feat(i18n): add content directories for 8 new languages

Created articles, docs, FAQ, and manifesto directories for es, fr, id, ja, pt, ru, tr, and zh-Hant locales.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: ericyangpan

content/articles/es/getting-started-with-ai-coding.mdx

@@ -0,0 +1,51 @@
+---
+title: 'Getting Started with AI Coding: A Comprehensive Guide'
+description: 'Learn how to set up your first AI-powered development environment with IDEs, CLIs, and the essential tools you need to boost your coding productivity.'
+date: '2025-01-15'
+---
+
+The rise of AI-powered coding tools has fundamentally transformed how developers write, debug, and maintain code. Whether you're a seasoned engineer or just starting your coding journey, integrating AI into your development workflow can dramatically boost your productivity and code quality.
+
+## Why AI Coding Matters
+
+Traditional coding involves writing every line manually, looking up documentation, and debugging through trial and error. AI coding assistants change this paradigm by:
+
+- Providing intelligent autocomplete that understands context
+- Generating boilerplate code from natural language descriptions
+- Catching bugs before they make it to production
+- Explaining complex code in plain English
+- Suggesting optimizations and best practices
+
+## Choosing Your IDE
+
+Your choice of IDE is crucial for AI coding. The two most popular options are:
+
+### Cursor
+
+Built from the ground up for AI pair programming. Cursor offers native AI chat, inline editing, and codebase-wide understanding. Best for developers who want AI deeply integrated into every aspect of their workflow.
+
+### VS Code + Extensions
+
+Combine the familiar VS Code interface with AI extensions like GitHub Copilot, Codeium, or Tabnine. Best for developers who want to keep their existing setup while adding AI capabilities.
+
+## Setting Up Your First AI Coding Environment
+
+Here's a step-by-step guide to get started:
+
+### 1. Choose Your IDE
+
+Download and install either Cursor or VS Code with your preferred AI extension.
+
+### 2. Configure API Keys
+
+Most AI coding tools require API keys from providers like OpenAI, Anthropic, or DeepSeek. Sign up for an account and generate your keys.
+
+### 3. Practice with Simple Tasks
+
+Start by asking your AI agent to generate simple functions, explain code snippets, or suggest improvements to existing code.
+
+## Next Steps
+
+Once you're comfortable with basic AI coding, explore advanced topics like prompt engineering for better code generation and integrating AI into your CI/CD pipeline.
+
+The AI coding ecosystem is evolving rapidly. Stay updated with the latest tools and best practices through the [AI Coding Stack](/ai-coding-stack) documentation.

content/articles/es/mcp-servers-explained.mdx

@@ -0,0 +1,100 @@
+---
+title: 'Understanding MCP Servers: The Future of AI Context'
+description: 'Deep dive into Model Context Protocol servers, how they work, and why they are crucial for building intelligent AI coding assistants that truly understand your project.'
+date: '2025-01-10'
+---
+
+Model Context Protocol (MCP) servers represent a paradigm shift in how AI agents interact with external tools, data sources, and APIs. They are the connective tissue that transforms isolated AI models into context-aware coding companions that truly understand your project.
+
+## What is MCP?
+
+The Model Context Protocol is an open standard developed by Anthropic that enables AI agents to securely connect to external data sources and tools. Think of MCP servers as specialized adapters that give your AI agent superpowers—whether that's reading from databases, searching the web, interacting with APIs, or accessing your filesystem.
+
+Unlike traditional API integrations that require custom code for each tool, MCP provides a standardized interface. This means any MCP-compatible AI agent can use any MCP server without custom integration work.
+
+## How MCP Servers Work
+
+An MCP server is a lightweight process that exposes three core primitives:
+
+### Resources
+
+Context and data that the AI can read. For example, a filesystem MCP server exposes your project files as resources.
+
+### Tools
+
+Actions the AI can perform. A web search MCP might expose a search tool, while a database MCP could provide query execution tools.
+
+### Prompts
+
+Templated interactions that guide the AI. These can include best practices or common workflows specific to the tool.
+
+## Popular MCP Servers
+
+The MCP ecosystem is rapidly growing. Here are some essential servers:
+
+- **Filesystem** - Access local files and directories
+- **Playwright** - Browser automation and web scraping
+- **Context7** - Up-to-date library documentation
+- **PostgreSQL** - Database queries and schema inspection
+- **GitHub** - Repository management and code search
+- **Brave Search** - Real-time web search capabilities
+
+## Why MCP Matters for AI Coding
+
+Before MCP, AI coding assistants were essentially blind to your development environment. They could generate code based on training data, but couldn't access your actual project structure, read your configuration files, or interact with your development tools.
+
+MCP changes this fundamentally. With MCP servers, your AI agent can:
+
+- Understand your entire codebase structure through filesystem access
+- Look up current documentation for libraries you're using
+- Execute database queries to understand your data model
+- Search the web for the latest solutions to your specific problem
+- Interact with your Git repository to understand commit history
+
+## Getting Started with MCP
+
+Most modern AI coding tools now support MCP. Setting up your first MCP server typically involves:
+
+### 1. Choose an MCP Server
+
+Start with something simple like the filesystem or web search MCP server.
+
+### 2. Install the Server
+
+Most MCP servers are distributed as npm packages or Python modules. Follow the installation instructions for your chosen server.
+
+### 3. Configure Your IDE
+
+Add the MCP server configuration to your IDE's settings. The exact format varies by tool, but typically involves specifying the server's command and any required arguments.
+
+Here's an example configuration for Claude Code:
+
+```json
+{
+  "mcpServers": {
+    "filesystem": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/your/project"]
+    },
+    "brave-search": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
+      "env": {
+        "BRAVE_API_KEY": "your-api-key-here"
+      }
+    }
+  }
+}
+```
+
+### 4. Test the Integration
+
+Ask your AI agent to use the newly connected tools. For example, if you installed a filesystem MCP, try asking it to read a specific file from your project.
+
+## The Future of MCP
+
+The MCP ecosystem is still in its early stages, but the potential is enormous. As more developers build MCP servers for specialized tools and services, AI agents will become increasingly powerful and context-aware.
+
+We're moving toward a future where your AI coding assistant has seamless access to every tool in your development workflow—from your IDE to your cloud infrastructure. MCP is the protocol that makes this vision possible.
+
+Explore the full catalog of available MCP servers in our [MCP Servers directory](/ai-coding-stack/mcp-servers) to find the right integrations for your workflow.

