 17 and Spring Boot 3.4.1 support, a new Admin API, default authentication, AI‑focused MCP, unified namespaces, beta distributed lock and fuzzy listening features, plus native xDS protocol support—positioning it as a powerful cloud‑native service discovery and configuration platform.
This guide outlines a comprehensive data development roadmap, covering infrastructure setup, governance frameworks, automated pipelines, BI and analytics tools, AI/ML integration, cultural adoption, and continuous performance monitoring to enable intelligent business transformation.
This article provides a comprehensive overview of the Model Context Protocol (MCP), explaining its origins, architecture, and how it standardises AI tool calling by enabling developers to build MCP servers and clients with TypeScript and Python, register resources, tools and prompts, and orchestrate model‑driven tool execution via JSON‑RPC.
This article explores the Model Context Protocol (MCP) as a universal USB‑C‑like interface for AI, detailing its client‑server architecture, the STDIO and SSE transport mechanisms, a stdio‑to‑SSE proxy, serverless deployment on Function Compute, performance benchmarks, and practical tips for production use.
Model Context Protocol (MCP) is an open standard that standardizes how large language models interact with external tools and data, enabling seamless function calls, simplifying prompt engineering, and allowing developers to build modular AI applications without handling low‑level integration details.
Discover the Model Context Protocol (MCP)—an open standard from Anthropic that unifies AI model access to external data sources, enabling real‑time search, tool integration, and bidirectional communication, with practical examples, setup guides, and code snippets for developers to quickly build AI‑driven applications.
This article examines the technical challenges of modernizing legacy J2EE monoliths and presents Alibaba Cloud Application Server's progressive containerization, AI integration, and observability solutions that enable seamless migration to intelligent, cloud‑native architectures.
This article explains chaos engineering—its definition, core principles, experimental workflow, tool selection, AI‑driven enhancements, and practical case studies—providing a comprehensive guide for building resilient distributed systems across backend, cloud‑native, mobile, and AI‑enabled environments.
This guide walks you through the VS Code extension architecture and provides a step‑by‑step example that creates a simple AI text‑explanation plugin, covering preparation, project scaffolding, command registration, API integration, debugging, and best‑practice security tips.
This guide walks through converting a traditional Spring Boot book‑management API into a Model Context Protocol (MCP) service that can be invoked via natural‑language prompts, covering dependency setup, proxy configuration, @Tool annotations, MCP server registration, chat client wiring, data initialization, and end‑to‑end testing.
This guide shows how to use the Model Context Protocol (MCP) to build a lightweight server that links Grafana dashboards to large language models, covering MCP concepts, FastMCP setup, Python client implementation, environment preparation, and integration with Cherry Studio for seamless AI-driven data access.
This article walks through the Model Context Protocol (MCP) as an AI‑centric "USB‑C" for tool integration, explains its core capabilities, shows how to register and call MCP tools using the TypeScript SDK, details the end‑to‑end workflow from ZIP file handling to React code generation, and shares practical implementation tips and future improvements.
The discussion introduces the Model Context Protocol (MCP) as an open‑source framework that repositions AI from a peripheral add‑on to the central “brain” of software, allowing AI‑first integration with business APIs, reducing rewrites, unifying user interfaces, and promising a rapid open‑source surge that could reshape development, production, and decentralized ecosystems.
This guide shows how to convert a traditional Spring Boot book‑management service into a Model Context Protocol (MCP) server that lets large language models interact with the service via natural‑language commands, covering dependency setup, MCP configuration, tool annotations, chat client integration, and end‑to‑end testing.
This article demonstrates how to convert a traditional Spring Boot book‑management service into an AI‑enabled MCP server, covering dependency setup, entity definition, tool annotations, MCP configuration, chat client integration, data initialization, and testing, with complete code examples.
This article explains the evolution from Spring AI FunctionCall to the newer ToolCalling, introduces the Model Context Protocol (MCP), and provides step‑by‑step code examples for building MCP servers and clients in Java, demonstrating how to integrate large language models with backend services.
