diff --git a/.github/workflows/jan-astro-docs.yml b/.github/workflows/jan-astro-docs.yml deleted file mode 100644 index 511b1cc96..000000000 --- a/.github/workflows/jan-astro-docs.yml +++ /dev/null @@ -1,75 +0,0 @@ -name: Deploy Astro Docs (v2) - -on: - push: - branches: - - dev - paths: - - 'website/**' - - '.github/workflows/jan-astro-docs.yml' - pull_request: - paths: - - 'website/**' - - '.github/workflows/jan-astro-docs.yml' - workflow_dispatch: - -jobs: - deploy: - name: Deploy to v2.jan.ai - env: - # IMPORTANT: You will need to create a new Cloudflare Pages project - # and name it "jan-v2" or update this value to your new project name. - CLOUDFLARE_PROJECT_NAME: jan-v2 - runs-on: ubuntu-latest - permissions: - contents: read - deployments: write - pull-requests: write - steps: - - name: Checkout repository - uses: actions/checkout@v4 - - - name: Setup Node.js - uses: actions/setup-node@v3 - with: - node-version: 20 - - - name: Install dependencies - working-directory: website - run: npm install - - - name: Build Astro Docs - working-directory: website - # No PUBLIC_BASE_PATH is set, so it builds for the root, which is correct for a subdomain - run: npm run build - - - name: Publish to Cloudflare Pages (PR Preview) - if: github.event_name == 'pull_request' - uses: cloudflare/pages-action@v1 - id: deployPreview - with: - apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }} - accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }} - projectName: ${{ env.CLOUDFLARE_PROJECT_NAME }} - directory: ./website/dist - gitHubToken: ${{ secrets.GITHUB_TOKEN }} - - - name: Add PR Comment with Preview URL - if: github.event_name == 'pull_request' - uses: mshick/add-pr-comment@v2 - with: - message: | - 🚀 Astro docs preview is ready! - URL: ${{ steps.deployPreview.outputs.url }} - - - name: Publish to Cloudflare Pages (Production) - if: (github.event_name == 'push' && github.ref == 'refs/heads/dev') || (github.event_name == 'workflow_dispatch' && github.ref == 'refs/heads/dev') - uses: cloudflare/pages-action@v1 - with: - apiToken: ${{ secrets.CLOUDFLARE_API_TOKEN }} - accountId: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }} - projectName: ${{ env.CLOUDFLARE_PROJECT_NAME }} - directory: ./website/dist - # This deploys to the production branch of your new Cloudflare project - branch: main - gitHubToken: ${{ secrets.GITHUB_TOKEN }} diff --git a/docs/src/pages/_meta.json b/docs/src/pages/_meta.json index 128c8c97b..5e43c5481 100644 --- a/docs/src/pages/_meta.json +++ b/docs/src/pages/_meta.json @@ -15,16 +15,16 @@ "type": "page", "title": "Products" }, - "local-server": { - "type": "page", - "title": "Jan Local Server", - "display": "hidden" - }, "cortex": { "type": "page", "title": "Cortex", "display": "hidden" }, + "platforms": { + "type": "page", + "title": "Platforms", + "display": "hidden" + }, "integrations": { "type": "page", "title": "Integrations", diff --git a/docs/src/pages/local-server/_assets/api-server-logs.png b/docs/src/pages/docs/_assets/api-server-logs.png similarity index 100% rename from docs/src/pages/local-server/_assets/api-server-logs.png rename to docs/src/pages/docs/_assets/api-server-logs.png diff --git a/docs/src/pages/docs/_assets/api-server-ui.png b/docs/src/pages/docs/_assets/api-server-ui.png new file mode 100644 index 000000000..828402bab Binary files /dev/null and b/docs/src/pages/docs/_assets/api-server-ui.png differ diff --git a/docs/src/pages/local-server/_assets/ls.png b/docs/src/pages/docs/_assets/ls.png similarity index 100% rename from docs/src/pages/local-server/_assets/ls.png rename to docs/src/pages/docs/_assets/ls.png diff --git a/docs/src/pages/docs/_meta.json b/docs/src/pages/docs/_meta.json index 3d3f8a8ca..3b78a2628 100644 --- a/docs/src/pages/docs/_meta.json +++ b/docs/src/pages/docs/_meta.json @@ -40,6 +40,15 @@ "mcp": "Model Context Protocol", "mcp-examples": "MCP Examples", + "localserver": { + "title": "LOCAL SERVER", + "type": "separator" + }, + "api-server": "Server Setup", + "llama-cpp-server": "LlamaCpp Server", + "server-settings": "Server Settings", + "server-troubleshooting": "Server Troubleshooting", + "server-examples": "Integrations", "reference-separator": { "title": "REFERENCE", "type": "separator" diff --git a/docs/src/pages/docs/api-server.mdx b/docs/src/pages/docs/api-server.mdx new file mode 100644 index 000000000..7f4e9874e --- /dev/null +++ b/docs/src/pages/docs/api-server.mdx @@ -0,0 +1,100 @@ +--- +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 { Callout, Steps } from 'nextra/components' + +# Local API Server + +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. + +![Jan's Local API Server Settings UI](./_assets/api-server-ui.png) + +## 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. + +## 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 + + +Ensure **Verbose Server Logs** are enabled to get detailed error messages in the "Server Logs" view. + + +- **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. \ No newline at end of file diff --git a/docs/src/pages/local-server/llama-cpp.mdx b/docs/src/pages/docs/llama-cpp-server.mdx similarity index 92% rename from docs/src/pages/local-server/llama-cpp.mdx rename to docs/src/pages/docs/llama-cpp-server.mdx index 2cddb3251..d89f48139 100644 --- a/docs/src/pages/local-server/llama-cpp.mdx +++ b/docs/src/pages/docs/llama-cpp-server.mdx @@ -21,7 +21,11 @@ import { Settings } from 'lucide-react' # Local AI Engine (llama.cpp) -llama.cpp is the engine that runs AI models locally on your computer. It's what makes Jan work without needing internet or cloud services. +`llama.cpp` is the core **inference engine** Jan uses to run AI models locally on your computer. This section covers the settings for the engine itself, which control *how* a model processes information on your hardware. + + +Looking for API server settings (like port, host, CORS)? They have been moved to the dedicated [**Local API Server**](/docs/local-server/api-server) page. + ## Accessing Engine Settings diff --git a/docs/src/pages/docs/llama-cpp.mdx b/docs/src/pages/docs/llama-cpp.mdx index 3274f48b7..4210b80df 100644 --- a/docs/src/pages/docs/llama-cpp.mdx +++ b/docs/src/pages/docs/llama-cpp.mdx @@ -155,7 +155,7 @@ These control how models use your computer's memory: ### KV Cache Types Explained - **f16**: Most stable, uses more memory -- **q8_0**: Balanced memory usage and quality +- **q8_0**: Balanced memory usage and quality - **q4_0**: Uses least memory, slight quality loss ## Troubleshooting Common Issues diff --git a/docs/src/pages/local-server/continue-dev.mdx b/docs/src/pages/docs/server-examples/continue-dev.mdx similarity index 100% rename from docs/src/pages/local-server/continue-dev.mdx rename to docs/src/pages/docs/server-examples/continue-dev.mdx diff --git a/docs/src/pages/local-server/llmcord.mdx b/docs/src/pages/docs/server-examples/llmcord.mdx similarity index 100% rename from docs/src/pages/local-server/llmcord.mdx rename to docs/src/pages/docs/server-examples/llmcord.mdx diff --git a/docs/src/pages/local-server/n8n.mdx b/docs/src/pages/docs/server-examples/n8n.mdx similarity index 100% rename from docs/src/pages/local-server/n8n.mdx rename to docs/src/pages/docs/server-examples/n8n.mdx diff --git a/docs/src/pages/local-server/tabby.mdx b/docs/src/pages/docs/server-examples/tabby.mdx similarity index 100% rename from docs/src/pages/local-server/tabby.mdx rename to docs/src/pages/docs/server-examples/tabby.mdx diff --git a/docs/src/pages/local-server/settings.mdx b/docs/src/pages/docs/server-settings.mdx similarity index 88% rename from docs/src/pages/local-server/settings.mdx rename to docs/src/pages/docs/server-settings.mdx index 545531baa..80d2cc0b2 100644 --- a/docs/src/pages/local-server/settings.mdx +++ b/docs/src/pages/docs/server-settings.mdx @@ -164,41 +164,17 @@ Jan stores everything locally on your computer in standard file formats. This duplicates your data to the new location - your original files stay safe. -## Network Settings +## Local API Server -### HTTPS Proxy Setup +All settings for running Jan as a local, OpenAI-compatible server have been moved to their own dedicated page for clarity. -If you need to connect through a corporate network or want enhanced privacy: +This includes configuration for: +- Server Host and Port +- API Keys +- CORS (Cross-Origin Resource Sharing) +- Verbose Logging -1. **Enable** the proxy toggle -2. Enter your proxy details: -``` -http://:@: -``` - -**Example:** -``` -http://user:pass@proxy.company.com:8080 -``` - -![HTTPS Proxy](./_assets/settings-13.png) - - -Proxy connections may slow down model downloads but don't affect local model performance. - - -### SSL Certificate Handling - -**Ignore SSL Certificates:** Only enable this for: -- Corporate networks with internal certificates -- Development/testing environments -- Trusted network setups - -![Ignore SSL Certificates](./_assets/settings-14.png) - - -Only enable if you trust your network environment completely. - +[**Go to Local API Server Settings →**](/docs/local-server/api-server) ## Emergency Options diff --git a/docs/src/pages/local-server/troubleshooting.mdx b/docs/src/pages/docs/server-troubleshooting.mdx similarity index 100% rename from