This article explains how Helidon 4.2 integrates the LangChain4j framework to simplify adding large‑language‑model capabilities, covering core features, Maven setup, configuration, component creation, dependency injection, annotations, custom tools, and sample applications such as a coffee‑shop assistant.
This article explains why AI often mishandles Obsidian’s markdown, introduces the open‑source obsidian‑skills package that equips AI with native markdown control, canvas generation, and database manipulation, and provides step‑by‑step setup, real‑world prompts, and tips for reliable use.
The article outlines eight pivotal 2026 Spring Boot trends—from mandatory migration to Java 17 and Jakarta namespaces, GraalVM native images, reactive WebFlux, OpenAPI/GraphQL APIs, zero‑trust Spring Security 6, Spring Cloud governance, Actuator observability, to AI integration—explaining why each matters and how to prepare.
This guide explains how to install Symfony AI components, use the Gemini platform, and create a Webman endpoint that leverages AI for chat, RAG search, and intelligent agents, providing full code examples and response handling.
This article explains the fundamental differences between Model Context Protocol (MCP) and Agent Skills, covering their design philosophy, technical architecture, token costs, security considerations, practical use‑cases, and how they complement each other in building powerful AI agents.
This article provides a comprehensive analysis of Spring AI’s Model Context Protocol (MCP), covering its layered client‑server architecture, core interaction sequences, key source‑code components, and step‑by‑step debugging of the SSE‑based initialization flow, enabling developers to integrate AI capabilities into Java applications with confidence.
MCP (Model Context Protocol) is an open standard that lets AI applications connect to external systems through a unified client‑server model, exposing Tools, Resources, and Prompts, while addressing security, permission, and audit concerns to make large‑model deployments more reusable and controllable.
This article walks through creating a Spring AI 1.1 MCP Server using the new annotation‑based programming model, covering project setup, Maven dependencies, YAML configuration, replacing @Tool with @McpTool, exploring the new context parameters, and demonstrating dynamic schema support with concrete code examples and test results.
The article introduces the Model Context Protocol (MCP), an open Anthropic standard that creates a secure, standardized, bidirectional bridge between large language models and external tools, then walks through its architecture, core components, Python server and client code, OpenAI integration, usage flow, ecosystem and future outlook.
The "AI‑Led Cross‑Platform Technology New Paradigm" salon gathered experts from Kuaishou, Alipay, Tencent, and JetBrains to share deep technical insights on Kotlin Multiplatform, large‑scale multi‑device architecture, AI integration, and real‑world migration experiences across iOS, Android and HarmonyOS.
The article examines how widespread AI anxiety among executives forces product teams to translate lofty, sometimes unrealistic AI visions into concrete, user‑centered features, detailing the design philosophy of "precise echo", the clash over perceived "fabrication", and the evolution toward a more exploratory "soul insight" approach.
This guide explains how to integrate large language models into modern web applications using Vercel AI SDK, covering core modules, package responsibilities, when to choose each package, installation steps, example code for both backend API routes and React front‑end, and a complete quick‑start workflow.
This guide explains why PHP applications need AI, outlines core AI use cases such as intelligent content processing, computer vision, personalization, and chatbots, and provides step‑by‑step implementation paths, tools, best‑practice recommendations, real‑world case studies, and future trends for developers.
The article examines how the wave of AI anxiety among executives reshapes product logic, detailing a three‑act saga of a precise‑echo AI requirement generator, a boss‑driven “fabrication” critique, and the evolution to a “soul‑insight” version, while offering concrete translation tactics and a taxonomy of boss behaviors.
The 2025 Apache Paimon release brings major performance boosts, AI‑centric multimodal storage, deeper streaming‑batch integration, and broader engine compatibility, detailing query and write optimizations, memory management tweaks, and a unified lake format for structured and unstructured data.
Spring AI 1.1.0 arrives with a major overhaul, adding out‑of‑the‑box Model Context Protocol support, five‑mode prompt caching that can cut LLM costs by up to 90%, reasoning APIs, recursive advisors, a broadened model ecosystem, enhanced vector‑store and chat‑memory options, and richer observability integrations.
This article explains how Spring AI handles prompts, covering the Prompt and Message APIs, role-based message types, PromptTemplate creation, custom template renderers, tokenization basics, and practical code examples to help developers craft effective AI prompts.
