- )
-}
-
-export default ScalarApiReferenceMulti
diff --git a/website/src/config/README.md b/website/src/config/README.md
deleted file mode 100644
index 255de0f59..000000000
--- a/website/src/config/README.md
+++ /dev/null
@@ -1,101 +0,0 @@
-# Navigation Configuration
-
-This directory contains configuration files for managing navigation across Jan's documentation sites.
-
-## Overview
-
-As Jan grows to include multiple products (Jan Desktop, Jan Server, Jan Mobile, etc.), we need a scalable way to manage navigation across different documentation sections. This configuration approach allows us to:
-
-1. **Maintain consistency** across different products
-2. **Avoid duplication** in navigation code
-3. **Scale easily** as new products are added
-4. **Separate concerns** between regular docs and API reference pages
-
-## Structure
-
-### `navigation.js`
-Central navigation configuration file containing:
-- Product-specific navigation links
-- API deployment configurations
-- Helper functions for navigation management
-- Feature flags for navigation behavior
-
-## Navigation Strategy
-
-### Regular Documentation Pages
-- Navigation is injected via `astro.config.mjs`
-- Shows "Docs" and "API Reference" links
-- Appears in the main header next to search
-
-### API Reference Pages
-- Have their own navigation via `ApiReferenceLayout.astro`
-- Navigation is built into the layout (not injected)
-- Prevents duplicate navigation elements
-
-## Adding New Products
-
-To add navigation for a new product:
-
-1. Update `navigation.js`:
-```javascript
-products: {
- janServer: {
- name: 'Jan Server',
- links: [
- { href: '/server', text: 'Server Docs', isActive: (path) => path.startsWith('/server') },
- { href: '/server/api', text: 'Server API', isActive: (path) => path.startsWith('/server/api') }
- ]
- }
-}
-```
-
-2. Update `astro.config.mjs` if needed to handle product-specific logic
-
-3. Create corresponding layout components if the product needs custom API reference pages
-
-## Configuration in astro.config.mjs
-
-The navigation injection in `astro.config.mjs` is kept minimal and clean:
-
-```javascript
-const JAN_NAV_CONFIG = {
- links: [/* navigation links */],
- excludePaths: [/* paths that have their own navigation */]
-};
-```
-
-This configuration:
-- Is easy to read and modify
-- Doesn't interfere with API reference pages
-- Can be extended for multiple products
-- Maintains clean separation of concerns
-
-## Best Practices
-
-1. **Keep it simple**: Navigation configuration should be declarative, not complex logic
-2. **Avoid duplication**: Use the configuration to generate navigation, don't hardcode it multiple places
-3. **Test changes**: Always verify navigation works on both regular docs and API reference pages
-4. **Document changes**: Update this README when adding new products or changing navigation strategy
-
-## Testing Navigation
-
-After making changes, verify:
-1. Navigation appears correctly on regular docs pages
-2. Navigation doesn't duplicate on API reference pages
-3. Active states work correctly
-4. Mobile responsiveness is maintained
-5. Theme switching doesn't break navigation
-
-## Future Considerations
-
-- **Product switcher**: Add a dropdown to switch between different product docs
-- **Version selector**: Add version switching for API documentation
-- **Search integration**: Integrate product-specific search scopes
-- **Analytics**: Track navigation usage to improve UX
-
-## Related Files
-
-- `/astro.config.mjs` - Navigation injection for regular docs
-- `/src/components/ApiReferenceLayout.astro` - API reference navigation
-- `/src/pages/api.astro` - API documentation landing page
-- `/src/pages/api-reference/*.astro` - API reference pages
\ No newline at end of file
diff --git a/website/src/config/navigation.js b/website/src/config/navigation.js
deleted file mode 100644
index f72c77890..000000000
--- a/website/src/config/navigation.js
+++ /dev/null
@@ -1,138 +0,0 @@
-/**
- * Navigation Configuration
- *
- * Centralized navigation configuration for Jan documentation.
- * This makes it easy to manage navigation across multiple products
- * and maintain consistency across different documentation sections.
- */
-
-export const NAVIGATION_CONFIG = {
- // Main product navigation links
- products: {
- jan: {
- name: 'Jan',
- links: [
- {
- href: '/',
- text: 'Docs',
- isActive: (path) => path === '/' || (path.startsWith('/') && !path.startsWith('/api')),
- description: 'Jan documentation and guides'
- },
- {
- href: '/api',
- text: 'API Reference',
- isActive: (path) => path.startsWith('/api'),
- description: 'OpenAI-compatible API documentation'
- }
- ]
- },
- // Future products can be added here
- // Example:
- // janServer: {
- // name: 'Jan Server',
- // links: [
- // { href: '/server', text: 'Server Docs', isActive: (path) => path.startsWith('/server') },
- // { href: '/server/api', text: 'Server API', isActive: (path) => path.startsWith('/server/api') }
- // ]
- // }
- },
-
- // API deployment configurations
- apiDeployments: {
- local: {
- name: 'Local API',
- defaultServers: [
- { url: 'http://127.0.0.1:1337', description: 'Local Jan Server (Default)' },
- { url: 'http://localhost:1337', description: 'Local Jan Server (localhost)' },
- { url: 'http://localhost:8080', description: 'Local Jan Server (Alternative Port)' }
- ],
- requiresAuth: false,
- engine: 'llama.cpp'
- },
- cloud: {
- name: 'Jan Server',
- defaultServers: [
- { url: 'https://api.jan.ai/v1', description: 'Jan Server (Production)' },
- { url: 'http://localhost:8000/v1', description: 'Jan Server (Local Development)' }
- ],
- requiresAuth: true,
- engine: 'vLLM'
- }
- },
-
- // Navigation styles configuration
- styles: {
- navLink: {
- base: 'nav-link',
- active: 'nav-link-active'
- },
- container: {
- base: 'custom-nav-links',
- mobile: 'custom-nav-links-mobile'
- }
- },
-
- // Feature flags for navigation behavior
- features: {
- persistCustomServer: true,
- allowUrlParams: true,
- showProductSwitcher: false, // For future multi-product support
- mobileMenuBreakpoint: 768
- },
-
- // Helper functions
- helpers: {
- /**
- * Get navigation links for current product
- * @param {string} productKey - The product identifier
- * @returns {Array} Navigation links for the product
- */
- getProductNav(productKey = 'jan') {
- return this.products[productKey]?.links || [];
- },
-
- /**
- * Determine if current path should show API reference navigation
- * @param {string} path - Current pathname
- * @returns {boolean} Whether to show API reference navigation
- */
- isApiReferencePage(path) {
- return path.startsWith('/api-reference/') || path.startsWith('/api/');
- },
-
- /**
- * Get server configuration for deployment type
- * @param {string} deployment - 'local' or 'cloud'
- * @returns {Object} Server configuration
- */
- getServerConfig(deployment) {
- return this.apiDeployments[deployment] || this.apiDeployments.local;
- },
-
- /**
- * Build navigation HTML for injection
- * @param {string} currentPath - Current page path
- * @param {string} productKey - Product identifier
- * @returns {string} HTML string for navigation
- */
- buildNavigationHTML(currentPath, productKey = 'jan') {
- const links = this.getProductNav(productKey);
-
- return links.map(link => `
-
- ${link.text}
-
- `).join('');
- }
- }
-};
-
-// Export for use in browser context
-if (typeof window !== 'undefined') {
- window.JanNavigationConfig = NAVIGATION_CONFIG;
-}
-
-export default NAVIGATION_CONFIG;
diff --git a/website/src/content.config.ts b/website/src/content.config.ts
deleted file mode 100644
index 69d64c7c7..000000000
--- a/website/src/content.config.ts
+++ /dev/null
@@ -1,10 +0,0 @@
-import { defineCollection, z } from 'astro:content'
-import { docsLoader } from '@astrojs/starlight/loaders'
-import { docsSchema } from '@astrojs/starlight/schema'
-
-export const collections = {
- docs: defineCollection({
- loader: docsLoader(),
- schema: docsSchema(),
- }),
-}
diff --git a/website/src/content/docs/browser/index.mdx b/website/src/content/docs/browser/index.mdx
deleted file mode 100644
index 967ba90f2..000000000
--- a/website/src/content/docs/browser/index.mdx
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: Jan Browser Extension
-description: Bring your favorite AI models to any website with Jan's browser extension.
-keywords:
- [
- Jan Browser Extension,
- Jan AI,
- Browser AI,
- Chrome extension,
- Firefox addon,
- local AI,
- ChatGPT alternative
- ]
-banner:
- content: 'Coming in September 2025. Currently testing it with selected users and internally. ๐ค'
----
-
-import { Aside, Card, CardGrid } from '@astrojs/starlight/components';
-
-
-
-## Your AI Models, Anywhere on the Web
-
-The Jan Browser Extension brings AI assistance directly to your browsing experience.
-Connect to your local Jan installation or any remote AI provider to get contextual help
-on any website without switching tabs.
-
-
-
-Access your preferred models without leaving your current page. Whether you're using local
-Jan models or remote providers, get instant AI assistance while reading, writing, or researching
-online.
-
-### Core Features Planned:
-- **Universal Access**: Use any Jan-compatible model from any website
-- **Context Integration**: Highlight text and get AI assistance instantly
-- **Privacy Options**: Choose between local processing or remote providers
-- **Seamless Experience**: No tab switching or workflow interruption required
diff --git a/website/src/content/docs/index.mdx b/website/src/content/docs/index.mdx
deleted file mode 100644
index 87cf331db..000000000
--- a/website/src/content/docs/index.mdx
+++ /dev/null
@@ -1,282 +0,0 @@
----
-title: Jan
-description: Working towards open superintelligence through community-driven AI
-keywords:
- [
- Jan,
- Jan AI,
- open superintelligence,
- AI ecosystem,
- local AI,
- private AI,
- self-hosted AI,
- llama.cpp,
- Model Context Protocol,
- MCP,
- GGUF models,
- large language model,
- LLM,
- ]
-banner:
- content: |
- We just launched something cool! ๐Jan now supports image ๐ผ๏ธ attachments ๐
----
-
-import { Aside, LinkCard } from '@astrojs/starlight/components';
-
-
-
-
-## Jan's Goal
-
-> We're working towards open superintelligence to make a viable open-source alternative to platforms like ChatGPT
-and Claude that anyone can own and run.
-
-## What is Jan Today
-
-Jan is an open-source AI platform that runs on your hardware. We believe AI should be in the hands of many, not
-controlled by a few tech giants.
-
-Today, Jan is:
-- **A desktop app** that runs AI models locally or connects to cloud providers
-- **A model hub** making the latest open-source models accessible
-- **A connector system** that lets AI interact with real-world tools via MCP
-
-Tomorrow, Jan aims to be a complete ecosystem where open models rival or exceed closed alternatives.
-
-
-
-## The Jan Ecosystem
-
-### Jan Apps
-**Available Now:**
-- **Desktop**: Full-featured AI workstation for Windows, Mac, and Linux
-
-**Coming Late 2025:**
-- **Mobile**: Jan on your phone
-- **Web**: Browser-based access at jan.ai
-- **Server**: Self-hosted for teams
-- **Extensions**: Browser extension for Chrome-based browsers
-
-### Jan Model Hub
-Making open-source AI accessible to everyone:
-- **Easy Downloads**: One-click model installation
-- **Jan Models**: Our own models optimized for local use
- - **Jan-v1**: 4B reasoning model specialized in web search
- - **Research Models**
- - **Jan-Nano (32k/128k)**: 4B model for web search with MCP tools
- - **Lucy**: 1.7B mobile-optimized for web search
-- **Community Models**: Any GGUF from Hugging Face works in Jan
-- **Cloud Models**: Connect your API keys for OpenAI, Anthropic, Gemini, and more
-
-
-### Jan Connectors Hub
-Connect AI to the tools you use daily via [Model Context Protocol](./mcp):
-
-**Creative & Design:**
-- **Canva**: Generate and edit designs
-
-**Data & Analysis:**
-- **Jupyter**: Run Python notebooks
-- **E2B**: Execute code in sandboxes
-
-**Web & Search:**
-- **Browserbase & Browser Use**: Browser automation
-- **Exa, Serper, Perplexity**: Advanced web search
-- **Octagon**: Deep research capabilities
-
-**Productivity:**
-- **Linear**: Project management
-- **Todoist**: Task management
-
-## Core Features
-
-- **Run Models Locally**: Download any GGUF model from Hugging Face, use OpenAI's gpt-oss models,
-or connect to cloud providers
-- **OpenAI-Compatible API**: Local server at `localhost:1337` works with tools like
-[Continue](./server-examples/continue-dev) and [Cline](https://cline.bot/)
-- **Extend with MCP Tools**: Browser automation, web search, data analysis, and design tools, all
-through natural language
-- **Your Choice of Infrastructure**: Run on your laptop, self-host on your servers (soon), or use
-cloud when you need it
-
-## Philosophy
-
-Jan is built to be user-owned:
-- **Open Source**: Apache 2.0 license
-- **Local First**: Your data stays on your device. Internet is optional
-- **Privacy Focused**: We don't collect or sell user data. See our [Privacy Policy](./privacy)
-- **No Lock-in**: Export your data anytime. Use any model. Switch between local and cloud
-
-
-
-## The Path Forward
-
-### What Works Today
-- Run powerful models locally on consumer hardware
-- Connect to any cloud provider with your API keys
-- Use MCP tools for real-world tasks
-
-### What We're Building
-- More specialized models that excel at specific tasks
-- Expanded app ecosystem (mobile, self-hosted server, web, extensions)
-- Richer connector ecosystem
-- An evaluation framework to build better models
-
-### The Long-Term Vision
-We're working towards open superintelligence where:
-- Open models match or exceed closed alternatives
-- Anyone can run powerful AI on their own hardware
-- The community drives innovation, not corporations
-- AI capabilities are owned by users, not rented
-
-
-
-## Quick Start
-
-1. [Download Jan](./quickstart) for your operating system
-2. Choose a model - download locally or add cloud API keys
-3. Start chatting or connect tools via MCP
-4. Build with our [local API](./api-server)
-5. Explore the [API Reference](/api) for Local and Server endpoints
-
-
-
-## Acknowledgements
-
-Jan is built on the shoulders of giants:
-- [Llama.cpp](https://github.com/ggerganov/llama.cpp) for inference
-- [Model Context Protocol](https://modelcontextprotocol.io) for tool integration
-- The open-source community that makes this possible
-
-## FAQs
-
-
-What is Jan?
-
-Jan is an open-source AI platform working towards a viable alternative to Big Tech AI. Today it's a desktop app that runs models locally or connects to cloud providers. Tomorrow it aims to be a complete ecosystem rivaling platforms like ChatGPT and Claude.
-
-
-
-How is this different from other AI platforms?
-
-Other platforms are models behind APIs you rent. Jan is a complete AI ecosystem you own. Run any model, use real tools through MCP, keep your data private, and never pay subscriptions for local use.
-
-
-
-What models can I use?
-
-**Jan Models:**
-- Jan-Nano (32k/128k) - Research and analysis with MCP integration
-- Lucy - Mobile-optimized search (1.7B)
-- Jan-v1 - Reasoning and tool use (4B)
-
-**Open Source:**
-- OpenAI's gpt-oss models (120b and 20b)
-- Any GGUF model from Hugging Face
-
-**Cloud (with your API keys):**
-- OpenAI, Anthropic, Mistral, Groq, and more
-
-
-
-What are MCP tools?
-
-MCP (Model Context Protocol) lets AI interact with real applications. Instead of just generating text, your AI can create designs in Canva, analyze data in Jupyter, browse the web, and execute code - all through conversation.
-
-
-
-Is Jan compatible with my system?
-
-**Supported OS**:
-- [Windows 10+](/docs/desktop/windows#compatibility)
-- [macOS 12+](/docs/desktop/mac#compatibility)
-- [Linux (Ubuntu 20.04+)](/docs/desktop/linux)
-
-**Hardware**:
-- Minimum: 8GB RAM, 10GB storage
-- Recommended: 16GB RAM, GPU (NVIDIA/AMD/Intel/Apple), 50GB storage
-
-
-
-How realistic is 'open superintelligence'?
-
-Honestly? It's ambitious and uncertain. We believe the combination of rapidly improving open models, better consumer hardware, community innovation, and specialized models working together can eventually rival closed platforms. But this is a multi-year journey with no guarantees. What we can guarantee is that we'll keep building in the open, with the community, towards this goal.
-
-
-
-What can Jan actually do today?
-
-Right now, Jan can:
-- Run models like Llama, Mistral, and our own Jan models locally
-- Connect to cloud providers if you want more power
-- Use MCP tools to create designs, analyze data, browse the web, and more
-- Work completely offline once models are downloaded
-- Provide an OpenAI-compatible API for developers
-
-
-
-Is Jan really free?
-
-**Local use**: Always free, no catches
-**Cloud models**: You pay providers directly (we add no markup)
-**Jan cloud**: Optional paid services coming 2025
-
-The core platform will always be free and open source.
-
-
-
-How does Jan protect privacy?
-
-- Runs 100% offline once models are downloaded
-- All data stored locally in [Jan Data Folder](/docs/data-folder)
-- No telemetry without explicit consent
-- Open source code you can audit
-
-
-
-
-
-Can I self-host Jan?
-
-Yes. Download directly or build from [source](https://github.com/menloresearch/jan). Jan Server for production deployments coming late 2025.
-
-
-
-When will mobile/web versions launch?
-
-- **Jan Web**: Beta late 2025
-- **Jan Mobile**: Late 2025
-- **Jan Server**: Late 2025
-
-All versions will sync seamlessly.
-
-
-
-How can I contribute?
-
-- Code: [GitHub](https://github.com/menloresearch/jan)
-- Community: [Discord](https://discord.gg/FTk2MvZwJH)
-- Testing: Help evaluate models and report bugs
-- Documentation: Improve guides and tutorials
-
-
-
-Are you hiring?
-
-Yes! We love hiring from our community. Check [Careers](https://menlo.bamboohr.com/careers).
-
diff --git a/website/src/content/docs/jan/assistants.mdx b/website/src/content/docs/jan/assistants.mdx
deleted file mode 100644
index ca6d78ed6..000000000
--- a/website/src/content/docs/jan/assistants.mdx
+++ /dev/null
@@ -1,97 +0,0 @@
----
-title: Assistants
-description: A step-by-step guide on customizing and managing your assistants.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- manage assistants,
- assistants,
- ]
----
-
-Jan allows you to give models specific sets of instructions without having to repeat yourself. We called these
-models with your instructions, Assistants. Each of these assistants can also have their own set of configuration
-which can help guide how the AI model should behave and respond to your inputs. You can add, edit, or delete
-assistants, and customize their instructions and settings from the Assistants tab.
-
-
-
-To find the Assistants tab:
-
-1. Open Jan and look at the left sidebar.
-2. Click on the **Assistants** tab (see highlighted section in the screenshot above).
-3. The main panel will display all your current assistants.
-
-## Managing Assistants
-
-- **Add a New Assistant**: Click the `+` button in the Assistants panel to create a new assistant with your instructions.
-- **Edit an Assistant**: Click the pencil (โ๏ธ) icon on any assistant card to update its name, description, or instructions.
-- **Delete an Assistant**: Click the trash (๐๏ธ) icon to remove an assistant you no longer need.
-
-## Customizing Assistant Instructions
-
-Each assistant can have its own set of instructions to guide its behavior. For example:
-
-```
-Act as a software engineering mentor focused on Python and JavaScript.
-Provide detailed explanations with code examples when relevant.
-Use markdown formatting for code blocks.
-```
-
-Or:
-
-```
-Respond in a casual, friendly tone. Keep explanations brief and use simple language.
-Provide examples when explaining complex topics.
-```
-
-Or:
-
-```
-Respond in a casual, friendly tone. Keep explanations brief and use simple language.
-Provide examples when explaining complex topics.
-```
-
-## Best Practices
-- Be clear and specific about the desired behavior for each assistant.
-- Include preferences for formatting, tone, or style.
-- Include examples to increase the model's compliance with your request.
-- Use different assistants for different tasks (e.g., translation, travel planning, financial advice).
-
-
-## Switching and Managing Assistants in Chat
-
-You can quickly switch between assistants, or create and edit them, directly from the Chat screen using the
-assistant dropdown menu at the top:
-
-
-
-- Click the assistant's name (e.g., "Travel Planner") at the top of the Chat screen to open the dropdown menu.
-- The dropdown lists all of your assistants. Click on any of the assistants available to switch to it for the
-current chat session.
-- To create a new assistant, select **Create Assistant** at the bottom of the dropdown. This opens the Add Assistant dialog:
-
-
-
-- To edit an existing assistant, click the gear (โ๏ธ) icon next to its name in the dropdown. This opens the Edit Assistant dialog:
-
-
-
-### Add/Edit Assistant Dialogs
-- Set an (optional) emoji and name for your assistant.
-- Optionally add a description.
-- Enter detailed instructions to guide the assistant's behavior.
-- Adjust the predefined parameters (like Temperature, Top P, etc.) or add custom parameters as needed.
-- Click **Save** to apply your changes.
-
-This workflow allows you to seamlessly manage and switch between assistants while chatting, making it easy to tailor
-Jan to your needs in real time.
diff --git a/website/src/content/docs/jan/custom-provider.mdx b/website/src/content/docs/jan/custom-provider.mdx
deleted file mode 100644
index 23ad87a67..000000000
--- a/website/src/content/docs/jan/custom-provider.mdx
+++ /dev/null
@@ -1,288 +0,0 @@
----
-title: Custom Providers
-description: Connect Jan to any OpenAI-compatible AI service, from major cloud providers to local inference servers.
-keywords:
- [
- Jan,
- custom providers,
- OpenAI API,
- Together AI,
- vLLM,
- LMStudio,
- transformers,
- SGLang,
- API integration,
- local AI,
- cloud AI,
- ]
-sidebar:
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan's custom provider system lets you connect to any OpenAI-compatible API service. Whether you're using cloud providers like Together AI, Fireworks, or Replicate, or running local inference servers like vLLM, LMStudio, or transformers, Jan can integrate with them seamlessly.
-
-## What You Can Connect
-
-**Cloud Providers:**
-- Together AI, Fireworks, Replicate
-- Perplexity, DeepInfra, Anyscale
-- Any OpenAI-compatible API service
-
-**Local Inference Servers:**
-- vLLM, LMStudio, Ollama
-- SGLang, transformers, text-generation-webui
-- TensorRT-LLM, LocalAI
-
-**Self-Hosted Solutions:**
-- Your own API deployments
-- Enterprise AI gateways
-- Custom model endpoints
-
-## Setup Process
-
-### Add a New Provider
-
-Navigate to **Settings > Model Providers** and click **Add Provider**.
-
-
-
-Enter a name for your provider. We'll use Together AI as our example.
-
-
-
-### Get Your API Credentials
-
-For cloud providers, you'll need an account and API key. Here's Together AI's dashboard showing your credits and API key location.
-
-
-
-
-
-### Configure the Provider
-
-Back in Jan, fill in your provider's details:
-
-**API Base URL:** The endpoint for your service (e.g., `https://api.together.xyz/`)
-**API Key:** Your authentication token
-
-
-
-Common endpoints for popular services:
-- **Together AI:** `https://api.together.xyz/`
-- **Fireworks:** `https://api.fireworks.ai/`
-- **Replicate:** `https://api.replicate.com/`
-- **Local vLLM:** `http://localhost:8000/` (default)
-- **LMStudio:** `http://localhost:1234/` (default)
-
-### Add Model IDs
-
-Click the `+` button to add specific models you want to access. Each provider offers different models with various capabilities.
-
-
-
-For Together AI, we're adding `Qwen/Qwen3-235B-A22B-Thinking-2507`, one of the most capable reasoning models available.
-
-### Configure Model Features
-
-After adding a model, click the pencil icon to enable additional features like tools or vision capabilities.
-
-
-
-Enable tools if your model supports function calling. This allows integration with Jan's MCP system for web search, code execution, and more.
-
-
-
-### Start Using Your Custom Model
-
-Open a new chat and select your custom model from the provider dropdown.
-
-
-
-If you enabled tools, click the tools icon to activate MCP integrations. Here we have Serper MCP enabled for web search capabilities.
-
-
-
-
-
-### Example in Action
-
-Here's the Qwen model thinking through a complex query, searching the web, and providing detailed information about Sydney activities.
-
-
-
-**Prompt used:** "What is happening in Sydney, Australia this week? What fun activities could I attend?"
-
-The model demonstrated reasoning, web search integration, and comprehensive response formattingโall through Jan's custom provider system.
-
-## Provider-Specific Setup
-
-### Together AI
-- **Endpoint:** `https://api.together.xyz/`
-- **Popular Models:** `meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo`, `Qwen/Qwen2.5-Coder-32B-Instruct`
-- **Features:** Fast inference, competitive pricing, latest models
-- **Best For:** Production applications, latest model access
-
-### Fireworks AI
-- **Endpoint:** `https://api.fireworks.ai/`
-- **Popular Models:** `accounts/fireworks/models/llama-v3p1-405b-instruct`, `accounts/fireworks/models/qwen2p5-coder-32b-instruct`
-- **Features:** Ultra-fast inference, function calling support
-- **Best For:** Real-time applications, tool usage
-
-### vLLM (Local)
-- **Endpoint:** `http://localhost:8000/` (configurable)
-- **Setup:** Install vLLM, run `vllm serve MODEL_NAME --api-key YOUR_KEY`
-- **Models:** Any HuggingFace model compatible with vLLM
-- **Best For:** Self-hosted deployments, custom models
-
-### LMStudio (Local)
-- **Endpoint:** `http://localhost:1234/` (default)
-- **Setup:** Download LMStudio, load a model, start local server
-- **Models:** GGUF models from HuggingFace
-- **Best For:** Easy local setup, GUI management
-
-### Ollama (Local)
-- **Endpoint:** `http://localhost:11434/` (with OpenAI compatibility)
-- **Setup:** Install Ollama, run `OLLAMA_HOST=0.0.0.0 ollama serve`
-- **Models:** Ollama model library (llama3, qwen2.5, etc.)
-- **Best For:** Simple local deployment, model management
-
-## Example Prompts to Try
-
-### Advanced Reasoning
-```
-I'm planning to start a sustainable urban garden on my apartment balcony. Consider my location (temperate climate), space constraints (4x6 feet), budget ($200), and goals (year-round fresh herbs and vegetables). Provide a detailed plan including plant selection, container setup, watering system, and seasonal rotation schedule.
-```
-
-### Research and Analysis
-```
-Compare the environmental impact of electric vehicles vs hydrogen fuel cell vehicles in 2024. Include manufacturing emissions, energy sources, infrastructure requirements, and lifecycle costs. Provide specific data and cite recent studies.
-```
-
-### Creative Problem Solving
-```
-Design a mobile app that helps people reduce food waste. Consider user psychology, practical constraints, monetization, and social impact. Include wireframes description, key features, and go-to-market strategy.
-```
-
-### Technical Deep Dive
-```
-Explain how large language models use attention mechanisms to understand context. Start with the basics and build up to transformer architecture, including mathematical foundations and practical implications for different model sizes.
-```
-
-### Planning and Strategy
-```
-I have 6 months to learn machine learning from scratch and land an ML engineering job. Create a week-by-week study plan including theory, practical projects, portfolio development, and job search strategy. Consider my background in software development.
-```
-
-## Advanced Configuration
-
-### Authentication Methods
-
-**API Key Header (Most Common):**
-- Standard: `Authorization: Bearer YOUR_KEY`
-- Custom: `X-API-Key: YOUR_KEY`
-
-**Query Parameters:**
-- Some services use `?api_key=YOUR_KEY`
-
-**Custom Headers:**
-- Enterprise gateways may require specific headers
-
-### Request Customization
-
-Most providers support OpenAI's standard parameters:
-- `temperature`: Response creativity (0.0-1.0)
-- `max_tokens`: Response length limit
-- `top_p`: Token selection probability
-- `frequency_penalty`: Repetition control
-- `presence_penalty`: Topic diversity
-
-### Model Naming Conventions
-
-Different providers use various naming schemes:
-- **HuggingFace:** `organization/model-name`
-- **Together AI:** `meta-llama/Llama-2-70b-chat-hf`
-- **Ollama:** `llama3:latest`
-- **Local:** Often just the model name
-
-## Troubleshooting
-
-### Connection Issues
-- Verify the API endpoint URL is correct
-- Check if the service is running (for local providers)
-- Confirm network connectivity and firewall settings
-
-### Authentication Failures
-- Ensure API key is copied correctly (no extra spaces)
-- Check if the key has necessary permissions
-- Verify the authentication method matches provider requirements
-
-### Model Not Found
-- Confirm the model ID exists on the provider
-- Check spelling and capitalization
-- Some models require special access or approval
-
-### Rate Limiting
-- Most providers have usage limits
-- Implement delays between requests if needed
-- Consider upgrading to higher tier plans
-
-### Performance Issues
-- Local providers may need more powerful hardware
-- Cloud providers vary in response times
-- Check provider status pages for service issues
-
-## Cost Management
-
-### Cloud Provider Pricing
-- Most charge per token (input + output)
-- Prices vary significantly between models
-- Monitor usage through provider dashboards
-
-### Local Provider Costs
-- Hardware requirements (RAM, GPU)
-- Electricity consumption
-- Initial setup and maintenance time
-
-### Optimization Tips
-- Use smaller models for simple tasks
-- Implement caching for repeated queries
-- Set appropriate max_tokens limits
-- Monitor and track usage patterns
-
-## Best Practices
-
-### Security
-- Store API keys securely
-- Use environment variables in production
-- Rotate keys regularly
-- Monitor for unauthorized usage
-
-### Performance
-- Choose models appropriate for your tasks
-- Implement proper error handling
-- Cache responses when possible
-- Use streaming for long responses
-
-### Reliability
-- Have fallback providers configured
-- Implement retry logic
-- Monitor service availability
-- Test regularly with different models
-
-## Next Steps
-
-Once you have custom providers configured, explore advanced integrations:
-- Combine with [MCP tools](./mcp-examples/search/serper) for enhanced capabilities
-- Set up multiple providers for different use cases
-- Create custom assistants with provider-specific models
-- Build workflows that leverage different model strengths
-
-Custom providers unlock Jan's full potential, letting you access cutting-edge models and maintain complete control over your AI infrastructure. Whether you prefer cloud convenience or local privacy, Jan adapts to your workflow.
