Category: Blog

🤖 Claude Code CLI

Claude Code CLI is launched by Anthropic. Based on its large Claude models (such as Opus 4, Sonnet 4), it is a command - line intelligent programming assistant that emphasizes strong reasoning ability and in - depth code understanding.

Advantages:

• In - depth Code Understanding and Complex Task Handling: Claude Code can deeply understand the structure of code libraries and complex logical relationships. It supports a context window of hundreds of thousands of tokens, enabling efficient multi - file linkage operations and cross - file context understanding. It is particularly good at handling medium - to - large - scale projects.
• Sub - agent Architecture and Powerful Toolset: It supports the sub - agent architecture, which can intelligently split complex tasks into multiple subtasks for parallel processing, achieving multi - agent - like collaboration. The built - in toolset is rich and professional, including more refined file operations (such as MultiEdit for batch modification), efficient file retrieval (Grep tool), task management and planning (TodoWrite/Read, Task sub - agent), and profound Git/GitHub integration capabilities, such as understanding PRs, code review, and handling comments.
• Integration with Enterprise - level Toolchains: Claude Code can not only be seamlessly integrated with IDEs, directly showing code changes in the IDE's difference view, but also be integrated into the CI/CD process in the form of GitHub Actions. It allows @claude in the comments of PRs or Issues to automatically analyze code or fix errors.
• Fine - grained Permission Control and Security: It provides a very complete and fine - grained permission control mechanism, allowing users to precisely control the permissions of each tool through configuration files or command - line parameters. For example, it can allow or prohibit a certain Bash command, limit the read - write range of files, and set different permission modes (such as the plan mode which is read - only and not writable). In an enterprise environment, system administrators can also enforce security policies that users cannot override.

Disadvantages:

• It is a commercial paid product with relatively high subscription fees.
• Its image recognition ability is relatively weak: When dealing with the understanding and analysis of interface screenshots and the task of converting design drafts into code, its accuracy and restoration degree may be inferior to some competitors.

Scope of Capabilities:

Claude Code CLI is very suitable for medium - to - large - scale project development, code libraries that need long - term maintenance, and scenarios where high code quality is required, and AI assistance is needed for in - depth debugging, refactoring, or optimization. It is relatively mature in terms of enterprise - level security, functional integrity, and ecosystem.

Usage:

It is usually installed globally via npm: npm install -g @anthropic - ai/claude - code. After installation, run claude login to go through the OAuth authentication process. The first time it runs, it will guide you through account authorization and theme selection. After completion, you can enter the interactive mode. Users can command the AI to complete code generation, debugging, refactoring, etc. through natural language instructions.

🔮 Gemini CLI

Gemini CLI is an open - source command - line AI tool by Google. Based on the powerful Gemini 2.5 Pro model, it aims to turn the terminal into an active development partner.

Advantages:

• Free and Open - source with Generous Quota: It is open - source under the Apache 2.0 license, with high transparency. Personal Google account users can enjoy a free quota of 60 requests per minute and 1000 requests per day, which is highly competitive among similar tools.
• Ultra - long Context Support: It supports a context window of up to 1 million tokens, easily handling large - scale code libraries, and can even read an entire project at once, which is very suitable for large - scale projects.
• Terminal - native and Powerful Agent Capability: Designed specifically for the command - line interface, it minimizes developers' context switching. It adopts the "Think - Act" (ReAct) loop mechanism, combined with built - in tools (such as file operations, shell commands) and the Model Context Protocol (MCP) server, to complete complex tasks such as fixing errors and creating new functions.
• High Scalability: Through the MCP server, bundled extensions, and the GEMINI.md file for custom prompts and instructions, it has a high degree of customizability.

Disadvantages:

• The accuracy of instruction execution and intention understanding is sometimes not as good as Claude Code, with slightly inferior performance.
• There are potential data security risks in the free version. User data may be used for model training, making it unsuitable for handling sensitive or proprietary code.
• The output quality may fluctuate. User feedback shows that Gemini - 2.5 - pro sometimes automatically downgrades to the less powerful Gemini - 2.5 - flash model, resulting in a decline in output quality.
• Its integration with the enterprise - level development environment is relatively weak, and it is more positioned as an independent terminal tool.