The article explores the challenges faced by traditional DBAs, redefines the DBA role as a blend of data, business, administration and architecture, presents multiple real‑world cases, and shows how AI and a DBA² mindset can dramatically improve efficiency and career prospects.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
This article introduces four cutting‑edge open‑source AI projects—Glass, CraftGPT, AI Trend Publish, and Klavis—detailing how they read screens and audio, run language models inside Minecraft, auto‑generate AI‑focused articles, and integrate dozens of external tools for powerful, automated assistance.
FastMCP lets Python developers replace hundreds of lines of LLM‑to‑database and API glue code with a 30‑line, low‑code server that offers quick setup, built‑in security, and extensible tool chaining for AI applications.
Matomo is an open‑source, PHP‑based web analytics platform that combines real‑time traffic monitoring, SEO optimization tools, and AI‑driven keyword suggestions, offering privacy‑first, self‑hosted deployment via Docker and supporting GDPR compliance, making it a powerful alternative to commercial analytics solutions for marketers and developers.
This article explains the Model Context Protocol (MCP) – an open, universal standard that lets large language models seamlessly interact with external tools and data – covering its core architecture, why it’s needed, underlying principles, tool‑selection mechanics, a step‑by‑step Python server implementation, and practical usage tips.
This article chronicles GitHub’s transformation from its modest 2008 launch, through rapid growth phases, major feature rollouts, and the recent AI integration, illustrating how a simple code‑hosting service became the world’s largest open‑source platform.
This tutorial shows how to let large language models autonomously invoke external services—specifically a MySQL database—by using the Model Context Protocol (MCP), covering environment setup, MCP service implementation, agent integration, and real‑world execution results.
This article explains how LApiGateway leverages the Model Context Protocol (MCP) to enable AI clients to access existing HTTP RPC services without code changes, detailing the protocol basics, current challenges, the gateway's architecture, implementation steps, and the resulting operational benefits.
The Model Context Protocol (MCP) introduced by Anthropic addresses the scalability, integration, and context‑transfer limitations of traditional Function Calling by offering a standardized, bidirectional, and context‑aware communication layer that simplifies tool discovery, security, and workflow orchestration for LLM‑driven agents.
This article introduces the Model Context Protocol (MCP) Go SDK v1.0.0, explains its stable API guarantees, provides quick‑start examples for building MCP servers and clients in Go, and outlines the SDK's modular design and error‑handling strategy for AI‑enabled backend development.
The article analyzes why many workers reject AI adoption, examining technological maturity limits, lack of best‑practice guidance, human‑nature learning barriers, and misaligned incentives, while illustrating points with real‑world examples and personal observations from a podcast discussion.
The article explains how Spring Framework 6’s @HttpExchange annotation and the new HTTP service registry introduced in Spring Framework 7 enable developers to declaratively define, configure, and manage AI‑driven HTTP clients as Spring beans, reducing boilerplate, supporting multiple providers, and integrating seamlessly with Spring Boot, Cloud, and Security.
This tutorial walks through setting up a local TiDB MCP Server, configuring an AI client, and using natural‑language prompts to query TiDB databases, showcasing practical AI‑driven data access and highlighting real‑world application scenarios.
Microsoft’s Visual Studio 2026 Insiders preview brings AI deeply into the IDE, boosts performance, refreshes the UI, ensures extension compatibility, and replaces the old Preview channel with a monthly update stream for developers.
Oracle’s latest LTS release, JDK 25, introduces compact source files, streamlined module imports, enhanced constructors, memory‑efficient compact object headers, AI‑friendly features like pattern matching and the Vector API, plus performance and security improvements, while aiming to make Java more approachable for beginners.
This article analyzes how combining ontology with large language models can create a new intelligent application development paradigm that unites semantic understanding and executable behavior, proposing a three‑layer architecture, a Model Control Protocol, and real‑world case studies to illustrate its potential and challenges.
This edition of the Zhuanzhuan Frontend Weekly curates five insightful articles covering AI‑empowered frontend development, log system performance optimization, multi‑agent planning enhancements, AI‑driven workflow improvements, and an exclusive interview with an OpenAI researcher, offering practical techniques and forward‑looking perspectives.
In this interview, Legora’s 25‑year‑old founder Max Junestrand shares how his AI‑driven legal workspace, built on Word and a web app, scales massive queries, reshapes lawyer roles, avoids vendor lock‑in, and uses a top‑down sales strategy to win elite law firms worldwide.