This guide demonstrates how to create a Spring Boot backend that integrates DeepSeek's large language model via the Spring AI OpenAI starter, covering project setup, dependency configuration, API key management, and a sample controller that provides AI-powered chat responses such as weather forecasts.
This article analyses why current AI agents fall short of being true digital employees, identifies four major obstacles—undocumented knowledge, GUI‑only tools, lack of isolated test environments, and limited memory and initiative—and proposes a comprehensive, six‑step technical and cultural roadmap for creating AI‑native teams that treat AI as a collaborative team member.
This tutorial walks through creating a Model Context Protocol (MCP) server and client using Spring Boot 3, covering dependencies, configuration, tool implementation, registration, and sample code to enable AI models to access external services like weather and IP information.
This article explains how Spring AI's tool‑calling feature extends large language models by integrating custom Java tools for information retrieval and operations, and provides step‑by‑step Spring Boot 3 examples—including date‑time and weather tools—complete with configuration, code snippets, and execution results.
The Model Context Protocol (MCP) lets large language models safely and directly access diverse data sources and tools, breaking data silos and enabling seamless AI‑driven automation across development, operations, and multi‑agent workflows.
This guide explains how to quickly integrate Baidu Map's MCP protocol into Java AI applications using NPX, covering API key setup, configuration, core map APIs, and sample Java code for seamless location and routing services.
The article explains how Vite’s native ES‑module architecture and the Model Context Protocol (MCP) enable deep AI integration via the vite‑plugin‑vue‑mcp, allowing tools like Cursor to access component trees, state and routing, which the author claims can raise development speed by up to 200% and cut bugs by 65%.
The article explains Anthropic's open Model Context Protocol (MCP), detailing its client‑server architecture, resource and prompt definitions, tool discovery and execution, sampling workflow, security features, and provides a complete Python example that demonstrates building, running, and testing an MCP server and client for real‑time data retrieval.
This article provides an in‑depth technical overview of the Model Context Protocol (MCP), explaining its core concepts, client‑server architecture, communication layers, key benefits such as uniformity and security, and a wide range of real‑world application scenarios from code collaboration to third‑party API integration.
This guide demonstrates how to integrate the Cursor AI editor with Git to automate commit message generation, streamline branch management, perform intelligent code reviews, resolve merge conflicts, and generate commands, offering concrete prompts, shortcuts, and visual examples that enhance version‑control efficiency for development teams.
Amid the AI wave, Java backend developers focused solely on CRUD face replacement by automation, but by deepening business expertise, enhancing soft skills, gaining project experience, and expanding into AI and new technologies, they can transform their roles and stay valuable in the evolving software industry.
DeepSeek4j 1.4 introduces a Java‑centric, Spring Boot‑compatible framework that fully preserves DeepSeek's chain‑of‑thought capabilities, adds reactive streaming, and provides simple one‑line API integration, addressing previous limitations in mainstream frameworks and offering ready‑to‑use configuration and code examples.
This article provides a concise guide on using SpringAI to connect Java applications with the domestic large‑language model DeepSeek, covering design philosophy, configuration, code examples for chat, streaming, structured output, security hardening, performance tuning, and production best practices.
The article introduces DeepSeek4j 1.4, a Java Spring Boot library that overcomes existing framework limitations by preserving DeepSeek's chain‑of‑thought capabilities, adding full reactive streaming, and providing a simple one‑line API along with quick‑start instructions and code examples.
The article explains why developers must combine deep business understanding with AI tools, outlines a three‑layer domain model (core, generic, supporting), debunks common misconceptions, and provides a step‑by‑step methodology for mastering banking business concepts to boost ROI and career resilience.
This guide walks through setting up a local personal knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and demonstrates how to integrate the Continue AI code assistant into VSCode, covering installation, configuration, and usage tips for efficient, secure development.
This tutorial walks you through integrating DeepSeek via Spring AI into a Spring Boot project, covering Spring AI basics, obtaining an API key, adding dependencies and configuration, implementing controller endpoints, testing with Postman, and accessing the full source code.
This tutorial walks through configuring Alibaba Cloud AI services, creating sparse embedding and rerank endpoints, setting up Elasticsearch mappings, indexing Agatha Christie data, and combining semantic search, reranking, and completion APIs to achieve more relevant search results and a RAG‑style answer generation pipeline.