docs/src/pages/local-server/troubleshooting.mdx rename to docs/src/pages/docs/server-troubleshooting.mdx diff --git a/docs/src/pages/local-server/_assets/add_assistant.png 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- "title": "GETTING STARTED", - "type": "separator" - }, - "api-server": "Server Setup", - - "tutorials-separator": { - "title": "TUTORIALS", - "type": "separator" - }, - "continue-dev": "Continue.dev", - "llmcord": "LLMCord (Discord)", - "n8n": "n8n", - "tabby": "Tabby", - - "explanation-separator": { - "title": "EXPLANATION", - "type": "separator" - }, - "llama-cpp": "Local AI Engine", - "data-folder": "Jan Data Folder", - - "reference-separator": { - "title": "REFERENCE", - "type": "separator" - }, - "settings": "Server Settings", - "troubleshooting": "Troubleshooting" -} diff --git a/docs/src/pages/local-server/api-server.mdx b/docs/src/pages/local-server/api-server.mdx deleted file mode 100644 index c86ce1f9f..000000000 --- a/docs/src/pages/local-server/api-server.mdx +++ /dev/null @@ -1,142 +0,0 @@ ---- -title: Local API Server -description: Learn how to run Jan's local API server. - [ - Jan, - Customizable Intelligence, LLM, - local AI, - privacy focus, - free and open source, - private and offline, - conversational AI, - no-subscription fee, - large language models, - Jan Extensions, - Extensions, - ] ---- - -import { Callout, Steps } from 'nextra/components' -import { Settings, EllipsisVertical } from 'lucide-react' - -# Local API Server - -Jan includes a built-in API server that is compatible with OpenAI's API specification, allowing you to -interact with AI models through a local HTTP interface. This means you can use Jan as a drop-in replacement -for OpenAI's API, but running entirely on your computer. - -Jan uses **llama.cpp** as its core engine for running AI models. If you need a standalone API server without -Jan's desktop interface (for example, in server environments or for command-line usage), you can use it directly -as well after downloading it from [here](https://github.com/ggml-org/llama.cpp). - -
-![Local API Server](./_assets/api-server.png) -
- -## Start Server - - - -### Step 1: Start Server -1. Navigate to the **Local API Server** -2. Add an API Key (it can be anything) or fully configure the server at [Server Settings](/docs/api-server#server-settings) -3. Click **Start Server** button -4. Wait for the confirmation message in the logs panel, your server is ready when you see: `JAN API listening at: http://127.0.0.1:1337` -5. Make sure you add an API key, this can be anything you want, a word like "testing" or even a combination of numbers and letters. - -![Local API Server](./_assets/api-server2.png) - -### Step 2: Test Server -The easiest way to test your server is through the API Playground: -1. Click the **API Playground** button to open its testing interface -2. Select a model from the dropdown menu in Jan interface -3. Try a simple request -4. View the response in real-time -5. When you send requests from another app, you need to add the API key in the request headers. - -### Step 3: Use the API - -```sh -curl http://127.0.0.1:1337/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer testing-something" \ # here you need to add your API key - -d '{ - "model": "jan-nano-gguf", - "messages": [ - { - "role": "user", - "content": "Write a one-sentence bedtime story about a unicorn." - } - ] - }' - -``` - - - -## Server Settings - -#### Host Address Options -- **127.0.0.1 (Recommended)**: - - Only accessible from your computer - - Most secure option for personal use -- **0.0.0.0**: - - Makes server accessible from other devices on your network - - Use with caution and only when necessary - -#### Port Number -- Default: `1337` -- Can be any number between 1-65535 -- Avoid common ports (80, 443, 3000, 8080) that might be used by other applications - -#### API Prefix -- Default: `/v1` -- Defines the base path for all API endpoints -- Example: http://127.0.0.1:1337/v1/chat/completions - -#### Cross-Origin Resource Sharing (CORS) -CORS controls which websites can access your API, which is important for web applications running in browsers. - -**When to enable:** -- If you're building a web application that needs to access the API -- If you're using browser extensions - -**When to leave disabled:** -- If you're only using the API from your local applications -- If you're concerned about security - -#### Verbose Server Logs -Enable to show: -- Detailed information about each API request -- Error messages and debugging information -- Server status updates - -## Troubleshooting Guide - - -Enable **Verbose Server Logs** for detailed error messages. - - -**1. Common Issues** -- Confirm the server is running -- Check if model is successfully loaded in Jan -- Check if the port is already in use by another application -- Verify you have admin/sudo rights if needed -- Make sure your API endpoint matches your server settings. Example: Using `http://localhost:1337` when you set a different port. -- Make sure the model name in your API request matches exactly what's shown in Jan. Example: If you selected "Llama 3.2 1B Instruct Q8" in Jan, use `llama3.2-1b-instruct` in your API request. -- Verify your JSON request format is correct -- Verify firewall settings -- Look for detailed error messages in the logs -- Make sure you add an API key, this can be anything you want, a word like "testing" or even a combination of numbers and letters. -- Use the API Key in the request headers when sending requests from another app. - -**2. CORS Errors in Web Apps** -- Enable CORS in server settings if using from a webpage -- Verify the origin of the request -- Verify your web app's request URL matches the server address exactly -- Check browser console for specific error messages - -**3. Performance Issues** -- Monitor system resources (CPU, RAM, and GPU usage) -- Try to reduce the context length or `ngl` (number of GPU layers) -- Check for other resource-intensive applications diff --git a/docs/src/pages/local-server/data-folder.mdx b/docs/src/pages/local-server/data-folder.mdx deleted file mode 100644 index 7e3cd2169..000000000 --- a/docs/src/pages/local-server/data-folder.mdx +++ /dev/null @@ -1,179 +0,0 @@ ---- -title: Jan Data Folder -description: Understand where Jan stores your data and how to monitor server logs. -keywords: - [ - Jan, - local AI, - data folder, - logs, - server logs, - troubleshooting, - privacy, - local storage, - file structure, - ] ---- - -import { Tabs } from 'nextra/components' -import { Callout, Steps } from 'nextra/components' -import { Settings, FolderOpen } from 'lucide-react' - -# Jan Data Folder - -Jan stores all your data locally on your computer. No cloud storage, no external servers - everything stays on your machine. - -## Quick Access - -**Via Jan Interface:** -1. Go to Settings () > Advanced Settings -2. Click the folder icon - -![Open Jan Data Folder](./_assets/settings-11.png) - -**Via File Explorer:** - - - -```cmd -%APPDATA%\Jan\data -``` - - -```bash -~/Library/Application Support/Jan/data -``` - - -```bash -# Default installation -~/.config/Jan/data - -# Custom installation -$XDG_CONFIG_HOME/Jan/data -``` - - - -## Monitoring Server Logs - -When Jan's local server is running, you can monitor real-time activity in the logs folder: - -![API Server Logs](./_assets/api-server-logs.png) - -### Live Log Monitoring - -**Real-time logs show:** -- API requests and responses -- Model loading and inference activity -- Error messages and warnings -- Performance metrics -- Connection attempts from external applications - -**Accessing logs:** -- **In Jan**: System Monitor (footer) > App Log -- **File location**: `/logs/app.log` - -### Log Categories - -| Log Type | What It Shows | When It's Useful | -|----------|---------------|------------------| -| **[APP]** | Core application events | Startup issues, crashes, general errors | -| **[SERVER]** | API server activity | Connection problems, request failures | -| **[SPECS]** | Hardware information | Performance issues, compatibility problems | -| **[MODEL]** | Model operations | Loading failures, inference errors | - -## Data Structure - -``` -jan/ -├── assistants/ # AI personality settings -│ └── jan/ -│ └── assistant.json -├── engines/ # Engine configurations -│ └── llama.cpp/ -├── extensions/ # Add-on modules -│ └── extensions.json -├── logs/ # Server and application logs -│ └── app.log # Main log file -├── models/ # Downloaded AI models -│ └── huggingface.co/ -└── threads/ # Chat conversations - └── thread_id/ - ├── messages.jsonl - └── thread.json -``` - -## Key Folders Explained - -### `/logs/` - Server Activity Hub -Contains all application and server logs. Essential for troubleshooting and monitoring API activity. - -**What you'll find:** -- Real-time server requests -- Model loading status -- Error diagnostics -- Performance data - -### `/models/` - AI Model Storage -Where your downloaded models live. Each model includes: -- `model.gguf` - The actual AI model file -- `model.json` - Configuration and metadata - -### `/threads/` - Chat History -Every conversation gets its own folder with: -- `messages.jsonl` - Complete chat history -- `thread.json` - Thread metadata and settings - -### `/assistants/` - AI