content/articles/fr/getting-started-with-ai-coding.mdx

@@ -0,0 +1,51 @@
+---
+title: 'Getting Started with AI Coding: A Comprehensive Guide'
+description: 'Learn how to set up your first AI-powered development environment with IDEs, CLIs, and the essential tools you need to boost your coding productivity.'
+date: '2025-01-15'
+---
+
+The rise of AI-powered coding tools has fundamentally transformed how developers write, debug, and maintain code. Whether you're a seasoned engineer or just starting your coding journey, integrating AI into your development workflow can dramatically boost your productivity and code quality.
+
+## Why AI Coding Matters
+
+Traditional coding involves writing every line manually, looking up documentation, and debugging through trial and error. AI coding assistants change this paradigm by:
+
+- Providing intelligent autocomplete that understands context
+- Generating boilerplate code from natural language descriptions
+- Catching bugs before they make it to production
+- Explaining complex code in plain English
+- Suggesting optimizations and best practices
+
+## Choosing Your IDE
+
+Your choice of IDE is crucial for AI coding. The two most popular options are:
+
+### Cursor
+
+Built from the ground up for AI pair programming. Cursor offers native AI chat, inline editing, and codebase-wide understanding. Best for developers who want AI deeply integrated into every aspect of their workflow.
+
+### VS Code + Extensions
+
+Combine the familiar VS Code interface with AI extensions like GitHub Copilot, Codeium, or Tabnine. Best for developers who want to keep their existing setup while adding AI capabilities.
+
+## Setting Up Your First AI Coding Environment
+
+Here's a step-by-step guide to get started:
+
+### 1. Choose Your IDE
+
+Download and install either Cursor or VS Code with your preferred AI extension.
+
+### 2. Configure API Keys
+
+Most AI coding tools require API keys from providers like OpenAI, Anthropic, or DeepSeek. Sign up for an account and generate your keys.
+
+### 3. Practice with Simple Tasks
+
+Start by asking your AI agent to generate simple functions, explain code snippets, or suggest improvements to existing code.
+
+## Next Steps
+
+Once you're comfortable with basic AI coding, explore advanced topics like prompt engineering for better code generation and integrating AI into your CI/CD pipeline.
+
+The AI coding ecosystem is evolving rapidly. Stay updated with the latest tools and best practices through the [AI Coding Stack](/ai-coding-stack) documentation.