This step‑by‑step guide shows you how to obtain a DeepSeek API key, install VSCode and the open‑source Cline extension, configure the plugin to connect to DeepSeek, and use AI‑powered code assistance while covering common pitfalls and alternative models.
This tutorial explains what Spring AI is, walks through creating a Spring Boot project with Maven, adding the necessary dependencies, writing a simple controller that forwards user messages to a local DeepSeek model, configuring the application, and testing the AI‑powered endpoint.
The article explains why existing Java AI frameworks struggle with DeepSeek R1’s chain‑of‑thought and streaming features, introduces DeepSeek4j 1.4 as a targeted solution, details its core capabilities, and provides a step‑by‑step guide to integrate it with Spring Boot and Project Reactor.
Trae, a new AI‑driven IDE, offers a dark‑theme interface, integrated Chat Builder Beta, version‑control cues, and Claude‑3‑Sonnet support, and while it mirrors many Cursor features such as smart completion and chat, it adds educational prompts and context‑enhanced inputs, though it still faces WSL compatibility and beta stability issues.
DeepSeek4j 1.4 brings a Java‑native integration framework that fully preserves DeepSeek's chain‑of‑thought capabilities, adds reactive streaming, and offers a one‑line Spring Boot starter, enabling developers to quickly embed the model with simple configuration and rich debugging tools.
This article walks through installing Ollama, downloading the DeepSeek‑r1:1.5b model, configuring Spring AI in a Spring Boot 3 project, and exposing the model via a REST endpoint with Server‑Sent Events, providing complete code snippets and step‑by‑step screenshots for a functional AI integration.
This guide shows how developers can quickly integrate DeepSeek's powerful language model into WeChat mini‑programs using Tencent Cloud, covering three integration approaches, a three‑step voice‑interaction setup, and a concise list of related cloud products and resources.
This article summarizes a meetup talk by Alibaba Cloud expert Yu Deshui, detailing MaxCompute’s evolution, serverless architecture, AI‑enabled features, and the platform’s comprehensive solutions—including OpenLake, MaxFrame, Object Table, near‑real‑time computing, and AI Functions—to address the challenges of modern data‑centric AI workloads.
This guide walks through building a Java RAG system with DeepSeek R1, Milvus, and Spring, covering environment setup, vector model integration via OpenAI protocol, Maven dependencies, data embedding, and a chat endpoint that combines semantic retrieval with LLM generation.
This tutorial walks you through obtaining a DeepSeek API key, installing Docker, configuring the chatgpt‑on‑wechat project, creating a custom docker‑compose.yml, and deploying the Cow application to enable DeepSeek-powered chat in your personal WeChat, complete with code snippets and screenshots.
This article introduces deepseek4j, a Spring‑Boot‑compatible library that fully supports DeepSeek R1’s chain‑of‑thought and billing features, offering reactive streaming, easy configuration, and a built‑in debugging page, with step‑by‑step setup and code examples to help developers quickly integrate the model.
This step‑by‑step guide shows how to install IntelliJ IDEA, add the CodeGPT plugin, configure DeepSeek API keys and URLs, and use the DeepSeek Coder template for AI‑assisted code completion within the IDE.
This guide explains how the open‑source Model Context Protocol (MCP) enables natural‑language control of Docker containers, walks through environment setup, Spring AI integration, client project configuration, and demonstrates a full execution log showing automated image pulling, port mapping, volume mounting, and container startup.
This article introduces DeepSeek R1, a breakthrough reasoning‑focused large model that visualizes its chain‑of‑thought process, matches OpenAI O1 performance, offers open‑source advantages, and provides step‑by‑step Spring AI integration guidance, including dependency setup, configuration, and code examples.
The 2024 second‑half database industry review highlights accelerated growth, AI‑database integration, multimodal support, storage‑compute separation, and a comprehensive roundup of major product releases and feature enhancements across RDBMS, NoSQL, NewSQL, cloud, and big‑data ecosystems, with links to detailed changelogs and download resources.