This article explains the Model Context Protocol (MCP) as an open standard for secure AI‑tool interaction, details its client‑server architecture, showcases a Figma‑focused MCP implementation with code examples, and discusses data simplification, transformers, and image handling that enable AI assistants to generate accurate front‑end code from design files.
This article defines the Model Context Protocol (MCP), explains why it is needed to unify LLM and external tool interactions, details its client‑host‑server architecture, compares its communication modes, outlines Java/Spring AI support, and discusses current adoption trends and open challenges.
When integrating generative AI into existing enterprise systems, architects must address reliability, performance, and security by applying patterns such as circuit breakers, retries with exponential backoff, asynchronous processing, caching, request hedging, input/output guards, sandboxes, and security proxies to ensure continuous, fast, and safe AI‑driven functionality.
The article examines how The Browser Company's AI‑native browser Dia, recently bought by Atlassian for $610 million, aims to reshape web interaction amid Chrome’s antitrust win, detailing product features, market competition, and the broader race for the next AI‑driven internet entry point.
Atlassian’s $6.1 billion cash acquisition of The Browser Company aims to embed AI into its suite, sparking a debate on the viability of AI‑enhanced browsers like Dia, Perplexity Comet, Edge Copilot and Brave Leo, and exploring their potential to reshape productivity, privacy, and the future of web interaction.
On September 5, 2025 the PHP Foundation announced the launch of an official PHP Model Context Protocol (MCP) SDK, detailing its purpose to standardize AI integration, its collaboration with existing community projects, key features like framework support and cross‑platform compatibility, and the early development milestones achieved.
This article explains the Model Context Protocol (MCP) introduced by Anthropic, compares it with Function Call, details its architecture and communication flow, and provides a step‑by‑step Spring AI guide—including server and client development, configuration, code examples, and testing—so developers can integrate AI models with back‑end services efficiently.
This guide explains what the Model Context Protocol (MCP) is, lists key MCP resources, and provides a step‑by‑step tutorial for creating a token, linking a Figma design, configuring Cursor, and using MCP to automatically generate front‑end HTML and Tailwind CSS from a Figma file.
This article demonstrates how to create diverse AI applications—such as a children’s picture‑book generator, a guessing‑game app, and an enterprise website with knowledge‑base Q&A—entirely through natural‑language prompts in VibeCoding, covering the setup, step‑by‑step construction, code‑free publishing, integration of various AI services, deployment configurations, and practical tips for reliable development.
This article analyses the evolution of software factories over five decades, defines the third‑generation model focused on data governance, AI integration, and high‑frequency iteration, presents an applicability assessment framework, compares AI‑centric and traditional software stacks, and illustrates the concepts with military and commercial case studies.
This article explores how AI is reshaping software testing, detailing practical challenges, integration pitfalls, human‑AI collaboration dilemmas, and concrete lessons from the AITest project, while offering a roadmap from assisted testing to fully autonomous AI‑driven testing.
This weekly roundup curates the latest front‑end breakthroughs, from Vue 3 component libraries and large‑scale engineering quality platforms to AI‑driven operations, open‑source tools, and a new WebSocket debugging extension, offering developers fresh ideas and practical resources.
This article chronicles ODPS's 15‑year journey from its exploratory beginnings to a modern, AI‑enabled big data platform, detailing its four development phases, architectural layers, SQL engine upgrades, real‑time processing, lakehouse integration, and the new Data+AI capabilities offered by MaxCompute and DataWorks.
The Model Context Protocol (MCP) is an open‑source interface that standardizes how large language models interact with external data sources and tools, offering a USB‑C‑like universal connector for AI applications, with built‑in session management, security, and flexible HTTP/SSE transport for seamless real‑world integration.
The article analyzes BCG’s "Turbulent Times Call for a New People Strategy" report, outlining why economic volatility, geopolitical shifts, and rapid AI advances demand a resilient, future‑ready workforce and presenting a three‑pillar talent triangle with actionable guidance for CEOs and CHROs.
The article explains how the MCP protocol standardizes AI model interactions with external services, outlines the challenges of deploying MCP servers in enterprises, and demonstrates how Huawei Cloud FunctionGraph's serverless workflow offers a low‑cost, scalable, and secure solution with step‑by‑step deployment guidance.