\ No newline at end of file
diff --git a/website/src/content/docs/jan/data-folder.mdx b/website/src/content/docs/jan/data-folder.mdx
deleted file mode 100644
index 98d605a03..000000000
--- a/website/src/content/docs/jan/data-folder.mdx
+++ /dev/null
@@ -1,216 +0,0 @@
----
-title: Jan Data Folder
-description: A guide to Jan's data structure.
-sidebar_position: 2
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- quickstart,
- getting started,
- using AI model,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan stores your data locally in JSON format. Your data is yours alone.
-
-## Open Jan Data Folder
-
-Via Jan:
-1. **Settings** > **General**
-2. Click on the **Change Location** button.
-
-
-
-
-Via Terminal:
-
-```bash
-# Windows
-cd %APPDATA%/Jan/data
-
-# Mac
-cd ~/Library/Application\ Support/Jan/data
-
-# Linux
-cd $XDG_CONFIG_HOME/Jan/data # Custom install
-cd ~/.config/Jan/data # Default install
-```
-
-## Directory Structure
-
-
-
-```
-/assistants/
- /jan/
- assistant.json
-/engines/
- /llama.cpp/
-/extensions/
- extensions.json
-/@janhq/
- /assistant-extension/
- /conversational-extension/
- /download-extension/
- /engine-management-extension/
- /hardware-management-extension/
- /inference-cortex-extension/
- /model-extension/
-/files/
-/logs/
- app.log
-/models/
- /huggingface.co/
- /Model_Provider_A/
- /Model_A
- model_A.gguf
- model_A.yaml
-/threads/
- /thread_A/
- messages.jsonl
- thread.json
-
-```
-
-### `assistants/`
-Where AI personalities live. The default one (`/assistants/jan/`):
-
-```json
-{
- "avatar": "๐",
- "id": "jan",
- "object": "assistant",
- "created_at": 1750945742.536,
- "name": "Jan",
- "description": "Jan is a helpful AI assistant that can use tools and help complete tasks for its users.",
- "model": "*",
- "instructions": "You have access to a set of tools to help you answer the userโs question. You can use only one tool per message, and youโll receive the result of that tool in the userโs next response. To complete a task, use tools step by stepโeach step should be guided by the outcome of the previous one.\nTool Usage Rules:\n1. Always provide the correct values as arguments when using tools. Do not pass variable namesโuse actual values instead.\n2. You may perform multiple tool steps to complete a task.\n3. Avoid repeating a tool call with exactly the same parameters to prevent infinite loops.",
- "tools": [
- {
- "type": "retrieval",
- "enabled": false,
- "useTimeWeightedRetriever": false,
- "settings": {
- "top_k": 2,
- "chunk_size": 1024,
- "chunk_overlap": 64,
- "retrieval_template": "Use the following pieces of context to answer the question at the end.\n----------------\nCONTEXT: {CONTEXT}\n----------------\nQUESTION: {QUESTION}\n----------------\nHelpful Answer:"
- }
- }
- ],
- "file_ids": []
-}
-```
-
-Parameters:
-
-| Parameter | Description | Type | Default |
-|------------------------|--------------------------------------------------------------|---------|---------|
-| id | Assistant identifier | string | jan |
-| avatar | Assistant image | string | None |
-| object | OpenAI API compatibility marker | string | None |
-| created_at | Creation timestamp | string | None |
-| name | Display name | string | Jan |
-| description | Role description | string | Default |
-| model | Allowed models (* = all) | string | * |
-| instructions | Default thread instructions | string | None |
-| file_ids | OpenAI compatibility field | string | None |
-| tools | Available tools (retrieval only currently) | array | retrieval|
-| type | Tool type | string | retrieval|
-| enabled | Tool status | boolean | true |
-| useTimeWeightedRetriever| Time-weighted retrieval toggle | boolean | false |
-| settings | Tool configuration | object | None |
-| top_k | Max retrieval results | number | 2 |
-| chunk_size | Text chunk size | number | 1024 |
-| chunk_overlap | Chunk overlap amount | number | 64 |
-| retrieval_template | Response format template | string | None |
-
-### `extensions/`
-Add-on central. Organization extensions live in `@janhq/`, solo ones in root.
-
-### `logs/`
-Debugging headquarters (`/logs/app.txt`):
-- **[APP]**: Core logs
-- **[SERVER]**: API drama
-- **[SPECS]**: Hardware confessions
-
-### `models/`
-The silicon brain collection. Each model has its own `model.json`.
-
-
-
-### `threads/`
-Chat archive. Each thread (`/threads/jan_unixstamp/`) contains:
-
-- `messages.jsonl`:
-```json
- {
- "completed_at": 0,
- "content": [
- {
- "text": {
- "annotations": [],
- "value": "Hello! I can help you with various tasks. I can search for information on the internet, including news, videos, images, shopping, and more. I can also scrape webpages to extract specific information. Let me know what you need!"
- },
- "type": "text"
- }
- ],
- "created_at": 1751012639307,
- "id": "01JYR7S0JB5ZBGMJV52KWMW5VW",
- "metadata": {
- "assistant": {
- "avatar": "๐",
- "id": "jan",
- "instructions": "You have access to a set of tools to help you answer the user's question. You can use only one tool per message, and you'll receive the result of that tool in the user's next response. To complete a task, use tools step by stepโeach step should be guided by the outcome of the previous one.\nTool Usage Rules:\n1. Always provide the correct values as arguments when using tools. Do not pass variable namesโuse actual values instead.\n2. You may perform multiple tool steps to complete a task.\n3. Avoid repeating a tool call with exactly the same parameters to prevent infinite loops.",
- "name": "Jan",
- "parameters": ""
- },
- "tokenSpeed": {
- "lastTimestamp": 1751012637097,
- "message": "01JYR7S0GW5M9PSHMRE7T8VQJM",
- "tokenCount": 49,
- "tokenSpeed": 22.653721682847895
- }
- },
- "object": "thread.message",
- "role": "assistant",
- "status": "ready",
- "thread_id": "8f2c9922-db49-4d1e-8620-279c05baf2d0",
- "type": "text"
- }
-```
-
-- `thread.json`:
-
-| Parameter | Description |
-|------------|------------------------------------------------|
-| assistants | Assistant configuration clone |
-| created | Creation timestamp |
-| id | Thread identifier |
-| metadata | Additional thread data |
-| model | Active model settings |
-| object | OpenAI compatibility marker |
-| title | Thread name |
-| updated | Updated timestamp |
-
-
-
-
-## Delete Jan Data
-
-Uninstall guides: [Mac](./installation/mac#step-2-clean-up-data-optional),
-[Windows](./installation/windows#step-2-handle-jan-data), or [Linux](./installation/linux#uninstall-jan).
diff --git a/website/src/content/docs/jan/explanation/model-parameters.mdx b/website/src/content/docs/jan/explanation/model-parameters.mdx
deleted file mode 100644
index 038fc09d9..000000000
--- a/website/src/content/docs/jan/explanation/model-parameters.mdx
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: Model Parameters
-description: Customize how your AI models behave and perform.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- model settings,
- parameters,
- ]
----
-import { Aside, Steps } from '@astrojs/starlight/components'
-
-Model parameters control how your AI thinks and responds. Think of them as the AI's personality settings and performance controls.
-
-## How to Access Settings
-
-**For individual conversations:**
-- In **Threads**, click the **gear icon** next to your selected model
-
-**For permanent model settings:**
-- Go to **Settings > Model Providers > Llama.cpp**, click the **gear icon** next to a model
-
-**For model capabilities:**
-- Click the **edit button** next to a model to enable features like vision or tools
-
-## Performance Settings (Gear Icon)
-
-These settings control how the model thinks and performs:
-
-| Setting | What It Does | Simple Explanation |
-|---------|-------------|-------------------|
-| **Context Size** | How much text the model remembers | Like the model's working memory. Larger = remembers more of your conversation, but uses more computer memory. |
-| **GPU Layers** | How much work your graphics card does | More layers on GPU = faster responses, but needs more graphics memory. Start high and reduce if you get errors. |
-| **Temperature** | How creative vs. predictable responses are | Low (0.1-0.3) = focused, consistent answers. High (0.7-1.0) = creative, varied responses. Try 0.7 for general use. |
-| **Top K** | How many word choices the model considers | Smaller numbers (20-40) = more focused. Larger numbers (80-100) = more variety. Most people don't need to change this. |
-| **Top P** | Another way to control word variety | Works with Top K. Values like 0.9 work well. Lower = more focused, higher = more creative. |
-| **Min P** | Minimum chance a word needs to be chosen | Prevents very unlikely words. Usually fine at default settings. |
-| **Repeat Last N** | How far back to check for repetition | Helps prevent the model from repeating itself. Default values usually work well. |
-| **Repeat Penalty** | How much to avoid repeating words | Higher values (1.1-1.3) reduce repetition. Too high makes responses awkward. |
-| **Presence Penalty** | Encourages talking about new topics | Higher values make the model explore new subjects instead of staying on one topic. |
-| **Frequency Penalty** | Reduces word repetition | Similar to repeat penalty but focuses on how often words are used. |
-
-
-
-## Model Capabilities (Edit Button)
-
-These toggle switches enable special features:
-
-- **Vision**: Let the model see and analyze images you share
-- **Tools**: Enable advanced features like web search, file operations, and code execution
-- **Embeddings**: Allow the model to create numerical representations of text (for advanced users)
-- **Web Search**: Let the model search the internet for current information
-- **Reasoning**: Enable step-by-step thinking for complex problems
-
-
-
-
-## Hardware Settings
-
-These control how efficiently the model runs on your computer:
-
-### GPU Layers
-Think of your model as a stack of layers, like a cake. Each layer can run on either your main processor (CPU) or graphics card (GPU). Your graphics card is usually much faster.
-
-- **More GPU layers** = Faster responses, but uses more graphics memory
-- **Fewer GPU layers** = Slower responses, but uses less graphics memory
-
-Start with the maximum number and reduce if you get out-of-memory errors.
-
-### Context Length
-This is like the model's short-term memory - how much of your conversation it can remember at once.
-
-- **Longer context** = Remembers more of your conversation, better for long discussions
-- **Shorter context** = Uses less memory, runs faster, but might "forget" earlier parts of long conversations
-
-
-
-## Quick Setup Guide
-
-**For most users:**
-1. Enable **Tools** if you want web search and code execution
-2. Set **Temperature** to 0.7 for balanced creativity
-3. Max out **GPU Layers** (reduce only if you get memory errors)
-4. Leave other settings at defaults
-
-**For creative writing:**
-- Increase **Temperature** to 0.8-1.0
-- Increase **Top P** to 0.95
-
-**For factual/technical work:**
-- Decrease **Temperature** to 0.1-0.3
-- Enable **Tools** for web search and calculations
-
-**Troubleshooting:**
-- **Responses too repetitive?** Increase Temperature or Repeat Penalty
-- **Out of memory errors?** Reduce GPU Layers or Context Size
-- **Responses too random?** Decrease Temperature
-- **Model running slowly?** Increase GPU Layers (if you have VRAM) or reduce Context Size
diff --git a/website/src/content/docs/jan/installation/linux.mdx b/website/src/content/docs/jan/installation/linux.mdx
deleted file mode 100644
index 4897e2c9c..000000000
--- a/website/src/content/docs/jan/installation/linux.mdx
+++ /dev/null
@@ -1,262 +0,0 @@
----
-title: Linux
-description: Get started quickly with Jan, an AI chat application that runs 100% offline on your desktop & mobile (*coming soon*).
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- quickstart,
- getting started,
- using AI model,
- installation,
- "desktop"
- ]
----
-
-import { Aside, Tabs, TabItem } from '@astrojs/starlight/components';
-
-Instructions for installing Jan on Linux.
-
-## Compatibility
-System requirements:
-
-
-
- #### Debian-based (Supports `.deb` and `AppImage`)
-
- - Debian
- - Ubuntu and derivatives:
- - Ubuntu Desktop LTS (official)/Ubuntu Server LTS (only for server)
- - Edubuntu
- - Kubuntu
- - Lubuntu
- - Ubuntu Budgie
- - Ubuntu Cinnamon
- - Ubuntu Kylin
- - Ubuntu MATE
- - Linux Mint
- - Pop!_OS
-
- #### RHEL-based (Supports `.rpm` and `AppImage`)
-
- - RHEL-based (Server only)
- - Fedora
-
- #### Arch-based
-
- - Arch Linux
- - SteamOS
-
- #### Independent
-
- - openSUSE
-
-
-
- - Haswell processors (Q2 2013) and newer
- - Tiger Lake (Q3 2020) and newer for Celeron and Pentium processors
- - Excavator processors (Q2 2015) and newer
-
-
-
-
-
- - 8GB โ up to 3B parameter models (int4)
- - 16GB โ up to 7B parameter models (int4)
- - 32GB โ up to 13B parameter models (int4)
-
-
-
-
-
- - 6GB โ up to 3B parameter models (int4)
- - 8GB โ up to 7B parameter models (int4)
- - 12GB โ up to 13B parameter models (int4)
-
-
-
-
-
- Minimum 10GB of free disk space required.
-
-
-
-## Install Jan
-
-Installation steps:
-
-
-### Step 1: Download Application
-
-Available releases:
-
-
- Stable release:
- - Ubuntu: [jan.deb](https://app.jan.ai/download/latest/linux-amd64-deb)
- - Others: [Jan.AppImage](https://app.jan.ai/download/latest/linux-amd64-appimage)
- - Official Website: https://jan.ai/download
-
-
-
- Development build:
- - Ubuntu: [jan.deb](https://app.jan.ai/download/nightly/linux-amd64-deb)
- - Others: [Jan.AppImage](https://app.jan.ai/download/nightly/linux-amd64-appimage)
-
-
-
-
-
-### Step 2: Install Application
-
-Installation commands:
-
-
-
- ##### dpkg
-
- ```bash
- sudo dpkg -i jan-linux-amd64-{version}.deb
- ```
-
- ##### apt-get
-
- ```bash
- sudo apt-get install ./jan-linux-amd64-{version}.deb
- ```
-
-
-
- From the terminal, run the following commands:
-
- ```bash
- chmod +x jan-linux-x86_64-{version}.AppImage
- ./jan-linux-x86_64-{version}.AppImage
- ```
-
-
-
-
-
-## Data Folder
-
-Default locations:
-
-```bash
-# Custom installation directory
-$XDG_CONFIG_HOME = /home/username/custom_config
-
-# or
-
-# Default installation directory
-~/.config/Jan/data
-
-```
-See [Jan Data Folder](/docs/data-folder) for details.
-
-
-## GPU Acceleration
-Configuration for GPU support:
-
-
-
- ### Step 1: Verify Hardware & Install Dependencies
-
- **1.1. Check GPU Detection**
-
- ```sh
- lspci | grep -i nvidia
- ```
-
- **1.2. Install Required components**
-
- **NVIDIA Driver:**
-
- 1. Install the [NVIDIA Driver](https://www.nvidia.com/en-us/drivers/), ideally via your package manager.
- 2. Verify:
-
- ```sh
- nvidia-smi
- ```
-
- **CUDA Toolkit:**
-
- 1. Install the [CUDA toolkit](https://developer.nvidia.com/cuda-downloads), ideally from your package manager (**11.7+**)
- 2. Verify:
-
- ```sh
- nvcc --version
- ```
-
- **Additional Requirements:**
-
- ```sh
- sudo apt update
- sudo apt install gcc-11 g++-11 cpp-11
- export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
- ```
- [Documentation](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions)
-
- ### Step 2: Enable GPU Acceleration
-
- 1. Navigate to **Settings** > **Local Engine** > **Llama.cpp**
- 2. Select appropriate backend in **llama-cpp Backend**. Details in our [guide](/docs/local-engines/llama-cpp).
-
-
-
-
-
- Requires Vulkan support.
-
- 1. Navigate to **Settings** > **Hardware** > **GPUs**
- 2. Select appropriate backend in **llama-cpp Backend**. Details in our [guide](/docs/local-engines/llama-cpp).
-
-
-
- Requires Vulkan support.
-
- 1. Navigate to **Settings** > **Hardware** > **GPUs**
- 2. Select appropriate backend in **llama-cpp Backend**. Details in our [guide](/docs/local-engines/llama-cpp).
-
-
-
-## Uninstall Jan
-
-Removal commands:
-
-
- ```bash
- sudo apt-get remove jan
- rm -rf Jan
- rm -rf ~/.config/Jan/data
- rm -rf ~/.config/Jan/cache
- ```
-
-
-
- ```bash
- rm jan-linux-x86_64-{version}.AppImage
- rm -rf ~/.config/Jan
- ```
-
-
-
-
diff --git a/website/src/content/docs/jan/installation/mac.mdx b/website/src/content/docs/jan/installation/mac.mdx
deleted file mode 100644
index 80a8cf40f..000000000
--- a/website/src/content/docs/jan/installation/mac.mdx
+++ /dev/null
@@ -1,131 +0,0 @@
----
-title: Mac
-description: Get started quickly with Jan - a local AI that runs on your computer. Install Jan and pick your model to start chatting.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- quickstart,
- getting started,
- using AI model,
- installation,
- "desktop"
- ]
----
-
-import { Aside, Tabs, TabItem } from '@astrojs/starlight/components';
-
-
-Jan runs natively on both Apple Silicon and Intel-based Macs.
-
-## Compatibility
-
-### Minimum Requirements
-
-Your Mac needs:
-- **Operating System:** MacOSX 13.6 or higher
-- **Memory:**
- - 8GB โ up to 3B parameter models
- - 16GB โ up to 7B parameter models
- - 32GB โ up to 13B parameter models
-- **Storage:** 10GB+ free space
-
-### Mac Performance Guide
-
-
-
-**Apple Silicon (M1, M2, M3)**
-- Metal acceleration enabled by default
-- GPU-accelerated processing
-
-**Intel-based Mac**
-- CPU processing only
-- Standard performance
-
-_Check your Mac's processor: Apple menu โ About This Mac_
-
-## Install Jan
-
-Installation steps:
-
-### Step 1: Download Application
-
-Select version:
-
-
-
- Get Jan from here:
- - [Download Jan's Stable Version](https://app.jan.ai/download/latest/mac-universal)
- - Official Website: https://jan.ai/download
-
-
-
-
- Nightly: Latest features, less stable.
-
- [Download Jan's Nightly Version](https://app.jan.ai/download/nightly/mac-universal)
-
-
-
-
-
-
-### Step 2: Install Application
-
-1. Open the Jan installer (`.dmg` file)
-2. Drag Jan to **Applications**
-3. Wait a moment
-4. Launch Jan
-
-
-## Jan Data Folder
-
-Default location:
-
-```sh
-# Default installation directory
-~/Library/Application\ Support/Jan/data
-```
-
-See [Jan Data Folder](../data-folder) for details.
-
-
-## Uninstall Jan
-
-
-### Step 1: Remove Application
-
-1. Close Jan if it's running
-2. Open **Finder**
-3. Go to **Applications**
-4. Find Jan
-5. Pick your removal method:
- - Drag to **Trash**
- - Right-click โ **Move to Trash**
- - **Command-Delete**
-
-### Step 2: Clean Up Data (Optional)
-
-Run this in **Terminal** to remove all data:
-
-```bash
-rm -rf ~/Library/Application\ Support/Jan/data
-```
-
-
diff --git a/website/src/content/docs/jan/installation/windows.mdx b/website/src/content/docs/jan/installation/windows.mdx
deleted file mode 100644
index 45e80fad2..000000000
--- a/website/src/content/docs/jan/installation/windows.mdx
+++ /dev/null
@@ -1,189 +0,0 @@
----
-title: Windows
-description: Run AI models locally on your Windows machine with Jan. Quick setup guide for local inference and chat.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- quickstart,
- getting started,
- using AI model,
- installation,
- "desktop"
- ]
----
-
-import { Aside, Tabs, TabItem } from '@astrojs/starlight/components';
-
-
-## Compatibility
-
-**System requirements:**
-- **Operating System**: Windows 10 or higher.
-- **CPU**
-
-
-
- - Intel: Haswell (Q2 2013) or newer
- - Intel Celeron/Pentium: Tiger Lake (Q3 2020) or newer
-
-
- - Excavator processors (Q2 2015) and newer.
-
-
-
-
-
-**Memory (RAM)**
-- 8GB โ up to 3B parameter models (int4)
-- 16GB โ up to 7B parameter models (int4)
-- 32GB โ up to 13B parameter models (int4)
-
-
-
-**GPU**:
-- 6GB โ up to 3B parameter models
-- 8GB โ up to 7B parameter models
-- 12GB โ up to 13B parameter models
-
-
-
-**Storage:** 10GB free space minimum for app and models
-
-
-## Install Jan
-
-### Step 1: Download Application
-
-
-
- - [Download Stable Jan](https://app.jan.ai/download/latest/win-x64)
- - Official Website: [Download Jan](https://jan.ai/download)
-
-
-
- Nightly: Development build with latest features
-
- [Download Nightly Jan](https://app.jan.ai/download/nightly/win-x64)
-
-
-
-
-
-### Step 2: Install Application
-
-1. Run the downloaded `.exe` file
-2. Wait for installation to complete
-3. Launch Jan
-
-## Data Folder
-
-Default installation path:
-
-```sh
-# Default installation directory
-~\Users\\AppData\Roaming\Jan\data
-```
-
-See [Jan Data Folder](/docs/data-folder) for complete folder structure details.
-
-
-## GPU Acceleration
-
-
-
-
-### Step 1: Verify Hardware & Install Dependencies
-**1.1. Check GPU Detection**
-
-Verify GPU is recognized:
-- Right-click desktop > NVIDIA Control Panel
-- Or check Device Manager > Display Adapters
-
-**1.2. Install Required components**
-**NVIDIA Driver:**
-1. Install [NVIDIA Driver](https://www.nvidia.com/en-us/drivers/) (version **470.63.01 or higher**)
-2. Verify installation:
-
-```sh
-nvidia-smi
-```
-
-**CUDA Toolkit:**
-1. Install [CUDA toolkit](https://developer.nvidia.com/cuda-downloads) (**11.7 or higher**)
-2. Verify installation:
-
-```sh
-nvcc --version
-```
-### Step 2: Enable GPU Acceleration
-
-Navigate to **Settings** > **Hardware** > **GPUs**
-and toggle the **ON** switch if not enabled.
-
-
-
-
- AMD GPUs require **Vulkan** support.
-
- Navigate to **Settings** > **Hardware** > **GPUs**
- and toggle the **ON** switch if not enabled.
-
-
-
-
- Intel Arc GPUs require **Vulkan** support.
-
- Navigate to **Settings** > **Hardware** > **GPUs**
- and toggle the **ON** switch if not enabled.
-
-
-
-
-
-
-## Uninstall Jan
-
-### Step 1: Remove Application through Control Panel
-
-1. Open **Control Panels**
-2. Go to **Programs** section
-3. Click **Uninstall Program**
-4. Search for **Jan**
-5. Click the **Three Dots Icon** > **Uninstall**
-6. Click **Uninstall** again to confirm
-7. Click **OK**
-
-### Step 2: Clean Up Remaining Files
-
-Remove app data:
-
-1. Navigate to `C:\Users\[username]\AppData\Roaming`
-2. Delete Jan folder
-
-or via **Terminal**:
-
-```sh
-cd C:\Users\%USERNAME%\AppData\Roaming
-rmdir /S Jan
-```
-
-
diff --git a/website/src/content/docs/jan/jan-models/jan-nano-128.mdx b/website/src/content/docs/jan/jan-models/jan-nano-128.mdx
deleted file mode 100644
index 03ee1f17c..000000000
--- a/website/src/content/docs/jan/jan-models/jan-nano-128.mdx
+++ /dev/null
@@ -1,137 +0,0 @@
----
-title: Jan Nano 128k
-description: Jan Models
-keywords:
- [
- Jan,
- Jan Models,
- Jan Model,
- Jan Model List,
- Menlo Models,
- Menlo Model,
- Jan-Nano-Gguf,
- ReZero,
- Model Context Protocol,
- MCP,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-> Enabling deeper research through extended context understanding.
-
-Jan-Nano-128k represents a notable advancement in compact language models for different applications. Building upon the
-success of Jan-Nano-32k, this enhanced version features a native 128k context window that enables deeper, more comprehensive
-research capabilities without the performance degradation typically associated with context extension methods.
-
-You can have a look at all of our models, and download them from the HuggingFace [Menlo Models page](https://huggingface.co/Menlo).
-
-**Key Improvements:**
-
-- ๐ Deeper Research: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations
-- โก Native 128k Window: Built to handle long contexts efficiently, maintaining performance across the full context range
-- ๐ Enhanced Performance: Unlike traditional context extension methods, Jan-Nano-128k's performance remains consistent with longer contexts
-
-This model maintains full compatibility with Model Context Protocol (MCP) servers while dramatically expanding the scope of research
-tasks it can handle in a single session.
-
-
-## Why Jan-Nano-128k?
-
-Most small models hit a wall at 8-32k tokens. Jan-Nano-128k goes beyond this limitation with a native 128k context windowโthat's roughly
-300 pages of text or an entire novel's worth of information processed simultaneously.
-
-Unlike YaRN or PI methods that retrofit models beyond their limits and degrade performance, Jan-Nano-128k was architecturally rewired for
-128k contexts from the ground up. The result: an inverse scaling behavior where performance actually improves with longer contexts,
-maintaining consistent accuracy from 1k to 128k tokens as the model leverages more information for synthesis.
-
-
-
-
-**Applications unlocked:**
-- **Academic**: Extract key findings from 50+ papers simultaneously
-- **Legal**: Pinpoint relevant clauses across thousand-page contracts
-- **Code**: Trace specific functions through massive codebases
-- **Business**: Distill insights from quarters of financial data
-- **Content**: Maintain narrative coherence across book-length outputs
-
-**MCP Usage:** Jan-Nano-128k doesn't memorize, it orchestrates. With MCP integration, it becomes a research conductor that fetches dozens
-of sources, holds everything in active memory, extracts precisely what's needed, and synthesizes findings across a marathon research session. It's
-not about understanding every word; it's about finding the needle in a haystack of haystacks.
-
-## Evaluation
-
-Jan-Nano-128k has been rigorously evaluated on the SimpleQA benchmark using our MCP-based methodology, demonstrating superior performance compared to its predecessor:
-
-
-
-**Key findings:**
-- 15% improvement over Jan-Nano-32k on complex multi-document tasks
-- Consistent performance across all context lengths (no cliff at 64k like other extended models)
-- Superior citation accuracy when handling 10+ sources simultaneously
-
-## ๐ฅ๏ธ How to Run Locally
-
-### Demo
-
-
-
-### Quick Start Guide
-
-1. **Download Jan**
-2. **Download Jan-Nano-128k**
-3. **Enable MCP**, the serper or the exa MCPs work very well with Jan-Nano-128k
-4. **Start researching**
-
-### Usage
-
-Deploy using VLLM:
-
-```bash
-vllm serve Menlo/Jan-nano-128k \
- --host 0.0.0.0 \
- --port 1234 \
- --enable-auto-tool-choice \
- --tool-call-parser hermes \
- --rope-scaling '{"rope_type":"yarn","factor":3.2,"original_max_position_embeddings":40960}' --max-model-len 131072
-```
-
-Or with `llama-server` from `llama.cpp`:
-
-```bash
-llama-server ... --rope-scaling yarn --rope-scale 3.2 --yarn-orig-ctx 40960
-```
-
-**Note:** The chat template is included in the tokenizer. For troubleshooting, download the [Non-think chat template](https://qwen.readthedocs.io/en/latest/_downloads/c101120b5bebcc2f12ec504fc93a965e/qwen3_nonthinking.jinja).
-
-### Recommended Sampling Parameters
-
-```yaml
-Temperature: 0.7
-Top-p: 0.8
-Top-k: 20
-Min-p: 0.0
-```
-
-### Hardware Requirements
-- **Minimum**: 16GB RAM for Q4 quantization
-- **Recommended**: 24GB RAM for Q8 quantization
-- **Optimal**: 32GB+ RAM for full precision
-
-## ๐ค Community & Support
-- **Discussions**: [HuggingFace Community](https://huggingface.co/Menlo/Jan-nano-128k/discussions)
-- **Issues**: [GitHub Repository](https://github.com/menloresearch/deep-research/issues)
-- **Discord**: Join our research community for tips and best practices
diff --git a/website/src/content/docs/jan/jan-models/jan-nano-32.mdx b/website/src/content/docs/jan/jan-models/jan-nano-32.mdx
deleted file mode 100644
index c50771ec8..000000000
--- a/website/src/content/docs/jan/jan-models/jan-nano-32.mdx
+++ /dev/null
@@ -1,135 +0,0 @@
----
-title: Jan Nano 32k
-description: Jan-Nano-Gguf Model
-keywords:
- [
- Jan,
- Jan Models,
- Jan Model,
- Jan Model List,
- Menlo Models,
- Menlo Model,
- Jan-Nano-Gguf,
- ReZero,
- Model Context Protocol,
- MCP,
- ]
-sidebar:
- order: 1
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-
-## Why Jan Nano?
-
-Most language models face a fundamental tradeoff where powerful capabilities require a lot of computational resources. Jan
-Nano breaks this constraint through a focused design philosophy where instead of trying to know everything, it excels at
-knowing how to find anything.
-
-
-## What is Jan Nano?
-
-Jan Nano is a compact 4-billion parameter language model specifically designed and trained for deep research tasks.
-This model has been optimized to work seamlessly with Model Context Protocol (MCP) servers, enabling efficient integration
-with various research tools and data sources.