Personalities -Configuration files that define how your AI assistants behave, including their instructions and available tools. - -## Privacy & Security - -**Your data stays local:** -- No cloud backups or syncing -- Files stored in standard JSON/JSONL formats -- Complete control over your data -- Easy to backup or migrate - -**File permissions:** -- Only you and Jan can access these files -- Standard user-level permissions -- No elevated access required - - -When using cloud AI services through Jan, those conversations follow the cloud provider's data policies. Local model conversations never leave your computer. - - -## Common Tasks - -### Backup Your Data -Copy the entire Jan data folder to backup: -- All chat history -- Model configurations -- Assistant settings -- Extension data - -### Clear Chat History -Delete individual thread folders in `/threads/` or use Jan's interface to delete conversations. - -### Export Conversations -Thread files are in standard JSON format - readable by any text editor or compatible with other applications. - -### Troubleshooting Data Issues -1. Check `/logs/app.log` for error messages -2. Verify folder permissions -3. Ensure sufficient disk space -4. Restart Jan if files appear corrupted - -## Uninstalling Jan - -If you need to completely remove Jan and all data: - -**Keep data (reinstall later):** Just uninstall the application -**Remove everything:** Delete the Jan data folder after uninstalling - -Detailed uninstall guides: -- [macOS](/docs/desktop/mac#step-2-clean-up-data-optional) -- [Windows](/docs/desktop/windows#step-2-handle-jan-data) -- [Linux](/docs/desktop/linux#uninstall-jan) diff --git a/docs/src/pages/local-server/index.mdx b/docs/src/pages/local-server/index.mdx deleted file mode 100644 index d5e9c110a..000000000 --- a/docs/src/pages/local-server/index.mdx +++ /dev/null @@ -1,201 +0,0 @@ ---- -title: Jan Local Server -description: Run Jan as a local AI server with OpenAI-compatible API for building AI applications. -keywords: - [ - Jan, - local AI server, - OpenAI API, - local API, - AI development, - self-hosted AI, - offline AI, - privacy-focused AI, - API integration, - local LLM server, - ] ---- - -import { Callout } from 'nextra/components' -import FAQBox from '@/components/FaqBox' - -# Jan Local Server - -![Jan's Cover Image](./_assets/ls.png) - -Jan Local Server provides an OpenAI-compatible API that runs entirely on your computer. Build AI applications using familiar API patterns while keeping complete control over your data and models. - -## How It Works - -Jan runs a local server powered by [llama.cpp](https://github.com/ggerganov/llama.cpp) that provides an OpenAI-compatible API. By default, it runs at `https://localhost:1337` and works completely offline. - -**What this enables:** -- Connect development tools like [Continue](./continue-dev) and [Cline](https://cline.bot/) to Jan -- Build AI applications without cloud dependencies -- Use both local and cloud models through the same API -- Maintain full privacy for local model interactions - -## Key Features - -**Local AI Models** -- Download popular open-source models (Llama, Gemma, Qwen) from Hugging Face -- Import any GGUF files from your computer -- Run models completely offline - -**Cloud Integration** -- Connect to cloud services (OpenAI, Anthropic, Mistral, Groq) -- Use your own API keys -- Switch between local and cloud models seamlessly - -**Developer-Friendly** -- OpenAI-compatible API for easy integration -- Chat interface for testing and configuration -- Model parameter customization - -**Complete Privacy** -- All data stored locally -- No cloud dependencies for local models -- You control what data leaves your machine - -## Why Choose Jan? - -**Truly Open Source** -- Apache 2.0 license - no restrictions -- Community-driven development -- Full transparency - -**Local-First Design** -- Works 100% offline with local models -- Data stays on your machine -- No vendor lock-in - -**Flexible Model Support** -- Your choice of AI models -- Both local and cloud options -- Easy model switching - -**No Data Collection** -- We don't collect or sell user data -- Local conversations stay local -- [Read our Privacy Policy](./privacy) - - -Jan follows [local-first principles](https://www.inkandswitch.com/local-first) - your data belongs to you and stays on your device. - - -## Philosophy - -Jan is built to be **user-owned**. This means: -- **True open source** - Apache 2.0 license with no hidden restrictions -- **Local data storage** - following [local-first principles](https://www.inkandswitch.com/local-first) -- **Internet optional** - works completely offline -- **Free choice** - use any AI models you want -- **No surveillance** - we don't collect or sell your data - -Read more about our [philosophy](/about#philosophy). - -## Inspiration - -Jan draws inspiration from [Calm Computing](https://en.wikipedia.org/wiki/Calm_technology) and the Disappearing Computer - technology that works quietly in the background without demanding constant attention. - -## Built With - -Jan stands on the shoulders of excellent open-source projects: -- [llama.cpp](https://github.com/ggerganov/llama.cpp) - Local AI model inference -- [Scalar](https://github.com/scalar/scalar) - API documentation - -## Frequently Asked Questions - - - Jan is a privacy-focused AI assistant that runs locally on your computer. It's an alternative to ChatGPT, Claude, and other cloud-based AI tools, with optional cloud AI support when you want it. - - - - Download Jan, add a model (either download locally or add a cloud API key), and start chatting. Check our [Quick Start guide](/docs/quickstart) for detailed setup instructions. - - - - Jan works on all major operating systems: - - [macOS](/docs/desktop/mac#compatibility) - Intel and Apple Silicon - - [Windows](/docs/desktop/windows#compatibility) - x64 systems - - [Linux](/docs/desktop/linux) - Most distributions - - Jan supports various hardware: - - NVIDIA GPUs (CUDA acceleration) - - AMD GPUs (Vulkan support) - - Intel Arc GPUs (Vulkan support) - - Any GPU with Vulkan support - - CPU-only operation - - - - Jan prioritizes privacy through: - - **100% offline operation** with local models - - **Local data storage** - everything stays on your device - - **Open-source transparency** - you can verify what Jan does - - **No data collection** - we never see your conversations - - - When using cloud AI services, their privacy policies apply. Jan doesn't add any tracking. - - - All your files and chat history are stored locally in the [Jan Data Folder](./data-folder). See our complete [Privacy Policy](./privacy). - - - - **Local models:** - - Download optimized models from the [Jan Hub](/docs/manage-models) - - Import GGUF models from Hugging Face - - Use any compatible local model files - - **Cloud models:** - - OpenAI (GPT-4, ChatGPT) - - Anthropic (Claude) - - Mistral, Groq, and others - - Bring your own API keys - - - - Yes! Jan is completely free and open-source with no subscription fees. - - **What's free:** - - Jan application and all features - - Local model usage (once downloaded) - - Local server and API - - **What costs money:** - - Cloud model usage (you pay providers directly) - - We add no markup to cloud service costs - - - - Absolutely! Once you download a local model, Jan works completely offline with no internet connection needed. This is one of Jan's core features. - - - - **Get help:** - - Join our [Discord community](https://discord.gg/qSwXFx6Krr) - - Check the [Troubleshooting guide](./troubleshooting) - - Ask in [#🆘|jan-help](https://discord.com/channels/1107178041848909847/1192090449725358130) - - **Contribute:** - - Contribute on [GitHub](https://github.com/menloresearch/jan) - - No permission needed to submit improvements - - Help other users in Discord - - - - Yes! We fully support self-hosting. You can: - - Download Jan directly for personal use - - Fork the [GitHub repository](https://github.com/menloresearch/jan) - - Build from source - - Deploy on your own infrastructure - - - - "Just a Name" - we admit we're not great at marketing! 😄 - - - - Yes! We love hiring from our community. Check our open positions at [Careers](https://menlo.bamboohr.com/careers). - diff --git a/docs/src/pages/platforms/_meta.json b/docs/src/pages/platforms/_meta.json new file mode 100644 index 000000000..bfee4c12e --- /dev/null +++ b/docs/src/pages/platforms/_meta.json @@ -0,0 +1,9 @@ +{ + "-- Switcher": { + "type": "separator", + "title": "Switcher" + }, + "index": { + "display": "hidden" + } +} diff --git a/docs/src/pages/platforms/index.mdx b/docs/src/pages/platforms/index.mdx new file mode 100644 index 000000000..1ba0ff3a7 --- /dev/null +++ b/docs/src/pages/platforms/index.mdx @@ -0,0 +1,87 @@ +--- +title: Coming Soon +description: Exciting new features and platforms are on the way. Stay tuned for Jan Web, Jan Mobile, and our API Platform. +keywords: + [ + Jan, + Customizable Intelligence, LLM, + local AI, + privacy focus, + free and open source, + private and offline, + conversational AI, + no-subscription fee, + large language models, + coming soon, + Jan Web, + Jan Mobile, + API Platform, + ] +--- + +import { Callout } from 'nextra/components' + +
+
+