Scope of Capabilities:

Gemini CLI, with its large context window and free features, is very suitable for individual developers, rapid prototyping, and exploratory programming tasks. It is suitable for handling large code libraries but is relatively weak in complex logic understanding and deep integration with enterprise - level toolchains.

Usage:

Install via npm: npm install -g @google/gemini - cli. After installation, run the gemini command. The first time it runs, it will guide users to authorize their Google accounts or configure the Gemini API Key (by setting the environment variable export GEMINI_API_KEY = "your API Key").

🌐 Qwen Code CLI

Qwen Code CLI is a command - line tool developed and optimized by Alibaba based on Gemini CLI, specifically designed to unleash the potential of its Qwen3 - Coder model in agent - based programming tasks.

Advantages:

• Deep Optimization for Qwen3 - Coder: It has customized prompts and function call protocols for the Qwen3 - Coder series of models (such as qwen3 - coder - plus), maximizing its performance in Agentic Coding tasks.
• Support for Ultra - long Context: Relying on the Qwen3 - Coder model, it natively supports 256K tokens and can be extended to 1 million tokens, suitable for handling medium - to - large - scale projects.
• Open - source and Supports OpenAI SDK Format: It is convenient for developers to call the model through compatible APIs.
• Wide Range of Programming Language Support: The model natively supports up to 358 programming and markup languages.

Disadvantages:

• Token consumption may be relatively fast, especially when using large - parameter models (such as 480B), resulting in higher costs. Users need to pay close attention to usage.
• The understanding and execution of complex tasks may sometimes get into loops or perform worse than top - tier models.
• The understanding accuracy of tool calls may sometimes deviate.

Scope of Capabilities:

Qwen Code CLI is particularly suitable for developers who are interested in or prefer the Qwen model, as well as scenarios that require code understanding, editing, and certain workflow automation. It performs well in agent - based coding and long - context processing.

Usage:

Install via npm: npm install -g @qwen - code/qwen - code. After installation, you need to configure environment variables to point to the Alibaba Cloud DashScope endpoint that is compatible with the OpenAI API and set the corresponding API Key: export OPENAI_API_KEY = "your API key", export OPENAI_BASE_URL = "https://dashscope - intl.aliyuncs.com/compatible - mode/v1", export OPENAI_MODEL = "qwen3 - coder - plus".

🚀 CodeBuddy

CodeBuddy is an AI programming assistant launched by Tencent Cloud. Strictly speaking, it is not just a CLI tool but an AI programming assistant that integrates IDE plugins and other forms. However, its core capabilities overlap and are comparable to CLI tools, and it deeply integrates Tencent's self - developed Hunyuan large model and DeepSeek V3 model.

Advantages:

• Integration of Product, Design, and R & D: It integrates functions such as requirement document generation, design draft to code conversion (such as converting Figma to production - level code with a restoration degree of up to 99.9%), and cloud deployment, achieving end - to - end AI - integrated development from product design to R & D deployment.
• Localization Optimization and Tencent Ecosystem Integration: Optimized specifically for Chinese developers, it provides better Chinese support and deeply integrates Tencent Cloud services (such as CloudBase), supporting one - click deployment.
• Dual - model Driven: It integrates Tencent's Hunyuan large model and DeepSeek V3 model, providing high - precision code suggestions.
• Visual Experience: It provides a Webview function, allowing direct preview of code debugging results within the IDE, with a smooth interactive experience.

Disadvantages:

• The interaction of some functions (such as @ symbol interaction) may need to be further simplified to improve operational convenience.
• The code scanning speed may be slow in large projects.
• The plugin compatibility with editors such as VSCode still needs to be enhanced.
• Currently, an invitation code may be required for use.

Scope of Capabilities:

CodeBuddy is very suitable for developers and enterprises that need full - stack development support, hope for end - to - end AI assistance from design to deployment, and are deeply integrated into the Tencent Cloud ecosystem. It is especially suitable for quickly validating MVPs and accelerating product iterations.