-
-The model and its different model variants are fully supported by Jan.
-
-
-
-
-## System Requirements
-
-- Minimum Requirements:
- - 8GB RAM (with iQ4_XS quantization)
- - 12GB VRAM (for Q8 quantization)
- - CUDA-compatible GPU
-- Recommended Setup:
- - 16GB+ RAM
- - 16GB+ VRAM
- - Latest CUDA drivers
- - RTX 30/40 series or newer
-
-
-## Using Jan-Nano-32k
-
-**Step 1**
-Download Jan from [here](https://jan.ai/docs/desktop/).
-
-**Step 2**
-Go to the Hub Tab, search for Jan-Nano-Gguf, and click on the download button to the best model size for your system.
-
-
-
-**Step 3**
-Go to **Settings** > **Model Providers** > **Llama.cpp** click on the pencil icon and enable tool use for Jan-Nano-Gguf.
-
-**Step 4**
-To take advantage of Jan-Nano's full capabilities, you need to enable MCP support. We're going to use it with Serper's
-API. You can get a free API key from [here](https://serper.dev/). Sign up and they will immediately generate one for you.
-
-**Step 5**
-Add the serper MCP to Jan via the **Settings** > **MCP Servers** tab.
-
-
-
-**Step 6**
-Open up a new chat and ask Jan-Nano to search the web for you.
-
-
-
-## Queries to Try
-
-Here are some example queries to showcase Jan-Nano's web search capabilities:
-
-1. **Current Events**: What are the latest developments in renewable energy adoption in Germany and Denmark?
-2. **International Business**: What is the current status of Tesla's Gigafactory in Berlin and how has it impacted the local economy?
-3. **Technology Trends**: What are the newest AI developments from Google, Microsoft, and Meta that were announced this week?
-4. **Global Weather**: What's the current weather forecast for Tokyo, Japan for the next 5 days?
-5. **Stock Market**: What are the current stock prices for Apple, Samsung, and Huawei, and how have they performed this month?
-6. **Sports Updates**: What are the latest results from the Premier League matches played this weekend?
-7. **Scientific Research**: What are the most recent findings about climate change impacts in the Arctic region?
-8. **Cultural Events**: What major music festivals are happening in Europe this summer and who are the headliners?
-9. **Health & Medicine**: What are the latest developments in mRNA vaccine technology and its applications beyond COVID-19?
-10. **Space Exploration**: What are the current missions being conducted by NASA, ESA, and China's space program?
-
-
-## FAQ
-
-- What are the recommended GGUF quantizations?
- - Q8 GGUF is recommended for best performance
- - iQ4_XS GGUF for very limited VRAM setups
- - Avoid Q4_0 and Q4_K_M as they show significant performance degradation
-
-- Can I run this on a laptop with 8GB RAM?
- - Yes, but use the recommended quantizations (iQ4_XS)
- - Note that performance may be limited with Q4 quantizations
-
-- How much did the training cost?
- - Training was done on internal A6000 clusters
- - Estimated cost on RunPod would be under $100 using H200
- - Hardware used:
- - 8xA6000 for training code
- - 4xA6000 for vllm server (inferencing)
-
-- What frontend should I use?
- - Jan Beta (recommended) - Minimalistic and polished interface
- - Download link: https://jan.ai/docs/desktop/beta
-
-- Getting Jinja errors in LM Studio?
- - Use Qwen3 template from other LM Studio compatible models
- - Disable โthinkingโ and add the required system prompt
- - Fix coming soon in future GGUF releases
-- Having model loading issues in Jan?
- - Use latest beta version: Jan-beta_0.5.18-rc6-beta
- - Ensure proper CUDA support for your GPU
- - Check VRAM requirements match your quantization choice
-
-## Resources
-
-- [Jan-Nano Model on Hugging Face](https://huggingface.co/Menlo/Jan-nano)
-- [Jan-Nano GGUF on Hugging Face](https://huggingface.co/Menlo/Jan-nano-gguf)
diff --git a/website/src/content/docs/jan/jan-models/jan-v1.mdx b/website/src/content/docs/jan/jan-models/jan-v1.mdx
deleted file mode 100644
index 2aca52a15..000000000
--- a/website/src/content/docs/jan/jan-models/jan-v1.mdx
+++ /dev/null
@@ -1,121 +0,0 @@
----
-title: Jan-v1
-description: 4B parameter model with strong performance on reasoning benchmarks
-sidebar:
- order: 0
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-## Overview
-
-Jan-v1 is a 4B parameter model based on Qwen3-4B-thinking, designed for reasoning and problem-solving tasks. The model achieves 91.1% accuracy on SimpleQA through model scaling and fine-tuning approaches.
-
-## Performance
-
-### SimpleQA Benchmark
-
-Jan-v1 demonstrates strong factual question-answering capabilities:
-
-
-
-At 91.1% accuracy, Jan-v1 outperforms several larger models on SimpleQA, including Perplexity's 70B model. This performance represents effective scaling and fine-tuning for a 4B parameter model.
-
-### Chat and Creativity Benchmarks
-
-Jan-v1 has been evaluated on conversational and creative tasks:
-
-
-
-These benchmarks (EQBench, CreativeWriting, and IFBench) measure the model's ability to handle conversational nuance, creative expression, and instruction following.
-
-## Requirements
-
-- **Memory**:
- - Minimum: 8GB RAM (with Q4 quantization)
- - Recommended: 16GB RAM (with Q8 quantization)
-- **Hardware**: CPU or GPU
-- **API Support**: OpenAI-compatible at localhost:1337
-
-## Using Jan-v1
-
-### Quick Start
-
-1. Download Jan Desktop
-2. Select Jan-v1 from the model list
-3. Start chatting - no additional configuration needed
-
-### Demo
-
-
-
-### Deployment Options
-
-**Using vLLM:**
-```bash
-vllm serve janhq/Jan-v1-4B \
- --host 0.0.0.0 \
- --port 1234 \
- --enable-auto-tool-choice \
- --tool-call-parser hermes
-```
-
-**Using llama.cpp:**
-```bash
-llama-server --model jan-v1.gguf \
- --host 0.0.0.0 \
- --port 1234 \
- --jinja \
- --no-context-shift
-```
-
-### Recommended Parameters
-
-```yaml
-temperature: 0.6
-top_p: 0.95
-top_k: 20
-min_p: 0.0
-max_tokens: 2048
-```
-
-## What Jan-v1 Does Well
-
-- **Question Answering**: 91.1% accuracy on SimpleQA
-- **Reasoning Tasks**: Built on thinking-optimized base model
-- **Tool Calling**: Supports function calling through hermes parser
-- **Instruction Following**: Reliable response to user instructions
-
-## Limitations
-
-- **Model Size**: 4B parameters limits complex reasoning compared to larger models
-- **Specialized Tasks**: Optimized for Q&A and reasoning, not specialized domains
-- **Context Window**: Standard context limitations apply
-
-## Available Formats
-
-### GGUF Quantizations
-
-- **Q4_K_M**: 2.5 GB - Good balance of size and quality
-- **Q5_K_M**: 2.89 GB - Better quality, slightly larger
-- **Q6_K**: 3.31 GB - Near-full quality
-- **Q8_0**: 4.28 GB - Highest quality quantization
-
-## Models Available
-
-- [Jan-v1 on Hugging Face](https://huggingface.co/janhq/Jan-v1-4B)
-- [Jan-v1 GGUF on Hugging Face](https://huggingface.co/janhq/Jan-v1-4B-GGUF)
-
-## Technical Notes
-
-
-
-## Community
-
-- **Discussions**: [HuggingFace Community](https://huggingface.co/janhq/Jan-v1-4B/discussions)
-- **Support**: Available through Jan App at [jan.ai](https://jan.ai)
diff --git a/website/src/content/docs/jan/jan-models/lucy.mdx b/website/src/content/docs/jan/jan-models/lucy.mdx
deleted file mode 100644
index bb9e9327d..000000000
--- a/website/src/content/docs/jan/jan-models/lucy.mdx
+++ /dev/null
@@ -1,108 +0,0 @@
----
-title: Lucy
-description: Compact 1.7B model optimized for web search with tool calling
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-
-## Overview
-
-Lucy is a 1.7B parameter model built on Qwen3-1.7B, optimized for web search through tool calling. The model has been trained to work effectively with search APIs like Serper, enabling web search capabilities in resource-constrained environments.
-
-## Performance
-
-Lucy achieves competitive performance on SimpleQA despite its small size:
-
-
-
-The benchmark shows Lucy (1.7B) compared against models ranging from 4B to 600B+ parameters. While larger models generally perform better, Lucy demonstrates that effective web search integration can partially compensate for smaller model size.
-
-## Requirements
-
-- **Memory**:
- - Minimum: 4GB RAM (with Q4 quantization)
- - Recommended: 8GB RAM (with Q8 quantization)
-- **Search API**: Serper API key required for web search functionality
-- **Hardware**: Runs on CPU or GPU
-
-
-
-## Using Lucy
-
-### Quick Start
-
-1. Download Jan Desktop
-2. Download Lucy from the Hub
-3. Configure Serper MCP with your API key
-4. Start using web search through natural language
-
-### Demo
-
-
-
-### Deployment Options
-
-**Using vLLM:**
-```bash
-vllm serve Menlo/Lucy-128k \
- --host 0.0.0.0 \
- --port 1234 \
- --enable-auto-tool-choice \
- --tool-call-parser hermes \
- --rope-scaling '{"rope_type":"yarn","factor":3.2,"original_max_position_embeddings":40960}' \
- --max-model-len 131072
-```
-
-**Using llama.cpp:**
-```bash
-llama-server model.gguf \
- --host 0.0.0.0 \
- --port 1234 \
- --rope-scaling yarn \
- --rope-scale 3.2 \
- --yarn-orig-ctx 40960
-```
-
-### Recommended Parameters
-
-```yaml
-Temperature: 0.7
-Top-p: 0.9
-Top-k: 20
-Min-p: 0.0
-```
-
-## What Lucy Does Well
-
-- **Web Search Integration**: Optimized to call search tools and process results
-- **Small Footprint**: 1.7B parameters means lower memory requirements
-- **Tool Calling**: Reliable function calling for search APIs
-
-## Limitations
-
-- **Requires Internet**: Web search functionality needs active connection
-- **API Costs**: Serper API has usage limits and costs
-- **Context Processing**: While supporting 128k context, performance may vary with very long inputs
-- **General Knowledge**: Limited by 1.7B parameter size for tasks beyond search
-
-## Models Available
-
-- [Lucy on Hugging Face](https://huggingface.co/Menlo/Lucy-128k)
-- [Lucy GGUF on Hugging Face](https://huggingface.co/Menlo/Lucy-128k-gguf)
-
-## Citation
-
-```bibtex
-@misc{dao2025lucyedgerunningagenticweb,
- title={Lucy: edgerunning agentic web search on mobile with machine generated task vectors},
- author={Alan Dao and Dinh Bach Vu and Alex Nguyen and Norapat Buppodom},
- year={2025},
- eprint={2508.00360},
- archivePrefix={arXiv},
- primaryClass={cs.CL},
- url={https://arxiv.org/abs/2508.00360},
-}
-```
diff --git a/website/src/content/docs/jan/manage-models.mdx b/website/src/content/docs/jan/manage-models.mdx
deleted file mode 100644
index b85cdc3c4..000000000
--- a/website/src/content/docs/jan/manage-models.mdx
+++ /dev/null
@@ -1,190 +0,0 @@
----
-title: Models Overview
-description: Manage AI models in Jan - local and cloud options
-keywords:
- [
- Jan,
- AI models,
- local models,
- cloud models,
- GGUF,
- Llama.cpp,
- model management,
- OpenAI,
- Anthropic,
- model selection,
- hardware requirements,
- privacy,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-AI models power Jan's conversations. You can run models locally on your device for privacy, or connect to cloud providers for more power.
-
-## Quick Start
-
-**New to Jan?** Start with **Jan-v1** (4B) - it runs on most computers
-**Limited hardware?** Use cloud models with your API keys
-**Privacy focused?** Download any local model - your data never leaves your device
-
-## Local Models
-
-Local models are managed through [Llama.cpp](https://github.com/ggerganov/llama.cpp), and these models are in a format called GGUF. When you run them locally, they will use your computer's memory (RAM) and processing power, so please make sure that you download models that match the hardware specifications for your operating system:
-- [Mac](/docs/desktop/mac#compatibility)
-- [Windows](/docs/desktop/windows#compatibility)
-- [Linux](/docs/desktop/linux#compatibility)
-
-### Adding Local Models
-
-#### 1. Download from Jan Hub (Recommended)
-
-The easiest way to get started is using Jan's built-in model hub (connected to [HuggingFace's Model Hub](https://huggingface.co/models)):
-1. Go to the **Hub** tab
-2. Browse available models and click on any model to see details
-3. Choose a model that fits your needs & hardware specifications
-4. Click **Download** on your chosen model
-
-
-
-
-
-#### 2. Import from Hugging Face
-
-You can download models with a direct link from Hugging Face:
-
-**Note:** Some models require a Hugging Face Access Token. Enter your token in **Settings > Model Providers > Hugging Face** before importing.
-
-1. Visit [Hugging Face Models](https://huggingface.co/models)
-2. Find a GGUF model that fits your computer
-3. Copy the **model ID** (e.g., TheBloke/Mistral-7B-v0.1-GGUF)
-4. In Jan, paste the model ID to the **Search** bar in **Hub** page
-5. Select your preferred quantized version to download
-
-**Copy the model ID:**
-
-
-**Paste it in Jan's Hub Search Bar:**
-
-
-#### 3. Import Local Files
-
-If you already have GGUF model files on your computer:
-1. Go to **Settings > Model Providers > Llama.cpp**
-2. Click **Import** and select your GGUF file(s)
-3. Choose how to import:
- - **Link Files:** Creates symbolic links (saves space)
- - **Duplicate:** Copies files to Jan's directory
-4. Click **Import** to complete
-
-
-
-
-
-#### 4. Manual Setup
-
-For advanced users who want to add models not available in Jan Hub:
-
-##### Step 1: Create Model File
-
-1. Navigate to the [Jan Data Folder](./data-folder)
-2. Open `models` folder
-3. Create a new folder for your model
-4. Add your `model.gguf` file
-5. Add a `model.yml` configuration file. Example:
-
-```yaml
-model_path: llamacpp/models/Jan-v1-4B-Q4_K_M/model.gguf
-name: Jan-v1-4B-Q4_K_M
-size_bytes: 2497281632
-```
-
-That's it! Jan now uses a simplified YAML format. All other parameters (temperature, context length, etc.) can be configured directly in the UI when you select the model.
-
-##### Step 2: Customize in the UI
-
-Once your model is added:
-1. Select it in a chat
-2. Click the gear icon next to the model
-3. Adjust any parameters you need
-
-
-
-### Delete Local Models
-
-1. Go to **Settings > Model Providers > Llama.cpp**
-2. Find the model you want to remove
-3. Click the three dots icon and select **Delete Model**
-
-
-
-## Cloud Models
-
-Jan supports connecting to various AI cloud providers through OpenAI-compatible APIs, including OpenAI (GPT-4o, o1), Anthropic (Claude), Groq, Mistral, and more.
-
-
-
-### Setting Up Cloud Models
-
-1. Navigate to **Settings**
-2. Under **Model Providers** in the left sidebar, choose your provider
-3. Enter your API key
-4. Activated cloud models appear in your model selector
-
-
-
-Once you add your API key, you can select any of that provider's models in the chat interface:
-
-
-
-## Choosing Between Local and Cloud
-
-### Local Models
-**Best for:**
-- Privacy-sensitive work
-- Offline usage
-- Unlimited conversations without costs
-- Full control over model behavior
-
-**Requirements:**
-- 8GB RAM minimum (16GB+ recommended)
-- 10-50GB storage per model
-- CPU or GPU for processing
-
-### Cloud Models
-**Best for:**
-- Advanced capabilities (GPT-4, Claude 3)
-- Limited hardware
-- Occasional use
-- Latest model versions
-
-**Requirements:**
-- Internet connection
-- API keys from providers
-- Usage-based payment
-
-## Hardware Guidelines
-
-| RAM | Recommended Model Size |
-|-----|----------------------|
-| 8GB | 1-3B parameters |
-| 16GB | 7B parameters |
-| 32GB | 13B parameters |
-| 64GB+ | 30B+ parameters |
-
-
-
-## Next Steps
-
-- [Explore Jan Models](./jan-models/jan-v1) - Our optimized models
-- [Set up Cloud Providers](./remote-models/openai) - Connect external services
-- [Learn Model Parameters](./explanation/model-parameters) - Fine-tune behavior
-- [Create AI Assistants](./assistants) - Customize models with instructions
diff --git a/website/src/content/docs/jan/mcp-examples/browser/browserbase.mdx b/website/src/content/docs/jan/mcp-examples/browser/browserbase.mdx
deleted file mode 100644
index a8963d029..000000000
--- a/website/src/content/docs/jan/mcp-examples/browser/browserbase.mdx
+++ /dev/null
@@ -1,273 +0,0 @@
----
-title: Browserbase MCP
-description: Control browsers with natural language through Browserbase's cloud infrastructure.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Browserbase,
- browser automation,
- web scraping,
- Stagehand,
- headless browser,
- tool calling,
- ]
----
-
-import { Aside, Steps } from '@astrojs/starlight/components'
-
-[Browserbase MCP](https://docs.browserbase.com/integrations/mcp/introduction) gives AI models actual browser control through cloud infrastructure. Built on Stagehand, it lets you navigate websites, extract data, and interact with web pages using natural language commands.
-
-The integration provides real browser sessions that AI can control, enabling tasks that go beyond simple web search APIs.
-
-## Available Tools
-
-
-
-### Multi-Session Tools
-- `multi_browserbase_stagehand_session_create`: Create parallel browser sessions
-- `multi_browserbase_stagehand_session_list`: Track active sessions
-- `multi_browserbase_stagehand_session_close`: Clean up sessions
-- `multi_browserbase_stagehand_navigate_session`: Navigate in specific session
-
-### Core Browser Actions
-- `browserbase_stagehand_navigate`: Navigate to URLs
-- `browserbase_stagehand_act`: Perform actions ("click the login button")
-- `browserbase_stagehand_extract`: Extract text content
-- `browserbase_stagehand_observe`: Find page elements
-- `browserbase_screenshot`: Capture screenshots
-
-### Session Management
-- `browserbase_session_create`: Create or reuse sessions
-- `browserbase_session_close`: Close active sessions
-
-## Prerequisites
-
-- Jan with MCP enabled
-- Browserbase account (includes 60 minutes free usage)
-- Model with strong tool calling support
-- Node.js installed
-
-
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Get Browserbase Credentials
-
-1. Sign up at [browserbase.com](https://browserbase.com)
- - Email verification required
- - Phone number authentication
- - Thorough security process
-
-2. Access your dashboard and copy:
- - **API Key**
- - **Project ID**
-
-
-
-### Configure MCP Server
-
-Click `+` in MCP Servers section:
-
-**NPM Package Configuration:**
-- **Server Name**: `browserbase`
-- **Command**: `npx`
-- **Arguments**: `@browserbasehq/mcp-server-browserbase`
-- **Environment Variables**:
- - Key: `BROWSERBASE_API_KEY`, Value: `your-api-key`
- - Key: `BROWSERBASE_PROJECT_ID`, Value: `your-project-id`
-
-
-
-### Verify Setup
-
-Check the tools bubble in chat to confirm Browserbase tools are available:
-
-
-
-## Real Usage Example
-
-### Live Information Query
-
-```
-Which sports matches are happening right now in Australia (irrespective of the sport)?
-```
-
-This simple query demonstrates browser automation in action:
-
-1. **Tool Activation**
- - Model creates browser session
- - Navigates to sports websites
- - Extracts current match data
-
-
-
-2. **Results Delivery**
- - Real-time match information
- - Multiple sports covered
- - Current scores and timings
-
-
-
-The AI successfully found:
-- AFL matches with live scores
-- NRL games in progress
-- Upcoming Rugby Union fixtures
-
-## Common Issues
-
-### Tool Call Failures
-
-Sometimes tool calls fail due to parsing issues:
-
-
-
-**Solutions:**
-- Try rephrasing your prompt
-- Disable unnecessary tools
-- Use simpler, more direct requests
-- Switch to Claude 3.5+ Sonnet if using another model
-
-### Model Limitations
-
-Most models struggle with multiple tools. If experiencing issues:
-- Start with single-purpose requests
-- Build complexity gradually
-- Consider which tools are actually needed
-- Expect some trial and error initially
-
-## Usage Limits
-
-**Free Tier:**
-- 60 minutes of browser time included
-- Sessions auto-terminate after 5 minutes inactivity
-- Can adjust timeout in Browserbase dashboard
-- Usage visible in dashboard analytics
-
-**Session Management:**
-- Each browser session counts against time
-- Close sessions when done to conserve minutes
-- Multi-session operations consume time faster
-
-## Practical Use Cases
-
-### Real-Time Data Collection
-```
-Check current prices for MacBook Pro M4 at major Australian retailers and create a comparison table.
-```
-
-### Form Testing
-```
-Navigate to myservice.gov.au and walk through the Medicare claim process, documenting each required field.
-```
-
-### Content Monitoring
-```
-Visit ABC News Australia and extract the top 5 breaking news headlines with their timestamps.
-```
-
-### Multi-Site Analysis
-```
-Compare flight prices from Sydney to Tokyo next week across Qantas, Jetstar, and Virgin Australia.
-```
-
-### Automated Verification
-```
-Check if our company is listed correctly on Google Maps, Yelp, and Yellow Pages, noting any discrepancies.
-```
-
-## Advanced Techniques
-
-### Session Reuse
-```
-Create a browser session, log into LinkedIn, then search for "AI engineers in Melbourne" and extract the first 10 profiles.
-```
-
-### Parallel Operations
-```
-Create three browser sessions: monitor stock prices on ASX, check crypto on CoinSpot, and track forex on XE simultaneously.
-```
-
-### Sequential Workflows
-```
-Go to seek.com.au, search for "data scientist" jobs in Sydney, apply filters for $150k+, then extract job titles and companies.
-```
-
-## Optimization Tips
-
-**Prompt Engineering:**
-- Be specific about what to extract
-- Name exact websites when possible
-- Break complex tasks into steps
-- Specify output format clearly
-
-**Tool Selection:**
-- Use multi-session only when needed
-- Close sessions promptly
-- Choose observe before act when possible
-- Screenshot sparingly to save time
-
-**Error Recovery:**
-- Have fallback prompts ready
-- Start simple, add complexity
-- Watch for timeout warnings
-- Monitor usage in dashboard
-
-## Troubleshooting
-
-**Connection Issues:**
-- Verify API key and Project ID
-- Check Browserbase service status
-- Ensure NPX can download packages
-- Restart Jan after configuration
-
-**Browser Failures:**
-- Some sites block automation
-- Try different navigation paths
-- Check if site requires login
-- Verify target site is accessible
-
-**Performance Problems:**
-- Reduce concurrent sessions
-- Simplify extraction requests
-- Check remaining time quota
-- Consider upgrading plan
-
-**Model Struggles:**
-- Too many tools overwhelm most models
-- Claude 3.5+ Sonnet most reliable
-- Reduce available tools if needed
-- Use focused, clear instructions
-
-
-
-## Browserbase vs Browser Use
-
-| Feature | Browserbase | Browser Use |
-|---------|-------------|-------------|
-| **Infrastructure** | Cloud browsers | Local browser |
-| **Setup Complexity** | API key only | Python environment |
-| **Performance** | Consistent | System dependent |
-| **Cost** | Usage-based | Free (local resources) |
-| **Reliability** | High | Variable |
-| **Privacy** | Cloud-based | Fully local |
-
-## Next Steps
-
-Browserbase MCP provides genuine browser automation capabilities, not just web search. This enables complex workflows like form filling, multi-site monitoring, and data extraction that would be impossible with traditional APIs.
-
-The cloud infrastructure handles browser complexity while Jan maintains conversational privacy. Just remember: with great browser power comes occasional parsing errors.
diff --git a/website/src/content/docs/jan/mcp-examples/data-analysis/e2b.mdx b/website/src/content/docs/jan/mcp-examples/data-analysis/e2b.mdx
deleted file mode 100644
index 1c1bfcf7d..000000000
--- a/website/src/content/docs/jan/mcp-examples/data-analysis/e2b.mdx
+++ /dev/null
@@ -1,284 +0,0 @@
----
-title: E2B Code Sandbox
-description: Execute Python code securely in isolated sandbox environments with E2B.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- E2B,
- code execution,
- sandbox,
- data analysis,
- Python,
- secure computing,
- tool calling,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-E2B MCP provides isolated Python execution environments. Your AI can run actual code instead of just describing what code might do.
-
-The real value emerges when you combine secure remote execution with Jan's flexible model selection. You can use
-local models for conversation and reasoning while offloading actual computation to E2B's sandboxes. This means you
-get the privacy and control of local models plus the computational power of cloud infrastructure, without the
-complexity of managing Python environments or dependencies locally.
-
-## Setup
-
-### Prerequisites
-
-- Jan with MCP enabled
-- E2B API key from [e2b.dev](https://e2b.dev/)
-- Node.js installed
-- Model with tool calling support
-
-### Configuration
-
-1. **Enable MCP**: Go to **Settings** > **MCP Servers**, toggle **Allow All MCP Tool Permission** ON
-
-
-
-2. **Get API Key**: Sign up at [e2b.dev](https://e2b.dev/), generate an API key
-
-
-
-Add a meaningful name to your key.
-
-
-
-3. **Add MCP Server**: Click `+` in MCP Servers section
-
-Configure:
-- **Server Name**: `e2b-server`
-- **Command**: `npx`
-- **Arguments**: `@e2b/mcp-server`
-- **Environment Variables**:
- - Key: `E2B_API_KEY`
- - Value: `your-api-key`
-
-
-
-4. **Verify**: Check server shows as active
-
-
-
-
-## Pre-installed Libraries
-
-The sandbox includes these packages by default:
-
-**Data Analysis & Science:**
-- `pandas` (1.5.3) - Data manipulation
-- `numpy` (1.26.4) - Numerical computing
-- `scipy` (1.12.0) - Scientific computing
-- `scikit-learn` (1.4.1) - Machine learning
-- `sympy` (1.12) - Symbolic mathematics
-
-**Visualization:**
-- `matplotlib` (3.8.3) - Static plots
-- `seaborn` (0.13.2) - Statistical visualization
-- `plotly` (5.19.0) - Interactive charts
-- `bokeh` (3.3.4) - Web-ready visualizations
-
-**Data Processing:**
-- `requests` (2.26.0) - HTTP requests
-- `beautifulsoup4` (4.12.3) - HTML/XML parsing
-- `openpyxl` (3.1.2) - Excel files
-- `python-docx` (1.1.0) - Word documents
-
-**Text & NLP:**
-- `nltk` (3.8.1) - Natural language processing
-- `spacy` (3.7.4) - Advanced NLP
-- `textblob` (0.18.0) - Text processing
-- `gensim` (4.3.2) - Topic modeling
-
-**Image & Audio:**
-- `opencv-python` (4.9.0) - Computer vision
-- `scikit-image` (0.22.0) - Image processing
-- `imageio` (2.34.0) - Image I/O
-- `librosa` (0.10.1) - Audio analysis
-
-Additional packages can be installed as needed.
-
-## Examples
-
-
-For the following examples, we'll use Claude 4 Sonnet but you can use any local or remote
-model with tool calling capabilities you'd like.
-
-
-
-
-
-### Basic Data Analysis
-
-Start small. Open a new chat, confirm that the model has tools enabled and ask it to create a small dataset of 100 students with grades and study hours.
-
-
-
-
-```
-Create a small dataset of 100 students with grades and study hours.
-Calculate the correlation and create a scatter plot.
-```
-
-The model will:
-1. Generate data with pandas (100 rows)
-2. Calculate correlation coefficient
-3. Create a matplotlib scatter plot
-4. Add trend line
-
-
-
-
-
-
-
-
-
-
-### Statistical Computing
-
-```
-Run a Monte Carlo simulation with 10,000 iterations to estimate ฯ.
-```
-
-Expected output:
-- Numerical computation with numpy
-- Convergence plot showing estimate improvement
-- Final ฯ estimate
-
-
-For more intensive simulations, increase iterations gradually and monitor performance.
-
-### Machine Learning
-
-```
-Create a simple 2-class dataset with 200 samples. Train a logistic regression
-model and visualize the decision boundary.
-```
-
-The model will:
-- Generate synthetic 2D classification data
-- Train a single scikit-learn model
-- Plot data points and decision boundary
-
-
-### Time Series Analysis
-
-```
-Generate daily temperature data for one year. Calculate moving averages
-and identify seasonal patterns.
-```
-
-Output includes:
-- Line plot of temperature data
-- Moving average overlay
-- Simple seasonal decomposition
-
-
-### Scaling Up
-
-Once basic examples work, you can increase complexity:
-- Larger datasets (1000+ samples)
-- Multiple models for comparison
-- Complex visualizations with subplots
-- Advanced statistical tests
-
-The sandbox handles moderate computational loads well. For very large datasets or intensive ML training, consider breaking work into smaller chunks.
-
-## Chart Generation
-
-E2B automatically detects and extracts charts from matplotlib code. Charts are returned as base64-encoded images and downloadable files.
-
-### Static Charts
-
-```python
-import matplotlib.pyplot as plt
-import numpy as np
-
-x = np.linspace(0, 10, 100)
-y = np.sin(x)
-
-plt.figure(figsize=(10, 6))
-plt.plot(x, y)
-plt.title('Sine Wave')
-plt.xlabel('x')
-plt.ylabel('sin(x)')
-plt.show()
-```
-
-E2B captures the plot and makes it available for download.