+ 🚀 Coming Soon +

+

+ We're working on the next stage of Jan - making our local assistant more powerful and available in more platforms. +

+
+ +
+
+
🌐
+

Jan Web

+

+ Access Jan directly from your browser with our powerful web interface +

+
+ +
+
📱
+

Jan Mobile

+

+ Take Jan on the go with our native mobile applications +

+
+ +
+
⚡
+

Jan Server

+

+ Integrate Jan's capabilities into your applications with our API +

+
+
+ + + **Stay Updated**: Follow our [GitHub repository](https://github.com/menloresearch/jan) and join our [Discord community](https://discord.com/invite/FTk2MvZwJH) for the latest updates on these exciting releases! + + +
+

What to Expect

+
+
+ ✓ +
+ Seamless Experience: Unified interface across all platforms +
+
+
+ ✓ +
+ Privacy First: Same privacy-focused approach you trust +
+
+
+ ✓ +
+ Developer Friendly: Robust APIs and comprehensive documentation +
+
+
+
+
diff --git a/docs/src/pages/products/_assets/jan-server.png b/docs/src/pages/products/_assets/jan-server.png new file mode 100644 index 000000000..e49499f01 Binary files /dev/null and b/docs/src/pages/products/_assets/jan-server.png differ diff --git a/website/src/assets/jan_desktop.png b/docs/src/pages/products/_assets/jan_desktop.png similarity index 100% rename from website/src/assets/jan_desktop.png rename to docs/src/pages/products/_assets/jan_desktop.png diff --git a/website/src/assets/jan_everywhere.png b/docs/src/pages/products/_assets/jan_everywhere.png similarity index 100% rename from website/src/assets/jan_everywhere.png rename to docs/src/pages/products/_assets/jan_everywhere.png diff --git a/website/src/assets/jan_mobile.png b/docs/src/pages/products/_assets/jan_mobile.png similarity index 100% rename from website/src/assets/jan_mobile.png rename to docs/src/pages/products/_assets/jan_mobile.png diff --git a/website/src/assets/jan_mobile2.png b/docs/src/pages/products/_assets/jan_mobile2.png similarity index 100% rename from website/src/assets/jan_mobile2.png rename to docs/src/pages/products/_assets/jan_mobile2.png diff --git a/website/src/assets/jan_mobile3.png b/docs/src/pages/products/_assets/jan_mobile3.png similarity index 100% rename from website/src/assets/jan_mobile3.png rename to docs/src/pages/products/_assets/jan_mobile3.png diff --git a/website/src/assets/jan_mobile4.png b/docs/src/pages/products/_assets/jan_mobile4.png similarity index 100% rename from website/src/assets/jan_mobile4.png rename to docs/src/pages/products/_assets/jan_mobile4.png diff --git a/website/src/assets/jan_web.png b/docs/src/pages/products/_assets/jan_web.png similarity index 100% rename from website/src/assets/jan_web.png rename to docs/src/pages/products/_assets/jan_web.png diff --git a/docs/src/pages/products/index.mdx b/docs/src/pages/products/index.mdx index 8e33feea0..9b2a2be7d 100644 --- a/docs/src/pages/products/index.mdx +++ b/docs/src/pages/products/index.mdx @@ -1,10 +1,14 @@ --- -title: Product Overview -description: AI that runs where you need it, how you need it. Jan is a full-stack, self-hostable AI solution. +title: Products +description: AI that runs where you need it, how you need it --- + import { Callout } from 'nextra/components' -Jan is moving from a local AI application to a complete full-stack AI solution that you can self-host. This includes models, applications, and tools that delight users and help them solve their problems. +# Products + +Jan is moving from a local AI application to a complete full-stack AI solution that you can self-host. This +includes models, applications, and tools that delights users and help them solve their problems. ## What We're Building @@ -13,91 +17,143 @@ Jan is moving from a local AI application to a complete full-stack AI solution t Unlike other AI assistants that do specific tasks with one model or have many models with a myriad of solutions, Jan provides: - Its own specialised models that are optimised at specific tasks like web-search, creative writing, and translation - Applications that work across all of your devices in an integrated way -- Tools that actually get things done +- Tools to help youget things done -## Two Modes, One Experience +## Two Modes + +### Local Mode -### Local (Incognito) Mode Run AI models entirely on your device, giving you complete privacy with no internet required. +![Jan Desktop](./_assets/jan_desktop.png) + ### Cloud Mode + Connect to more powerful models when needed - either self-hosted or via jan.ai. - - Users shouldn't need to understand models, APIs, or technical details. Just choose Local for privacy or Cloud for power. +![Jan Everywhere](./_assets/jan_everywhere.png) + + +Users shouldn't need to understand models, APIs, or technical details. Just choose Local for privacy or Cloud for power. ## Our Product Principles ### 1) It Just Works -1. Open Jan, start chatting -2. Onboarding is fully available but optional -3. Setting up an API key is optional -4. Selecting a local model is optional -5. Become a power user at your own pace, if you want to + +1. Open Jan, start chatting +2. Onboarding is fully available but optional +3. Setting up an API key is optional +4. Selecting a local model is optional +5. Become a power user at your own pace, if you want to We handle the complexity. ### 2) Cloud When Needed + Start completely locally and own your AI models. Add cloud capabilities only when you choose to. ### 3) Solve Problems, Not Settings -We help users get to answers quickly, not configuration options. Power users can dig deeper, but it's never required. + +We help users get to answers quickly and leave the configurations as optional. Power users can +dig deeper, but it's never required. ## Available on Every Device -
-
-

Jan Desktop

-

- **Available Now**
- Your personal AI workstation. Run models locally or connect to the cloud. Powers your other devices. -

- Learn more → -
-
-

Jan Web

-

- **Beta Launch Soon**
- Access Jan from any browser with no setup. The default cloud backend for mobile and team collaboration. -

- Learn more → -
-
-

Jan Mobile

-

- **Coming Q4 2025**
- Connect to your Desktop or Server, or run models like Jan Nano locally for a seamless on-the-go experience. -

- Learn more → -
-
-

Jan Server

-

- **Coming Q2 2025**
- A self-hosted solution for teams and enterprises. Your own private AI cloud with enterprise features. -

- Learn more → -
-
+### Jan Desktop -## Jan Mobile: Three Modes, One Experience +This is how Jan started and it has been available since day 1. -Jan Mobile brings the same AI experience to your phone. Connect to your desktop, your server, or run models locally. +Jan Desktop stives to be: + +> Your personal AI workstation that helps with your use cases and powers other devices. Run +models locally right away or bring an API key to connect to your favorite cloud-based models. + +**Key Features:** +- Runs models locally on your hardware +- GPU acceleration support +- Powers other devices via network connection +- Complete privacy and control +- Windows, macOS, and Linux support + +**Requirements:** +- Minimum 8GB RAM +- 10GB+ storage space +- Optional: NVIDIA GPU for acceleration + +### Jan Web + +**Status:** Beta Launch Soon + +![Jan Web](./_assets/jan_web.png) + +Web-based version of 👋 Jan with no setup required. Same default cloud mode for mobile and desktop users. + +**Key Features:** +- No installation needed +- Instant access from any browser +- Automatic updates and maintenance +- Default cloud backend for mobile apps +- Team collaboration features + - Share prompts + - Share workflows + - Collaborate on threads + - Share threads + +**Pricing:** +- Free for everyone +- Pro: + - Access our latest models + - Access other cloud providers, no need to bring their API keys +- Enterprise: + - Self-host or we host it for you + - Active support and SLAs + - SSO + - Team featues + +### Jan Mobile + +**Status:** Coming Q4 2025 + +Connect to Desktop/Server, run local mode with Jan Nano or our upcoming Jan v1, same experience everywhere. -### How It Works Jan Mobile adapts to your situation: -- **At Home:** Connect to your Jan Desktop over WiFi. `Your Phone → WiFi → Your Desktop → Response` -- **At Work:** Connect to your company Jan Server. `Your Phone → Internet → Company Server → Response` -- **On the Go:** Run Jan Nano on your phone or talk to your favorite cloud-based model. `Your Phone → Jan Nano (6GB) → Response` +At Home, you can connect to your Jan Desktop over WiFi -No configuration needed. It just works. +![Jan Mobile Home](./_assets/jan_mobile2.png) + +At Work, you can connect to your company Jan Server + +![Jan Mobile Work](./_assets/jan_mobile3.png) + +On the Go, you can run Jan Nano on your phone or talk to your favourite cloud-based model + +![Jan Mobile Go](./_assets/jan_mobile4.png) + +**Key Features:** +- iOS and Android support +- Three adaptive modes (Desktop, Server, Local) +- Voice-first interface +- Seamless device switching +- Jan Nano for on-device AI + +### Jan Server + +**Status:** Coming Q3 2025 + +Self-hosted solution or connect to Jan via API. + +**Key Features:** +- Docker and Kubernetes deployment +- Admin dashboard +- Team knowledge sharing + +**Deployment Options:** +- Docker compose: Single command setup +- Kubernetes: Enterprise scale +- Bare metal: Maximum control -### Key Features -- **Seamless Switching:** Move from home to office to airplane. One-click and Jan adapts immediately. -- **Voice First:** Talk to Jan naturally. Responses can be spoken too. -- **Sync Everything:** Conversations, settings, and preferences follow you across devices. ## What Makes Jan Different @@ -113,20 +169,21 @@ No configuration needed. It just works. Jan is actively developed with regular releases. Our development follows these key milestones: ### Current Focus -- **Jan Desktop:** Continuous improvements and model support -- **Jan Web:** Beta launch preparation -- **Model Development:** Jan Nano and Lucy optimization +- **Jan Desktop**: Continuous improvements and model support +- **Jan Web**: Beta launch preparation +- **Model Development**: Jan Nano optimization and v1 launch ### Next 6 Months -- Jan Web public beta -- Mobile app development -- Server deployment tools +- Jan Web public beta +- Mobile app development +- Server deployment tools ### Future Vision -- Complete AI Agent platform -- Advanced tool integration -- Enterprise features +- Complete full-stack AI solution +- Advanced tool integration +- Enterprise features -We're building AI that respects your choices. Run it locally for privacy, connect to cloud for power, or self-host for both. - \ No newline at end of file +We're building AI that respects your choices. Run it locally and power other app, connect to +cloud for power, or self-host for both. + diff --git a/docs/src/pages/products/models/jan-nano.mdx b/docs/src/pages/products/models/jan-nano.mdx index 308d0e958..6c3fb89d3 100644 --- a/docs/src/pages/products/models/jan-nano.mdx +++ b/docs/src/pages/products/models/jan-nano.mdx @@ -29,7 +29,3 @@ Jan Nano is a 4-billion parameter model designed for research and information re ## Philosophy Most models try to be encyclopedias. Jan Nano is a research assistant. It doesn't memorize the internet—it knows how to navigate it. - ---- - -[Download Jan Desktop](https://jan.ai/download) | [Model Details](https://huggingface.co/Menlo/Jan-nano) | [MCP Documentation](https://jan.ai/docs/mcp) diff --git a/docs/src/pages/products/models/jan-v1.mdx b/docs/src/pages/products/models/jan-v1.mdx index 786415b81..b2522b18d 100644 --- a/docs/src/pages/products/models/jan-v1.mdx +++ b/docs/src/pages/products/models/jan-v1.mdx @@ -8,7 +8,9 @@ import { Callout } from 'nextra/components' ## Our Foundational Model Family -Jan V1 is our in-house family of models designed to compete directly with leading models like GPT-4 and Claude. We're building powerful, general-purpose models from the ground up to solve real-world problems with a focus on efficiency and privacy. +Jan V1 is our in-house, still in training, family of models designed to compete directly with leading +models. We're building powerful, general-purpose models from the ground up to solve real-world problems +with a focus on efficiency and privacy. ### Planned Model Lineup @@ -17,14 +19,11 @@ Jan V1 is our in-house family of models designed to compete directly with leadin | Jan V1-7B | 4-8GB | Fast, efficient daily tasks | Coming Soon | | Jan V1-13B | 8-16GB | Balanced power and performance | Coming Soon | | Jan V1-70B | 40-64GB | Deep analysis, professional work | Coming Soon | -| Jan V1-180B | 100GB+ | Frontier research, complex tasks | Planned 2026 | +| Jan V1-2350B | 100GB+ | Frontier research, complex tasks | Planned 2026 | ### What to Expect - **Competitive Performance**: Aiming for results on par with leading closed-source models. - **Optimized for Local Use**: Efficient quantized versions for running on your own hardware. - **Privacy-Centric**: Trainable and runnable in your own environment, ensuring your data stays yours. - **Seamless Integration**: Designed to work perfectly within the Jan ecosystem. - ---- - -[Follow our development →](https://jan.ai/v1-updates) +- **Fine-tuning support**: Easy to adapt to specific tasks or domains. diff --git a/docs/src/pages/products/platforms/desktop.mdx b/docs/src/pages/products/platforms/desktop.mdx index 58228e71c..c1a8b9ec7 100644 --- a/docs/src/pages/products/platforms/desktop.mdx +++ b/docs/src/pages/products/platforms/desktop.mdx @@ -4,9 +4,12 @@ description: AI that runs on your computer, not someone else's. Your personal AI --- import { Callout, Tabs, Tab } from 'nextra/components' -This is how Jan started and it has been available since day 1. Jan Desktop strives to be: +This is how Jan started and it has been available since day 1. -> Your personal AI workstation that helps with our use cases and powers other devices. Run models locally right away or bring an API key to connect to your favorite cloud-based models. +Jan Desktop strives to be: + +> Your personal AI workstation that helps with your use cases and powers other devices. Run models +locally right away or bring an API key to connect to your favorite cloud-based models. Jan Desktop is where it all starts. Download it, open it, and start chatting. Your AI runs on your computer with zero setup required. @@ -19,7 +22,8 @@ Your conversations stay on your computer. No internet needed. Complete privacy. Connect to more powerful models when you need them. Your choice of provider. - First time opening Jan? It just works. No API keys, no model downloads, no settings required. + As of today, when you first open Jan you do have to download a model or connect to a cloud provider, + but that is about to change soon. ## What You Get @@ -31,7 +35,7 @@ Connect to more powerful models when you need them. Your choice of provider.