The article walks through the DIKW model, illustrates how a homestay platform’s data stack evolves from a simple MVP to a complex, AI‑enabled distributed system, and details the design of a unified Data Warebase that combines database and data‑warehouse capabilities to meet performance, correctness, and real‑time demands, backed by concrete case studies and measurable improvements.
Baidu Intelligent Cloud’s Qianfan AppBuilder 3.0 offers an enterprise‑grade platform that simplifies large‑model application development by providing high‑accuracy RAG, robust agent scheduling, extensive integration, secure private‑or‑hybrid deployment, and a guided methodology, enabling industries to transform processes, add AI copilots, and create novel capabilities.
This guide walks you through setting up Ollama for local large‑model serving, creating a Spring Boot project with Spring AI support, writing a unit test to query the model, and explains how to add the necessary dependencies for AI integration in existing Java applications.
The talk outlines ApsaraMQ’s journey to a fully serverless, cloud‑native messaging platform, detailing its compute‑storage separation, stateless proxy functions, RDMA‑enhanced performance, elastic scaling mechanisms, and how its event‑driven architecture empowers real‑time AI applications through seamless data vectorization.
This article examines the challenges of traditional on‑premise software, explains how enterprise SaaS launchpads with dual front‑office and back‑office workbenches simplify user roles, improve digital asset management, and leverage AI to enhance efficiency, offering practical design patterns and service‑off‑boarding strategies.
This article introduces HarmonyOS NEXT's Intent Framework, explains its distribution capabilities such as habit and event recommendations, skill invocation, and local search, showcases practical scenarios including movie ticket reminders and JD Finance services, and provides step‑by‑step integration instructions with code examples.
This article explains HarmonyOS NEXT's Intent Framework, detailing its distribution capabilities, real‑world usage scenarios—including finance examples—along with step‑by‑step integration, code snippets, testing procedures, and future enhancements to improve mobile app engagement.
This article explains Robotic Process Automation (RPA), its cost‑saving and efficiency benefits, common use cases such as private‑domain operations, HR and e‑commerce, the business and technical architecture of a large‑scale RPA system, Android accessibility service fundamentals, sample code, and future AI‑driven enhancements.
Since 2017, Guangdong Mobile has advanced its digital transformation by integrating data‑weaving technology with AI large models to overcome data silos, improve governance, and support a rapidly growing mix of structured and unstructured data across its enterprise, culminating in a virtual data‑center architecture that drives efficient data storage, processing, and business innovation.
This article examines how large‑scale big‑data platforms can simplify AI application development, outlines the shift from model‑centric to data‑centric paradigms, and shares Alibaba Cloud’s practical experiences in building an integrated big‑data‑AI architecture, including MaxCompute, Hologres, MaxFrame, and vector search capabilities.
This article compares Kong, Gloo, and Higress on their AI‑related extensions—covering tech stacks, logging, proxy normalization, API‑key handling, caching, request/response rewriting, and RAG support—to help developers choose the most suitable gateway for emerging LLM workloads.
This article examines the three dominant AI integration forms on PC—immersive agents, accompanying co‑pilots, and embedded AI—detailing their capabilities, UI designs, real‑world examples, and design considerations to help creators choose the most effective approach for each scenario.
This case study examines how 58 Home’s maid‑booking service transformed its traditional broker‑driven model into a direct, zero‑intermediary experience, using market analysis, AI features, and conversion‑rate optimizations to overcome user confusion, low resume quality, and traffic challenges.
On August 10‑11, JD Group's Project Management Talent Development Channel showcased its expertise at the 3rd China PMO&PM Conference in Shanghai, delivering multiple expert talks on data‑driven project management, R&D efficiency, AI‑enabled workflows, overseas delivery, and fostering industry dialogue through round‑table discussions.
This article provides a comprehensive, step‑by‑step guide to designing, prototyping, and continuously improving an AI‑assisted software engineering (AI4SE) framework, covering goal definition, pain‑point identification, technology selection, cross‑disciplinary team building, metric evaluation, and real‑world examples for teams of all sizes.