-
-### Interactive Charts
-
-The system extracts chart data for frontend visualization:
-
-```python
-plt.bar(['A', 'B', 'C'], [10, 20, 15])
-plt.title('Sample Bar Chart')
-plt.show()
-```
-
-Returns structured data:
-```json
-{
- "type": "bar",
- "title": "Sample Bar Chart",
- "elements": [
- {"label": "A", "value": 10},
- {"label": "B", "value": 20},
- {"label": "C", "value": 15}
- ]
-}
-```
-
-Supported chart types: line, bar, scatter, pie, box plots.
-
-## Available Tools
-
-- **run_code**: Execute Python code
-- **install_package**: Add Python packages
-- **create_file**: Save files to sandbox
-- **read_file**: Access sandbox files
-- **list_files**: Browse sandbox contents
-
-## Troubleshooting
-
-**Connection Issues:**
-- Verify API key is correct
-- Check Node.js installation
-- Restart Jan if server won't start
-
-**Execution Problems:**
-- Free sandboxes have 2 cores and 1GB RAM - start with small datasets
-- Large computations may time out or run out of memory
-- Scale up complexity gradually after testing basic examples
-- Some packages may require explicit installation
-
-**Package Installation:**
-- Most data science packages install successfully
-- System dependencies may cause failures for some packages
-- Try alternative packages if installation fails
-
-
-
-## Use Cases
-
-E2B is useful for:
-
-- **Academic Research**: Statistical analysis, data visualization, hypothesis testing
-- **Data Science**: Exploratory data analysis, model prototyping, result validation
-- **Financial Analysis**: Portfolio optimization, risk calculations, market simulations
-- **Scientific Computing**: Numerical simulations, mathematical modeling, algorithm testing
-- **Prototyping**: Quick algorithm validation, proof-of-concept development
-
-The sandbox provides isolated execution without local environment setup or dependency management.
diff --git a/website/src/content/docs/jan/mcp-examples/data-analysis/jupyter.mdx b/website/src/content/docs/jan/mcp-examples/data-analysis/jupyter.mdx
deleted file mode 100644
index 64536b9cb..000000000
--- a/website/src/content/docs/jan/mcp-examples/data-analysis/jupyter.mdx
+++ /dev/null
@@ -1,335 +0,0 @@
----
-title: Jupyter MCP
-description: Real-time Jupyter notebook interaction and code execution through MCP integration.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Jupyter,
- data analysis,
- code execution,
- notebooks,
- Python,
- visualization,
- tool calling,
- GPT-5,
- OpenAI,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-[Jupyter MCP Server](https://jupyter-mcp-server.datalayer.tech/) enables real-time interaction with Jupyter notebooks, allowing AI models to edit, execute, and document code for data analysis and visualization. Instead of just generating code suggestions, AI can actually run Python code and see the results.
-
-This integration gives Jan the ability to execute analysis, create visualizations, and iterate based on actual results - turning your AI assistant into a capable data science partner.
-
-
-
-## Available Tools
-
-The Jupyter MCP Server provides [12 comprehensive tools](https://jupyter-mcp-server.datalayer.tech/tools/):
-
-### Core Operations
-- `append_execute_code_cell`: Add and run code cells at notebook end
-- `insert_execute_code_cell`: Insert and run code at specific positions
-- `execute_cell_simple_timeout`: Execute cells with timeout control
-- `execute_cell_streaming`: Long-running cells with progress updates
-- `execute_cell_with_progress`: Execute with timeout and monitoring
-
-### Cell Management
-- `append_markdown_cell`: Add documentation cells
-- `insert_markdown_cell`: Insert markdown at specific positions
-- `delete_cell`: Remove cells from notebook
-- `overwrite_cell_source`: Update existing cell content
-
-### Information & Reading
-- `get_notebook_info`: Retrieve notebook metadata
-- `read_cell`: Examine specific cell content
-- `read_all_cells`: Get complete notebook state
-
-
-
-## Prerequisites
-
-- Jan with MCP enabled
-- Python 3.8+ with uv package manager
-- Docker installed
-- OpenAI API key for GPT-5 access
-- Basic understanding of Jupyter notebooks
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Install uv Package Manager
-
-If you don't have uv installed:
-
-```bash
-# macOS and Linux
-curl -LsSf https://astral.sh/uv/install.sh | sh
-
-# Windows
-powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
-```
-
-### Create Python Environment
-
-Set up an isolated environment for Jupyter:
-
-```bash
-# Create environment with Python 3.13
-uv venv .venv --python 3.13
-
-# Activate environment
-source .venv/bin/activate # Linux/macOS
-# or
-.venv\Scripts\activate # Windows
-
-# Install Jupyter dependencies
-uv pip install jupyterlab==4.4.1 jupyter-collaboration==4.0.2 ipykernel
-uv pip uninstall pycrdt datalayer_pycrdt
-uv pip install datalayer_pycrdt==0.12.17
-
-# Add data science libraries
-uv pip install pandas numpy matplotlib altair
-```
-
-### Start JupyterLab Server
-
-Launch JupyterLab with authentication:
-
-```bash
-jupyter lab --port 8888 --IdentityProvider.token heyheyyou --ip 0.0.0.0
-```
-
-
-
-The server opens in your browser:
-
-
-
-### Create Target Notebook
-
-Create a new notebook named `for_jan.ipynb`:
-
-
-
-### Configure MCP Server in Jan
-
-Click `+` in MCP Servers section:
-
-**Configuration for macOS/Windows:**
-- **Server Name**: `jupyter`
-- **Command**: `docker`
-- **Arguments**:
- ```
- run -i --rm -e DOCUMENT_URL -e DOCUMENT_TOKEN -e DOCUMENT_ID -e RUNTIME_URL -e RUNTIME_TOKEN datalayer/jupyter-mcp-server:latest
- ```
-- **Environment Variables**:
- - Key: `DOCUMENT_URL`, Value: `http://host.docker.internal:8888`
- - Key: `DOCUMENT_TOKEN`, Value: `heyheyyou`
- - Key: `DOCUMENT_ID`, Value: `for_jan.ipynb`
- - Key: `RUNTIME_URL`, Value: `http://host.docker.internal:8888`
- - Key: `RUNTIME_TOKEN`, Value: `heyheyyou`
-
-
-
-## Using OpenAI's GPT-5
-
-### Configure OpenAI Provider
-
-Navigate to **Settings** > **Model Providers** > **OpenAI**:
-
-
-
-### Add GPT-5 Model
-
-Since GPT-5 is new, you'll need to manually add it to Jan:
-
-
-
-
-
-### Enable Tool Calling
-
-Ensure tools are enabled for GPT-5:
-
-
-
-## Usage
-
-### Verify Tool Availability
-
-Start a new chat with GPT-5. The tools bubble shows all available Jupyter operations:
-
-
-
-### Initial Test
-
-Start with establishing the notebook as your workspace:
-
-```
-You have access to a jupyter notebook, please use it as our data analysis scratchpad. Let's start by printing "Hello Jan" in a new cell.
-```
-
-GPT-5 creates and executes the code successfully:
-
-
-
-### Advanced Data Analysis
-
-Try a more complex task combining multiple operations:
-
-```
-Generate synthetic data with numpy, move it to a pandas dataframe and create a pivot table, and then make a cool animated plot using matplotlib. Your use case will be sales analysis in the luxury fashion industry.
-```
-
-
-
-Watch the complete output unfold:
-
-
-
-## Example Prompts to Try
-
-### Financial Analysis
-```
-Create a Monte Carlo simulation for portfolio risk analysis. Generate 10,000 scenarios, calculate VaR at 95% confidence, and visualize the distribution.
-```
-
-### Time Series Forecasting
-```
-Generate synthetic time series data representing daily website traffic over 2 years with weekly seasonality and trend. Build an ARIMA model and forecast the next 30 days.
-```
-
-### Machine Learning Pipeline
-```
-Build a complete classification pipeline: generate a dataset with 3 classes and 5 features, split the data, try multiple algorithms (RF, SVM, XGBoost), and create a comparison chart of their performance.
-```
-
-### Interactive Dashboards
-```
-Create an interactive visualization using matplotlib widgets showing how changing interest rates affects loan payments over different time periods.
-```
-
-### Statistical Testing
-```
-Generate two datasets representing A/B test results for an e-commerce site. Perform appropriate statistical tests and create visualizations to determine if the difference is significant.
-```
-
-## Performance Considerations
-
-
-
-### Context Management
-- Each tool call adds to conversation history
-- 12 available tools means substantial system prompt overhead
-- Local models may need reduced tool sets for reasonable performance
-- Consider disabling unused tools to conserve context
-
-### Cloud vs Local Trade-offs
-- **Cloud models (GPT-5)**: Handle multiple tools efficiently with large context windows
-- **Local models**: May require optimization, reduced tool sets, or smaller context sizes
-- **Hybrid approach**: Use cloud for complex multi-tool workflows, local for simple tasks
-
-## Security Considerations
-
-
-
-### Authentication Tokens
-- **Always use strong tokens** - avoid simple passwords
-- **Never commit tokens** to version control
-- **Rotate tokens regularly** for production use
-- **Use different tokens** for different environments
-
-### Network Security
-- JupyterLab is network-accessible with `--ip 0.0.0.0`
-- Consider using `--ip 127.0.0.1` for local-only access
-- Implement firewall rules to restrict access
-- Use HTTPS in production environments
-
-### Code Execution Risks
-- AI has full Python execution capabilities
-- Review generated code before execution
-- Use isolated environments for sensitive work
-- Monitor resource usage and set limits
-
-### Data Privacy
-- Notebook content is processed by AI models
-- When using cloud models like GPT-5, data leaves your system
-- Keep sensitive data in secure environments
-- Consider model provider's data policies
-
-## Best Practices
-
-### Environment Management
-- Use virtual environments for isolation
-- Document required dependencies
-- Version control your notebooks
-- Regular environment cleanup
-
-### Performance Optimization
-- Start with simple operations
-- Monitor memory usage during execution
-- Close unused notebooks
-- Restart kernels when needed
-
-### Effective Prompting
-- Be specific about desired outputs
-- Break complex tasks into steps
-- Ask for explanations with code
-- Request error handling in critical operations
-
-## Troubleshooting
-
-**Connection Problems:**
-- Verify JupyterLab is running
-- Check token matches configuration
-- Confirm Docker can reach host
-- Test with curl to verify connectivity
-
-**Execution Failures:**
-- Check Python package availability
-- Verify kernel is running
-- Look for syntax errors in generated code
-- Restart kernel if stuck
-
-**Tool Calling Errors:**
-- Ensure model supports tool calling
-- Verify all 12 tools appear in chat
-- Check MCP server is active
-- Review Docker logs for errors
-
-**API Rate Limits:**
-- Monitor OpenAI usage dashboard
-- Implement retry logic for transient errors
-- Consider fallback to local models
-- Cache results when possible
-
-## Conclusion
-
-The Jupyter MCP integration combined with GPT-5's advanced capabilities creates an exceptionally powerful data science environment. With GPT-5's built-in reasoning and expert-level intelligence, complex analyses that once required extensive manual coding can now be accomplished through natural conversation.
-
-Whether you're exploring data, building models, or creating visualizations, this integration provides the computational power of Jupyter with the intelligence of GPT-5 - all within Jan's privacy-conscious interface.
-
-Remember: with great computational power comes the responsibility to use it securely. Always validate generated code, use strong authentication, and be mindful of data privacy when using cloud-based models.
diff --git a/website/src/content/docs/jan/mcp-examples/deepresearch/octagon.mdx b/website/src/content/docs/jan/mcp-examples/deepresearch/octagon.mdx
deleted file mode 100644
index aba5cc9d9..000000000
--- a/website/src/content/docs/jan/mcp-examples/deepresearch/octagon.mdx
+++ /dev/null
@@ -1,259 +0,0 @@
----
-title: Octagon Deep Research MCP
-description: Finance-focused deep research with AI-powered analysis through Octagon's MCP integration.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Octagon,
- deep research,
- financial research,
- private equity,
- market analysis,
- technical research,
- tool calling,
- ]
----
-
-import { Aside, Steps } from '@astrojs/starlight/components'
-
-
-[Octagon Deep Research MCP](https://docs.octagonagents.com/guide/deep-research-mcp.html) provides specialized AI research capabilities with a strong focus on financial markets and business intelligence. Unlike general research tools, Octagon excels at complex financial analysis, market dynamics, and investment research.
-
-The integration delivers comprehensive reports that combine multiple data sources, cross-verification, and actionable insights - particularly useful for understanding market structures, investment strategies, and business models.
-
-## Available Tools
-
-### octagon-agent
-Orchestrates comprehensive market intelligence research, particularly strong in:
-- Financial market analysis
-- Private equity and M&A research
-- Corporate structure investigations
-- Investment strategy evaluation
-
-### octagon-scraper-agent
-Specialized web scraping for public and private market data:
-- SEC filings and regulatory documents
-- Company financials and metrics
-- Market transaction data
-- Industry reports and analysis
-
-### octagon-deep-research-agent
-Comprehensive research synthesis combining:
-- Multi-source data aggregation
-- Cross-verification of claims
-- Historical trend analysis
-- Actionable insights generation
-
-## Prerequisites
-
-- Jan with MCP enabled
-- Octagon account (includes 2-week Pro trial)
-- Model with tool calling support
-- Node.js installed
-
-
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Get Octagon API Key
-
-1. Sign up at [Octagon signup page](https://app.octagonai.co/signup/?redirectToAfterSignup=https://app.octagonai.co/api-keys)
-2. Navigate to the API playground
-3. Copy your API key from the dashboard
-
-
-
-### Configure MCP Server
-
-Click `+` in MCP Servers section:
-
-**NPM Package Configuration:**
-- **Server Name**: `octagon-mcp-server`
-- **Command**: `npx`
-- **Arguments**: `-y octagon-mcp@latest`
-- **Environment Variables**:
- - Key: `OCTAGON_API_KEY`, Value: `your-api-key`
-
-
-
-### Verify Setup
-
-Check the tools bubble in chat to confirm Octagon tools are available:
-
-
-
-## Real-World Example: Private Equity Analysis
-
-Here's an actual deep research query demonstrating Octagon's financial analysis capabilities:
-
-### The Prompt
-
-```
-Break apart the private equity paradox: How did an industry that promises to "unlock value" become synonymous with gutting companies, yet still attracts the world's smartest money?
-
-Start with the mechanicsโhow PE firms use other people's money to buy companies with borrowed cash, then charge fees for the privilege. Trace the evolution from corporate raiders of the 1980s to today's trillion-dollar titans like Blackstone, KKR, and Apollo. Use SEC filings, M&A databases, and bankruptcy records to map their empires.
-
-Dig into specific deals that illustrate the dual nature: companies genuinely transformed versus those stripped and flipped. Compare Toys "R" Us's death to Hilton's resurrection. Examine how PE-owned companies fare during economic downturnsโdo they really have "patient capital" or do they bleed portfolio companies dry through dividend recaps?
-
-Investigate the fee structure that makes partners billionaires regardless of performance. Calculate the real returns after the 2-and-20 (or worse) fee structures. Why do pension funds and endowments keep pouring money in despite academic studies showing they'd do better in index funds?
-
-Explore the revolving door between PE, government, and central banks. How many Fed officials and Treasury secretaries came from or went to PE? Map the political donations and lobbying expenditures that keep carried interest taxed as capital gains.
-
-Address the human cost through labor statistics and case studiesโwhat happens to employees when PE takes over? But also examine when PE genuinely saves failing companies and preserves jobs.
-
-Write this as if explaining to a skeptical but curious friend over drinksโclear language, no jargon without explanation, and enough dry humor to make the absurdities apparent. Think Michael Lewis meets Matt Levine. Keep it under 3,000 words but pack it with hard data and real examples. The goal: help readers understand why PE is simultaneously capitalism's most sophisticated expression and its most primitive.
-```
-
-
-
-### Research Process
-
-The AI engages multiple Octagon tools to gather comprehensive data:
-
-
-
-### The Results
-
-Octagon delivers a detailed analysis covering:
-
-**Part 1: The Mechanics Explained**
-
-
-**Part 2: Historical Analysis and Case Studies**
-
-
-**Part 3: Financial Engineering and Human Impact**
-
-
-The report demonstrates Octagon's ability to:
-- Access and analyze SEC filings
-- Compare multiple deal outcomes
-- Calculate real returns after fees
-- Track political connections
-- Assess human impact with data
-
-## Finance-Focused Use Cases
-
-### Investment Research
-```
-Analyze Tesla's vertical integration strategy vs traditional automakers. Include supply chain dependencies, margin analysis, and capital efficiency metrics from the last 5 years.
-```
-
-### Market Structure Analysis
-```
-Map the concentration of market makers in US equities. Who controls order flow, what are their profit margins, and how has this changed since zero-commission trading?
-```
-
-### Corporate Governance
-```
-Investigate executive compensation at the 10 largest US banks post-2008. Compare pay ratios, stock buybacks vs R&D spending, and correlation with shareholder returns.
-```
-
-### Private Market Intelligence
-```
-Track Series B+ funding rounds in AI/ML companies in 2024. Identify valuation trends, investor concentration, and compare to public market multiples.
-```
-
-### Regulatory Analysis
-```
-Examine how Basel III implementation differs across major markets. Which banks gained competitive advantages and why?
-```
-
-### M&A Strategy
-```
-Analyze Microsoft's acquisition strategy under Nadella. Calculate actual vs projected synergies, integration success rates, and impact on market position.
-```
-
-## Technical Research Capabilities
-
-While finance-focused, Octagon also handles technical research:
-
-### Framework Evaluation
-```
-Compare Kubernetes alternatives for edge computing. Consider resource usage, latency, reliability, and operational complexity with real deployment data.
-```
-
-### API Economics
-```
-Analyze the unit economics of major AI API providers. Include pricing history, usage patterns, and margin estimates based on reported compute costs.
-```
-
-### Open Source Sustainability
-```
-Research funding models for critical open source infrastructure. Which projects are at risk and what are the economic incentives misalignments?
-```
-
-## Research Quality
-
-Octagon's reports typically include:
-- **Primary Sources**: SEC filings, earnings calls, regulatory documents
-- **Quantitative Analysis**: Financial metrics, ratios, trend analysis
-- **Comparative Studies**: Peer benchmarking, historical context
-- **Narrative Clarity**: Complex topics explained accessibly
-- **Actionable Insights**: Not just data, but implications
-
-## Troubleshooting
-
-**Authentication Issues:**
-- Verify API key from Octagon dashboard
-- Check trial status hasn't expired
-- Ensure correct API key format
-- Contact Octagon support if needed
-
-**Research Failures:**
-- Some queries may exceed scope (try narrowing)
-- Financial data may have access restrictions
-- Break complex queries into parts
-- Allow time for comprehensive research
-
-**Tool Calling Problems:**
-- Not all models handle multiple tools well
-- Kimi-k2 via OpenRouter works reliably
-- Claude 3.5+ Sonnet also recommended
-- Enable tool calling in model settings
-
-**Performance Considerations:**
-- Deep research takes time (be patient)
-- Complex financial analysis may take minutes
-- Monitor API usage in dashboard
-- Consider query complexity vs urgency
-
-
-
-## Pricing After Trial
-
-After the 2-week Pro trial:
-- Check current pricing at octagonagents.com
-- Usage-based pricing for API access
-- Different tiers for research depth
-- Educational discounts may be available
-
-## Octagon vs Other Research Tools
-
-| Feature | Octagon | ChatGPT Deep Research | Perplexity |
-|---------|---------|----------------------|------------|
-| **Finance Focus** | Specialized | General | General |
-| **Data Sources** | Financial databases | Web-wide | Web-wide |
-| **SEC Integration** | Native | Limited | Limited |
-| **Market Data** | Comprehensive | Basic | Basic |
-| **Research Depth** | Very Deep | Deep | Moderate |
-| **Speed** | Moderate | Slow | Fast |
-
-## Next Steps
-
-Octagon Deep Research MCP excels at complex financial analysis that would typically require a team of analysts. The integration provides institutional-quality research capabilities within Jan's conversational interface.
-
-Whether analyzing market structures, evaluating investments, or understanding business models, Octagon delivers the depth and accuracy that financial professionals expect, while maintaining readability for broader audiences.
diff --git a/website/src/content/docs/jan/mcp-examples/design/canva.mdx b/website/src/content/docs/jan/mcp-examples/design/canva.mdx
deleted file mode 100644
index 008bff70f..000000000
--- a/website/src/content/docs/jan/mcp-examples/design/canva.mdx
+++ /dev/null
@@ -1,279 +0,0 @@
----
-title: Canva MCP
-description: Create and manage designs through natural language commands with Canva's official MCP server.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Canva,
- design automation,
- graphic design,
- presentations,
- templates,
- tool calling,
- ]
----
-
-import { Aside, Steps } from '@astrojs/starlight/components'
-
-[Canva MCP](https://www.canva.com/newsroom/news/deep-research-integration-mcp-server/) gives AI models the ability to create, search, and manage designs directly within Canva. As the first design platform with native MCP integration, it lets you generate presentations, logos, and marketing materials through conversation rather than clicking through design interfaces.
-
-The integration provides comprehensive design capabilities without leaving your chat, though actual editing still happens in Canva's interface.
-
-## Available Tools
-
-
-
-### Design Operations
-- **generate-design**: Create new designs using AI prompts
-- **search-designs**: Search docs, presentations, videos, whiteboards
-- **get-design**: Get detailed information about a Canva design
-- **get-design-pages**: List pages in multi-page designs
-- **get-design-content**: Extract content from designs
-- **resize-design**: Adapt designs to different dimensions
-- **get-design-resize-status**: Check resize operation status
-- **get-design-generation-job**: Track AI generation progress
-
-### Import/Export
-- **import-design-from-url**: Import files from URLs as new designs
-- **get-design-import-from-url**: Check import status
-- **export-design**: Export designs in various formats
-- **get-export-formats**: List available export options
-- **get-design-export-status**: Track export progress
-
-### Organization
-- **create-folder**: Create folders in Canva
-- **move-item-to-folder**: Organize designs and assets
-- **list-folder-items**: Browse folder contents
-
-### Collaboration
-- **comment-on-design**: Add comments to designs
-- **list-comments**: View design comments
-- **list-replies**: See comment threads
-- **reply-to-comment**: Respond to feedback
-
-### Legacy Tools
-- **search**: ChatGPT connector (use search-designs instead)
-- **fetch**: Content retrieval for ChatGPT
-
-## Prerequisites
-
-- Jan with MCP enabled
-- Canva account (free or paid)
-- Model with tool calling support
-- Node.js installed
-- Internet connection for Canva API access
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Configure Canva MCP Server
-
-Click `+` in MCP Servers section:
-
-**Configuration:**
-- **Server Name**: `Canva`
-- **Command**: `npx`
-- **Arguments**: `-y mcp-remote@latest https://mcp.canva.com/mcp`
-- **Environment Variables**: Leave empty (authentication handled via OAuth)
-
-
-
-### Authentication Process
-
-When you first use Canva tools:
-
-1. **Browser Opens Automatically**
- - Canva authentication page appears in your default browser
- - Log in with your Canva account
-
-
-
-2. **Team Selection & Permissions**
- - Select your team (if you have multiple)
- - Review permissions the AI will have
- - Click **Allow** to grant access
-
-
-
-The permissions include:
-- Reading your profile and designs
-- Creating new designs
-- Managing folders and content
-- Accessing team brand templates
-- Commenting on designs
-
-### Model Configuration
-
-Use a tool-enabled model:
-
-- **Anthropic Claude 3.5+ Sonnet**
-- **OpenAI GPT-4o**
-- **Google Gemini Pro**
-
-## Real-World Usage Example
-
-Here's an actual workflow creating a company logo:
-
-### Initial Setup Confirmation
-
-```
-Are you able to access my projects?
-```
-
-The AI explains available capabilities:
-
-
-
-### Design Creation Request
-
-```
-Create new designs with AI. Call it "VibeBusiness" and have it be a company focused on superintelligence for the benefit of humanity.
-```
-
-The AI initiates design generation:
-
-
-
-### Design Options
-
-The AI creates multiple logo variations:
-
-**First Option:**
-
-
-**Selected Design:**
-
-
-### Final Result
-
-After selection, the AI confirms:
-
-
-
-Clicking the design link opens it directly in Canva:
-
-
-
-## Practical Use Cases
-
-### Marketing Campaign Development
-```
-Create a social media campaign for our new product launch. Generate Instagram posts, Facebook covers, and LinkedIn banners with consistent branding.
-```
-
-### Presentation Automation
-```
-Search for our Q4 sales presentation and create a simplified 5-slide version for the board meeting.
-```
-
-### Brand Asset Management
-```
-List all designs in our "2025 Marketing" folder and export the approved ones as PDFs.
-```
-
-### Design Iteration
-```
-Find our company logo designs from last month and resize them for business cards, letterheads, and email signatures.
-```
-
-### Content Extraction
-```
-Extract all text from our employee handbook presentation so I can update it in our documentation.
-```
-
-### Collaborative Review
-```
-Add a comment to the new website mockup asking the design team about the color scheme choices.
-```
-
-## Workflow Tips
-
-### Effective Design Generation
-- **Be specific**: "Create a minimalist tech company logo with blue and silver colors"
-- **Specify format**: "Generate an Instagram story template for product announcements"
-- **Include context**: "Design a professional LinkedIn banner for a AI research company"
-- **Request variations**: Ask for multiple options to choose from
-
-### Organization Best Practices
-- Create folders before generating multiple designs
-- Use descriptive names for easy searching later
-- Move designs to appropriate folders immediately
-- Export important designs for backup
-
-### Integration Patterns
-- Generate designs โ Review options โ Select preferred โ Open in Canva for fine-tuning
-- Search existing designs โ Extract content โ Generate new versions
-- Create templates โ Resize for multiple platforms โ Export all variants
-
-## Limitations and Considerations
-
-**Design Editing**: While the MCP can create and manage designs, actual editing requires opening Canva's interface.
-
-**Project Access**: The integration may not access all historical projects immediately, focusing on designs created or modified after connection.
-
-**Generation Time**: AI design generation takes a few moments. The tool provides job IDs to track progress.
-
-**Team Permissions**: Access depends on your Canva team settings and subscription level.
-
-## Troubleshooting
-
-**Authentication Issues:**
-- Clear browser cookies for Canva
-- Try logging out and back into Canva
-- Ensure pop-ups aren't blocked for OAuth flow
-- Check team admin permissions if applicable
-
-**Design Generation Failures:**
-- Verify you have creation rights in selected team
-- Check Canva subscription limits
-- Try simpler design prompts first
-- Ensure stable internet connection
-
-**Tool Availability:**
-- Some tools require specific Canva plans
-- Team features need appropriate permissions
-- Verify MCP server is showing as active
-- Restart Jan after authentication
-
-**Search Problems:**
-- Use search-designs (not the legacy search tool)
-- Be specific with design types and names
-- Check folder permissions for team content
-- Allow time for new designs to index
-
-
-
-## Advanced Workflows
-
-### Batch Operations
-```
-Create 5 variations of our product announcement banner, then resize all of them for Twitter, LinkedIn, and Facebook.
-```
-
-### Content Migration
-```
-Import all designs from [URLs], organize them into a "2025 Campaign" folder, and add review comments for the team.
-```
-
-### Automated Reporting
-```
-Search for all presentation designs created this month, extract their content, and summarize the key themes.
-```
-
-## Next Steps
-
-Canva MCP bridges the gap between conversational AI and visual design. Instead of describing what you want and then manually creating it, you can generate professional designs directly through natural language commands.
-
-The real power emerges when combining multiple tools - searching existing assets, generating new variations, organizing content, and collaborating with teams, all within a single conversation flow.
diff --git a/website/src/content/docs/jan/mcp-examples/productivity/linear.mdx b/website/src/content/docs/jan/mcp-examples/productivity/linear.mdx
deleted file mode 100644
index 60824eadc..000000000
--- a/website/src/content/docs/jan/mcp-examples/productivity/linear.mdx
+++ /dev/null
@@ -1,265 +0,0 @@
----
-title: Linear MCP
-description: Manage software projects and issue tracking through natural language with Linear integration.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Linear,
- project management,
- issue tracking,
- agile,
- software development,
- tool calling,
- ]
-sidebar:
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-[Linear MCP](https://linear.app) provides comprehensive project management capabilities through natural conversation. Transform your software development workflow by managing issues, projects, and team collaboration directly through AI.
-
-## Available Tools
-
-Linear MCP offers extensive project management capabilities:
-
-### Issue Management
-- `list_issues`: View all issues in your workspace
-- `get_issue`: Get details of a specific issue
-- `create_issue`: Create new issues with full details
-- `update_issue`: Modify existing issues
-- `list_my_issues`: See your assigned issues
-- `list_issue_statuses`: View available workflow states
-- `list_issue_labels`: See and manage labels
-- `create_issue_label`: Create new labels
-
-### Project & Team
-- `list_projects`: View all projects
-- `get_project`: Get project details
-- `create_project`: Start new projects
-- `update_project`: Modify project settings
-- `list_teams`: See all teams
-- `get_team`: Get team information
-- `list_users`: View team members
-
-### Documentation & Collaboration
-- `list_documents`: Browse documentation
-- `get_document`: Read specific documents
-- `search_documentation`: Find information
-- `list_comments`: View issue comments
-- `create_comment`: Add comments to issues
-- `list_cycles`: View sprint cycles
-
-## Prerequisites
-
-- Linear account (free for up to 250 issues)
-- Model with strong tool calling support
-- Active internet connection
-
-
-
-## Setup
-
-### Create Linear Account
-
-1. Sign up at [linear.app](https://linear.app)
-2. Complete the onboarding process
-
-
-
-Once logged in, you'll see your workspace:
-
-
-
-### Enable MCP in Jan
-
-1. Go to **Settings > MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-### Configure Linear MCP
-
-Click the `+` button to add Linear MCP:
-
-**Configuration:**
-- **Server Name**: `linear`
-- **Command**: `npx`
-- **Arguments**: `-y mcp-remote https://mcp.linear.app/sse`
-
-
-
-### Authenticate with Linear
-
-When you first use Linear tools, a browser tab will open for authentication:
-
-
-
-Complete the OAuth flow to grant Jan access to your Linear workspace.