🛡️ Your Data Stays Yours

-

Everything stored in `~/jan`. No cloud backups unless you want them.

+

Everything stored in `~/.local/share/jan`. No cloud backups unless you want them.

🖥️ Powers Other Devices

@@ -72,7 +76,7 @@ Connect to more powerful models when you need them. Your choice of provider. ## Local Mode Features -- **Built-in Model:** Jan comes with a model that works immediately. No downloading, no waiting. +- **Select your favorite Model:** Jan allows you to download any GGUF model from the Hugging Face Hub. - **Smart Defaults:** Automatically uses your GPU if available and adjusts to your system's capabilities. - **Complete Privacy:** No telemetry by default, no account required, and no data leaves your machine. @@ -107,32 +111,11 @@ Your desktop can power AI across all your devices by automatically becoming a lo # Always running at localhost:1337 curl http://localhost:1337/v1/chat/completions \ -H "Content-Type: application/json" \ - -d '{"model": "default", "messages": [{"role": "user", "content": "Hello"}]}' + -d '{"model": "gemma3:3b", "messages": [{"role": "user", "content": "Hello"}]}' ``` -## Common Questions - -
- Do I need to download models? - No. Jan comes with a default model that works immediately. -
-
- Can I use it offline? - Yes. Local Mode works completely offline once installed. -
-
- How do I switch models? - Most users don't need to. Power users can explore the Model Hub. -
-
- Is it really private? - Yes. In Local Mode, nothing leaves your computer. -
- - - Having issues? Most problems are solved by restarting the app. If that doesn't work, check our [troubleshooting guide](/docs/troubleshooting). - ## The Bottom Line -Jan Desktop is AI that respects that your computer is YOUR computer, not a terminal to someone else's server. Just software that works for you. \ No newline at end of file +Jan Desktop is AI that respects that your computer is YOUR computer, not a terminal +to someone else's server. Just software that works for you. diff --git a/docs/src/pages/products/platforms/jan-ai.mdx b/docs/src/pages/products/platforms/jan-ai.mdx index 18ed8a6a8..5ae54eb89 100644 --- a/docs/src/pages/products/platforms/jan-ai.mdx +++ b/docs/src/pages/products/platforms/jan-ai.mdx @@ -4,16 +4,19 @@ description: Cloud AI that respects your privacy. Web-based access to Jan with n --- import { Callout } from 'nextra/components' -**Status:** Beta Launch Soon +![Jan Web](../_assets/jan_web.png) -Web-based version of Jan with no setup required. Same default cloud mode for mobile and desktop users. +**Status:** Beta Launch Soon 🚀 + +Web-based version of Jan with no setup required. Same default cloud mode for (soon) mobile and desktop users. ## What is Jan Web? -Jan Web is the cloud-hosted version of Jan that runs in your browser. No installation needed, instant access from any device, with the same AI experience you get locally. +Jan Web is the cloud-hosted version of Jan that runs in your browser. No installation needed, instant access +from any device, with the same AI experience you get locally. - Currently in beta development. Beta launch coming soon. + Currently in development with a beta launch coming soon. ## How It Works @@ -21,15 +24,15 @@ Jan Web is the cloud-hosted version of Jan that runs in your browser. No install

For Desktop Users

-

When you switch to Cloud Mode in Jan Desktop, it connects to Jan Web automatically.

+

You can sync conversations from Jan desktop to Jan Web.

For Mobile Users

-

Jan Web serves as the default cloud backend for mobile apps when not connected to your desktop.

+

Jan Web uses the same models you have access to on the go.

For Web Users

-

Visit jan.ai directly in your browser for instant access to AI without downloading anything.

+

Visit (soon) ask.jan.ai directly in your browser for instant access to AI without downloading anything.