A curated roundup of the latest frontend and AI development resources, covering React 2023 insights, new CSS font-size-adjust support, Astro 4.12 release, zero‑width character pitfalls in phone validation, the relevance of global state, form‑design strategies, and AI‑assisted coding workflows.
This article reviews the evolution of databases and large models, explains vector database fundamentals and RAG pipelines, and details Baidu's VectorDB architecture, performance advantages, and its role in AI‑enhanced database operations.
This article examines how the big‑data AI development paradigm has shifted from model‑centric to data‑centric workflows, outlines the challenges of integrating data and AI teams, and details Alibaba Cloud’s end‑to‑end, serverless big‑data platform—including MaxCompute, Hologres, MaxFrame, Object Table, and vector search—designed to accelerate large‑scale AI applications.
This article introduces Spring Cloud Alibaba AI, explains its relationship to Spring AI, and provides a step‑by‑step tutorial—including Maven setup, dependency configuration, code examples, and sample calls—to integrate Alibaba's Tongyi large‑model services for text QA, image generation, and speech synthesis in a Java Spring Boot application.
This article explores the technical maturity curve of indicator systems, covering data collection, modeling, production, management, governance, and application, while analyzing the security, stability, and usability requirements and discussing how large language models can enhance certain clear and complicated scenarios.
This tutorial teaches Java engineers how to build a JUnit4 Rule that captures test failures, extracts stack traces and source files, sends them to GPT for automated analysis, and logs the AI‑generated code improvement suggestions, complete with full example code and usage.
This tutorial introduces Spring AI and Spring Cloud Alibaba AI, explains their core features, shows how to set up a Maven project with the required dependencies, and provides step‑by‑step code examples for invoking text, image, and audio generation models using Spring Boot.
OpenAI and Stack Overflow have announced a partnership that will integrate large‑language models into the platform, allowing real‑time API access, AI‑enhanced search, and content linking in ChatGPT, while also raising concerns about community engagement, data usage, and future strategic direction.
This article explains how to build and apply a comprehensive metric system by addressing business, technical, and product challenges, outlining design, standardization, metadata management, and future AI‑driven use cases to support data‑driven decision making.
This guide explains how to add Moonshot's large language model to the Webman 5.0 framework, covering feature highlights, driver and processor implementation in PHP, configuration of model mappings, role settings, and practical code snippets for seamless AI integration.
Stack Overflow and Google Cloud have formed a strategic partnership to embed verified Stack Overflow answers into Google Cloud’s Gemini AI, while JetBrains’ survey reveals AI coding assistants dramatically boost developer productivity and satisfaction, highlighting a new era of AI‑enhanced software development.
This guide introduces Spring AI, shows how to add dependencies, configure OpenAI credentials, and demonstrates chat, image, and prompt APIs with code examples, then explores advanced function calling to build business‑level large language model integrations in Java.
Discover a Python 3.11‑based WeChat bot that leverages ChatGPT for intelligent replies, AI‑generated images, context memory, and a suite of automation features such as friend management, video downloads, and automated Moments posting, complete with system requirements, installation steps, and visual demos.
This article explains how ChatGPT is integrated into the Columbus Instant Design low‑code platform, detailing the platform’s core capabilities, AI‑driven workflow for creating and modifying UI floors, implementation architecture, token management, and future development plans.
The Bilibili video template system combines theme‑based editing, a layered architecture, and a cross‑platform Protobuf protocol to enable PGC and UGC creators to produce, review, distribute, and consume richly‑featured videos with standardized media formats, modular plugins, AI integration, and robust quality assurance.
CoUnit is an open‑source Rust‑based tool that uses local semantic search and LLMs to create a virtual team interface, enabling low‑cost, offline knowledge retrieval, API discovery, and cross‑team assistance for software development teams.
The article examines fundamental challenges of applying large language models to software engineering—such as scale limits, lack of abstract reasoning, hidden tacit knowledge, and maintenance difficulties—and proposes practical recommendations for integrating AI with disciplined development practices.
This article explores the rise of procedural materials in computer graphics, explaining how algorithm-driven textures in Unreal Engine can generate infinite variations, save time, enhance performance, and integrate with AI, while also discussing workflow, node systems, challenges, and future trends for game developers.