-
-## Usage
-
-### Select a Model with Tool Calling
-
-For this example, we'll use kimi-k2 from Groq:
-
-1. Add the model in Groq settings: `moonshotai/kimi-k2-instruct`
-
-
-
-2. Enable tools for the model:
-
-
-
-### Verify Available Tools
-
-You should see all Linear tools in the chat interface:
-
-
-
-### Epic Project Management
-
-Watch AI transform mundane tasks into epic narratives:
-
-
-
-## Creative Examples
-
-### ๐ญ Shakespearean Sprint Planning
-```
-Create Linear tickets in the '๐Jan' team for my AGI project as battles in a Shakespearean war epic. Each sprint is a military campaign, bugs are enemy spies, and merge conflicts are sword fights between rival houses. Invent unique epic titles and dramatic descriptions with battle cries and victory speeches. Characterize bugs as enemy villains and developers as heroic warriors in this noble quest for AGI glory. Make tasks like model training, testing, and deployment sound like grand military campaigns with honor and valor.
-```
-
-### ๐ Space Mission Development
-```
-Transform our mobile app redesign into a NASA space mission. Create issues where each feature is a mission objective, bugs are space debris to clear, and releases are launch windows. Add dramatic mission briefings, countdown sequences, and astronaut logs. Priority levels become mission criticality ratings.
-```
-
-### ๐ดโโ ๏ธ Pirate Ship Operations
-```
-Set up our e-commerce platform project as a pirate fleet adventure. Features are islands to conquer, bugs are sea monsters, deployments are naval battles. Create colorful pirate-themed tickets with treasure maps, crew assignments, and tales of high seas adventure.
-```
-
-### ๐ฎ Video Game Quest Log
-```
-Structure our API refactoring project like an RPG quest system. Create issues as quests with XP rewards, boss battles for major features, side quests for minor tasks. Include loot drops (completed features), skill trees (learning requirements), and epic boss fight descriptions for challenging bugs.
-```
-
-### ๐ณ Gordon Ramsay's Kitchen
-```
-Manage our restaurant app project as if Gordon Ramsay is the head chef. Create brutally honest tickets criticizing code quality, demanding perfection in UX like a Michelin star dish. Bugs are "bloody disasters" and successful features are "finally, some good code." Include Kitchen Nightmares-style rescue plans.
-```
-
-## Practical Workflows
-
-### Sprint Planning
-```
-Review all open issues in the Backend team, identify the top 10 by priority, and create a new sprint cycle called "Q1 Performance Sprint" with appropriate issues assigned.
-```
-
-### Bug Triage
-```
-List all bugs labeled "critical" or "high-priority", analyze their descriptions, and suggest which ones should be fixed first based on user impact. Update their status to "In Progress" for the top 3.
-```
-
-### Documentation Audit
-```
-Search our documentation for anything related to API authentication. Create issues for any gaps or outdated sections you find, labeled as "documentation" with detailed improvement suggestions.
-```
-
-### Team Workload Balance
-```
-Show me all active issues grouped by assignee. Identify anyone with more than 5 high-priority items and suggest redistributions to balance the workload.
-```
-
-### Release Planning
-```
-Create a project called "v2.0 Release" with milestones for: feature freeze, beta testing, documentation, and launch. Generate appropriate issues for each phase with realistic time estimates.
-```
-
-## Advanced Integration Patterns
-
-### Cross-Project Dependencies
-```
-Find all issues labeled "blocked" across all projects. For each one, identify what they're waiting on and create linked issues for the blocking items if they don't exist.
-```
-
-### Automated Status Updates
-```
-Look at all issues assigned to me that haven't been updated in 3 days. Add a comment with a status update based on their current state and any blockers.
-```
-
-### Smart Labeling
-```
-Analyze all unlabeled issues in our workspace. Based on their titles and descriptions, suggest appropriate labels and apply them. Create any missing label categories we need.
-```
-
-### Sprint Retrospectives
-```
-Generate a retrospective report for our last completed cycle. List what was completed, what was pushed to next sprint, and create discussion issues for any patterns you notice.
-```
-
-## Tips for Maximum Productivity
-
-- **Batch Operations**: Create multiple related issues in one request
-- **Smart Templates**: Ask AI to remember your issue templates
-- **Natural Queries**: "Show me what John is working on this week"
-- **Context Awareness**: Reference previous issues in new requests
-- **Automated Workflows**: Set up recurring management tasks
-
-## Troubleshooting
-
-**Authentication Issues:**
-- Clear browser cookies for Linear
-- Re-authenticate through the OAuth flow
-- Check Linear workspace permissions
-- Verify API access is enabled
-
-**Tool Calling Errors:**
-- Ensure model supports multiple tool calls
-- Try breaking complex requests into steps
-- Verify all required fields are provided
-- Check Linear service status
-
-**Missing Data:**
-- Refresh authentication token
-- Verify workspace access permissions
-- Check if issues are in archived projects
-- Ensure proper team selection
-
-**Performance Issues:**
-- Linear API has rate limits (see dashboard)
-- Break bulk operations into batches
-- Cache frequently accessed data
-- Use specific filters to reduce data
-
-
-
-## Integration Ideas
-
-Combine Linear with other MCP tools:
-
-- **Serper + Linear**: Research technical solutions, then create implementation tickets
-- **Jupyter + Linear**: Analyze project metrics, generate data-driven sprint plans
-- **Todoist + Linear**: Sync personal tasks with work issues
-- **E2B + Linear**: Run code tests, automatically create bug reports
-
-## Privacy & Security
-
-Linear MCP uses OAuth for authentication, meaning:
-- Your credentials are never shared with Jan
-- Access can be revoked anytime from Linear settings
-- Data stays within Linear's infrastructure
-- Only requested permissions are granted
-
-## Next Steps
-
-Linear MCP transforms project management from clicking through interfaces into natural conversation. Whether you're planning sprints, triaging bugs, or crafting epic development sagas, AI becomes your project management companion.
-
-Start with simple issue creation, then explore complex workflows like automated sprint planning and workload balancing. The combination of Linear's powerful platform with AI's creative capabilities makes project management both efficient and entertaining!
diff --git a/website/src/content/docs/jan/mcp-examples/productivity/todoist.mdx b/website/src/content/docs/jan/mcp-examples/productivity/todoist.mdx
deleted file mode 100644
index 15548a011..000000000
--- a/website/src/content/docs/jan/mcp-examples/productivity/todoist.mdx
+++ /dev/null
@@ -1,256 +0,0 @@
----
-title: Todoist MCP
-description: Manage your tasks and todo lists through natural language with Todoist integration.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Todoist,
- task management,
- productivity,
- todo list,
- tool calling,
- ]
-sidebar:
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-[Todoist MCP Server](https://github.com/abhiz123/todoist-mcp-server) enables AI models to manage your Todoist tasks through natural conversation. Instead of switching between apps, you can create, update, and complete tasks by simply chatting with your AI assistant.
-
-## Available Tools
-
-- `todoist_create_task`: Add new tasks to your todo list
-- `todoist_get_tasks`: Retrieve and view your current tasks
-- `todoist_update_task`: Modify existing tasks
-- `todoist_complete_task`: Mark tasks as done
-- `todoist_delete_task`: Remove tasks from your list
-
-## Prerequisites
-
-- Todoist account (free or premium)
-- Model with strong tool calling support
-- Node.js installed
-
-
-
-## Setup
-
-### Create Todoist Account
-
-1. Sign up at [todoist.com](https://todoist.com) or log in if you have an account
-2. Complete the onboarding process
-
-
-
-Once logged in, you'll see your main dashboard:
-
-
-
-### Get Your API Token
-
-1. Click **Settings** (gear icon)
-2. Navigate to **Integrations**
-3. Click on the **Developer** tab
-4. Copy your API token (it's already generated for you)
-
-
-
-### Enable MCP in Jan
-
-1. Go to **Settings > MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-### Configure Todoist MCP
-
-Click the `+` button to add a new MCP server:
-
-**Configuration:**
-- **Server Name**: `todoist`
-- **Command**: `npx`
-- **Arguments**: `-y @abhiz123/todoist-mcp-server`
-- **Environment Variables**:
- - Key: `TODOIST_API_TOKEN`, Value: `your_api_token_here`
-
-
-
-## Usage
-
-### Select a Model with Tool Calling
-
-Open a new chat and select a model that excels at tool calling. Make sure tools are enabled for your chosen model.
-
-
-
-### Verify Tools Available
-
-You should see the Todoist tools in the tools panel:
-
-
-
-### Start Managing Tasks
-
-Now you can manage your todo list through natural conversation:
-
-
-
-## Example Prompts
-
-### Blog Writing Workflow
-```
-I need to write a blog post about AI and productivity tools today. Please add some tasks to my todo list to make sure I have a good set of steps to accomplish this task.
-```
-
-The AI will create structured tasks like:
-- Research AI productivity tools
-- Create blog outline
-- Write introduction
-- Draft main sections
-- Add examples and screenshots
-- Edit and proofread
-- Publish and promote
-
-### Weekly Meal Planning
-```
-Help me plan meals for the week. Create a grocery shopping list and cooking schedule for Monday through Friday, focusing on healthy, quick dinners.
-```
-
-### Home Improvement Project
-```
-I'm renovating my home office this weekend. Break down the project into manageable tasks including shopping, prep work, and the actual renovation steps.
-```
-
-### Study Schedule
-```
-I have a statistics exam in 2 weeks. Create a study plan with daily tasks covering all chapters, practice problems, and review sessions.
-```
-
-### Fitness Goals
-```
-Set up a 30-day fitness challenge for me. Include daily workout tasks, rest days, and weekly progress check-ins.
-```
-
-### Event Planning
-```
-I'm organizing a surprise birthday party for next month. Create a comprehensive task list covering invitations, decorations, food, entertainment, and day-of coordination.
-```
-
-## Advanced Usage
-
-### Task Management Commands
-
-**View all tasks:**
-```
-Show me all my pending tasks for today
-```
-
-**Update priorities:**
-```
-Make "Write blog introduction" high priority and move it to the top of my list
-```
-
-**Bulk completion:**
-```
-Mark all my morning routine tasks as complete
-```
-
-**Clean up:**
-```
-Delete all completed tasks from last week
-```
-
-### Project Organization
-
-Todoist supports projects, though the MCP may have limitations. Try:
-```
-Create a new project called "Q1 Goals" and add 5 key objectives as tasks
-```
-
-### Recurring Tasks
-
-Set up repeating tasks:
-```
-Add a daily task to review my calendar at 9 AM
-Add a weekly task for meal prep on Sundays
-Add a monthly task to pay bills on the 1st
-```
-
-## Creative Use Cases
-
-### ๐ฎ Game Development Sprint
-```
-I'm participating in a 48-hour game jam. Create an hour-by-hour task schedule covering ideation, prototyping, art creation, programming, testing, and submission.
-```
-
-### ๐ Book Writing Challenge
-```
-I'm doing NaNoWriMo (writing a novel in a month). Break down a 50,000-word goal into daily writing tasks with word count targets and plot milestones.
-```
-
-### ๐ฑ Garden Planning
-```
-It's spring planting season. Create a gardening schedule for the next 3 months including soil prep, planting dates for different vegetables, watering reminders, and harvest times.
-```
-
-### ๐ Baking Business Launch
-```
-I'm starting a home bakery. Create tasks for getting permits, setting up social media, creating a menu, pricing strategy, and first week's baking schedule.
-```
-
-### ๐ Moving Checklist
-```
-I'm moving to a new apartment next month. Generate a comprehensive moving checklist including utilities setup, packing by room, change of address notifications, and moving day logistics.
-```
-
-## Tips for Best Results
-
-- **Be specific**: "Add task: Call dentist tomorrow at 2 PM" works better than "remind me about dentist"
-- **Use natural language**: The AI understands context, so chat naturally
-- **Batch operations**: Ask to create multiple related tasks at once
-- **Review regularly**: Ask the AI to show your tasks and help prioritize
-- **Iterate**: If the tasks aren't quite right, ask the AI to modify them
-
-## Troubleshooting
-
-**Tasks not appearing in Todoist:**
-- Verify API token is correct
-- Check Todoist website/app and refresh
-- Ensure MCP server shows as active
-
-**Tool calling errors:**
-- Confirm model supports tool calling
-- Enable tools in model settings
-- Try a different model (Claude 3.5+ or GPT-4o recommended)
-
-**Connection issues:**
-- Check internet connectivity
-- Verify Node.js installation
-- Restart Jan after configuration
-
-**Rate limiting:**
-- Todoist API has rate limits
-- Space out bulk operations
-- Wait a moment between large task batches
-
-
-
-## Privacy Note
-
-Your tasks are synced with Todoist's servers. While the MCP runs locally, task data is stored in Todoist's cloud for sync functionality. Review Todoist's privacy policy if you're handling sensitive information.
-
-## Next Steps
-
-Combine Todoist MCP with other tools for powerful workflows:
-- Use Serper MCP to research topics, then create action items in Todoist
-- Generate code with E2B, then add testing tasks to your todo list
-- Analyze data with Jupyter, then create follow-up tasks for insights
-
-Task management through natural language makes staying organized effortless. Let your AI assistant handle the overhead while you focus on getting things done!
diff --git a/website/src/content/docs/jan/mcp-examples/search/exa.mdx b/website/src/content/docs/jan/mcp-examples/search/exa.mdx
deleted file mode 100644
index 19c7a5dde..000000000
--- a/website/src/content/docs/jan/mcp-examples/search/exa.mdx
+++ /dev/null
@@ -1,224 +0,0 @@
----
-title: Exa Search MCP
-description: Connect Jan to real-time web search with Exa's AI-powered search engine.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- Exa,
- web search,
- real-time search,
- research,
- AI search,
- tool calling,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-[Exa MCP](https://docs.exa.ai/examples/exa-mcp) provides real-time web search capabilities for AI
-models. Instead of relying on training data, models can access current web content through Exa's search API.
-
-## Available Tools
-
-Exa MCP includes eight search functions:
-- `web_search_exa`: General web search with content extraction
-- `research_paper_search`: Academic papers and research content
-- `company_research`: Company analysis and business intelligence
-- `crawling`: Extract content from specific URLs
-- `competitor_finder`: Find business competitors
-- `linkedin_search`: Search LinkedIn profiles and companies
-- `wikipedia_search_exa`: Wikipedia content retrieval
-- `github_search`: Repository and code search
-
-## Prerequisites
-
-- Jan with MCP enabled
-- Exa API key from [dashboard.exa.ai](https://dashboard.exa.ai/api-keys)
-- Model with tool calling support
-- Node.js installed
-
-
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Get API Key
-
-1. Visit [dashboard.exa.ai/api-keys](https://dashboard.exa.ai/api-keys)
-2. Create account or sign in
-3. Generate API key
-4. Save the key
-
-
-
-### Configure MCP Server
-
-Click `+` in MCP Servers section:
-
-**Configuration:**
-- **Server Name**: `exa`
-- **Command**: `npx`
-- **Arguments**: `-y exa-mcp-server`
-- **Environment Variables**:
- - Key: `EXA_API_KEY`
- - Value: `your-api-key`
-
-
-
-### Verify Setup
-
-Check server status in the MCP Servers list.
-
-
-
-### Model Configuration
-
-Use a compatible model provider:
-
-- **Jan Nano 32k**
-- **Anthropic**
-- **OpenAI**
-- **OpenRouter**
-
-
-
-## Usage
-
-Start a new chat with a tool-enabled model. Exa tools will appear in the available tools list.
-
-
-
-### Example Queries
-
-**Current Events & Activities:**
-
-```
-What is happening this week, mid July 2025, in Sydney, Australia?
-```
-
-
-
-**Investment Research:**
-
-```
-Find recent research papers about quantum computing startups that received Series A funding in 2024-2025
-```
-
-**Tech Discovery:**
-
-```
-Find GitHub repositories for WebAssembly runtime engines written in Rust with active development
-```
-
-**Career Intelligence:**
-
-```
-Search LinkedIn for AI safety researchers at major tech companies who published papers in the last 6 months
-```
-
-**Competitive Analysis:**
-
-```
-Research emerging competitors to OpenAI in the large language model space, focusing on companies founded after 2023
-```
-
-**Travel & Local Research:**
-
-```
-Find authentic local food experiences in Tokyo that aren't in typical tourist guides, mentioned in recent travel blogs
-```
-
-**Academic Research:**
-
-```
-Find recent papers about carbon capture technology breakthroughs published in Nature or Science during 2025
-```
-
-**Creator Economy:**
-
-```
-Research successful creators who transitioned from TikTok to longer-form content platforms in 2024-2025
-```
-
-**Emerging Tech Trends:**
-
-```
-Find startups working on brain-computer interfaces that have raised funding in the past 12 months
-```
-
-**Health & Wellness:**
-
-```
-Extract information about the latest longevity research findings from Peter Attia's recent podcast episodes
-```
-
-**Regulatory Intelligence:**
-
-```
-Find recent AI regulation developments in the EU that could impact US companies, focusing on July 2025 updates
-```
-
-**Supply Chain Research:**
-
-```
-Research companies developing sustainable packaging alternatives that have partnerships with major retailers
-```
-
-## Use Cases
-
-### Academic Research
-Literature reviews, finding recent papers, tracking research trends.
-
-### Business Intelligence
-Competitor analysis, market research, company information gathering.
-
-### Technical Research
-Finding libraries, tools, and code repositories. Documentation research.
-
-### Content Analysis
-Extracting and analyzing content from specific URLs for research.
-
-### Professional Search
-LinkedIn searches for industry connections and expertise.
-
-## Troubleshooting
-
-**Connection Issues:**
-- Verify API key accuracy
-- Check Node.js installation
-- Restart Jan
-- Make sure you have enough credits in your Exa account
-
-**Tool Calling Problems:**
-- Confirm tool calling is enabled for your model
-- Try Jan Nano 32k, Claude, Gemini, GPT-4o and above models
-- Check MCP server status
-
-**Search Quality:**
-- Use specific, descriptive queries
-- Prefer natural language over keywords
-
-**API Errors:**
-- Verify API key at [dashboard.exa.ai](https://dashboard.exa.ai)
-- Check rate limits on your plan
-- Regenerate API key if needed
-
-
-
-## Next Steps
-
-Exa MCP enables real-time web search within Jan's privacy-focused environment. Models can access current
-information while maintaining local conversation processing.
diff --git a/website/src/content/docs/jan/mcp-examples/search/serper.mdx b/website/src/content/docs/jan/mcp-examples/search/serper.mdx
deleted file mode 100644
index 191b72c84..000000000
--- a/website/src/content/docs/jan/mcp-examples/search/serper.mdx
+++ /dev/null
@@ -1,157 +0,0 @@
----
-title: Serper Search MCP
-description: Connect Jan to real-time web search with Google results through Serper API.
-sidebar:
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-[Serper](https://serper.dev) provides Google search results through a simple API, making it
-perfect for giving AI models access to current web information. The Serper MCP integration
-enables Jan models to search the web and retrieve real-time information.
-
-## Available Tools
-
-- `google_search`: Search Google and retrieve results with snippets
-- `scrape`: Extract content from specific web pages
-
-## Prerequisites
-
-- Serper API key from [serper.dev](https://serper.dev)
-- Model with tool calling support (recommended: Jan v1)
-
-
-
-## Setup
-
-### Enable MCP
-
-1. Go to **Settings** > **MCP Servers**
-2. Toggle **Allow All MCP Tool Permission** ON
-
-
-
-### Get Serper API Key
-
-1. Visit [serper.dev](https://serper.dev)
-2. Sign up for a free account
-3. Copy your API key from the playground
-
-
-
-
-
-### Configure MCP Server
-
-Click `+` in MCP Servers section:
-
-**Configuration:**
-- **Server Name**: `serper`
-- **Command**: `npx`
-- **Arguments**: `-y serper-search-scrape-mcp-server`
-- **Environment Variables**:
- - Key: `SERPER_API_KEY`, Value: `your-api-key`
-
-
-
-### Download Jan v1
-
-Jan v1 is optimized for tool calling and works excellently with Serper:
-
-1. Go to the **Hub** tab
-2. Search for **Jan v1**
-3. Choose your preferred quantization
-4. Click **Download**
-
-
-
-### Enable Tool Calling
-
-Tool calling is now enabled by default on Jan.
-
-## Usage
-
-### Start a New Chat
-
-With Jan v1 selected, you'll see the available Serper tools:
-
-
-
-### Example Queries
-
-**Current Information:**
-```
-What are the latest developments in quantum computing this week?
-```
-
-**Comparative Analysis:**
-```
-What are the main differences between the Rust programming language and C++? Be spicy, hot
-takes are encouraged. ๐
-```
-
-
-**Research Tasks:**
-```
-Find the current stock price of NVIDIA and recent news about their AI chips.
-```
-
-**Fact-Checking:**
-```
-Is it true that the James Webb telescope found signs of life on an exoplanet? What's the latest?
-```
-
-**Local Information:**
-```
-What restaurants opened in San Francisco this month? Focus on Japanese cuisine.
-```
-
-## How It Works
-
-1. **Query Processing**: Jan v1 analyzes your question and determines what to search
-2. **Web Search**: Calls Serper API to get Google search results
-3. **Content Extraction**: Can scrape specific pages for detailed information
-4. **Synthesis**: Combines search results into a comprehensive answer
-
-## Tips for Best Results
-
-- **Be specific**: "Tesla Model 3 2024 price Australia" works better than "Tesla price"
-- **Request recent info**: Add "latest", "current", or "2024/2025" to get recent results
-- **Ask follow-ups**: Jan v1 maintains context for deeper research
-- **Combine with analysis**: Ask for comparisons, summaries, or insights
-
-## Troubleshooting
-
-**No search results:**
-- Verify API key is correct
-- Check remaining credits at serper.dev
-
-**Tools not appearing:**
-- Restart Jan after configuration changes
-- Ensure MCP Server shows as active
-
-**Poor search quality:**
-- Use more specific search terms
-- Try rephrasing your question
-- Check if Serper service is operational
-
-
-
-## API Limits
-
-- **Free tier**: 2,500 searches
-- **Paid plans**: Starting at $50/month for 50,000 searches
-- **Rate limits**: 100 requests per second
-
-## Next Steps
-
-Serper MCP enables models to access current web information, making them powerful research
-assistants. Combine with other MCP tools for even more capabilities - use Serper for search,
-then E2B for data analysis, or Jupyter for visualization.
diff --git a/website/src/content/docs/jan/mcp.mdx b/website/src/content/docs/jan/mcp.mdx
deleted file mode 100644
index 110108227..000000000
--- a/website/src/content/docs/jan/mcp.mdx
+++ /dev/null
@@ -1,298 +0,0 @@
----
-title: Model Context Protocol
-description: Extend Jan's capabilities with tools and external integrations through MCP.
-keywords:
- [
- Jan,
- MCP,
- Model Context Protocol,
- tools,
- integrations,
- AI tools,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- large language models,
- external APIs,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-## Tools in Jan
-
-Jan supports powerful tool integrations that extend your AI's capabilities beyond simple text generation. These tools are implemented through the **Model Context Protocol (MCP)**, allowing your AI to search the web, execute code, manage files, and interact with external services.
-
-**Available tool categories:**
-- **Web & Search** - Real-time web search, browser automation
-- **Code & Analysis** - Jupyter notebooks, code execution, data analysis
-- **Productivity** - Task management, calendar integration, note-taking
-- **Creative** - Design tools, content generation, media manipulation
-- **File Management** - Document processing, file operations, data extraction
-
-Tools work with both local and cloud models, though compatibility varies. Cloud models like GPT-4 and Claude typically offer the best tool-calling performance, while newer local models are rapidly improving their tool capabilities.
-
-```mermaid
-graph TD
- subgraph "What is MCP?"
- You[You using Jan Desktop]
- Claude[Jan AI Assistant]
-
- subgraph "Your Connected Tools"
- Files[๐ Your Files Documents, folders, text files]
- Database[๐ Your Data Spreadsheets, databases]
- WebServices[๐ Online Services GitHub, Slack, Google Drive]
- Custom[๐ง Custom Tools Special programs you've added]
- end
-
- subgraph "What Jan Can Do"
- Read[Read & Understand - View your files - Check your data - See updates]
- Action[Take Actions - Search for info - Create content - Run commands]
- Templates[Use Templates - Common tasks - Saved prompts - Workflows]
- end
- end
-
- You --> Claude
- Claude -->|"Can I see this file?"| Files
- Claude -->|"What's in my database?"| Database
- Claude -->|"Check my GitHub"| WebServices
- Claude -->|"Run this tool"| Custom
-
- Files --> Read
- Database --> Read
- WebServices --> Action
- Custom --> Templates
-
- style You fill:transparent
- style Claude fill:transparent
- style Files fill:transparent
- style Database fill:transparent
- style WebServices fill:transparent
- style Custom fill:transparent
- style Read fill:transparent
- style Action fill:transparent
- style Templates fill:transparent
-```
-
-## What is MCP?
-
-Jan supports the **Model Context Protocol (MCP)**, an open standard that allows AI models to interact with external tools and data sources in a secure, standardized way.
-
-MCP solves the integration challenge by creating a common interface between AI models and external tools. Instead of building custom connectors for every model-tool combination, MCP provides a universal protocol that any compatible model can use with any compatible tool.
-
-**How it works:**
-- **MCP Servers** provide tools, data sources, and capabilities
-- **MCP Clients** (like Jan) connect models to these servers
-- **Standardized Protocol** ensures compatibility across different implementations
-
-This architecture means you can easily add new capabilities to your AI without complex integrations, and tools built for one AI system work with others that support MCP.
-
-## Core Benefits
-
-**Standardization:** MCP eliminates the "M x N" integration problem where every AI model needs unique connectors for every tool. One standard interface works everywhere.
-
-**Extensibility:** Add powerful new capabilities to your AI models. Search local codebases, query databases, interact with web APIs, automate browser tasks, and more.
-
-**Flexibility:** Swap models and tools easily. Your MCP setup works whether you're using local models, Claude, GPT-4, or future AI systems.
-
-**Security:** User-controlled permissions ensure you decide which tools can access what resources. Tools run in isolated environments with explicit consent.
-
-
-
-## Model Compatibility Requirements
-
-
-
-## Security and Considerations
-
-MCP provides powerful capabilities that require careful security consideration:
-
-**Security Model:**
-- **Explicit permissions** for each tool and capability
-- **Isolated execution** prevents cross-tool interference
-- **User approval** required for sensitive operations
-- **Audit trails** track all tool usage and outputs
-
-**Performance Impact:**
-- **Context usage:** Active tools consume model context window space
-- **Response time:** More tools may slow generation slightly
-- **Resource usage:** Some tools require additional system resources
-
-**Best Practices:**
-- Enable only tools you actively need
-- Review tool permissions regularly
-- Monitor system resource usage
-- Keep MCP servers updated for security patches
-
-## Setting Up MCP in Jan
-
-### Prerequisites
-
-Ensure you have the required runtime environments:
-- **Node.js** - Download from [nodejs.org](https://nodejs.org/)
-- **Python** - Download from [python.org](https://www.python.org/)
-
-Most MCP tools require one or both of these environments.
-
-### Enable MCP Support
-
-Navigate to **Settings โ MCP Servers** and toggle **Allow All MCP Tool Permission** to ON.
-
-
-
-This global setting allows Jan to connect to MCP servers. You'll still control individual tool permissions.
-
-### Example: Browser MCP Setup
-
-Let's configure Browser MCP for web automation as a practical example:
-
-#### Step 1: Add MCP Server
-
-Click the `+` button in the MCP Servers section:
-
-
-
-#### Step 2: Configure Browser MCP
-
-Enter these details:
-- **Server Name:** `browsermcp`
-- **Command:** `npx`
-- **Arguments:** `@browsermcp/mcp`
-- **Environment Variables:** Leave empty
-
-
-
-#### Step 3: Verify Connection
-
-Confirm the server shows as active:
-
-
-
-#### Step 4: Install Browser Extension
-
-Install the [Browser MCP Chrome Extension](https://chromewebstore.google.com/detail/browser-mcp-automate-your/bjfgambnhccakkhmkepdoekmckoijdlc) to enable browser control:
-
-
-
-#### Step 5: Configure Extension
-
-Enable the extension for private browsing (recommended for clean sessions):
-
-
-
-Connect the extension to your MCP server:
-
-
-
-#### Step 6: Enable Model Tools
-
-Select a model with strong tool-calling capabilities and enable tools:
-
-
-
-Verify tool calling is active:
-
-
-
-## Available MCP Integrations
-
-Jan supports a growing ecosystem of MCP tools:
-
-### Web & Search
-- **Browser Control** - Automate web browsing tasks
-- **Web Search** - Real-time search with Serper, Exa
-- **Screenshot** - Capture and analyze web content
-
-### Development
-- **Code Execution** - Run code in secure sandboxes
-- **GitHub** - Repository management and analysis
-- **Documentation** - Generate and maintain docs
-
-### Productivity
-- **Task Management** - Todoist, Linear integration
-- **Calendar** - Schedule and meeting management
-- **Note Taking** - Obsidian, Notion connectivity
-
-### Creative
-- **Design Tools** - Canva integration for graphics
-- **Content Generation** - Blog posts, social media
-- **Media Processing** - Image and video manipulation
-
-Explore specific integrations in our [MCP Examples](./mcp-examples/browser/browserbase) section.
