Google's Gemini Spark, announced at I/O 2026, is a cloud‑based personal AI assistant that runs on Gemini 3.5 and the Antigravity framework, allowing users to schedule long‑running tasks without keeping a computer or browser open, and integrates with Google tools via the MCP protocol.
This guide walks developers through Codex’s advanced features—including layered AGENTS.md configuration, Context Compaction, Claude Code + Codex collaboration, sandbox and Rules security, MCP protocol integration, profile switching, pipeline workflow, and session management—showing how each can be combined to turn basic code generation into a high‑efficiency development engine.
A Beijing dumpling restaurant has open‑sourced an AI Skill that lets assistants query its location, menu, Wi‑Fi password and more via the MCP protocol, and the article explains the skill’s capabilities, technical implementation, and step‑by‑step usage instructions.
This guide walks experienced developers through Codex’s advanced capabilities—layered AGENTS.md configuration, Context Compaction to prevent memory loss, Claude‑Code hybrid workflows, sandbox and Rules security controls, the extensible MCP protocol, profile switching, pipeline integration, and session management—to transform casual use into expert mastery.
The article explains how poorly designed prompts cause AI agents to invoke unnecessary or incorrect tools, and shows how a structured prompt template combined with the Model Context Protocol (MCP) and three safety measures can raise tool‑calling accuracy from about 30% to over 95%.
This article examines the limitations of AI‑generated Mermaid diagrams and introduces Next AI Draw.io MCP, which lets AI produce editable XML draw.io files, enabling real‑time, editable architecture assets directly from code logic.
A product manager overwhelmed by manual competitor research discovers that configuring AI agents with specialized Skills—such as web‑browsing, code‑interpretation, and workflow automation—can turn a three‑hour, labor‑intensive task into a five‑minute, fully automated process, reshaping the role of PMs from operators to architects.
AEnvironment is an open‑source, unified environment platform for Agentic Reinforcement Learning that abstracts all resources as services via the MCP protocol, enabling trillion‑scale model training, rapid app generation, benchmark integration, and seamless deployment through a high‑performance ASandbox runtime.
An in‑depth review of the WEF‑Capgemini 2025 whitepaper reveals how AI agents are transitioning from simple chatbots to autonomous decision‑making partners, outlining a three‑layer architecture, new communication protocols, governance challenges, risk assessment frameworks, and practical steps for enterprises to deploy trustworthy agents.
This report outlines emerging AI trends for the coming year, highlighting Physical AI, AI agents, multimodal models, the MCP standard, and the growing role of AI‑driven DevOps, while also discussing security implications and adoption stages across the technology lifecycle.
This article presents a structured, step‑by‑step learning path that guides beginners from Python fundamentals through AI API mastery, Retrieval‑Augmented Generation, deep MCP protocol knowledge, and advanced multi‑agent development, complete with practical code examples and performance‑monitoring techniques.
This article breaks down the confusing AI buzzwords—user prompts, system prompts, agents, tool registration, function calling, and the MCP protocol—explaining how they work together to enable AI assistants that can perform real tasks beyond simple chat.
This article details Baidu's evolution from manual network fault analysis to a multi‑agent AI platform, describing how the Lingxi intelligent agent leverages large language models, MCP tools, and design patterns to automate latency queries, generate analysis reports, and integrate with existing monitoring services.
This article explains how the OpenTiny team uses the Model Context Protocol (MCP) to enable large language models to control web applications, describes the TinyVue intelligent component library, shows architecture and code examples, and provides step‑by‑step guidance for integrating AI‑driven automation into front‑end projects.
This article explains how AI‑driven agents, powered by large language models and the Model Control Protocol (MCP), can directly interact with internal and external APIs, eliminating the need for traditional front‑end code and reshaping application development toward capability orchestration.
This article examines how the rise of large language models reshapes core business assets, defines agents and tools, explores multi‑agent collaboration patterns, task allocation and conflict resolution mechanisms, and evaluates the MCP protocol and engineering requirements for building scalable, flexible agent platforms.
This article explains the limitations of the original Stdio and HTTP SSE communication modes for MCP, introduces the Streamable HTTP protocol that resolves those issues, and provides a step‑by‑step Python implementation of both a Streamable HTTP MCP server and a matching client, complete with environment setup, FastAPI code, JSON‑RPC handling, and tool‑calling examples.
This guide explains how the open‑source MCP protocol from Anthropic, now offered as a full‑lifecycle service on Alibaba Cloud Bailian, lets you quickly create a plug‑and‑play AI agent that integrates large models with external tools such as maps and weather APIs, all in under five minutes.
The article examines the Model Context Protocol (MCP), detailing its microkernel‑based technical architecture, development timeline from Anthropic’s 2024 release to industry adoption, hands‑on implementation examples, and business use cases such as multi‑agent QQ robots, highlighting MCP’s potential to standardize AI tool integration across industries.
This guide walks through setting up the MCP protocol on macOS, configuring a server, installing Bun, linking Cursor 0.47+ with Figma, importing the plugin manifest, enabling MCP in Cursor, and using prompt templates to automatically generate high‑quality iOS fitness app designs, complete with screenshots and practical tips.