Usage:

CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin), and it can also run in an independent IDE. Usually, users need to install the plugin and log in to their Tencent Cloud account to start experiencing features like code completion and the Craft mode.

In general, Claude Code CLI, Gemini CLI, Qwen Code CLI, and CodeBuddy each have their own focuses and are actively exploring how to better assist and transform the programming workflow with natural language. The choice depends on your specific needs, technology stack, budget, and preferences for different ecosystems. Understanding their technical principles and challenges can also help us view and apply these powerful tools more rationally, making AI a truly capable assistant in the development process. CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin) and can also run in an independent IDE. Users usually need to install the plugin and log in to their Tencent Cloud account to start experiencing features such as code completion and the Craft mode.In general, Claude Code CLI, Gemini CLI, Qwen Code CLI, and CodeBuddy each have their own focuses and are actively exploring how to better assist and transform the programming workflow with natural language. The choice depends on your specific needs, technology stack, budget, and preferences for different ecosystems. Understanding their technical principles and challenges can also help us view and apply these powerful tools more rationally, making AI a truly capable assistant in the development process. CodeBuddy is mainly used as an IDE plugin (such as the VS Code plugin) and can also run in an independent IDE. Users usually need to install the plugin and log in to their Tencent Cloud account to start experiencing features such as code completion and the Craft mode.

Poons, in today's rapidly evolving AI world, an awesome MCP protocol has been born! 🤩

MCP protocol, known as Model Context Protocol (Model Context Protocol), is an open standard protocol proposed by Anthropic and open source. Its appearance is simply too timely, a perfect solution to the problem of connecting AI assistants and various data systems, so that AI systems can more reliably obtain data and give relevant and high-quality responses, which brings a lot of convenience to developers and enterprises! 👏

🔍 Core components are ultra-critical

The MCP protocol core architecture has three important components:

• MCP Host: Like the commander, it is the system initiator and contains the MCP client application, which is responsible for sending requests to the MCP server to obtain data and functional support according to user requirements.
• MCP Client: As an intermediate bridge, it is responsible for communicating with the MCP server, accurately forwarding the requests from the MCP host, and then sending the results returned by the server back safely to ensure the smooth operation of the system.
• MCP Server: A back-end service that provides specific functionality. It is lightweight and can be a local Node.js or Python program, or a remote cloud service, adapting to various application scenarios and deployment needs.

📶 Ultra-flexible communication mechanisms

The MCP protocol communication mechanism is based on JSON-RPC2.0 protocol and supports two communication methods:

• Local communication: through the standard input and output and local server interaction, the data security requirements of high scenarios is super suitable, such as internal processing of sensitive data within the enterprise, can ensure that the data in the local security transmission.
• Remote communication: HTTP connection based on SSE (Server-Sent Events), with awesome support for cloud services, meeting large-scale data processing and distributed computing needs.

💥 Super wide range of application scenarios

The MCP protocol is used in a huge variety of scenarios, covering almost every area where AI needs to be tightly integrated with data systems. Although it is not mentioned in detail here, you can imagine that it can be very useful in many industries!

What do you think about the MCP protocol? Let's talk about it in the comments section!

#MCP Protocol #ModelContextProtocol #AI Protocol # Data Connection # Core Components # Communication Mechanisms

In technical exchange groups and community forums, I found that many front-end developers have difficulties when using AI: either asking vague questions and getting answers that can't be put into practice; or only using AI to do simple code completion, far from realizing its potential. This is like "begging for food with a golden bowl", obviously AI is a powerful tool in your hand, but you have only tapped into its value. In order to help you break these bottlenecks, I will share my practical experience and methodology for collaborating with AI in front-end development, which will help you efficiently master AI technology.

I. Redefining the relationship between front-end and AI

In the rapidly changing technology iteration, AI is no longer a bystander in the field of front-end development, but an important participant deeply integrated into the development process. As a developer who has been exploring the wave of front-end and AI convergence, I deeply realize that mastering the skills of using AI tools is only the foundation, and building a systematic AI thinking architecture is the key to stand out in the current competitive environment.