-
-## Troubleshooting
-
-### Connection Issues
-
-**MCP server won't connect:**
-- Verify Node.js and Python are installed correctly
-- Check command syntax in server configuration
-- Restart Jan after adding new servers
-- Review server logs for specific error messages
-
-**Tools not appearing:**
-- Ensure model has tool calling enabled
-- Verify MCP permissions are active
-- Check that the server status shows as running
-- Try with a different model known for good tool support
-
-### Performance Problems
-
-**Slow responses with tools:**
-- Reduce number of active tools
-- Use models with larger context windows
-- Monitor system resource usage
-- Consider using faster local models or cloud providers
-
-**Model not using tools effectively:**
-- Switch to models specifically trained for tool calling
-- Provide more explicit instructions about tool usage
-- Check model documentation for tool-calling examples
-- Test with proven tool-compatible models first
-
-### Model Compatibility
-
-**Local models not calling tools:**
-- Ensure the model supports function calling in its training
-- Enable tool calling in model capabilities settings
-- Try newer model versions with improved tool support
-- Consider switching to cloud models for complex tool workflows
-
-## Future Development
-
-MCP integration in Jan continues evolving with new capabilities:
-
-**Planned Features:**
-- **Visual tool builder** for custom MCP servers
-- **Tool marketplace** for easy discovery and installation
-- **Enhanced security** with granular permission controls
-- **Performance optimization** for faster tool execution
-
-**Ecosystem Growth:**
-- More professional tools (CRM, analytics, design)
-- Better local model tool-calling performance
-- Cross-platform mobile tool support
-- Enterprise-grade security and compliance features
-
-The MCP ecosystem enables increasingly sophisticated AI workflows. As more tools become available and models improve their tool-calling abilities, Jan becomes a more powerful platform for augmented productivity and creativity.
-
-Start with simple tools like web search or code execution, then gradually expand your toolkit as you discover new use cases and workflows that benefit from AI-tool collaboration.
\ No newline at end of file
diff --git a/website/src/content/docs/jan/multi-modal.mdx b/website/src/content/docs/jan/multi-modal.mdx
deleted file mode 100644
index c1e67310e..000000000
--- a/website/src/content/docs/jan/multi-modal.mdx
+++ /dev/null
@@ -1,175 +0,0 @@
----
-title: Multi-Modal Support
-description: Use images with AI models in Jan - local vision models and cloud providers with image understanding.
-keywords:
- [
- Jan,
- multi-modal,
- vision models,
- image recognition,
- Gemma3,
- Qwen3,
- Claude,
- GPT-4V,
- image attachment,
- visual AI,
- ]
-sidebar:
- badge:
- text: New
- variant: tip
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports image attachments with both local and cloud AI models. Upload images directly in your chats and get visual understanding, analysis, and creative responses from compatible models.
-
-## Local Vision Models
-
-Local models with image support work immediately without configuration. Popular vision models include the latest Gemma3 and Qwen3 series, which excel at image understanding while running entirely on your device.
-
-**Recommended Local Vision Models:**
-- **Gemma3 4B** - Excellent balance of performance and resource usage
-- **Qwen3 7B/14B** - Superior image analysis capabilities
-- **LLaVA models** - Specialized for visual question answering
-
-### Example: Image Analysis
-
-Here's Gemma3 4B analyzing a meme with some personality:
-
-
-
-Load a vision model like [Gemma3 4B](https://huggingface.co/unsloth/gemma-3-4b-it-GGUF) and attach your image:
-
-
-
-**Prompt used:** "Describe what you see in the image please. Be a bit sarcastic."
-
-The model delivers contextual analysis with the requested tone:
-
-
-
-
-
-## Cloud Vision Models
-
-Cloud providers like OpenAI (GPT-4V), Anthropic (Claude), and Google (Gemini) offer powerful vision capabilities. However, image support must be manually enabled for each model.
-
-### Enabling Vision for Cloud Models
-
-Navigate to your model settings and enable vision support:
-
-
-
-Toggle both **Tools** and **Vision** if you want to combine image understanding with web search or other MCP capabilities.
-
-### Example: Creative Image Analysis
-
-With Claude 3.5 Sonnet configured for vision, upload an image and get creative responses:
-
-
-
-**Prompt used:** "Write an AI joke about the image attached please."
-
-Claude combines image understanding with humor:
-
-
-
-## Supported Use Cases
-
-### Creative and Fun
-- Meme analysis and creation
-- Visual jokes and commentary
-- Art critique and style analysis
-- Creative writing from visual prompts
-
-### Practical Applications
-- Document analysis and OCR
-- Chart and graph interpretation
-- Product identification and comparison
-- Technical diagram explanation
-
-### Educational and Research
-- Historical photo analysis
-- Scientific image interpretation
-- Visual learning assistance
-- Research documentation
-
-## Model Capabilities Comparison
-
-| Model Type | Image Support | Setup Required | Privacy | Best For |
-|------------|---------------|----------------|---------|----------|
-| **Local (Gemma3, Qwen3)** | Automatic | None | Complete | Privacy, offline use |
-| **GPT-4V** | Manual enable | API key + toggle | Cloud processed | Advanced analysis |
-| **Claude 3.5 Sonnet** | Manual enable | API key + toggle | Cloud processed | Creative tasks |
-| **Gemini Pro Vision** | Manual enable | API key + toggle | Cloud processed | Multi-language |
-
-## Image Format Support
-
-Jan accepts common image formats:
-- **JPEG/JPG** - Most compatible
-- **PNG** - Full transparency support
-- **WebP** - Modern web format
-- **GIF** - Static images only
-
-
-
-## Example Prompts
-
-### Technical Analysis
-```
-Analyze this circuit diagram and explain how it works. Identify any potential issues or improvements.
-```
-
-### Creative Tasks
-```
-Look at this artwork and write a short story inspired by the mood and colors you see.
-```
-
-### Educational Support
-```
-Help me understand this math problem shown in the image. Walk through the solution step by step.
-```
-
-### Business Applications
-```
-Review this presentation slide and suggest improvements for clarity and visual impact.
-```
-
-### OCR and Document Processing
-```
-Extract all the text from this document and format it as a clean markdown list.
-```
-
-## Future Improvements
-
-We're actively improving multi-modal support:
-
-**Automatic Detection:** Models will show visual capabilities without manual configuration
-**Batch Processing:** Upload multiple images for comparison and analysis
-**Better Indicators:** Clear visual cues for vision-enabled models
-**Enhanced Formats:** Support for more image types and sizes
-
-## Performance Tips
-
-**Local Models:**
-- Ensure sufficient RAM (8GB+ recommended for vision models)
-- Use GPU acceleration for faster image processing
-- Start with smaller models if resources are limited
-
-**Cloud Models:**
-- Monitor API usage as vision requests typically cost more
-- Resize large images before upload to save bandwidth
-- Combine with tools for enhanced workflows
-
-## Privacy Considerations
-
-**Local Processing:** Images processed by local models never leave your device. Complete privacy for sensitive visual content.
-
-**Cloud Processing:** Images sent to cloud providers are processed on their servers. Check provider privacy policies for data handling practices.
-
-Multi-modal AI opens new possibilities for visual understanding and creative assistance. Whether you prefer local privacy or cloud capabilities, Jan makes it easy to work with images and text together.
diff --git a/website/src/content/docs/jan/privacy.mdx b/website/src/content/docs/jan/privacy.mdx
deleted file mode 100644
index 3e0d8301e..000000000
--- a/website/src/content/docs/jan/privacy.mdx
+++ /dev/null
@@ -1,140 +0,0 @@
----
-title: Jan Privacy Policy
-description: Jan's data collection practices, privacy measures, and your rights. Learn how we protect your data and maintain transparency.
-keywords:
- [
- Jan AI,
- Jan,
- local AI,
- private AI,
- conversational AI,
- no-subscription fee,
- large language model,
- about Jan,
- desktop application,
- privacy policy,
- data protection,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-
- Last Updated: January 16, 2025
-
-
-Jan is your AI. Here's what we do with data.
-
-
-
-## 1. Data Collection and Consent
-
-### No Data Collection Until You Allow It
-
-Menlo Research Pte Ltd (the "Company") doesn't collect anything until you explicitly allow tracking.
-
-### Your Choice
-
-You'll choose tracking preferences at first launch. Change them anytime in Settings or Privacy Settings.
-
-### Legal Basis (GDPR)
-
-Under EU GDPR Article 6(1)(a), we process data based on your explicit consent:
-
-- Clear consent required before any data collection
-- Withdraw consent anytime through Settings
-- Withdrawal doesn't affect previous lawful processing
-- Processing stops immediately upon withdrawal
-
-## 2. What We Never Collect
-
-Jan will **never** access your chats, settings, or model choices without permission:
-
-- **Chat History**: Your conversations stay private
-- **Chat Settings**: Your personalized settings remain with you
-- **Language Models**: We don't track which models you use
-- **Files**: No scanning, uploading, or viewing
-- **Personal Identity**: No personally identifiable information
-- **Prompts**: Your prompts and templates aren't monitored
-- **Conversation Metrics**: No context or conversation length tracking
-- **Model Usage**: Specific models and types aren't tracked
-
-## 3. Data We Track (With Permission)
-
-We track basic app usage to improve Jan.
-
-### Product Analytics
-
-When allowed, we collect:
-
-- **Active Users**: Daily active users to gauge engagement
-- **Retention**: User retention metrics to ensure ongoing value
-
-Everything's tied to a random ID - not your personal information. Your chats remain private.
-
-
-
-## 4. Cloud Model Use
-
-Cloud models (like GPT, Claude) need to see your messages to work. That's between you and the cloud provider - Jan facilitates the connection.
-
-- **API Processing**: Cloud providers process your messages directly
-- **Jan Access**: We don't access or store these messages
-- **Local Models**: Keep everything on your device with no external access
-
-## 5. Data Storage and Security
-
-### Analytics Provider
-
-[PostHog EU](https://posthog.com/eu) handles our analytics. All EU-based, GDPR-compliant data processing.
-
-### Security Measures
-
-- **Encryption**: All transfers use TLS encryption
-- **EU Processing**: Data processed within European Union
-- **Secure Storage**: PostHog manages data securely
-
-Details in their [GDPR docs](https://posthog.com/docs/privacy/gdpr-compliance).
-
-## 6. Data Retention
-
-- **Retention Period**: Analytics data kept for up to 12 months
-- **Deletion Requests**: Request deletion by emailing hello@jan.ai
-- **Legal Requirements**: May retain longer if legally required
-
-## 7. Your Rights
-
-- **Access and Control**: Modify tracking preferences anytime in Settings
-- **Data Requests**: Contact hello@jan.ai for any data-related requests
-- **Withdrawal**: Stop data collection immediately through Settings
-
-## 8. Children's Privacy
-
-Services not targeted at children under 13. We don't knowingly collect data from children under 13. If we become aware of such collection, we'll delete the information.
-
-## 9. Cookies and Tracking
-
-Our website uses cookies to:
-
-- Enhance user experience
-- Measure website traffic and usage
-
-Most browsers let you manage cookies and adjust privacy preferences. See our Cookie Policy for details.
-
-## 10. Policy Changes
-
-We may update this policy to reflect practice or legal changes. We'll notify you of significant changes via:
-
-- App notifications
-- Website announcements
-- Email (if provided)
-
-Continued use means you accept the changes.
-
-## 11. Contact Us
-
-Questions about privacy or data practices? Contact hello@menlo.ai.
diff --git a/website/src/content/docs/jan/quickstart.mdx b/website/src/content/docs/jan/quickstart.mdx
deleted file mode 100644
index 25b0cde2e..000000000
--- a/website/src/content/docs/jan/quickstart.mdx
+++ /dev/null
@@ -1,137 +0,0 @@
----
-title: QuickStart
-description: Get started with Jan and start chatting with AI in minutes.
-keywords:
- [
- Jan,
- local AI,
- LLM,
- chat,
- threads,
- models,
- download,
- installation,
- conversations,
- ]
-banner:
- content: |
- ๐Jan now supports image ๐ผ๏ธ attachments ๐
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Get up and running with Jan in minutes. This guide will help you install Jan, download a model, and start chatting immediately.
-
-### Step 1: Install Jan
-
-1. [Download Jan](/download)
-2. Install the app ([Mac](/docs/desktop/mac), [Windows](/docs/desktop/windows), [Linux](/docs/desktop/linux))
-3. Launch Jan
-
-### Step 2: Download Jan v1
-
-We recommend starting with **Jan v1**, our 4B parameter model optimized for reasoning and tool calling:
-
-1. Go to the **Hub Tab**
-2. Search for **Jan v1**
-3. Choose a quantization that fits your hardware:
- - **Q4_K_M** (2.5 GB) - Good balance for most users
- - **Q8_0** (4.28 GB) - Best quality if you have the RAM
-4. Click **Download**
-
-
-
-
-
-### Step 3: Start Chatting
-
-1. Click the **New Chat** icon
-2. Select your model in the input field dropdown
-3. Type your message and start chatting
-
-
-
-Try asking Jan v1 questions like:
-- "Explain quantum computing in simple terms"
-- "Help me write a Python function to sort a list"
-- "What are the pros and cons of electric vehicles?"
-
-
-
-## Managing Conversations
-
-Jan organizes conversations into threads for easy tracking and revisiting.
-
-### View Chat History
-
-- **Left sidebar** shows all conversations
-- Click any chat to open the full conversation
-- **Favorites**: Pin important threads for quick access
-- **Recents**: Access recently used threads
-
-
-
-### Edit Chat Titles
-
-1. Hover over a conversation in the sidebar
-2. Click the **three dots** icon
-3. Click **Rename**
-4. Enter new title and save
-
-
-
-### Delete Threads
-
-
-
-**Single thread:**
-1. Hover over thread in sidebar
-2. Click the **three dots** icon
-3. Click **Delete**
-
-**All threads:**
-1. Hover over `Recents` category
-2. Click the **three dots** icon
-3. Select **Delete All**
-
-## Advanced Features
-
-### Custom Assistant Instructions
-
-Customize how models respond:
-
-1. Use the assistant dropdown in the input field
-2. Or go to the **Assistant tab** to create custom instructions
-3. Instructions work across all models
-
-
-
-
-
-### Model Parameters
-
-Fine-tune model behavior:
-- Click the **Gear icon** next to your model
-- Adjust parameters in **Assistant Settings**
-- Switch models via the **model selector**
-
-
-
-### Connect Cloud Models (Optional)
-
-Connect to OpenAI, Anthropic, Groq, Mistral, and others:
-
-1. Open any thread
-2. Select a cloud model from the dropdown
-3. Click the **Gear icon** beside the provider
-4. Add your API key (ensure sufficient credits)
-
-
-
-For detailed setup, see [Remote APIs](/docs/remote-models/openai).
diff --git a/website/src/content/docs/jan/remote-models/anthropic.mdx b/website/src/content/docs/jan/remote-models/anthropic.mdx
deleted file mode 100644
index 595590a2c..000000000
--- a/website/src/content/docs/jan/remote-models/anthropic.mdx
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: Anthropic
-description: Learn how to integrate Anthropic with Jan for enhanced functionality.
-keywords:
- [
- Anthropic API,
- Jan,
- Jan AI,
- ChatGPT alternative,
- conversational AI,
- large language model,
- integration,
- Anthropic integration,
- API integration
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports all of [Anthropic's models](https://anthropic.com/) via API integration, allowing
-you to chat with Claude's latest Opus, Sonnet and Haiku models.
-
-## Integrate Anthropic API with Jan
-
-
-### Step 1: Get Your API Key
-
-1. Visit [Anthropic Console](https://console.anthropic.com/settings/keys) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **Anthropic**
-3. Insert your **API Key**
-
-
-
-### Step 3: Start Using Anthropic's Models
-
-1. In any existing **Chat** or create a new one
-2. Select an Anthropic model from **model selector**
-3. Start chatting
-
-
-## Available Anthropic Models
-
-Jan automatically includes Anthropic's available models. In case you want to use a specific Anthropic model
-that you cannot find in **Jan**, follow instructions in [Add Cloud Models](/docs/manage-models#add-models-1):
-- See list of available models in [Anthropic Models](https://docs.anthropic.com/claude/docs/models-overview).
-- The `id` property must match the model name in the list. For example, `claude-opus-4@20250514`, `claude-sonnet-4@20250514`, or `claude-3-5-haiku@20241022`.
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your Anthropic account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify Anthropic's system status
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Anthropic account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the
-[Anthropic's documentation](https://docs.anthropic.com/claude/docs).
diff --git a/website/src/content/docs/jan/remote-models/cohere.mdx b/website/src/content/docs/jan/remote-models/cohere.mdx
deleted file mode 100644
index 91ba75b10..000000000
--- a/website/src/content/docs/jan/remote-models/cohere.mdx
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: Cohere
-description: Learn how to integrate Cohere with Jan for enhanced functionality.
-keywords:
- [
- Cohere API,
- Jan,
- Jan AI,
- ChatGPT alternative,
- conversational AI,
- large language model,
- integration,
- Cohere integration,
- API integration
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports [Cohere](https://cohere.com/) API integration, allowing you to use Cohere's
-models (Command, Command-R and more) through Jan's interface.
-
-## Integrate Cohere API with Jan
-
-
-### Step 1: Get Your API Key
-
-1. Visit [Cohere Dashboard](https://dashboard.cohere.com/api-keys) and sign in
-2. Create a new API key and/or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **Cohere**
-3. Insert your **API Key**
-
-
-
-
-### Step 3: Start Using Cohere's Models
-
-1. Jump into any existing **Chat** or create a new one
-2. Select a Cohere model from **model selector** options
-3. Start chatting
-
-
-## Available Cohere Models
-
-Jan automatically includes Cohere's available models. In case you want to use a specific
-Cohere model that you cannot find in **Jan**, follow instructions in [Add Cloud Models](/docs/manage-models):
-- See list of available models in [Cohere Documentation](https://docs.cohere.com/v2/docs/models).
-- The `id` property must match the model name in the list. For example, `command-nightly` or `command-light`.
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your Cohere account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify Cohere's [system status](https://status.cohere.com/)
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Cohere account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the [Cohere documentation](https://docs.cohere.com).
diff --git a/website/src/content/docs/jan/remote-models/google.mdx b/website/src/content/docs/jan/remote-models/google.mdx
deleted file mode 100644
index 41aa7ed1c..000000000
--- a/website/src/content/docs/jan/remote-models/google.mdx
+++ /dev/null
@@ -1,75 +0,0 @@
----
-title: Google
-description: Learn how to integrate Google with Jan for enhanced functionality.
-keywords:
- [
- Anthropic API,
- Jan,
- Jan AI,
- ChatGPT alternative,
- conversational AI,
- large language model,
- integration,
- Anthropic integration,
- API integration
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports [Google](https://ai.google/get-started/our-models/) API integration, allowing you to use Google models (like Gemini series) through Jan's interface.
-
-## Integrate Google API with Jan
-
-### Step 1: Get Your API Key
-
-1. Visit [Google AI Studio](https://aistudio.google.com/app/apikey) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **Gemini**
-3. Insert your **API Key**
-
-
-
-
-### Step 3: Start Using Google's Models
-
-1. Got to any existing **Chat** or create a new one
-2. Select an Gemini model from **model selector**
-3. Start chatting
-
-
-## Available Google Models
-
-Jan automatically includes Google's available models like Gemini series. In case you want to use a specific
-Gemini model that you cannot find in **Jan**, follow instructions in [Add Cloud Models](/docs/manage-models#add-models-1):
-- See list of available models in [Google Models](https://ai.google.dev/gemini-api/docs/models/gemini).
-- The `id` property must match the model name in the list. For example, `gemini-1.5-pro` or `gemini-2.0-flash-lite-preview`.
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your Google account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify [Gemini's system status](https://www.google.com/appsstatus/dashboard/)
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Google account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH).
diff --git a/website/src/content/docs/jan/remote-models/groq.mdx b/website/src/content/docs/jan/remote-models/groq.mdx
deleted file mode 100644
index cd67a63c3..000000000
--- a/website/src/content/docs/jan/remote-models/groq.mdx
+++ /dev/null
@@ -1,74 +0,0 @@
----
-title: Groq API
-description: Learn how to integrate Groq API with Jan for enhanced functionality.
-keywords:
- [
- Groq API,
- Jan,
- Jan AI,
- ChatGPT alternative,
- conversational AI,
- large language model,
- integration,
- Groq integration,
- API integration
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports [Groq](https://groq.com/) API integration, allowing you to use Groq's high-performance LLM models (LLaMA 2, Mixtral and more) through Jan's interface.
-
-## Integrate Groq API with Jan
-
-### Step 1: Get Your API Key
-
-1. Visit [Groq Console](https://console.groq.com/keys) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **Groq**
-3. Insert your **API Key**
-
-
-
-
-### Step 3: Start Using Groq's Models
-
-1. Jump into any existing **Chat** or create a new one
-2. Select a Groq model from **model selector**
-3. Start chatting
-
-## Available Models Through Groq
-
-Jan automatically includes Groq's available models. In case you want to use a specific Groq model that
-you cannot find in **Jan**, follow the instructions in the [Add Cloud Models](/docs/manage-models#add-models-1):
-- See list of available models in [Groq Documentation](https://console.groq.com/docs/models).
-- The `id` property must match the model name in the list. For example, if you want to use Llama3.3 70B, you must set the `id` property to `llama-3.3-70b-versatile`.
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your Groq account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify Groq's system status
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Groq account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the [Groq documentation](https://console.groq.com/docs).
diff --git a/website/src/content/docs/jan/remote-models/huggingface.mdx b/website/src/content/docs/jan/remote-models/huggingface.mdx
deleted file mode 100644
index 32100ff41..000000000
--- a/website/src/content/docs/jan/remote-models/huggingface.mdx
+++ /dev/null
@@ -1,136 +0,0 @@
----
-title: Hugging Face
-description: Learn how to integrate Hugging Face models with Jan using the Router or Inference Endpoints.
-keywords:
- [
- Hugging Face,
- Jan,
- Jan AI,
- Hugging Face Router,
- Hugging Face Inference Endpoints,
- Hugging Face API,
- Hugging Face Integration,
- Hugging Face API Integration
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-
-Jan supports Hugging Face models through two methods: the new **HF Router** (recommended) and **Inference Endpoints**. Both methods require a Hugging Face token and **billing to be set up**.
-
-
-
-## Option 1: HF Router (Recommended)
-
-The HF Router provides access to models from multiple providers (Replicate, Together AI, SambaNova, Fireworks, Cohere, and more) through a single endpoint.
-
-### Step 1: Get Your HF Token
-
-Visit [Hugging Face Settings > Access Tokens](https://huggingface.co/settings/tokens) and create a token. Make sure you have billing set up on your account.
-
-### Step 2: Configure Jan
-
-1. Go to **Settings** > **Model Providers** > **HuggingFace**
-2. Enter your HF token
-3. Use this URL: `https://router.huggingface.co/v1`
-
-
-
-You can find out more about the HF Router [here](https://huggingface.co/docs/inference-providers/index).
-
-### Step 3: Start Using Models
-
-Jan comes with three HF Router models pre-configured. Select one and start chatting immediately.
-
-
-
-## Option 2: HF Inference Endpoints
-
-For more control over specific models and deployment configurations, you can use Hugging Face Inference Endpoints.
-
-### Step 1: Navigate to the HuggingFace Model Hub
-
-Visit the [Hugging Face Model Hub](https://huggingface.co/models) (make sure you are logged in) and pick the model you want to use.
-
-
-
-### Step 2: Configure HF Inference Endpoint and Deploy
-
-After you have selected the model you want to use, click on the **Deploy** button and select a deployment method. We will select HF Inference Endpoints for this one.
-
-
-
-This will take you to the deployment set up page. For this example, we will leave the default settings as they are under the GPU tab and click on **Create Endpoint**.
-
-
-
-Once your endpoint is ready, test that it works on the **Test your endpoint** tab.
-
-
-
-If you get a response, you can click on **Copy** to copy the endpoint URL and API key.
-
-
-
-### Step 3: Configure Jan
-
-If you do not have an API key you can create one under **Settings** > **Access Tokens** [here](https://huggingface.co/settings/tokens). Once you finish, copy the token and add it to Jan alongside your endpoint URL at **Settings** > **Model Providers** > **HuggingFace**.
-
-**3.1 HF Token**
-
-
-**3.2 HF Endpoint URL**
-
-
-**3.3 Jan Settings**
-
-
-
-
-**3.4 Add Model Details**
-
-
-### Step 4: Start Using the Model
-
-Now you can start using the model in any chat.
-
-
-
-If you want to learn how to use Jan Nano with MCP, check out [the guide here](../jan-models/jan-nano-32).
-
-## Available Hugging Face Models
-
-**Option 1 (HF Router):** Access to models from multiple providers as shown in the providers image above.
-
-**Option 2 (Inference Endpoints):** You can follow the steps above with a large amount of models on Hugging Face and bring them to Jan. Check out other models in the [Hugging Face Model Hub](https://huggingface.co/models).
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. Started a chat but the model is not responding**
-- Verify your API_KEY/HF_TOKEN is correct and not expired
-- Ensure you have billing set up on your HF account
-- For Inference Endpoints: Ensure the model you're trying to use is running again since, after a while, they go idle so that you don't get charged when you are not using it
-
-
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify Hugging Face's system status
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Hugging Face account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the
-[Hugging Face's documentation](https://docs.huggingface.co/en/inference-endpoints/index).
diff --git a/website/src/content/docs/jan/remote-models/mistralai.mdx b/website/src/content/docs/jan/remote-models/mistralai.mdx
deleted file mode 100644
index f2a6bbaab..000000000
--- a/website/src/content/docs/jan/remote-models/mistralai.mdx
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: Mistral AI API
-description: A step-by-step guide on integrating Jan with Mistral AI.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- Mistral integration,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports all models available via the [Mistral AI](https://mistral.ai/) API, allowing you to use Mistral's
-powerful models (Mistral Large, Mistral Medium, Mistral Small and more) through Jan's interface.
-
-## Integrate Mistral AI with Jan
-
-### Step 1: Get Your API Key
-
-1. Visit the [Mistral AI Platform](https://console.mistral.ai/api-keys/) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **Mistral AI**
-3. Insert your **API Key**
-
-
-
-### Step 3: Start Using Mistral's Models
-
-1. Open any existing **Chat** or create a new one
-2. Select a Mistral model from **model selector**
-3. Start chatting
-
-
-## Available Mistral Models
-
-Jan automatically includes Mistral's available models. In case you want to use a specific Mistral model
-that you cannot find in **Jan**, follow the instructions in [Add Cloud Models](/docs/manage-models#add-models-1):
-- See list of available models in [Mistral AI Documentation](https://docs.mistral.ai/platform/endpoints).
-- The `id` property must match the model name in the list. For example, if you want to use
-Mistral Large, you must set the `id` property to `mistral-large-latest`
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your Mistral AI account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify Mistral AI's system status
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your Mistral AI account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the [Mistral AI documentation](https://docs.mistral.ai/).
diff --git a/website/src/content/docs/jan/remote-models/openai.mdx b/website/src/content/docs/jan/remote-models/openai.mdx
deleted file mode 100644
index f1eb33ba5..000000000
--- a/website/src/content/docs/jan/remote-models/openai.mdx
+++ /dev/null
@@ -1,81 +0,0 @@
----
-title: OpenAI API
-description: A step-by-step guide on integrating Jan with Azure OpenAI.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- integration,
- Azure OpenAI Service,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-Jan supports most [OpenAI](https://openai.com/) as well as the many OpenAI-compatible APIs out there,
-allowing you to use all models from OpenAI (GPT-4o, o3 and even those from Together AI, DeepSeek, Fireworks
-and more) through Jan's interface.
-
-## Integrate OpenAI API with Jan
-
-### Step 1: Get Your API Key
-1. Visit the [OpenAI Platform](https://platform.openai.com/api-keys) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under Remote Engines, select OpenAI
-3. Insert your API Key
-
-
-
-
-### Step 3: Start Using OpenAI's Models
-
-In any existing Threads or create a new one
-Select an OpenAI model from model selector
-Start chatting
-
-
-## Available OpenAI Models
-
-Jan automatically includes popular OpenAI models. In case you want to use a specific model that you
-cannot find in Jan, follow instructions in [Add Cloud Models](/docs/manage-models#add-models-1):
-- See list of available models in [OpenAI Platform](https://platform.openai.com/docs/models/overview).
-- The id property must match the model name in the list. For example, if you want to use the
-[GPT-4.5](https://platform.openai.com/docs/models/), you must set the id property
-to respective one.
-
-## Troubleshooting
-
-Common issues and solutions:
-
-1. API Key Issues
-- Verify your API key is correct and not expired
-- Check if you have billing set up on your OpenAI account
-- Ensure you have access to the model you're trying to use
-
-2. Connection Problems
-- Check your internet connection
-- Verify OpenAI's [system status](https://status.openai.com)
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-3. Model Unavailable
-- Confirm your API key has access to the model
-- Check if you're using the correct model ID
-- Verify your OpenAI account has the necessary permissions
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the
-[OpenAI documentation](https://platform.openai.com/docs).
diff --git a/website/src/content/docs/jan/remote-models/openrouter.mdx b/website/src/content/docs/jan/remote-models/openrouter.mdx
deleted file mode 100644
index 614bc58e6..000000000
--- a/website/src/content/docs/jan/remote-models/openrouter.mdx
+++ /dev/null
@@ -1,90 +0,0 @@
----
-title: OpenRouter
-description: A step-by-step guide on integrating Jan with OpenRouter.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- OpenRouter integration,
- OpenRouter,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-## Integrate OpenRouter with Jan
-
-[OpenRouter](https://openrouter.ai/) is a tool that gathers AI model APIs and provides access to all
-via a unified API. Developers can use the API to interact with LLMs, generative image models, and
-even models that generate 3D objects, all with a competitive pricing.
-
-Jan supports the OpenRouter API, allowing you to use models from various providers (Anthropic, Google,
-Meta and more) and helping you avoid having to get an API from all of your favorite ones.