This article explores the evolution of business intelligence toward intelligent BI, detailing traditional BI limitations, agile BI improvements, and how StarRocks' MPP lakehouse engine combined with large language models enables natural‑language analytics, real‑time performance, AI‑driven insights, and scalable enterprise deployments.
This article summarizes the major Apache Flink 2.0 updates released in the first half of 2025, covering architecture separation, cloud‑native deployment, AI‑driven agents, SQL enhancements, data integration, operational tools, and performance optimizations for real‑time intelligent computing.
This article demonstrates how to set up a FastMCP server for MySQL, load the Sakila sample database, expose query tools to Gemini, experiment with different model versions and temperature settings, and observe the generated SQL queries and results, providing a practical guide for AI‑driven database access.
The article analyzes the emerging AI browser war, explaining why Perplexity sees building its own native browser Comet as essential for survival, detailing its strategic rationale, workflow‑centric business model, technical architecture, and the risks of relying on rival AI models.
This article walks through the challenges of traditional traffic replay, explains the design of a conventional replay system, and then details a novel LLM‑powered solution that automates data preparation, script generation, validation, and continuous integration for backend service testing.
This article introduces three emerging open‑source MCP projects—FastAPI‑MCP for converting FastAPI endpoints, ACI.dev as a one‑click MCP orchestrator with 600+ tool connectors, and Dive, a cross‑platform desktop client—detailing their purpose, key features, usage steps, and GitHub repositories.
This article introduces the Model Context Protocol (MCP), explains its core advantages such as capability expansion, standardized interfaces, and low‑code integration, and demonstrates a practical Sentry‑MCP implementation with Python code, detailed functions, and visual results for AI‑driven data access.
This article walks through the fundamentals of large language models, their stateless and structured-output nature, explains how Spring‑AI provides a Java‑friendly API for model integration, covers RAG architecture, the MCP protocol, and demonstrates end‑to‑end code examples for building intelligent agents.
The Model Context Protocol (MCP) is an open, standardized adapter that lets large language models seamlessly connect to tools, data sources, and workflows, offering plug‑and‑play intelligence, cross‑platform compatibility, security, and modular extensibility for building real‑world AI applications.
This article explains what the Model Context Protocol (MCP) is, outlines common pain points when using Cursor AI with MCP, analyzes the issues, and provides a detailed engineering solution—including SDK installation, configuration, tool definition, code structure, build steps, and deployment—culminating in a practical example and future outlook.
The article introduces Model Context Protocol (MCP), explains its architecture of Host, Client, and Server, describes its components—Resources, Tools, Prompts—and demonstrates practical integration with IDE plugins to extend LLM capabilities such as real‑time ticket queries, highlighting its significance for AI development.
The Model Context Protocol (MCP) is a standardized interface that enables seamless, secure communication between AI models and external tools or data sources, detailing its core components, architecture, server lifecycle, ecosystem, use cases, and associated security and privacy considerations.
This article outlines ByteDance’s four‑part framework for improving R&D efficiency—covering concepts, real‑world practices, future outlook, and underlying principles—while detailing the three‑layer model, the “golden triangle” of online, standardization, and digitization, and the roadmap toward platform engineering and AI‑augmented development.
This guide shows how to integrate Spring Boot with the Model Context Protocol (MCP) to let large language models interact with existing services via natural language, covering entity definitions, Maven dependencies, configuration, tool annotations, and testing to create an AI‑enabled book management API.
Apple’s WWDC 2025 revealed iOS 26 (formerly iOS 19) with a sweeping “Liquid Glass” UI redesign, AI‑powered photo tools, a dynamic lock‑screen clock, revamped camera and gallery, plus a new Foundation Models Framework for on‑device AI, while iPadOS gains multi‑window and file‑management features.
This step‑by‑step tutorial shows how to install Ollama, pull the DeepSeek‑R1 model, create a Spring Boot project with the Spring AI Ollama starter, code a ChatController, and test a local AI "Hello World" integration, illustrating AI‑enhanced backend development.
This article explains the Model Context Protocol (MCP), an open standard that lets AI models, tools, and agents share context and communicate through a central server, detailing its definition, key components, workflow, benefits for developers, and real‑world examples.
This article walks through how Java developers can connect to Python‑based AI services using the Model Context Protocol (MCP), compares STDIO and SSE transports, explains why Spring AI’s MCP support is limited, and shows a complete implementation with the raw MCP Java SDK and OpenAI client to invoke tools like Blender from Java code.