This article explains how integrating large language models into industrial configuration tools creates AI‑driven features such as knowledge Q&A, automatic application generation, smart drawing, script and material generation, and outlines the three‑layer architecture, prompt engineering, and implementation lessons for developers.
The article traces the evolution of databases, explains how vector databases leverage generative AI for similarity search and anomaly detection, showcases practical examples, details KX's KDB.AI pipeline, discusses market competition and privacy concerns, and highlights speed claims that make vector databases pivotal for modern data-intensive applications.
The article examines the rapid rise of GPT models, their technical capabilities and limitations, and how their integration is reshaping software interaction from command‑line to GUI‑plus‑Language UI, offering frontend engineers new opportunities, practical examples, and guidance on leveraging large‑model AI in product design.
This article explores whether AI agents such as ChatGPT can help business owners quickly perform data analysis by linking large language models with local MySQL databases via LangChain, presenting the experimental setup, schema creation, sample queries, results, and practical insights.
This article explores the challenges AI poses to designers, examines why low‑code platforms struggle with user experience, analyzes real‑world canvas issues through competitor studies, and presents a comprehensive redesign of the 58APAAS canvas that improves framework, visual feedback, interaction, semantics, and UI guidelines while hinting at future AI‑driven enhancements.
Integrating ChatGPT with low‑code platforms—using it to translate domain models, generate DSL specifications, and fill code snippets—can automate up to 90 % of routine coding while still requiring human review for complex logic, data sensitivity, and best‑practice compliance.
This guide shows how to install PandasAI, connect it to OpenAI's language model, and use natural‑language prompts to query and visualize Pandas DataFrames, including examples with sample data, database connections, and custom aggregations.
This guide walks you through preparing an OpenAI account, obtaining an API key, using the Completion endpoint with example JSON, calling the API from plain JavaScript/HTML, and integrating the same service into a Spring Boot application with Maven dependencies and Java code.
The article explores how AI's rise highlights human error, argues that fault‑tolerant design is essential for user‑centric products, outlines practical guidelines such as anticipatory guidance and constructive error recovery, and validates the approach with a VR‑tool experiment.
Windows' share of the US desktop market has fallen to its lowest level since 2009, dropping to 57.37% as macOS and ChromeOS gain traction, while Microsoft's AI-driven enhancements aim to revive its dominance.
This article summarizes Wang Chuanting’s DTCC2022 talk on Huawei Cloud GaussDB(DWS) 3.0, detailing its cloud‑native architecture, layered elasticity, lake‑warehouse integration, performance acceleration techniques, and how it seamlessly couples data‑processing pipelines with AI workloads for modern, real‑time analytics.
In this interview, Huawei Cloud’s chief architect Zeng Kai explains how data warehouses originated, outlines the five key fusion trends shaping their evolution, and reveals the innovative features of GaussDB(DWS) 3.0 that drive cloud‑native, real‑time, and AI‑integrated analytics.
This article demonstrates how to integrate ChatGPT with Siri using iOS Shortcuts to build a sophisticated smart home system, detailing JSON request structures, prompt engineering, handling of command, query, answer, and clarification actions, and providing step‑by‑step code examples for implementation.
Microsoft’s $100 billion investment in OpenAI aims to embed ChatGPT across Bing and Office, promising powerful AI-driven search and productivity tools while confronting challenges of cost, accuracy, and privacy, and signaling a major shift in the competitive AI landscape.
The paper describes how Bilibili transformed its danmaku service from a simple, limited‑recall pipeline into a ten‑fold larger, KV‑store‑backed recommendation architecture that unifies engineering and AI layers, uses dynamic sharding and Redis locks, and ultimately boosts recall pool size, exposure, and experiment speed while reducing downgrade rates.
The talk outlines the evolution of Alibaba Cloud’s integrated big data and AI platform, highlighting the three‑V fundamentals, the AI‑inspired usability‑scale‑efficiency triangle, open‑source trends, and how the platform unifies offline and real‑time analytics while simplifying governance and development.