-
-OpenRouter even offers a few free models! ๐
-
-## Integrate OpenRouter with Jan
-
-### Step 1: Get Your API Key
-1. Visit [OpenRouter](https://openrouter.ai/keys) and sign in
-2. Create & copy a new API key or copy your existing one
-
-
-
-### Step 2: Configure Jan
-
-1. Navigate to the **Settings** page
-2. Under **Model Providers**, select **OpenRouter**
-3. Insert your **API Key**
-
-
-
-### Step 3: Start Using OpenRouter Models
-
-1. Pick any existing **Chat** or create a new one
-2. Select any model from **model selector** under OpenRouter
-3. Start chatting
-
-## Available Models Through OpenRouter
-
-Jan automatically use your default OpenRouter's available models. For custom configurations:
-
-**Model Field Settings:**
-- Leave empty to use your account's default model
-- Specify a model using the format: `organization/model-name`
-- Available options can be found in [OpenRouter's Model Reference](https://openrouter.ai/models)
-
-**Examples of Model IDs:**
-- Claude 4 Opus: `anthropic/claude-opus-4`
-- Google Gemini 2.5 Pro: `google/gemini-2.5-pro-preview`
-- DeepSeek R1 Latest: `deepseek/deepseek-r1-0528`
-
-## Troubleshooting
-
-Common issues and solutions:
-
-**1. API Key Issues**
-- Verify your API key is correct and not expired
-- Check if you have sufficient credits in your OpenRouter account
-- Ensure you have access to the model you're trying to use
-
-**2. Connection Problems**
-- Check your internet connection
-- Verify OpenRouter's [system status](https://status.openrouter.ai)
-- Look for error messages in [Jan's logs](/docs/troubleshooting#how-to-get-error-logs)
-
-**3. Model Unavailable**
-- Confirm the model is currently available on OpenRouter
-- Check if you're using the correct model ID format
-- Verify the model provider is currently operational
-
-Need more help? Join our [Discord community](https://discord.gg/FTk2MvZwJH) or check the [OpenRouter documentation](https://openrouter.ai/docs).
diff --git a/website/src/content/docs/jan/settings.mdx b/website/src/content/docs/jan/settings.mdx
deleted file mode 100644
index baba03570..000000000
--- a/website/src/content/docs/jan/settings.mdx
+++ /dev/null
@@ -1,215 +0,0 @@
----
-title: Settings
-description: Explore how to adjust Jan's settings to suit your specific requirements.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- Advanced Settings,
- HTTPS Proxy,
- SSL,
- settings,
- Jan settings,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components';
-
-To access the **Settings**, click icon in the bottom left corner of Jan.
-
-## Model Management
-
-Manage your installed AI models in **Settings** > **Model Providers**:
-
-### Import Models
-- **From Hugging Face:**
- - Enter a model's Hugging Face ID (e.g., `org/model_name_or_id`) in the Hub's search bar.
- - **Note:** Some models require a Hugging Face Access Token. Enter your token in **Settings > Model Providers > Hugging Face**.
-- **From Local Files:**
- - Click **Import Model** and select your GGUF files.
-
-### Remove Models
-
-- Click the trash icon next to the **Start** button and then click **Delete**.
-
-### Start Models
-
-1. Open a new chat and select the model you want to start.
-2. Click the **Start** button on the **Settings > Model Providers**
-
-### Hugging Face Access Token
-To download models from Hugging Face that require authentication, for example, like the llama models from meta:
-1. Get your token from [Hugging Face Tokens](https://huggingface.co/docs/hub/en/security-tokens)
-2. Enter it in **Settings > Model Providers > Hugging Face**.
-
-## Model Settings (Gear Icon)
-
-
-
-Click the gear icon next to a model to configure advanced settings:
-- **Context Size**: Maximum prompt context length
-- **GPU Layers**: Number of model layers to offload to GPU. If you have an NVIDIA GPU and notice that your model won't fully load in it, you can reduce this value to load smaller parts of the model and try again.
-- **Temperature**: Controls randomness (higher = more random)
-- **Top K**: Limits token selection to K most likely next tokens (smaller K = more focused responses)
-- **Top P**: Limits token selection to tokens comprising P probability mass (smaller P = more focused responses)
-- **Min P**: Sets a minimum threshold for words the model can select (higher values filter out less likely words)
-- **Repeat Last N**: Determines how many recent words the model checks to avoid repetition
-- **Repeat Penalty**: Controls how strongly the model avoids repeating phrases (higher values reduce repetition)
-- **Presence Penalty**: Discourages reusing words that already appeared in the text (helps with variety)
-
-_See [Model Parameters](/docs/model-parameters) for a more detailed explanation._
-
-
-## Hardware
-
-Monitor and manage system resources at **Settings > Hardware**:
-- **CPU, RAM, GPU**: View usage and specs
-- **GPU Acceleration**: Enable/disable and configure GPU settings
-
-
-
-
-## Preferences
-
-### Appearance & Theme
-
-Control the visual theme of Jan's interface with any color combo you'd like. You can also control the color use in the code blocks.
-
-
-
-### Spell Check
-
-Jan includes a built-in spell check feature to help catch typing errors in your messages.
-
-
-
-## Privacy
-
-At **Settings** > **Privacy**, you can control anonymous analytics in Jan:
-
-### Analytics
-Jan is built with privacy at its core. By default, no data is collected. Everything stays local on your device.
-You can help improve Jan by sharing anonymous usage data:
-1. Toggle on **Analytics** to share anonymous data
-2. You can change this setting at any time
-
-
-
-
-
-### Log Management
-
-**1. View Logs**
-- Logs are stored at:
- - App log: `~/Library/Application\ Support/jan/data/logs/app.log`
- - Cortex log: `~/Library/Application\ Support/jan/data/logs/cortex.log`
-- To open logs from Jan's interface: at **Logs**, click icon to open App Logs & Cortex Logs:
-
-
-
-**2. Clear Logs**
-
-Jan retains your logs for only **24 hours**. To remove all logs from Jan, at **Clear Logs**, click the **Clear** button:
-
-
-
-
-
-
-### Jan Data Folder
-Jan stores your data locally in your own filesystem in a universal file format. See detailed [Jan Folder Structure](./data-folder#folder-structure).
-
-**1. Open Jan Data Folder**
-
-At **Jan Data Folder**, click icon to open Jan application's folder:
-
-
-
-**2. Edit Jan Data Folder**
-
-1. At **Jan Data Folder** icon to edit Jan application's folder
-2. Choose a new directory & click **Select**, make sure the new folder is empty
-3. Confirmation pop-up shows up:
-
-> Are you sure you want to relocate Jan Data Folder to `new directory`?
-Jan Data Folder will be duplicated into the new location while the original folder remains intact.
-An app restart will be required afterward.
-
-4. Click **Yes, Proceed**
-
-
-
-### HTTPs Proxy
-
-HTTPS Proxy encrypts data between your browser and the internet, making it hard for outsiders to intercept
-or read. It also helps you maintain your privacy and security while bypassing regional restrictions on the internet.
-
-
-
-1. **Enable** the proxy toggle
-2. Enter your proxy server details in the following format:
-
-```
-http://:@:
-```
-Where:
-- ``: Your proxy username (if authentication is required)
-- ``: Your proxy password (if authentication is required)
-- ``: Your proxy server's domain name or IP address
-- ``: The port number for the proxy server
-
-
-
-**Ignore SSL Certificates**
-
-This setting allows Jan to accept self-signed or unverified SSL certificates. This may be necessary when:
-- Working with corporate proxies using internal certificates
-- Testing in development environments
-- Connecting through specialized network security setups
-
-
-
-
-
-### Factory Reset
-
-Reset to Factory Settings restores Jan to its initial state by erasing all user data, including downloaded
-models and chat history. This action is irreversible and should only be used as a last resort when experiencing
-serious application issues.
-
-
-
-Only use factory reset if:
-- The application is corrupted
-- You're experiencing persistent technical issues that other solutions haven't fixed
-- You want to completely start fresh with a clean installation
-
-To begin the process:
-1. At **Reset to Factory Settings**, click **Reset** button
-
-
-
-2. In the confirmation dialog:
-- Type the word **RESET** to confirm
-- Optionally check **Keep the current app data location** to maintain the same data folder
-- Click **Reset Now**
-3. App restart is required upon confirmation
-
diff --git a/website/src/content/docs/jan/troubleshooting.mdx b/website/src/content/docs/jan/troubleshooting.mdx
deleted file mode 100644
index d2d417ad0..000000000
--- a/website/src/content/docs/jan/troubleshooting.mdx
+++ /dev/null
@@ -1,344 +0,0 @@
----
-title: Troubleshooting
-description: Fix common issues and optimize Jan's performance with this comprehensive guide.
-keywords:
- [
- Jan,
- troubleshooting,
- error fixes,
- performance issues,
- GPU problems,
- installation issues,
- common errors,
- local AI,
- technical support,
- ]
----
-
-import { Tabs, TabItem } from '@astrojs/starlight/components';
-import { Aside } from '@astrojs/starlight/components';
-import { Steps } from '@astrojs/starlight/components';
-
-## Getting Help: Error Logs
-
-When Jan isn't working properly, error logs help identify the problem. Here's how to get them:
-
-### Quick Access to Logs
-
-**In Jan Interface:**
-1. Look for **System Monitor** in the footer
-2. Click **App Log**
-
-
-
-**Via Terminal:**
-
-
-
-
-**Application Logs:**
-```bash
-tail -n 50 ~/Library/Application\ Support/Jan/data/logs/app.log
-```
-
-**Server Logs:**
-```bash
-tail -n 50 ~/Library/Application\ Support/Jan/data/logs/cortex.log
-```
-
-
-
-
-**Application Logs:**
-```cmd
-type %APPDATA%\Jan\data\logs\app.log
-```
-
-**Server Logs:**
-```cmd
-type %APPDATA%\Jan\data\logs\cortex.log
-```
-
-
-
-
-
-
-## Common Issues & Solutions
-
-### Jan Won't Start (Broken Installation)
-
-If Jan gets stuck after installation or won't start properly:
-
-
-
-
-**Clean Reinstall Steps:**
-
-1. **Uninstall Jan** from Applications folder
-
-2. **Delete all Jan data:**
-```bash
-rm -rf ~/Library/Application\ Support/Jan
-```
-
-3. **Kill any background processes** (for versions before 0.4.2):
-```bash
-ps aux | grep nitro
-# Find process IDs and kill them:
-kill -9
-```
-
-4. **Download fresh copy** from [jan.ai](/download)
-
-
-
-
-**Clean Reinstall Steps:**
-
-1. **Uninstall Jan** via Control Panel
-
-2. **Delete application data:**
-```cmd
-cd C:\Users\%USERNAME%\AppData\Roaming
-rmdir /S Jan
-```
-
-3. **Kill background processes** (for versions before 0.4.2):
-```cmd
-# Find nitro processes
-tasklist | findstr "nitro"
-# Kill them by PID
-taskkill /F /PID
-```
-
-4. **Download fresh copy** from [jan.ai](/download)
-
-
-
-
-**Clean Reinstall Steps:**
-
-1. **Uninstall Jan:**
-```bash
-# For Debian/Ubuntu
-sudo apt-get remove jan
-
-# For AppImage - just delete the file
-```
-
-2. **Delete application data:**
-```bash
-# Default location
-rm -rf ~/.config/Jan
-
-# Or custom location
-rm -rf $XDG_CONFIG_HOME/Jan
-```
-
-3. **Kill background processes** (for versions before 0.4.2):
-```bash
-ps aux | grep nitro
-kill -9
-```
-
-4. **Download fresh copy** from [jan.ai](/download)
-
-
-
-
-
-
-### NVIDIA GPU Not Working
-
-If Jan isn't using your NVIDIA graphics card for acceleration:
-
-#### Step 1: Verify Hardware and System Requirements
-
-**Check GPU Detection:**
-
-*Windows:* Right-click desktop โ NVIDIA Control Panel, or check Device Manager โ Display Adapters
-
-*Linux:* Run `lspci | grep -i nvidia`
-
-**Install Required Software:**
-
-**NVIDIA Driver (470.63.01 or newer):**
-1. Download from [nvidia.com/drivers](https://www.nvidia.com/drivers/)
-2. Test: Run `nvidia-smi` in terminal
-
-**CUDA Toolkit (11.7 or newer):**
-1. Download from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads)
-2. Test: Run `nvcc --version`
-
-**Linux Additional Requirements:**
-```bash
-# Install required packages
-sudo apt update && sudo apt install gcc-11 g++-11 cpp-11
-
-# Set CUDA environment
-export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
-```
-
-#### Step 2: Enable GPU Acceleration in Jan
-
-1. Open **Settings** > **Hardware**
-2. Turn on **GPU Acceleration**
-3. Check **System Monitor** (footer) to verify GPU is detected
-
-
-
-#### Step 3: Verify Configuration
-
-1. Go to **Settings** > **Advanced Settings** > **Data Folder**
-2. Open `settings.json` file
-3. Check these settings:
-
-```json
-{
- "run_mode": "gpu", // Should be "gpu"
- "nvidia_driver": {
- "exist": true, // Should be true
- "version": "531.18"
- },
- "cuda": {
- "exist": true, // Should be true
- "version": "12"
- },
- "gpus": [
- {
- "id": "0",
- "vram": "12282" // Your GPU memory in MB
- }
- ]
-}
-```
-
-#### Step 4: Restart Jan
-
-Close and restart Jan to apply changes.
-
-#### Tested Working Configurations
-
-**Desktop Systems:**
-- Windows 11 + RTX 4070Ti + CUDA 12.2 + Driver 531.18
-- Ubuntu 22.04 + RTX 4070Ti + CUDA 12.2 + Driver 545
-
-**Virtual Machines:**
-- Ubuntu on Proxmox + GTX 1660Ti + CUDA 12.1 + Driver 535
-
-
-
-### "Failed to Fetch" or "Something's Amiss" Errors
-
-When models won't respond or show these errors:
-
-**1. Check System Requirements**
-- **RAM:** Use models under 80% of available memory
- - 8GB system: Use models under 6GB
- - 16GB system: Use models under 13GB
-- **Hardware:** Verify your system meets [minimum requirements](/docs/desktop/)
-
-**2. Adjust Model Settings**
-- Open model settings in the chat sidebar
-- Lower the **GPU Layers (ngl)** setting
-- Start low and increase gradually
-
-**3. Check Port Conflicts**
-If logs show "Bind address failed":
-
-```bash
-# Check if ports are in use
-# macOS/Linux
-netstat -an | grep 1337
-
-# Windows
-netstat -ano | find "1337"
-```
-
-**Default Jan ports:**
-- API Server: `1337`
-- Documentation: `3001`
-
-**4. Try Factory Reset**
-1. **Settings** > **Advanced Settings**
-2. Click **Reset** under "Reset To Factory Settings"
-
-
-
-**5. Clean Reinstall**
-If problems persist, do a complete clean installation (see "Jan Won't Start" section above).
-
-### Permission Denied Errors
-
-If you see permission errors during installation:
-
-```bash
-# Fix npm permissions (macOS/Linux)
-sudo chown -R $(whoami) ~/.npm
-
-# Windows - run as administrator
-```
-
-### OpenAI API Issues ("Unexpected Token")
-
-For OpenAI connection problems:
-
-**1. Verify API Key**
-- Get valid key from [OpenAI Platform](https://platform.openai.com/)
-- Ensure sufficient credits and permissions
-
-**2. Check Regional Access**
-- Some regions have API restrictions
-- Try using a VPN from a supported region
-- Test network connectivity to OpenAI endpoints
-
-### Performance Issues
-
-**Models Running Slowly:**
-- Enable GPU acceleration (see NVIDIA section)
-- Use appropriate model size for your hardware
-- Close other memory-intensive applications
-- Check Task Manager/Activity Monitor for resource usage
-
-**High Memory Usage:**
-- Switch to smaller model variants
-- Reduce context length in model settings
-- Enable model offloading in engine settings
-
-**Frequent Crashes:**
-- Update graphics drivers
-- Check system temperature
-- Reduce GPU layers if using GPU acceleration
-- Verify adequate power supply (desktop systems)
-
-## Need More Help?
-
-If these solutions don't work:
-
-**1. Gather Information:**
-- Copy your error logs (see top of this page)
-- Note your system specifications
-- Describe what you were trying to do when the problem occurred
-
-**2. Get Community Support:**
-- Join our [Discord](https://discord.com/invite/FTk2MvZwJH)
-- Post in the **#๐|jan-help** channel
-- Include your logs and system info
-
-**3. Check Resources:**
-- [System requirements](./installation)
-- [Model compatibility guides](./manage-models)
-- [Hardware setup guides](./installation)
-
-
diff --git a/website/src/content/docs/local-server/api-server.mdx b/website/src/content/docs/local-server/api-server.mdx
deleted file mode 100644
index 491c31db9..000000000
--- a/website/src/content/docs/local-server/api-server.mdx
+++ /dev/null
@@ -1,100 +0,0 @@
----
-title: Local API Server
-description: Run Jan's OpenAI-compatible API server on your local machine for private, offline AI development.
-keywords:
- [
- Jan,
- local AI server,
- OpenAI API,
- local API,
- self-hosted AI,
- offline AI,
- privacy-focused AI,
- API integration,
- local LLM server,
- llama.cpp,
- CORS,
- API key
- ]
----
-import { Aside, Steps } from '@astrojs/starlight/components'
-
-Jan provides a built-in, OpenAI-compatible API server that runs entirely on your computer, powered by `llama.cpp`. Use it as a drop-in replacement for cloud APIs to build private, offline-capable AI applications.
-
-
-
-## Quick Start
-
-### Start the Server
-1. Navigate to **Settings** > **Local API Server**.
-2. Enter a custom **API Key** (e.g., `secret-key-123`). This is required for all requests.
-3. Click **Start Server**.
-
-The server is ready when the logs show `JAN API listening at http://127.0.0.1:1337`.
-
-### Test with cURL
-Open a terminal and make a request. Replace `YOUR_MODEL_ID` with the ID of an available model in Jan.
-
-```bash
-curl http://127.0.0.1:1337/v1/chat/completions \
- -H "Content-Type: application/json" \
- -H "Authorization: Bearer secret-key-123" \
- -d '{
- "model": "YOUR_MODEL_ID",
- "messages": [{"role": "user", "content": "Tell me a joke."}]
- }'
-```
-
-## Server Configuration
-
-These settings control the network accessibility and basic behavior of your local server.
-
-### Server Host
-The network address the server listens on.
-- **`127.0.0.1`** (Default): The server is only accessible from your own computer. This is the most secure option for personal use.
-- **`0.0.0.0`**: The server is accessible from other devices on your local network (e.g., your phone or another computer). Use this with caution.
-
-### Server Port
-The port number for the API server.
-- **`1337`** (Default): A common alternative port.
-- You can change this to any available port number (e.g., `8000`).
-
-### API Prefix
-The base path for all API endpoints.
-- **`/v1`** (Default): Follows OpenAI's convention. The chat completions endpoint would be `http://127.0.0.1:1337/v1/chat/completions`.
-- You can change this or leave it empty if desired.
-
-### API Key
-A mandatory secret key to authenticate requests.
-- You must set a key. It can be any string (e.g., `a-secure-password`).
-- All API requests must include this key in the `Authorization: Bearer YOUR_API_KEY` header.
-
-### Trusted Hosts
-A comma-separated list of hostnames allowed to access the server. This provides an additional layer of security when the server is exposed on your network.
-
-### Request timeout
-Request timeout for local model response in seconds.
-- **`600`** (Default): You can change this to any suitable value.
-
-## Advanced Settings
-
-### Cross-Origin Resource Sharing (CORS)
-- **(Enabled by default)** Allows web applications (like a custom web UI you are building) running on different domains to make requests to the API server.
-- **Disable this** if your API will only be accessed by non-browser-based applications (e.g., scripts, command-line tools) for slightly improved security.
-
-### Verbose Server Logs
-- **(Enabled by default)** Provides detailed, real-time logs of all incoming requests, responses, and server activity.
-- This is extremely useful for debugging application behavior and understanding exactly what is being sent to the models.
-
-## Troubleshooting
-
-
-
-- **Connection Refused:** The server is not running, or your application is pointing to the wrong host or port.
-- **401 Unauthorized:** Your API Key is missing from the `Authorization` header or is incorrect.
-- **404 Not Found:**
- - The `model` ID in your request body does not match an available model in Jan.
- - Your request URL is incorrect (check the API Prefix).
-- **CORS Error (in a web browser):** Ensure the CORS toggle is enabled in Jan's settings.
diff --git a/website/src/content/docs/local-server/index.mdx b/website/src/content/docs/local-server/index.mdx
deleted file mode 100644
index 76c7de6b7..000000000
--- a/website/src/content/docs/local-server/index.mdx
+++ /dev/null
@@ -1,114 +0,0 @@
----
-title: Local API Server
-description: Build AI applications with Jan's OpenAI-compatible API server.
----
-
-import { Aside, LinkCard } from '@astrojs/starlight/components';
-
-Jan provides an OpenAI-compatible API server that runs entirely on your computer. Use the same API patterns you know from OpenAI, but with complete control over your models and data.
-
-## Features
-
-- **OpenAI-compatible** - Drop-in replacement for OpenAI API
-- **Local models** - Run GGUF models via llama.cpp
-- **Cloud models** - Proxy to OpenAI, Anthropic, and others
-- **Privacy-first** - Local models never send data externally
-- **No vendor lock-in** - Switch between providers seamlessly
-
-## Quick Start
-
-Start the server in **Settings > Local API Server** and make requests to `http://localhost:1337/v1`:
-
-```bash
-curl http://localhost:1337/v1/chat/completions \
- -H "Content-Type: application/json" \
- -H "Authorization: Bearer YOUR_API_KEY" \
- -d '{
- "model": "MODEL_ID",
- "messages": [{"role": "user", "content": "Hello!"}]
- }'
-```
-
-## Documentation
-
-- [**API Reference**](/api) - Interactive API documentation with Try It Out
-- [**API Configuration**](./api-server) - Server settings, authentication, CORS
-- [**Engine Settings**](./llama-cpp) - Configure llama.cpp for your hardware
-- [**Server Settings**](./settings) - Advanced configuration options
-
-
-
-## Integration Examples
-
-### Continue (VS Code)
-```json
-{
- "models": [{
- "title": "Jan",
- "provider": "openai",
- "baseURL": "http://localhost:1337/v1",
- "apiKey": "YOUR_API_KEY",
- "model": "MODEL_ID"
- }]
-}
-```
-
-### Python (OpenAI SDK)
-```python
-from openai import OpenAI
-
-client = OpenAI(
- base_url="http://localhost:1337/v1",
- api_key="YOUR_API_KEY"
-)
-
-response = client.chat.completions.create(
- model="MODEL_ID",
- messages=[{"role": "user", "content": "Hello!"}]
-)
-```
-
-### JavaScript/TypeScript
-```javascript
-const response = await fetch('http://localhost:1337/v1/chat/completions', {
- method: 'POST',
- headers: {
- 'Content-Type': 'application/json',
- 'Authorization': 'Bearer YOUR_API_KEY'
- },
- body: JSON.stringify({
- model: 'MODEL_ID',
- messages: [{ role: 'user', content: 'Hello!' }]
- })
-});
-```
-
-## Supported Endpoints
-
-| Endpoint | Description |
-|----------|-------------|
-| `/v1/chat/completions` | Chat completions (streaming supported) |
-| `/v1/models` | List available models |
-| `/v1/models/{id}` | Get model information |
-
-
-
-## Why Use Jan's API?
-
-**Privacy** - Your data stays on your machine with local models
-**Cost** - No API fees for local model usage
-**Control** - Choose your models, parameters, and hardware
-**Flexibility** - Mix local and cloud models as needed
-
-## Related Resources
-
-- [Models Overview](/docs/jan/manage-models) - Available models
-- [Data Storage](/docs/jan/data-folder) - Where Jan stores data
-- [Troubleshooting](/docs/jan/troubleshooting) - Common issues
-- [GitHub Repository](https://github.com/janhq/jan) - Source code
diff --git a/website/src/content/docs/local-server/integrations/continue-dev.mdx b/website/src/content/docs/local-server/integrations/continue-dev.mdx
deleted file mode 100644
index 6be87d943..000000000
--- a/website/src/content/docs/local-server/integrations/continue-dev.mdx
+++ /dev/null
@@ -1,97 +0,0 @@
----
-title: Continue.dev
-description: A step-by-step guide on integrating Jan with Continue and VS Code.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- Continue integration,
- VSCode integration,
- ]
----
-
-import { Tabs, TabItem } from '@astrojs/starlight/components';
-
-## Integrate with Continue VS Code
-
-[Continue](https://continue.dev/docs/intro) is an open-source autopilot compatible with Visual Studio Code and JetBrains, offering the simplest method to code with any LLM (Local Language Model).
-
-To integrate Jan with a local AI language model, follow the steps below:
-
-1. **Installing Continue on Visual Studio Code**
- - Follow this [guide](https://continue.dev/docs/quickstart) to install the Continue extension on Visual Studio Code.
-2. **Enable the Jan API Server**
- To set up Continue for use with Jan's Local Server, you must activate the Jan API Server with your chosen model.
- 1. Press the `โ๏ธ Settings` button.
- 2. Locate `Local API Server`.
- 3. Setup the server, which includes the **IP Port**, **Cross-Origin-Resource-Sharing (CORS)** and **Verbose Server Logs**.
- 4. Include your user-defined API Key.
- 5. Press the **Start Server** button
-3. **Configure Continue to Use Jan's Local Server**
- 1. Go to the `~/.continue` directory.
-
-
- ```bash
- cd ~/.continue
- ```
-
-
- ```bash
- C:/Users//.continue
- ```
-
-
- ```bash
- cd ~/.continue
- ```
-
-
-
- ```yaml title="~/.continue/config.yaml"
- name: Local Assistant
- version: 1.0.0
- schema: v1
- models:
- - name: Jan
- provider: openai
- model: #MODEL_NAME (e.g. qwen3:0.6b)
- apiKey: #YOUR_USER_DEFINED_API_KEY_HERE (e.g. hello)
- apiBase: http://localhost:1337/v1
- context:
- - provider: code
- - provider: docs
- - provider: diff
- - provider: terminal
- - provider: problems
- - provider: folder
- - provider: codebase
- ```
- 2. Ensure the file has the following configurations:
- - Ensure `openai` is selected as the `provider`.
- - Match the `model` with the one enabled in the Jan API Server.
- - Set `apiBase` to `http://localhost:1337/v1`.
-4. **Ensure the Using Model Is Activated in Jan**
- 1. Navigate to `Settings` > `Model Providers`.
- 2. Under Llama.cpp, find the model that you would want to use.
- 3. Select the **Start Model** button to activate the model.
-
-## Use Jan with Continue in Visual Studio Code
-
-### 1. Exploring Code with Jan
-
-1. Highlight a code.
-2. Press `Command + Shift + M` to open the Left Panel.
-3. Click "Jan" at the bottom of the panel and submit your query, such as `Explain this code`.
-
-### 2. Enhancing Code with the Help of a Large Language Model
-
-1. Select a code snippet.
-2. Press `Command + Shift + L`.
-3. Type in your specific request, for example, `Add comments to this code`.
diff --git a/website/src/content/docs/local-server/integrations/llmcord.mdx b/website/src/content/docs/local-server/integrations/llmcord.mdx
deleted file mode 100644
index 4724e2d27..000000000
--- a/website/src/content/docs/local-server/integrations/llmcord.mdx
+++ /dev/null
@@ -1,63 +0,0 @@
----
-title: llmcord (Discord)
-description: A step-by-step guide on integrating Jan with a Discord bot.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- Discord integration,
- Discord,
- bot,
- ]
----
-
-import { Aside, Steps } from '@astrojs/starlight/components';
-
-## Integrate llmcord.py with Jan
-
-[llmcord.py](https://github.com/jakobdylanc/discord-llm-chatbot) lets you and your friends chat with LLMs directly in your Discord server.
-
-To integrate Jan with llmcord.py, follow the steps below:
-
-
-
-1. **Clone the Repository**
- Clone the discord bot's [repository](https://github.com/jakobdylanc/discord-llm-chatbot) by using the following command:
- ```bash
- git clone https://github.com/jakobdylanc/discord-llm-chatbot.git
- ```
-2. **Install the Required Libraries**
- After cloning the repository, run the following command:
- ```bash
- pip install -r requirements.txt
- ```
-
-3. **Set the Environment**
- 1. Create a copy of `.env.example`.
- 2. Change the name to `.env`.
- 3. Set the environment with the following options:
-
- | Setting | Instructions |
- | :----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
- | `DISCORD_BOT_TOKEN` | Create a new Discord bot at [discord.com/developers/applications](https://discord.com/developers/applications), obtain a token from the Bot tab, and enable MESSAGE CONTENT INTENT. |
- | `DISCORD_CLIENT_ID` | Found under the OAuth2 tab of the Discord bot you just made. |
- | `LLM` | For Jan, set to `local/openai/(MODEL_NAME)`, where `(MODEL_NAME)` is your loaded model's name. |
- | `LLM_SYSTEM_PROMPT` | Adjust the bot's behavior as needed. |
- | `LOCAL_SERVER_URL` | URL of your local API server. For Jan, set it to `http://localhost:1337/v1`. |
-
- For more configuration options, refer to llmcord.py's [README](https://github.com/jakobdylanc/discord-llm-chatbot/tree/main?tab=readme-ov-file#instructions).
-4. **Run the Bot**
- Run the bot by using the following command in your command prompt:
- ```bash
- python llmcord.py
- ```
- The bot's invite URL will be printed in the console. Use it to add the bot to your server.
-
-
diff --git a/website/src/content/docs/local-server/integrations/n8n.mdx b/website/src/content/docs/local-server/integrations/n8n.mdx
deleted file mode 100644
index 80b89a0b5..000000000
--- a/website/src/content/docs/local-server/integrations/n8n.mdx
+++ /dev/null
@@ -1,72 +0,0 @@
----
-title: n8n
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- n8n integration,
- n8n,
- ]
-description: A step-by-step guide on integrating Jan with n8n.