content/articles/fr/mcp-servers-explained.mdx

@@ -0,0 +1,100 @@
+---
+title: 'Understanding MCP Servers: The Future of AI Context'
+description: 'Deep dive into Model Context Protocol servers, how they work, and why they are crucial for building intelligent AI coding assistants that truly understand your project.'
+date: '2025-01-10'
+---
+
+Model Context Protocol (MCP) servers represent a paradigm shift in how AI agents interact with external tools, data sources, and APIs. They are the connective tissue that transforms isolated AI models into context-aware coding companions that truly understand your project.
+
+## What is MCP?
+
+The Model Context Protocol is an open standard developed by Anthropic that enables AI agents to securely connect to external data sources and tools. Think of MCP servers as specialized adapters that give your AI agent superpowers—whether that's reading from databases, searching the web, interacting with APIs, or accessing your filesystem.
+
+Unlike traditional API integrations that require custom code for each tool, MCP provides a standardized interface. This means any MCP-compatible AI agent can use any MCP server without custom integration work.
+
+## How MCP Servers Work
+
+An MCP server is a lightweight process that exposes three core primitives:
+
+### Resources
+
+Context and data that the AI can read. For example, a filesystem MCP server exposes your project files as resources.
+
+### Tools
+
+Actions the AI can perform. A web search MCP might expose a search tool, while a database MCP could provide query execution tools.
+
+### Prompts
+
+Templated interactions that guide the AI. These can include best practices or common workflows specific to the tool.
+
+## Popular MCP Servers
+
+The MCP ecosystem is rapidly growing. Here are some essential servers:
+
+- **Filesystem** - Access local files and directories
+- **Playwright** - Browser automation and web scraping
+- **Context7** - Up-to-date library documentation
+- **PostgreSQL** - Database queries and schema inspection
+- **GitHub** - Repository management and code search
+- **Brave Search** - Real-time web search capabilities
+
+## Why MCP Matters for AI Coding
+
+Before MCP, AI coding assistants were essentially blind to your development environment. They could generate code based on training data, but couldn't access your actual project structure, read your configuration files, or interact with your development tools.
+
+MCP changes this fundamentally. With MCP servers, your AI agent can:
+
+- Understand your entire codebase structure through filesystem access
+- Look up current documentation for libraries you're using
+- Execute database queries to understand your data model
+- Search the web for the latest solutions to your specific problem
+- Interact with your Git repository to understand commit history
+
+## Getting Started with MCP
+
+Most modern AI coding tools now support MCP. Setting up your first MCP server typically involves:
+
+### 1. Choose an MCP Server
+
+Start with something simple like the filesystem or web search MCP server.
+
+### 2. Install the Server
+
+Most MCP servers are distributed as npm packages or Python modules. Follow the installation instructions for your chosen server.
+
+### 3. Configure Your IDE
+
+Add the MCP server configuration to your IDE's settings. The exact format varies by tool, but typically involves specifying the server's command and any required arguments.
+
+Here's an example configuration for Claude Code:
+
+```json
+{
+  "mcpServers": {
+    "filesystem": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/your/project"]
+    },
+    "brave-search": {
+      "command": "npx",
+      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
+      "env": {
+        "BRAVE_API_KEY": "your-api-key-here"
+      }
+    }
+  }
+}
+```
+
+### 4. Test the Integration
+
+Ask your AI agent to use the newly connected tools. For example, if you installed a filesystem MCP, try asking it to read a specific file from your project.
+
+## The Future of MCP
+
+The MCP ecosystem is still in its early stages, but the potential is enormous. As more developers build MCP servers for specialized tools and services, AI agents will become increasingly powerful and context-aware.
+
+We're moving toward a future where your AI coding assistant has seamless access to every tool in your development workflow—from your IDE to your cloud infrastructure. MCP is the protocol that makes this vision possible.
+
+Explore the full catalog of available MCP servers in our [MCP Servers directory](/ai-coding-stack/mcp-servers) to find the right integrations for your workflow.

content/articles/id/getting-started-with-ai-coding.mdx

@@ -0,0 +1,51 @@
+---
+title: 'Getting Started with AI Coding: A Comprehensive Guide'
+description: 'Learn how to set up your first AI-powered development environment with IDEs, CLIs, and the essential tools you need to boost your coding productivity.'
+date: '2025-01-15'
+---
+
+The rise of AI-powered coding tools has fundamentally transformed how developers write, debug, and maintain code. Whether you're a seasoned engineer or just starting your coding journey, integrating AI into your development workflow can dramatically boost your productivity and code quality.
+
+## Why AI Coding Matters
+
+Traditional coding involves writing every line manually, looking up documentation, and debugging through trial and error. AI coding assistants change this paradigm by:
+
+- Providing intelligent autocomplete that understands context
+- Generating boilerplate code from natural language descriptions
+- Catching bugs before they make it to production
+- Explaining complex code in plain English
+- Suggesting optimizations and best practices
+
+## Choosing Your IDE
+
+Your choice of IDE is crucial for AI coding. The two most popular options are:
+
+### Cursor
+
+Built from the ground up for AI pair programming. Cursor offers native AI chat, inline editing, and codebase-wide understanding. Best for developers who want AI deeply integrated into every aspect of their workflow.
+
+### VS Code + Extensions
+
+Combine the familiar VS Code interface with AI extensions like GitHub Copilot, Codeium, or Tabnine. Best for developers who want to keep their existing setup while adding AI capabilities.
+
+## Setting Up Your First AI Coding Environment
+
+Here's a step-by-step guide to get started:
+
+### 1. Choose Your IDE
+
+Download and install either Cursor or VS Code with your preferred AI extension.
+
+### 2. Configure API Keys
+
+Most AI coding tools require API keys from providers like OpenAI, Anthropic, or DeepSeek. Sign up for an account and generate your keys.
+
+### 3. Practice with Simple Tasks
+
+Start by asking your AI agent to generate simple functions, explain code snippets, or suggest improvements to existing code.
+
+## Next Steps
+
+Once you're comfortable with basic AI coding, explore advanced topics like prompt engineering for better code generation and integrating AI into your CI/CD pipeline.
+
+The AI coding ecosystem is evolving rapidly. Stay updated with the latest tools and best practices through the [AI Coding Stack](/ai-coding-stack) documentation.