The Model Context Protocol (MCP) is an open, client‑server protocol introduced by Anthropic that standardizes how applications provide context to large language models, offering a USB‑C‑like interface for tools, data sources, and services to enable reliable, extensible, and secure AI interactions.
The article announces the official release of Spring AI 1.0, explains the meaning of GA, provides the Maven dependency configuration, describes how Spring AI simplifies AI integration for Java applications, and outlines its key features and supported model types.
This article introduces the Model Context Protocol (MCP) as an open AI‑model integration standard, explains its client‑server architecture and components, shares practical Node/Python development pitfalls and debugging tips, discusses hallucination and error‑retry strategies, lists useful tools, and reflects on the broader implications of AI‑driven conversational services.
Model Context Protocol (MCP) is an open standard that creates secure bidirectional links between data sources and AI tools, offering ecosystem plugins, cross‑model compatibility, and data‑privacy benefits; the article compares MCP with function calls and agents, outlines its architecture, and demonstrates practical implementations at Huolala.
This guide walks Java developers through integrating Spring AI with large‑model services such as DeepSeek, covering setup, API key configuration, code examples for synchronous and streaming calls, reactive implementation, monitoring with Actuator, and compatibility with OpenAI‑style APIs.
This article explains the Model Context Protocol (MCP) for AI development, detailing its definition, core components, communication methods, execution process, relationship with agents and function calling, ecosystem growth, and future implications and challenges.
WeddingJoy, a comprehensive wedding service platform, transformed its data infrastructure by adopting Alibaba Cloud DataWorks, addressing challenges of massive heterogeneous data, improving data governance, enabling AI-driven insights, and boosting development efficiency across its ecosystem.
The article analyses how privacy concerns, data‑security priorities and an application‑first strategy cause both Apple’s Apple Intelligence and WeChat’s AI features to lag behind hype, examining product decisions, technical constraints, and the potential future of AI agents within these ecosystems.
This article introduces the Model Context Protocol (MCP), explains its client‑server architecture, shares practical development experiences and debugging tips for Node.js and Python implementations, discusses common pitfalls such as environment setup, hallucinations, and error handling, and reflects on the broader implications of AI‑driven services.
This guide explains the Model Context Protocol (MCP) that lets AI models securely access files, databases, APIs, and other resources, and presents a curated list of standout MCP servers across categories such as aggregators, web automation, cloud platforms, finance, security, and more, helping developers enhance AI workflows.
The article reviews the shortcomings of traditional large language models, explains how function calling extends LLMs beyond pure text, introduces the Model Context Protocol (MCP) as a standardized USB‑C‑like interface for AI tools, and demonstrates a Python MCP example that integrates LLMs with Tencent Advertising APIs.
This article introduces Spring AI, explains its core features for integrating various AI models, and walks through creating a Spring Boot chat application that connects to the DeepSeek model using both synchronous and streaming endpoints.
This article walks through Amap's Model Context Protocol (MCP) service, explaining its purpose, installation steps, configuration in the Cursor client, and practical examples such as travel planning and location‑based queries, while also evaluating its strengths and current limitations.
Android 16 adds the AppFunctions API, letting apps expose specific functions through an AppFunctionService that the system indexes via AppSearch and invokes with AppFunctionManager, so AI assistants and system search can discover and execute actions like ordering pizza or booking flights while respecting required permissions and threading constraints.
The article explains Anthropic’s Model Context Protocol, outlines its architecture, and provides a step‑by‑step guide to creating a Spring AI‑based MCP server in Java—including adding the starter, defining @Tool‑annotated services, packaging the jar, configuring the Cline plugin, and demonstrating advanced tools such as Elasticsearch queries.
This article summarizes the key changes from the Nacos 3.0 beta to the official release, covering JDK and Spring Boot upgrades, new Admin APIs, default authentication, AI Model Content Protocol, unified namespaces, beta distributed lock, fuzzy listening, and native xDS support for cloud‑native microservice architectures.
This article introduces the Model Context Protocol (MCP) released by Anthropic, explains its core features and client‑server architecture, walks through building a Go‑based MCP server and client with time, weather, and schedule tools, demonstrates testing with MCP Inspector, and highlights MCP's advantages and typical AI application scenarios.
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.