----
-
-import { Steps } from '@astrojs/starlight/components';
-
-## Integrate n8n with Jan
-
-[n8n](https://n8n.io/) is an open-source workflow automation tool that allows you to connect to more than 400+ integrations and services to automate repetitive tasks. With its visual interface, you can create complex workflows conveniently. To integrate n8n with Jan, follow the steps below:
-
-
-1. **Run your preferred model with Jan server**
- 1. Open Jan app.
- 2. Go to the **Hub** and download your preferred model
- 3. Run the Jan server
-2. **Start n8n service**
- Start n8n immediately using npx:
- ```sh
- npx n8n
- ```
-
- Or deploy with Docker:
- ```sh
- docker run -it --rm --name n8n -p 5678:5678 docker.n8n.io/n8nio/n8n
- ```
-3. **Integrate Jan with n8n service using HTTP Request**
- Integrate Jan by selecting the HTTP Request node in n8n and importing the following cURL command:
-
- ```bash
- curl -X 'POST' \
- 'http://127.0.0.1:1337/v1/chat/completions' \
- -H 'accept: application/json' \
- -H 'Content-Type: application/json' \
- -d '{
- "messages": [
- {
- "content": "You are a helpful assistant.",
- "role": "system"
- },
- {
- "content": "Hello!",
- "role": "user"
- }
- ],
- "model": "tinyllama-1.1b",
- "stream": true,
- "max_tokens": 2048,
- "stop": [
- "hello"
- ],
- "frequency_penalty": 0,
- "presence_penalty": 0,
- "temperature": 0.7,
- "top_p": 0.95
- }'
- ```
-
diff --git a/website/src/content/docs/local-server/integrations/tabby.mdx b/website/src/content/docs/local-server/integrations/tabby.mdx
deleted file mode 100644
index 1d69f5635..000000000
--- a/website/src/content/docs/local-server/integrations/tabby.mdx
+++ /dev/null
@@ -1,88 +0,0 @@
----
-title: Tabby
-description: A step-by-step guide on integrating Jan with Tabby and VSCode, JetBrains, or other IDEs.
-keywords:
- [
- Jan,
- Customizable Intelligence, LLM,
- local AI,
- privacy focus,
- free and open source,
- private and offline,
- conversational AI,
- no-subscription fee,
- large language models,
- Tabby integration,
- VSCode integration,
- JetBrains integration,
- ]
----
-
-import { Steps } from '@astrojs/starlight/components';
-
-## Integrate Jan with Tabby and Your Favorite IDEs
-
-[Tabby](https://www.tabbyml.com/) is an open-source, self-hosted AI coding assistant.
-With Tabby, teams can easily set up their own LLM-powered code completion server.
-
-Tabby provides integrations with VSCode, JetBrains, and other IDEs to help developers code more efficiently,
-and it can be used with various LLM services, including Jan.
-
-To integrate Jan with Tabby, follow these steps:
-
-
-
-1. **Enable the Jan API Server**
- To set up Tabby with Jan's Local Server, you must activate the Jan API Server with your chosen model.
- 1. Click the `Local API Server` (`<>`) button above the Settings. Jan will direct you to the **Local API Server** section.
- 2. Configure the server, including the **IP Port**, **Cross-Origin Resource Sharing (CORS)**, and **Verbose Server Logs**.
- 3. Press the **Start Server** button.
-2. **Find the Model ID and Ensure the Model is Activated**
- 1. Go to `Settings` > `My Models`.
- 2. Models are listed with their **Model ID** beneath their names.
- 3. Click the **three dots (โฎ)** button next to the model.
- 4. Select **Start Model** to activate the model.
-3. **Installing Tabby Server**
- Use the following documentation to install the Tabby server:
- - [Docker](https://tabby.tabbyml.com/docs/quick-start/installation/docker/)
- - [Apple Silicon](https://tabby.tabbyml.com/docs/quick-start/installation/apple/)
- - [Linux](https://tabby.tabbyml.com/docs/quick-start/installation/linux/)
- - [Windows](https://tabby.tabbyml.com/docs/quick-start/installation/windows/)
- Then, follow the steps to connect Jan with the Tabby server:
- [Connect Jan with Tabby](https://tabby.tabbyml.com/docs/references/models-http-api/jan.ai/).
- For example, to connect Jan with Tabby, save the following configuration under `~/.tabby/config.toml`:
-
- ```toml
- # ~/.tabby/config.toml
- [model.chat.http]
- kind = "openai/chat"
- model_name = "model_id"
- api_endpoint = "http://localhost:1337/v1"
- api_key = ""
- ```
- Currently, the Jan completion and embedding API is under construction.
- Once completed, you can also connect Jan with Tabby for completion and embedding tasks.
-
-4. **Installing Tabby on Your Favorite IDEs**
- Refer to the following documentation to install the Tabby extension on your favorite IDEs:
- - [Visual Studio Code](https://tabby.tabbyml.com/docs/extensions/installation/vscode/)
- - [JetBrains IntelliJ Platform](https://tabby.tabbyml.com/docs/extensions/installation/intellij/)
- - [VIM / NeoVIM](https://tabby.tabbyml.com/docs/extensions/installation/vim/)
-
-
-
-## How to Use Tabby with Jan Integration
-
-### Answer Engine: Chat with Your Codes and Documentation
-
-Tabby offers an [Answer Engine](https://tabby.tabbyml.com/docs/administration/answer-engine/) on the homepage,
-which can leverage the Jan LLM and related contexts like code, documentation, and web pages to answer user questions.
-
-Simply open the Tabby homepage at [localhost:8080](http://localhost:8080) and ask your questions.
-
-### IDE Chat Sidebar
-
-After installing the Tabby extension on your preferred IDEs, you can engage in a conversation with Jan to:
-
-1. Discuss your code, receive suggestions, and seek assistance.
-2. Request Jan to inline edit your code, and then review and accept the proposed changes.
diff --git a/website/src/content/docs/local-server/llama-cpp.mdx b/website/src/content/docs/local-server/llama-cpp.mdx
deleted file mode 100644
index 4263c80c3..000000000
--- a/website/src/content/docs/local-server/llama-cpp.mdx
+++ /dev/null
@@ -1,388 +0,0 @@
----
-title: llama.cpp Engine
-description: Configure Jan's local AI engine for optimal performance on your hardware.
-keywords:
- [
- Jan,
- local AI,
- llama.cpp,
- AI engine,
- local models,
- performance,
- GPU acceleration,
- CPU processing,
- model optimization,
- CUDA,
- Metal,
- Vulkan,
- ]
----
-
-import { Aside, Tabs, TabItem } from '@astrojs/starlight/components';
-
-## What is llama.cpp?
-
-llama.cpp is the core inference engine that powers Jan's ability to run AI models locally on your computer. Created by Georgi Gerganov, it's designed to run large language models efficiently on consumer hardware without requiring specialized AI accelerators or cloud connections.
-
-**Key benefits:**
-- Run models entirely offline after download
-- Use your existing hardware (CPU, GPU, or Apple Silicon)
-- Complete privacy - conversations never leave your device
-- No API costs or subscription fees
-
-## Accessing Engine Settings
-
-Navigate to **Settings** > **Model Providers** > **Llama.cpp**:
-
-
-
-
-
-## Engine Management
-
-| Feature | What It Does | When to Use |
-|---------|-------------|-------------|
-| **Engine Version** | Shows current llama.cpp version | Check when models require newer engine |
-| **Check Updates** | Downloads latest engine | Update for new model support or bug fixes |
-| **Backend Selection** | Choose hardware-optimized version | After hardware changes or performance issues |
-
-## Selecting the Right Backend
-
-Different backends are optimized for specific hardware. Choose the one that matches your system:
-
-
-
-
-### NVIDIA Graphics Cards
-Check your CUDA version in NVIDIA Control Panel, then select:
-
-**CUDA 12.0 (Most Common):**
-- `llama.cpp-avx2-cuda-12-0` - Modern CPUs with AVX2
-- `llama.cpp-avx512-cuda-12-0` - Newer Intel/AMD CPUs with AVX512
-- `llama.cpp-avx-cuda-12-0` - Older CPUs without AVX2
-
-**CUDA 11.7 (Older Drivers):**
-- `llama.cpp-avx2-cuda-11-7` - Modern CPUs
-- `llama.cpp-avx-cuda-11-7` - Older CPUs
-
-### CPU Only
-- `llama.cpp-avx2` - Most modern CPUs (2013+)
-- `llama.cpp-avx512` - High-end Intel/AMD CPUs
-- `llama.cpp-avx` - Older CPUs (2011-2013)
-- `llama.cpp-noavx` - Very old CPUs (pre-2011)
-
-### AMD/Intel Graphics
-- `llama.cpp-vulkan` - AMD Radeon, Intel Arc, Intel integrated
-
-
-
-
-
-
-
-### Apple Silicon (M1/M2/M3/M4)
-- `llama.cpp-mac-arm64` - Automatically uses GPU acceleration via Metal
-
-### Intel Macs
-- `llama.cpp-mac-amd64` - CPU-only processing
-
-
-
-
-
-
-
-### NVIDIA Graphics Cards
-- `llama.cpp-avx2-cuda-12-0` - CUDA 12.0+ with modern CPU
-- `llama.cpp-avx2-cuda-11-7` - CUDA 11.7+ with modern CPU
-
-### CPU Only
-- `llama.cpp-avx2` - x86_64 modern CPUs
-- `llama.cpp-avx512` - High-end Intel/AMD CPUs
-- `llama.cpp-arm64` - ARM processors (Raspberry Pi, etc.)
-
-### AMD/Intel Graphics
-- `llama.cpp-vulkan` - Open-source GPU acceleration
-
-
-
-
-## Performance Settings
-
-Configure how the engine processes requests:
-
-### Core Performance
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **Auto-update engine** | Automatically updates llama.cpp to latest version | Enabled | Disable if you need version stability |
-| **Auto-Unload Old Models** | Frees memory by unloading unused models | Disabled | Enable if switching between many models |
-| **Threads** | CPU cores for text generation (`-1` = all cores) | -1 | Reduce if you need CPU for other tasks |
-| **Threads (Batch)** | CPU cores for batch processing | -1 | Usually matches Threads setting |
-| **Context Shift** | Removes old text to fit new text in memory | Disabled | Enable for very long conversations |
-| **Max Tokens to Predict** | Maximum response length (`-1` = unlimited) | -1 | Set a limit to control response size |
-
-**Simple Analogy:** Think of threads like workers in a factory. More workers (threads) means faster production, but if you need workers elsewhere (other programs), you might want to limit how many the factory uses.
-
-### Batch Processing
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **Batch Size** | Logical batch size for prompt processing | 2048 | Lower if you have memory issues |
-| **uBatch Size** | Physical batch size for hardware | 512 | Match your GPU's capabilities |
-| **Continuous Batching** | Process multiple requests at once | Enabled | Keep enabled for efficiency |
-
-**Simple Analogy:** Batch size is like the size of a delivery truck. A bigger truck (batch) can carry more packages (tokens) at once, but needs a bigger garage (memory) and more fuel (processing power).
-
-### Multi-GPU Settings
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **GPU Split Mode** | How to divide model across GPUs | Layer | Change only with multiple GPUs |
-| **Main GPU Index** | Primary GPU for processing | 0 | Select different GPU if needed |
-
-**When to tweak:** Only adjust if you have multiple GPUs and want to optimize how the model is distributed across them.
-
-## Memory Configuration
-
-Control how models use system and GPU memory:
-
-### Memory Management
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **Flash Attention** | Optimized memory usage for attention | Enabled | Disable only if having stability issues |
-| **Disable mmap** | Turn off memory-mapped file loading | Disabled | Enable if experiencing crashes |
-| **MLock** | Lock model in RAM (no swap to disk) | Disabled | Enable if you have plenty of RAM |
-| **Disable KV Offload** | Keep conversation memory on CPU | Disabled | Enable if GPU memory is limited |
-
-**Simple Analogy:** Think of your computer's memory like a desk workspace:
-- **mmap** is like keeping reference books open to specific pages (efficient)
-- **mlock** is like gluing papers to your desk so they can't fall off (uses more space but faster access)
-- **Flash Attention** is like using sticky notes instead of full pages (saves space)
-
-### KV Cache Configuration
-
-| Setting | What It Does | Options | When to Adjust |
-|---------|-------------|---------|----------------|
-| **KV Cache K Type** | Precision for "keys" in memory | f16, q8_0, q4_0 | Lower precision saves memory |
-| **KV Cache V Type** | Precision for "values" in memory | f16, q8_0, q4_0 | Lower precision saves memory |
-| **KV Cache Defragmentation Threshold** | When to reorganize memory (0.1 = 10% fragmented) | 0.1 | Increase if seeing memory errors |
-
-**Memory Precision Guide:**
-- **f16** (default): Full quality, uses most memory - like HD video
-- **q8_0**: Good quality, moderate memory - like standard video
-- **q4_0**: Acceptable quality, least memory - like compressed video
-
-**When to adjust:** Start with f16. If you run out of memory, try q8_0. Only use q4_0 if absolutely necessary.
-
-## Advanced Settings
-
-### RoPE (Rotary Position Embeddings)
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **RoPE Scaling Method** | How to extend context length | None | For contexts beyond model's training |
-| **RoPE Scale Factor** | Context extension multiplier | 1 | Increase for longer contexts |
-| **RoPE Frequency Base** | Base frequency (0 = auto) | 0 | Leave at 0 unless specified |
-| **RoPE Frequency Scale Factor** | Frequency adjustment | 1 | Advanced users only |
-
-**Simple Analogy:** RoPE is like the model's sense of position in a conversation. Imagine reading a book:
-- **Normal**: You remember where you are on the page
-- **RoPE Scaling**: Like using a magnifying glass to fit more words on the same page
-- Scaling too much can make the text (context) blurry (less accurate)
-
-**When to use:** Only adjust if you need conversations longer than the model's default context length and understand the quality tradeoffs.
-
-### Mirostat Sampling
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **Mirostat Mode** | Alternative text generation method | Disabled | Try for more consistent output |
-| **Mirostat Learning Rate** | How quickly it adapts (eta) | 0.1 | Lower = more stable |
-| **Mirostat Target Entropy** | Target randomness (tau) | 5 | Lower = more focused |
-
-**Simple Analogy:** Mirostat is like cruise control for text generation:
-- **Regular sampling**: You manually control speed (randomness) with temperature
-- **Mirostat**: Automatically adjusts to maintain consistent "speed" (perplexity)
-- **Target Entropy**: Your desired cruising speed
-- **Learning Rate**: How quickly the cruise control adjusts
-
-**When to use:** Enable Mirostat if you find regular temperature settings produce inconsistent results. Start with defaults and adjust tau (3-7 range) for different styles.
-
-### Structured Output
-
-| Setting | What It Does | Default | When to Adjust |
-|---------|-------------|---------|----------------|
-| **Grammar File** | BNF grammar to constrain output | None | For specific output formats |
-| **JSON Schema File** | JSON schema to enforce structure | None | For JSON responses |
-
-**Simple Analogy:** These are like templates or forms the model must fill out:
-- **Grammar**: Like Mad Libs - the model can only put words in specific places
-- **JSON Schema**: Like a tax form - specific fields must be filled with specific types of data
-
-**When to use:** Only when you need guaranteed structured output (like JSON for an API). Most users won't need these.
-
-## Quick Optimization Guide
-
-### For Best Performance
-1. **Enable**: Flash Attention, Continuous Batching
-2. **Set Threads**: -1 (use all CPU cores)
-3. **Batch Size**: Keep defaults (2048/512)
-
-### For Limited Memory
-1. **Enable**: Auto-Unload Models, Flash Attention
-2. **KV Cache**: Set both to q8_0 or q4_0
-3. **Reduce**: Batch Size to 512/128
-
-### For Long Conversations
-1. **Enable**: Context Shift
-2. **Consider**: RoPE scaling (with quality tradeoffs)
-3. **Monitor**: Memory usage in System Monitor
-
-### For Multiple Models
-1. **Enable**: Auto-Unload Old Models
-2. **Disable**: MLock (saves RAM)
-3. **Use**: Default memory settings
-
-## Troubleshooting Settings
-
-**Model crashes or errors:**
-- Disable mmap
-- Reduce Batch Size
-- Switch KV Cache to q8_0
-
-**Out of memory:**
-- Enable Auto-Unload
-- Reduce KV Cache precision
-- Lower Batch Size
-
-**Slow performance:**
-- Check Threads = -1
-- Enable Flash Attention
-- Verify GPU backend is active
-
-**Inconsistent output:**
-- Try Mirostat mode
-- Adjust temperature in model settings
-- Check if Context Shift is needed
-
-## Model-Specific Settings
-
-Each model can override engine defaults. Access via the gear icon next to any model:
-
-
-
-| Setting | What It Controls | Impact |
-|---------|-----------------|---------|
-| **Context Length** | Conversation history size | Higher = more memory usage |
-| **GPU Layers** | Model layers on GPU | Higher = faster but more VRAM |
-| **Temperature** | Response randomness | 0.1 = focused, 1.0 = creative |
-| **Top P** | Token selection pool | Lower = more focused responses |
-
-
-
-## Troubleshooting
-
-### Models Won't Load
-1. **Wrong backend:** Try CPU-only backend first (`avx2` or `avx`)
-2. **Insufficient memory:** Check RAM/VRAM requirements
-3. **Outdated engine:** Update to latest version
-4. **Corrupted download:** Re-download the model
-
-### Slow Performance
-1. **No GPU acceleration:** Verify correct CUDA/Vulkan backend
-2. **Too few GPU layers:** Increase in model settings
-3. **CPU bottleneck:** Check thread count matches cores
-4. **Memory swapping:** Reduce context size or use smaller model
-
-### Out of Memory
-1. **Reduce quality:** Switch KV Cache to q8_0 or q4_0
-2. **Lower context:** Decrease context length in model settings
-3. **Fewer layers:** Reduce GPU layers
-4. **Smaller model:** Use quantized versions (Q4 vs Q8)
-
-### Crashes or Instability
-1. **Backend mismatch:** Use more stable variant (avx vs avx2)
-2. **Driver issues:** Update GPU drivers
-3. **Overheating:** Monitor temperatures, improve cooling
-4. **Power limits:** Check PSU capacity for high-end GPUs
-
-## Performance Benchmarks
-
-Typical performance with different configurations:
-
-| Hardware | Model Size | Backend | Tokens/sec |
-|----------|------------|---------|------------|
-| RTX 4090 | 7B Q4 | CUDA 12 | 80-120 |
-| RTX 3070 | 7B Q4 | CUDA 12 | 40-60 |
-| M2 Pro | 7B Q4 | Metal | 30-50 |
-| Ryzen 9 | 7B Q4 | AVX2 | 10-20 |
-
-
-
-## Advanced Configuration
-
-### Custom Compilation
-
-For maximum performance, compile llama.cpp for your specific hardware:
-
-```bash
-# Clone and build with specific optimizations
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-
-# Examples for different systems
-make LLAMA_CUDA=1 # NVIDIA GPUs
-make LLAMA_METAL=1 # Apple Silicon
-make LLAMA_VULKAN=1 # AMD/Intel GPUs
-```
-
-### Environment Variables
-
-Fine-tune behavior with environment variables:
-
-```bash
-# Force specific GPU
-export CUDA_VISIBLE_DEVICES=0
-
-# Thread tuning
-export OMP_NUM_THREADS=8
-
-# Memory limits
-export GGML_CUDA_NO_PINNED=1
-```
-
-## Best Practices
-
-**For Beginners:**
-1. Use default settings
-2. Start with smaller models (3-7B parameters)
-3. Enable GPU acceleration if available
-
-**For Power Users:**
-1. Match backend to hardware precisely
-2. Tune memory settings for your VRAM
-3. Experiment with parallel slots for multi-tasking
-
-**For Developers:**
-1. Enable verbose logging for debugging
-2. Use consistent settings across deployments
-3. Monitor resource usage during inference
-
-## Related Resources
-
-- [Model Parameters Guide](/docs/jan/explanation/model-parameters) - Fine-tune model behavior
-- [Troubleshooting Guide](/docs/jan/troubleshooting) - Detailed problem-solving
-- [Hardware Requirements](/docs/desktop/mac#compatibility) - System specifications
-- [API Server Settings](./api-server) - Configure the local API
diff --git a/website/src/content/docs/local-server/settings.mdx b/website/src/content/docs/local-server/settings.mdx
deleted file mode 100644
index a95691de6..000000000
--- a/website/src/content/docs/local-server/settings.mdx
+++ /dev/null
@@ -1,125 +0,0 @@
----
-title: Server Settings
-description: Configure advanced server settings for Jan's local API.
-keywords:
- [
- Jan,
- local server,
- settings,
- configuration,
- API server,
- performance,
- logging,
- ]
----
-
-import { Aside } from '@astrojs/starlight/components'
-
-This page covers server-specific settings for Jan's local API. For general Jan settings, see the main [Settings Guide](/docs/jan/settings).
-
-## Accessing Server Settings
-
-Navigate to **Settings** in Jan to configure server-related options.
-
-## Server Configuration
-
-### API Server Settings
-
-Configure the local API server at **Settings > Local API Server**:
-
-- **Host & Port** - Network binding configuration
-- **API Key** - Authentication for API requests
-- **CORS** - Cross-origin resource sharing
-- **Verbose Logging** - Detailed request/response logs
-
-See our [API Configuration Guide](./api-server) for complete details.
-
-### Engine Configuration
-
-Configure llama.cpp engine at **Settings > Model Providers > Llama.cpp**:
-
-- **Backend Selection** - Hardware-optimized versions
-- **Performance Settings** - Batching, threading, memory
-- **Model Defaults** - Context size, GPU layers
-
-See our [Engine Settings Guide](./llama-cpp) for optimization tips.
-
-## Logging & Monitoring
-
-### Server Logs
-
-Monitor API activity in real-time:
-
-1. Enable **Verbose Server Logs** in API settings
-2. View logs at **System Monitor** > **App Log**
-3. Filter by `[SERVER]` tags for API-specific events
-
-### Log Management
-
-- **Location**: Stored in [Jan Data Folder](/docs/jan/data-folder)
-- **Retention**: Automatically cleared after 24 hours
-- **Manual Clear**: Settings > Advanced > Clear Logs
-
-
-
-## Performance Tuning
-
-### Memory Management
-
-For optimal server performance:
-
-- **High Traffic**: Increase parallel slots in engine settings
-- **Limited RAM**: Reduce KV cache quality (q8_0 or q4_0)
-- **Multiple Models**: Enable model unloading after idle timeout
-
-### Network Configuration
-
-Advanced networking options:
-
-- **Local Only**: Use `127.0.0.1` (default, most secure)
-- **LAN Access**: Use `0.0.0.0` (allows network connections)
-- **Custom Port**: Change from default `1337` if conflicts exist
-
-## Security Considerations
-
-### API Authentication
-
-- Always set a strong API key
-- Rotate keys regularly for production use
-- Never expose keys in client-side code
-
-### Network Security
-
-- Keep server on `localhost` unless LAN access is required
-- Use firewall rules to restrict access
-- Consider VPN for remote access needs
-
-## Troubleshooting Server Issues
-
-### Common Problems
-
-**Server won't start:**
-- Check port availability (`netstat -an | grep 1337`)
-- Verify no other instances running
-- Try different port number
-
-**Connection refused:**
-- Ensure server is started
-- Check host/port configuration
-- Verify firewall settings
-
-**Authentication failures:**
-- Confirm API key matches configuration
-- Check Authorization header format
-- Ensure no extra spaces in key
-
-For more issues, see our [Troubleshooting Guide](/docs/jan/troubleshooting).
-
-## Related Resources
-
-- [API Configuration](./api-server) - Detailed API settings
-- [Engine Settings](./llama-cpp) - Hardware optimization
-- [Data Folder](/docs/jan/data-folder) - Storage locations
-- [Models Overview](/docs/jan/manage-models) - Model management
\ No newline at end of file
diff --git a/website/src/content/docs/mobile/index.mdx b/website/src/content/docs/mobile/index.mdx
deleted file mode 100644
index ed62d4b6a..000000000
--- a/website/src/content/docs/mobile/index.mdx
+++ /dev/null
@@ -1,35 +0,0 @@
----
-title: Jan Mobile
-description: Your AI assistant, on the go. Get ready for a seamless mobile experience with local and cloud capabilities.
-keywords:
- [
- Jan Mobile,
- Jan AI,
- mobile AI,
- local AI on phone,
- private AI app,
- iOS,
- Android,
- offline AI,
- ChatGPT alternative mobile
- ]
-banner:
- content: 'Coming Q4 2025: Jan Mobile is currently in development.'
----
-import { Aside, Card, CardGrid } from '@astrojs/starlight/components';
-
-## Your AI, Everywhere
-
-Jan Mobile brings the full power of a private, local-first AI to your iOS and Android devices. Connect to your home desktop, your company's server, or run models directly on your phone for complete offline privacy.
-
-
-
-The goal is a seamless experience that adapts to your environment without requiring you to change settings.
-
-### Core Features Planned:
-- **Three Connection Modes**: Seamlessly switch between Local, Desktop, and Server modes.
-- **Offline Capability**: Run `Jan Nano` or other small models directly on your device.
-- **Voice-First Interface**: Interact with your AI naturally through voice commands.
-- **Privacy by Design**: End-to-end encryption and full control over your data.
diff --git a/website/src/content/docs/server/index.mdx b/website/src/content/docs/server/index.mdx
deleted file mode 100644
index 6da7d4f7a..000000000
--- a/website/src/content/docs/server/index.mdx
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Jan Server
-description: Your self-hosted, private AI cloud for teams and enterprises.
-keywords:
- [
- Jan Server,
- Jan AI,
- self-hosted AI,
- private AI cloud,
- local LLM server,
- enterprise AI,
- Docker,
- Kubernetes,
- on-premise AI
- ]
-banner:
- content: 'Coming Q3 2025: Jan Server is currently in development.'
----
-import { Aside, Card } from '@astrojs/starlight/components';
-
-## Your Private AI Cloud
-
-Jan Server allows you to deploy a powerful, multi-user AI environment on your
-own infrastructure. It's designed for teams and enterprises that require full
-data control, privacy, and predictable costs without sacrificing performance.
-
-
-
-By self-hosting, you ensure that your sensitive data and intellectual property never leave your network.
-
-### Core Features Planned:
-- **Multi-User Management**: Control access with individual accounts and API keys.
-- **Enterprise Authentication**: Integrate with your existing SSO, LDAP, or AD.
-- **Flexible Deployment**: Deploy easily via Docker, Kubernetes, or on bare metal.
-- **Centralized Admin Dashboard**: Monitor usage, manage models, and oversee system health.
diff --git a/website/src/pages/api-reference.astro b/website/src/pages/api-reference.astro
deleted file mode 100644
index 81e7c3f87..000000000
--- a/website/src/pages/api-reference.astro
+++ /dev/null
@@ -1,22 +0,0 @@
-
-
-
-
-
- Redirecting to API Documentation | Jan
-
-
-
-
-
-
-
-
diff --git a/website/src/pages/api-reference/cloud.astro b/website/src/pages/api-reference/cloud.astro
deleted file mode 100644
index 7fb634e58..000000000
--- a/website/src/pages/api-reference/cloud.astro
+++ /dev/null
@@ -1,331 +0,0 @@
----
-import ApiReferenceLayout from '../../components/ApiReferenceLayout.astro'
-import ScalarApiReferenceMulti from '../../components/react/ScalarApiReferenceMulti.jsx'
-
-const title = 'Jan Server API Reference'
-const description = 'OpenAI-compatible API documentation for Jan Server powered by vLLM'
----
-
-
-
- Self-hostable Jan Server powered by vLLM for high-throughput serving
-
-
-
- Base URL:
- http://your-server:8000/v1
-
-
- Engine:
- vLLM
-
-
- Format:
- OpenAI Compatible
-
-
-
-
-
-
-
-
-
-
- Authentication Required: All requests to Jan Server require authentication.
- Include your API key in the Authorization header as Bearer YOUR_API_KEY.
- Configure authentication in your server settings.
-
-
-
-
-
-
-
-
-
High Performance
-
Powered by vLLM's PagedAttention for efficient memory usage and high throughput
-
-
-
-
-
-
Auto-Scaling
-
Automatically scales to handle your workload with intelligent load balancing
-
-
-
-
-
-
Multi-Model Support
-
Support for various model formats and sizes with optimized serving configurations
-
-
-
-
-
-
-
-
-
diff --git a/website/src/pages/api-reference/local.astro b/website/src/pages/api-reference/local.astro
deleted file mode 100644
index f860620e3..000000000
--- a/website/src/pages/api-reference/local.astro
+++ /dev/null
@@ -1,222 +0,0 @@
----
-import ApiReferenceLayout from '../../components/ApiReferenceLayout.astro'
-import ScalarApiReferenceMulti from '../../components/react/ScalarApiReferenceMulti.jsx'
-
-const title = 'Jan Local API Reference'
-const description = 'OpenAI-compatible API documentation for Jan running locally with llama.cpp'
----
-
-
-
- Run Jan locally on your machine with llama.cpp's high-performance inference engine
-
-
-
- Base URL:
- http://localhost:1337
-
-
- Engine:
- llama.cpp
-
-
- Format:
- OpenAI Compatible
-
-
-
-
-
-
-
-
-
-
- Getting Started: Make sure Jan is running locally on your machine.
- You can start the server by launching the Jan application or running the CLI command.
- Default port is 1337, but you can configure it in your settings.
-
-
-
-
-
-
-
-
-
diff --git a/website/src/pages/api.astro b/website/src/pages/api.astro
deleted file mode 100644
index 5f28fe3b3..000000000
--- a/website/src/pages/api.astro
+++ /dev/null
@@ -1,257 +0,0 @@
----
-import ApiReferenceLayout from '../components/ApiReferenceLayout.astro'
-
-const title = 'Jan API Documentation'
-const description = 'OpenAI-compatible API for local and server deployments'
----
-
-
-
-
-
๐Jan API Documentation
-
OpenAI-compatible API for local and server deployments