In the past, we viewed AI as a tool to assist in writing code and finding bugs, a perception that greatly limited its value. Today, AI has become a partner that can deeply collaborate with developers. In actual projects, I have faced complex performance optimization problems, and the traditional way requires a lot of time for code analysis and solution verification. With AI, through reasonable questions and interactions, it can not only quickly provide a variety of optimization ideas, but also evaluate solutions in the context of the actual project, significantly reducing the development cycle. This collaboration model shows that AI is no longer a "machine" that passively executes instructions, but an "intelligent body" that can think and solve problems together with developers.

II. Four-quadrant framework for AI dialogues: building a mindset model for efficient collaboration

Quadrant 1: Open (AI knows, people know)

When both the developer and AI have a clear understanding of the problem, this is the most direct and efficient collaboration scenario. For example, when developing React components, if the clear requirement is to realize anti-shake function with React Hook, you can directly give AI the instruction of "realize an anti-shake component with React Hook, require concise code with comments", and then you can get the result quickly. However, it should be noted that the more structured the instruction is (e.g. "step-by-step instructions + code examples + notes"), the lower the communication cost.

Quadrant 2: Blind (AI knows, people don't)

When facing unfamiliar technical issues, such as optimizing front-end page load speed, direct questions often get general answers. At this point, we should adopt a layered questioning strategy: first understand the common dimensions of performance optimization, then explore the priority of network request and rendering optimization, then ask about the specific optimization means of the React project, and finally ask for actual cases. Through the progressive questioning of "what→why→how→case", AI can avoid outputting invalid information.

Quadrant III: Unknown (AI does not know, people do not know)

When exploring the integration of new technologies, such as the combination of 3D models generated by AIGC and WebGL to realize interactive virtual exhibition halls, there is no ready answer for both humans and machines. In this case, AI should be regarded as a creative stimulation partner, obtaining ideas through cross-border questioning, and then combining with its own technical capabilities to make feasibility judgments and program iterations.AI's answers are creative materials, and developers need to sift, combine and verify them.

Quadrant 4: Hidden (AI doesn't know, people know)

Involving project-specific knowledge, such as the company's own research component library development specifications, you need to take the initiative to "feed" information to the AI. Uploading relevant documents and code snippets and then giving instructions will enable the AI to generate content that better fits the actual needs. Enterprises can establish a technical knowledge base and use RAG technology to realize the rapid invocation of internal data by AI; individual developers should also be clearly informed of the project constraints before collaboration to avoid AI generating unrealistic solutions.

From Tool Use to Thinking Architecture: The AI Advancement Path for Front-End Developers

1. Create a sense of positioning for AI collaboration

Before every interaction with AI, think about three questions: what is the nature of the problem, and what is the AI's mastery of the relevant technology stack? What proprietary information should be added? Take debugging a React component as an example, if the type of error is clear, it belongs to the Open quadrant, and you can directly seek a solution; if the cause of the error is vague, you need to enter the Blind quadrant, and adopt a layered questioning strategy.

2. Developing structured questioning skills

Especially in the Blind Quadrant, the "onion peeling" method of questioning can effectively improve the quality of information acquisition. Take learning WebAssembly as an example, from the core principle (what), to the reasons for solving the performance bottleneck of JavaScript (why), to the integration method in the React project (how), and finally to the actual application cases (scenario-based validation), we will go deeper and deeper. At the same time, the use of "if... then..." sentences to test the depth of understanding and strengthen the learning effect.

3. Building a Personal AI Collaboration Intelligence Repository

Organize commonly used tech stack documents, team code specifications, and historical project pitfall records into a Markdown format "AI Collaboration Manual". Asking questions with links to key chapters or explicitly referencing specifications in instructions enables AI to quickly understand the technical context and generate content that is more in line with expectations.

4. Stimulating innovative thinking and exploring uncharted territories

Use a "technical domain + non-technical domain + target scenario" questioning formula, e.g., "When ChatGPT learns front-end engineering, can it automatically generate scaffolding that meets the team's specifications? What data training is needed?" Encourage AI to think out of the box and explore the new boundaries of technology together. At the same time, we screen the solutions through technical feasibility analysis and carry out iterative optimization.

5. Dynamic adaptation of collaboration strategies

Regularly follow the updates of front-end AI tools and test the adaptability of new features in real projects. Record the types of problems encountered during AI collaboration and the quadrants they belong to, and analyze the distribution of your collaboration ability in different quadrants. If the Hidden quadrant has frequent problems, improve the internal knowledge base; if the Blind quadrant has more problems, strengthen the training on question disassembly.

Practical tool recommendation: covering the whole quadrant of the front-end AI collaboration matrix

Open Quadrant (AI is known to all)

Tools / Methods	Core competencies and scenarios
Cursor	- Natural language generation of complete code for React/Vue components (including Hook logic) - Support for real-time code debugging and bug fixing (e.g., automatic handling of Promise exceptions)
Codeium	- Context-based code completion (e.g., enteruseEffect(Automatically prompts for dependency array writes) - Generate test cases (Jest/React Testing Library)
Tabnine	- Smart function name recommendation (e.g. enterfetchDataFromauto-completeAPI) - Generate TypeScript type definitions (inferring return value types from function arguments)

Blind Quadrant (AI knows but no one knows)

Tools / Methods	Core competencies and scenarios
Raycast AI	- Breaking down complex problems (e.g. generating a layered solution for "React Performance Optimization": Network Optimization → Rendering Optimization → Component Optimization) - Querying framework source code in real-time (e.g. automatically parsing the implementation logic of React Router v6 Hooks)
WizNote AI Assistant	- Structured questioning of technical documentation (e.g. "How to integrate WASM in React" after uploading official WebAssembly docs) - Generation of knowledge brain maps (automated sorting of CSS-in-JS scenarios with comparative strengths and weaknesses)
DevDocs AI	- Cross-document retrieval (e.g., query MDN + React official website + community blogs at the same time, integrate "useContext best practices") - Code sample adaptation (automatically convert Vue3 examples to React writing style)

Hidden quadrant (known but not known to AI)

Tools / Methods	Core competencies and scenarios
PrivateGPT (Enterprise Edition)	- Uploading the team's component library specification and generating code that conforms to the specification (e.g., generating a Button component based on the Ant Design specification). - Parsing internal business documents (e.g., generating form validation logic based on e-commerce order system documents)
RAG-Stack (self-built knowledge base)	- Access to enterprise Git repositories, AI automatically learns historical project architecture (e.g., identifying a project's micro-front-end splitting strategy) - Generates problem troubleshooting processes based on internal failure documentation (e.g., diagnostic steps for "white screen problems")
LocalAI + Vector Database	- Secure handling of sensitive code (e.g., cryptographic algorithm modules for financial projects) - Generation of code styles that conform to team conventions (e.g., automatically formatting code according to team ESLint configurations)

Unknown quadrant.

Tools / Methods	Core competencies and scenarios
GitHub Copilot X	- Collaborative exploration of new architectures (e.g. AI to generate technical solution sketches for "React+WebAssembly for 3D editor") - Automated generation of technical feasibility reports (with performance estimates and risk point analysis)
Replit AI Workspace	- Multiplayer real-time co-creation (front-end / back-end / UI synchronized iteration of AIGC-generated virtual showroom scenarios) - One-click deployment of experimental scenarios (e.g., publishing AI-generated WebGL interaction demos directly to preview environments)
AI Architect	- Generate cross-domain technology combinations (e.g. "LLM + Front-End Route Guard" for dynamic privilege control) - Provide technology roadmaps (e.g. migration steps from "Legacy SPA" to "PWA Provide a technology roadmap (e.g. migration steps from "Traditional SPA" to "PWA + Server Components").

V. Conclusion: Embracing AI, Reconstructing Front-End Development Thinking

The application of AI in the front-end field is not only an upgrade of tools, but also a change in the way of thinking. Mastering the four-quadrant framework of AI dialogue and building a systematic AI thinking architecture will enable us to transform from "AI tool users" to "intelligent collaboration leaders". In the future of front-end development, those developers who can master AI and collaborate with it in depth will surely have a head start in the wave of technology. We look forward to exploring more possibilities for the integration of AI and front-end development with your peers, and welcome you to share your practical experience and thoughts.