Team Collaboration

@@ -44,29 +47,3 @@ Jan Web is the cloud-hosted version of Jan that runs in your browser. No install | **Free** | Free for everyone | $0 | | **Pro** | Access our latest models
Access other cloud providers without API keys | Coming Soon | | **Enterprise** | Self-host or we host it for you
Active support and SLAs
SSO integration
Team features | Contact Sales | - -## For Developers - -### API Access -```javascript -// Same API as local Jan -const response = await fetch('https://api.jan.ai/v1/chat/completions', { - headers: { 'Authorization': 'Bearer YOUR_KEY' }, - body: JSON.stringify({ - model: 'jan-nano', - messages: [{ role: 'user', content: 'Hello' }] - }) -}); -``` -### OpenAI Compatible -```python -# Just change the base URL -client = OpenAI( - base_url="https://api.jan.ai/v1", - api_key="your-jan-key" -) -``` - ---- - -[Join Beta Waitlist](https://jan.ai/beta) | [Contact Sales](https://jan.ai/enterprise) | [API Documentation](/docs/api) diff --git a/docs/src/pages/products/platforms/mobile.mdx b/docs/src/pages/products/platforms/mobile.mdx index 9b8c5f8fd..7709c81dd 100644 --- a/docs/src/pages/products/platforms/mobile.mdx +++ b/docs/src/pages/products/platforms/mobile.mdx @@ -4,6 +4,8 @@ description: Your AI assistant on the go. Seamlessly connect to local, desktop, --- import { Callout } from 'nextra/components' +![Jan Mobile](../_assets/jan_mobile.png) + **Status:** Coming Q4 2025 Jan Mobile brings the same AI experience to your phone. Connect to your desktop, your server, or run models locally. @@ -32,15 +34,11 @@ No connection? No problem. Run models like 'Jan Nano' directly on your phone for
**Sync Everything**
**iOS and Android**
**Adaptive Modes**
-
**Jan Nano Integration**
+
**Jan Models Integration**
## Development Status -- Core architecture completed -- Desktop/Server connection protocols implemented +- Core architecture in progress +- Desktop/Server connection protocols next - Jan Nano mobile optimization in progress -- Closed beta planned for Q3 2025 - ---- - -[Get Notified](https://jan.ai/mobile) | [Follow on Discord](https://discord.gg/jan) +- Beta launchplanned for Q4 2025 diff --git a/docs/src/pages/products/platforms/server.mdx b/docs/src/pages/products/platforms/server.mdx index 525ada752..f25baecb8 100644 --- a/docs/src/pages/products/platforms/server.mdx +++ b/docs/src/pages/products/platforms/server.mdx @@ -4,12 +4,18 @@ description: Your own private AI cloud. Self-hosted AI for teams and enterprises --- import { Callout } from 'nextra/components' +![Jan Server](../_assets/jan-server.png) + **Status:** Coming Q2 2025 -Jan Server is Jan Desktop with multi-user support. Deploy it on your hardware to create your own private AI cloud for your team or organization. +Jan Server is a powerful AI API platform with multi-user support that you can self-host. Deploy it on your +hardware to create your own private AI cloud for your team or organization, or run it at scale with Jan's +managed service. ## Why Organizations Need This -Jan Server gives you complete control over your AI infrastructure, ensuring total privacy, predictable costs, and compliance readiness. + +Jan Server gives you complete control over your AI infrastructure, ensuring total privacy, predictable +costs, and compliance readiness. ## Key Features
@@ -23,14 +29,10 @@ Jan Server gives you complete control over your AI infrastructure, ensuring tota ## Deployment Options - **Docker:** Single command setup -- **Kubernetes:** Enterprise scale and high availability +- **Kubernetes:** Scale with your needs - **Bare Metal:** Maximum control and performance ## Scaling Guidelines -- **Small Teams (5-10 users):** Single powerful GPU (e.g., RTX 4090) -- **Departments (10-50 users):** 2-4 GPU cluster nodes -- **Enterprise (50+ users):** DGX cluster or custom configurations - ---- - -[Join Early Access](https://jan.ai/server) | [Hardware Guide](/docs/server/hardware) +- **Small Teams (5-10 users):** Small GPU cluster +- **Departments (10-50 users):** 4-8 GPU cluster nodes +- **Enterprise (50+ users):** Multi-cluster setup with custom configurations diff --git a/docs/src/pages/products/tools/browseruse.mdx b/docs/src/pages/products/tools/browseruse.mdx index 548fd5779..a7c2850fe 100644 --- a/docs/src/pages/products/tools/browseruse.mdx +++ b/docs/src/pages/products/tools/browseruse.mdx @@ -8,12 +8,14 @@ import { Callout } from 'nextra/components' ## Let Jan Use Your Browser -'BrowserUse' is a native tool being developed for the Jan ecosystem that will allow the AI to securely control a browser to perform tasks, automate workflows, and interact with websites just like a human would. +Jan allows you to give your AI models control of your browser to accomplish tasks automate workflows, and interact with +websites just like you would. -Think of it as an integrated, privacy-first automation layer that turns Jan from a conversational AI into a powerful agent for action. +Think of it as an integrated, automation layer that turns Jan from a conversational AI into a powerful agent for action. ### Built on MCP -The tool will be implemented as a native **Model Context Protocol (MCP)** server within Jan, ensuring secure and standardized communication between the AI model and the browser. +The tool will be implemented as a native **Model Context Protocol (MCP)** server within Jan, ensuring secure and +standardized communication between the AI model and the browser. ### Planned Core Features: - **Secure Sessions** in an isolated, sandboxed environment. diff --git a/docs/src/pages/products/tools/deepresearch.mdx b/docs/src/pages/products/tools/deepresearch.mdx index f94030c91..f351a55ad 100644 --- a/docs/src/pages/products/tools/deepresearch.mdx +++ b/docs/src/pages/products/tools/deepresearch.mdx @@ -8,12 +8,16 @@ import { Callout } from 'nextra/components' ## Your Personal Research Analyst -'DeepResearch' is a planned native tool for Jan that transforms it into a powerful research agent. Give it a complex question, and it will autonomously browse, analyze, and synthesize information from numerous sources to deliver a comprehensive, structured report. +DeepResearch is a planned native tool for Jan that transforms it into a powerful research agent. Give +it a complex question, and it will autonomously browse, analyze, and synthesize information from numerous +sources to deliver a comprehensive, structured report. -Think of it as Jan's answer to the advanced research capabilities seen in **OpenAI's ChatGPT** and **Google's Gemini**, but built with privacy and user control at its core. +Think of it as Jan's answer to the advanced research capabilities seen in **OpenAI's ChatGPT** and +**Google's Gemini**, but built in the open and with user control at its core. ### How It Will Work -Unlike a simple web search that returns a list of links, 'DeepResearch' will understand your goal, create a research plan, execute it, and deliver a final, synthesized document with citations. +Unlike a simple web search that returns a list of links, DeepResearch will understand your goal, create +a research plan that you can edit, execute it, and deliver a final, synthesized document with citations. ### Planned Core Features: - **Autonomous Multi-Step Research** diff --git a/docs/theme.config.tsx b/docs/theme.config.tsx index 3c63fc16b..148079484 100644 --- a/docs/theme.config.tsx +++ b/docs/theme.config.tsx @@ -73,15 +73,15 @@ const config: DocsThemeConfig = { return (
{[ - { title: 'Jan', path: '/docs', Icon: LibraryBig }, + { title: 'Jan Desktop', path: '/docs', Icon: LibraryBig }, { - title: 'Jan Local Server', - path: '/local-server', + title: 'Jan Mobile', + path: '/platforms', Icon: BrainCircuit, }, // { title: 'Jan Mobile', path: '/platforms', Icon: Blocks }, { - title: 'Jan API Platform', + title: 'Jan Server', path: '/platforms', Icon: Computer, }, diff --git a/website/.gitignore b/website/.gitignore deleted file mode 100644 index 6240da8b1..000000000 --- a/website/.gitignore +++ /dev/null @@ -1,21 +0,0 @@ -# build output -dist/ -# generated types -.astro/ - -# dependencies -node_modules/ - -# logs -npm-debug.log* -yarn-debug.log* -yarn-error.log* -pnpm-debug.log* - - -# environment variables -.env -.env.production - -# macOS-specific files -.DS_Store diff --git a/website/.vscode/extensions.json b/website/.vscode/extensions.json deleted file mode 100644 index 22a15055d..000000000 --- a/website/.vscode/extensions.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "recommendations": ["astro-build.astro-vscode"], - "unwantedRecommendations": [] -} diff --git a/website/.vscode/launch.json b/website/.vscode/launch.json deleted file mode 100644 index d64220976..000000000 --- a/website/.vscode/launch.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "version": "0.2.0", - "configurations": [ - { - "command": "./node_modules/.bin/astro dev", - "name": "Development server", - "request": "launch", - "type": "node-terminal" - } - ] -} diff --git a/website/PRODUCTS_PAGE_README.MD b/website/PRODUCTS_PAGE_README.MD deleted file mode 100644 index e4a1188be..000000000 --- a/website/PRODUCTS_PAGE_README.MD +++ /dev/null @@ -1,306 +0,0 @@ -# Jan Products Page - Retro-Tech Design System - -A futuristic products showcase page that combines the nostalgic aesthetics of RadioShack's electronic components with PostHog's modern data visualization approach. This creates a unique "retro-tech" visual language that feels both familiar and cutting-edge. - -## Design Philosophy - -### RadioShack + PostHog = Retro-Tech -- **RadioShack Heritage**: Circuit board patterns, electronic components, terminal interfaces, amber/green CRT colors -- **PostHog Influence**: Clean data cards, status indicators, system metrics, dashboard layouts -- **Result**: A cohesive aesthetic that makes AI products feel like sophisticated electronic equipment - -### Color Palette -```css ---retro-green: #00ff41 /* Matrix green, primary brand */ ---retro-amber: #ffb000 /* Classic terminal amber */ ---retro-blue: #00bfff /* Processing/loading states */ ---retro-red: #ff0040 /* Error/warning states */ ---retro-cyan: #00ffff /* Accent highlights */ ---retro-magenta: #ff00ff /* Special effects */ -``` - -## Architecture - -### File Structure -``` -jan/website/src/ -├── pages/ -│ └── prods.astro # Main products page -├── components/ -│ ├── CircuitBackground.astro # Animated circuit patterns -│ ├── StatusIndicator.astro # System status components -│ ├── RetroCard.astro # Terminal-style cards -│ └── FloatingNav.astro # Floating navigation menu -├── layouts/ -│ └── Layout.astro # Base layout with GSAP -└── styles/ - └── retro-effects.css # Advanced visual effects -``` - -### Component Hierarchy -``` -Layout -└── Products Page - ├── FloatingNav (right-side navigation) - ├── Hero Section - │ └── CircuitBackground (animated) - ├── Table of Contents - ├── Models Section - │ └── RetroCard (circuit variant) - ├── Platforms Section - │ └── RetroCard (data/system variants) - ├── Tools Section - │ └── RetroCard (terminal variant) - └── Integration Section -``` - -## Components Deep Dive - -### CircuitBackground.astro -Creates animated circuit board patterns with electronic components. - -**Props:** -- `variant`: 'subtle' | 'prominent' | 'animated' -- `color`: 'green' | 'amber' | 'blue' | 'multi' -- `density`: 'low' | 'medium' | 'high' -- `animated`: boolean -- `opacity`: number (0-1) - -**Features:** -- Procedural circuit patterns -- Animated electronic components (LEDs, resistors, capacitors) -- Flowing data lines -- Responsive grid scaling - -### StatusIndicator.astro -Terminal-style system status indicators with pulsing animations. - -**Props:** -- `status`: 'active' | 'warning' | 'success' | 'error' | 'idle' -- `label`: string -- `pulse`: boolean -- `size`: 'small' | 'medium' | 'large' - -**Features:** -- Animated status dots with glow effects -- Color-coded status states -- Accessibility compliant -- Reduced motion support - -### RetroCard.astro -Terminal-inspired cards with scanlines, CRT effects, and interactive animations. - -**Props:** -- `variant`: 'terminal' | 'circuit' | 'data' | 'system' -- `color`: 'green' | 'amber' | 'blue' | 'red' | 'multi' -- `size`: 'small' | 'medium' | 'large' -- `glitch`: boolean -- `scanlines`: boolean -- `glow`: boolean -- `interactive`: boolean -- `status`: 'online' | 'offline' | 'error' | 'warning' | 'processing' - -**Features:** -- CRT monitor bezels with power indicators -- Animated scanlines and glitch effects -- Terminal-style headers with traffic light controls -- Interactive hover states with 3D transforms -- Corner brackets for retro-futuristic framing - -### FloatingNav.astro -Floating navigation with terminal interface and system monitoring. - -**Props:** -- `sections`: Array of navigation items with icons and status -- `position`: 'left' | 'right' -- `theme`: 'dark' | 'matrix' | 'circuit' -- `compact`: boolean - -**Features:** -- Terminal-style command prompt -- Real-time system status indicators -- Animated progress bars -- Circuit connection lines -- Smooth scrolling with GSAP integration - -## Animations & Effects - -### GSAP Integration -The page uses GSAP (GreenSock Animation Platform) for sophisticated animations: - -**Timeline Animations:** -- Hero section entrance with staggered reveals -- Section headers with typewriter effects -- Card appearances with 3D transforms - -**Scroll Triggers:** -- Elements animate into view as user scrolls -- Parallax effects on background elements -- Progressive content revelation - -**Interactive Animations:** -- Magnetic cursor effects on cards -- 3D hover transformations -- Ripple effects on clicks - -### CSS-Only Effects -Enhanced with pure CSS animations for performance: - -**Visual Effects:** -- CRT scanlines and monitor bezels -- Matrix-style character rain -- Glitch text distortions -- Neon glow and pulsing lights - -**Component Animations:** -- Electronic component blinking (LEDs) -- Data stream flowing effects -- Circuit trace pulsing -- Status indicator breathing - -## Performance Optimizations - -### Lazy Loading -- GSAP libraries loaded from CDN -- Animations initialized after DOM ready -- Circuit patterns use CSS transforms for GPU acceleration - -### Reduced Motion Support -```css -@media (prefers-reduced-motion: reduce) { - /* All animations disabled for accessibility */ -} -``` - -### Mobile Optimizations -- Responsive grid layouts -- Touch-friendly interactive elements -- Optimized animation performance -- Reduced visual effects on smaller screens - -## Accessibility Features - -### WCAG Compliance -- High contrast mode support -- Focus indicators for keyboard navigation -- Screen reader friendly markup -- Semantic HTML structure - -### Keyboard Navigation -- Tab order follows logical flow -- Escape key closes floating navigation -- Arrow keys for menu navigation -- Enter/Space for activation - -### Color Accessibility -- Sufficient contrast ratios maintained -- Status information not solely color-dependent -- Alternative text for decorative elements - -## Customization Guide - -### Adding New Sections -1. Add section to main page -2. Update FloatingNav sections array -3. Add corresponding GSAP animations -4. Update TOC links - -### Creating New Card Variants -```astro - - - -``` - -### Custom Circuit Patterns -```astro - -``` - -### Color Theme Customization -Override CSS custom properties: -```css -:root { - --retro-green: #your-color; - --retro-amber: #your-color; - /* etc. */ -} -``` - -## Dependencies - -### Required -- Astro ^5.6.1 -- GSAP (loaded from CDN) -- JetBrains Mono font - -### Optional Enhancements -- Sharp for image optimization -- Additional GSAP plugins for advanced effects - -## Browser Support - -### Fully Supported -- Chrome 88+ -- Firefox 85+ -- Safari 14+ -- Edge 88+ - -### Graceful Degradation -- Older browsers receive static layout -- CSS fallbacks for unsupported features -- Progressive enhancement approach - -## Development Commands - -```bash -# Start development server -npm run dev - -# Build for production -npm run build - -# Preview production build -npm run preview -``` - -## Future Enhancements - -### Planned Features -- Sound effects for interactions -- More electronic component animations -- Additional card variants -- Theme switching capability -- Advanced glitch effects - -### Performance Improvements -- WebGL circuit backgrounds -- Intersection Observer for animations -- Service worker for offline support -- Image lazy loading optimization - -## Contributing - -When adding new components or effects: - -1. Follow the retro-tech design language -2. Ensure accessibility compliance -3. Add reduced motion alternatives -4. Test on multiple devices -5. Document new props and features - ---- - -*Created with ❤️ and a healthy dose of 80s nostalgia* diff --git a/website/PRODUCTS_PAGE_README.md b/website/PRODUCTS_PAGE_README.md deleted file mode 100644 index 54299c01b..000000000 --- a/website/PRODUCTS_PAGE_README.md +++ /dev/null @@ -1,306 +0,0 @@ -# Jan Products Page - Retro-Tech Design System - -A futuristic products showcase page that combines the nostalgic aesthetics of RadioShack's electronic components with PostHog's modern data visualization approach. This creates a unique "retro-tech" visual language that feels both familiar and cutting-edge. - -## Design Philosophy - -### RadioShack + PostHog = Retro-Tech -- **RadioShack Heritage**: Circuit board patterns, electronic components, terminal interfaces, amber/green CRT colors -- **PostHog Influence**: Clean data cards, status indicators, system metrics, dashboard layouts -- **Result**: A cohesive aesthetic that makes AI products feel like sophisticated electronic equipment - -### Color Palette -```css ---retro-green: #00ff41 /* Matrix green, primary brand */ ---retro-amber: #ffb000 /* Classic terminal amber */ ---retro-blue: #00bfff /* Processing/loading states */ ---retro-red: #ff0040 /* Error/warning states */ ---retro-cyan: #00ffff /* Accent highlights */ ---retro-magenta: #ff00ff /* Special effects */ -``` - -## Architecture - -### File Structure -``` -jan/website/src/ -├── pages/ -│ └── prods.astro # Main products page -├── components/ -│ ├── CircuitBackground.astro # Animated circuit patterns -│ ├── StatusIndicator.astro # System status components -│ ├── RetroCard.astro # Terminal-style cards -│ └── FloatingNav.astro # Floating navigation menu -├── layouts/ -│ └── Layout.astro # Base layout with GSAP -└── styles/ - └── retro-effects.css # Advanced visual effects -``` - -### Component Hierarchy -``` -Layout -└── Products Page - ├── FloatingNav (right-side navigation) - ├── Hero Section - │ └── CircuitBackground (animated) - ├── Table of Contents - ├── Models Section - │ └── RetroCard (circuit variant) - ├── Platforms Section - │ └── RetroCard (data/system variants) - ├── Tools Section - │ └── RetroCard (terminal variant) - └── Integration Section -``` - -## Components Deep Dive - -### CircuitBackground.astro -Creates animated circuit board patterns with electronic components. - -**Props:** -- `variant`: 'subtle' | 'prominent' | 'animated' -- `color`: 'green' | 'amber' | 'blue' | 'multi' -- `density`: 'low' | 'medium' | 'high' -- `animated`: boolean -- `opacity`: number (0-1) - -**Features:** -- Procedural circuit patterns -- Animated electronic components (LEDs, resistors, capacitors) -- Flowing data lines -- Responsive grid scaling - -### StatusIndicator.astro -Terminal-style system status indicators with pulsing animations. - -**Props:** -- `status`: 'active' | 'warning' | 'success' | 'error' | 'idle' -- `label`: string -- `pulse`: boolean -- `size`: 'small' | 'medium' | 'large' - -**Features:** -- Animated status dots with glow effects -- Color-coded status states -- Accessibility compliant -- Reduced motion support - -### RetroCard.astro -Terminal-inspired cards with scanlines, CRT effects, and interactive animations. - -**Props:** -- `variant`: 'terminal' | 'circuit' | 'data' | 'system' -- `color`: 'green' | 'amber' | 'blue' | 'red' | 'multi' -- `size`: 'small' | 'medium' | 'large' -- `glitch`: boolean -- `scanlines`: boolean -- `glow`: boolean -- `interactive`: boolean -- `status`: 'online' | 'offline' | 'error' | 'warning' | 'processing' - -**Features:** -- CRT monitor bezels with power indicators -- Animated scanlines and glitch effects -- Terminal-style headers with traffic light controls -- Interactive hover states with 3D transforms -- Corner brackets for retro-futuristic framing - -### FloatingNav.astro -Floating navigation with terminal interface and system monitoring. - -**Props:** -- `sections`: Array of navigation items with icons and status -- `position`: 'left' | 'right' -- `theme`: 'dark' | 'matrix' | 'circuit' -- `compact`: boolean - -**Features:** -- Terminal-style command prompt -- Real-time system status indicators -- Animated progress bars -- Circuit connection lines -- Smooth scrolling with GSAP integration - -## Animations & Effects - -### GSAP Integration -The page uses GSAP (GreenSock Animation Platform) for sophisticated animations: - -**Timeline Animations:** -- Hero section entrance with staggered reveals -- Section headers with typewriter effects -- Card appearances with 3D transforms - -**Scroll Triggers:** -- Elements animate into view as user scrolls -- Parallax effects on background elements -- Progressive content revelation - -**Interactive Animations:** -- Magnetic cursor effects on cards -- 3D hover transformations -- Ripple effects on clicks - -### CSS-Only Effects -Enhanced with pure CSS animations for performance: - -**Visual Effects:** -- CRT scanlines and monitor bezels -- Matrix-style character rain -- Glitch text distortions -- Neon glow and pulsing lights - -**Component Animations:** -- Electronic component blinking (LEDs) -- Data stream flowing effects -- Circuit trace pulsing -- Status indicator breathing - -## Performance Optimizations - -### Lazy Loading -- GSAP libraries loaded from CDN -- Animations initialized after DOM ready -- Circuit patterns use CSS transforms for GPU acceleration - -### Reduced Motion Support -```css -@media (prefers-reduced-motion: reduce) { - /* All animations disabled for accessibility */ -} -``` - -### Mobile Optimizations -- Responsive grid layouts -- Touch-friendly interactive elements -- Optimized animation performance -- Reduced visual effects on smaller screens - -## Accessibility Features - -### WCAG Compliance -- High contrast mode support -- Focus indicators for keyboard navigation -- Screen reader friendly markup -- Semantic HTML structure - -### Keyboard Navigation -- Tab order follows logical flow -- Escape key closes floating navigation -- Arrow keys for menu navigation -- Enter/Space for activation - -### Color Accessibility -- Sufficient contrast ratios maintained -- Status information not solely color-dependent -- Alternative text for decorative elements - -## Customization Guide - -### Adding New Sections -1. Add section to main page -2. Update FloatingNav sections array -3. Add corresponding GSAP animations -4. Update TOC links - -### Creating New Card Variants -```astro - - - -``` - -### Custom Circuit Patterns -```astro - -``` - -### Color Theme Customization -Override CSS custom properties: -```css -:root { - --retro-green: #your-color; - --retro-amber: #your-color; - /* etc. */ -} -``` - -## Dependencies - -### Required -- Astro ^5.6.1 -- GSAP (loaded from CDN) -- JetBrains Mono font - -### Optional Enhancements -- Sharp for image optimization -- Additional GSAP plugins for advanced effects - -## Browser Support - -### Fully Supported -- Chrome 88+ -- Firefox 85+ -- Safari 14+ -- Edge 88+ - -### Graceful Degradation -- Older browsers receive static layout -- CSS fallbacks for unsupported features -- Progressive enhancement approach - -## Development Commands - -```bash -# Start development server -npm run dev - -# Build for production -npm run build - -# Preview production build -npm run preview -``` - -## Future Enhancements - -### Planned Features -- Sound effects for interactions -- More electronic component animations -- Additional card variants -- Theme switching capability -- Advanced glitch effects - -### Performance Improvements -- WebGL circuit backgrounds -- Intersection Observer for animations -- Service worker for offline support -- Image lazy loading optimization - -## Contributing - -When adding new components or effects: - -1. Follow the retro-tech design language -2. Ensure accessibility compliance -3. Add reduced motion alternatives -4. Test on multiple devices -5. Document new props and features - ---- - -*Created with ❤️ and a healthy dose of 80s nostalgia* \ No newline at end of file diff --git a/website/README.md b/website/README.md deleted file mode 100644 index cec200784..000000000 --- a/website/README.md +++ /dev/null @@ -1,28 +0,0 @@ -# Jan's Website - -This website is [built with Starlight](https://starlight.astro.build) - - -Starlight looks for `.md` or `.mdx` files in the `src/content/docs/` directory. Each file is exposed -as a route based on its file name. - -Images can be added to `src/assets/` and embedded in Markdown with a relative link. - -Static assets, like favicons, can be placed in the `public/` directory. - -If you want to add new pages, these can go in the `src/pages/` directory. Because of the topics plugin -we are using ([starlight sidebar topics](https://starlight-sidebar-topics.netlify.app/docs/guides/excluded-pages/)) -you will need to exclude them from the sidebar by adding them to the exclude list in `astro.config.mjs`, e.g., `exclude: ['/example'],`. - -## 🧞 Commands - -All commands are run from the root of the project, from a terminal: - -| Command | Action | -| :------------------------ | :----------------------------------------------- | -| `bun install` | Installs dependencies | -| `bun dev` | Starts local dev server at `localhost:4321` | -| `bun build` | Build your production site to `./dist/` | -| `bun preview` | Preview your build locally, before deploying | -| `bun astro ...` | Run CLI commands like `astro add`, `astro check` | -| `bun astro -- --help` | Get help using the Astro CLI | diff --git a/website/astro.config.mjs b/website/astro.config.mjs deleted file mode 100644 index c13290f1d..000000000 --- a/website/astro.config.mjs +++ /dev/null @@ -1,174 +0,0 @@ -// @ts-check -import { defineConfig } from 'astro/config' -import starlight from '@astrojs/starlight' -import starlightThemeRapide from 'starlight-theme-rapide' -import starlightSidebarTopics from 'starlight-sidebar-topics' -import mermaid from 'astro-mermaid' - -// https://astro.build/config -export default defineConfig({ - // Deploy to the new v2 subdomain - site: 'https://v2.jan.ai', - // No 'base' property is needed, as this will be deployed to the root of the subdomain. - integrations: [ - mermaid({ - theme: 'default', - autoTheme: true, - }), - starlight({ - title: '👋 Jan', - favicon: 'jan2.png', - plugins: [ - starlightThemeRapide(), - starlightSidebarTopics( - [ - { - label: 'Jan Desktop', - link: '/', - icon: 'rocket', - items: [ - { - label: 'HOW TO', - items: [ - { - label: 'Install 👋 Jan', - collapsed: false, - autogenerate: { directory: 'jan/installation' }, - }, - { label: 'Start Chatting', slug: 'jan/threads' }, - { - label: 'Use Jan Models', - collapsed: true, - autogenerate: { directory: 'jan/jan-models' }, - }, - { label: 'Assistants', slug: 'jan/assistants' }, - ], - }, - { - label: 'Cloud Providers', - items: [ - { label: 'Anthropic', slug: 'jan/remote-models/anthropic' }, - { label: 'OpenAI', slug: 'jan/remote-models/openai' }, - { label: 'Gemini', slug: 'jan/remote-models/google' }, - { - label: 'OpenRouter', - slug: 'jan/remote-models/openrouter', - }, - { label: 'Cohere', slug: 'jan/remote-models/cohere' }, - { label: 'Mistral', slug: 'jan/remote-models/mistralai' }, - { label: 'Groq', slug: 'jan/remote-models/groq' }, - ], - }, - { - label: 'EXPLANATION', - items: [ - { - label: 'Local AI Engine', - slug: 'jan/explanation/llama-cpp', - }, - { - label: 'Model Parameters', - slug: 'jan/explanation/model-parameters', - }, - ], - }, - { - label: 'ADVANCED', - items: [ - { label: 'Manage Models', slug: 'jan/manage-models' }, - { label: 'Model Context Protocol', slug: 'jan/mcp' }, - { - label: 'MCP Examples', - collapsed: true, - items: [ - { - label: 'Code Sandbox (E2B)', - slug: 'jan/mcp-examples/data-analysis/e2b', - }, - { - label: 'Web Search with Exa', - slug: 'jan/mcp-examples/search/exa', - }, - ], - }, - ], - }, - { - label: 'Local Server', - items: [ - { label: 'Server Setup', slug: 'local-server/api-server' }, - { - label: 'Jan Data Folder', - slug: 'local-server/data-folder', - }, - { label: 'Server Settings', slug: 'local-server/settings' }, - { - label: 'Llama.cpp Server', - slug: 'local-server/llama-cpp', - }, - { - label: 'Integrations', - collapsed: true, - autogenerate: { directory: 'local-server/integrations' }, - }, - ], - }, - { - label: 'REFERENCE', - items: [ - { label: 'Settings', slug: 'jan/settings' }, - { label: 'Jan Data Folder', slug: 'jan/data-folder' }, - { label: 'Troubleshooting', slug: 'jan/troubleshooting' }, - { label: 'Privacy Policy', slug: 'jan/privacy' }, - ], - }, - ], - }, - { - label: 'Jan Mobile', - link: '/mobile/', - badge: { text: 'Coming Soon', variant: 'caution' }, - icon: 'phone', - items: [{ label: 'Overview', slug: 'mobile' }], - }, - { - label: 'Jan Server', - link: '/server/', - badge: { text: 'Coming Soon', variant: 'caution' }, - icon: 'forward-slash', - items: [{ label: 'Overview', slug: 'server' }], - }, - ], - { - exclude: [ - '/prods', - '/api-reference', - '/products', - '/products/**/*', - ], - } - ), - ], - social: [ - { - icon: 'github', - label: 'GitHub', - href: 'https://github.com/menloresearch/jan', - }, - { - icon: 'x.com', - label: 'X', - href: 'https://twitter.com/jandotai', - }, - { - icon: 'discord', - label: 'Discord', - href: 'https://discord.com/invite/FTk2MvZwJH', - }, - ], - components: { - Header: './src/components/CustomNav.astro', - }, - }), - ], -}) diff --git a/website/bun.lock b/website/bun.lock deleted file mode 100644 index 9ec213e17..000000000 --- a/website/bun.lock +++ /dev/null @@ -1,1298 +0,0 @@ -{ - "lockfileVersion": 1, - "workspaces": { - "": { - "name": "website", - "dependencies": { - "@astrojs/starlight": "^0.35.1", - "@lorenzo_lewis/starlight-utils": "^0.3.2", - "astro": "^5.6.1", - "astro-mermaid": "^1.0.4", - "gsap": "^3.13.0", - "phosphor-astro": "^2.1.0", - "sharp": "^0.34.2", - "starlight-openapi": "^0.19.1", - "starlight-sidebar-topics": "^0.6.0", - "starlight-theme-rapide": "^0.5.1", - "starlight-videos": "^0.3.0", - 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"starlight-theme-rapide": "^0.5.1", - "starlight-videos": "^0.3.0" - } -} diff --git a/website/public/favicon.svg b/website/public/favicon.svg deleted file mode 100644 index cba5ac140..000000000 --- a/website/public/favicon.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/website/public/gifs/jan-nano-demo.gif b/website/public/gifs/jan-nano-demo.gif deleted file mode 100644 index a2b87619f..000000000 Binary files a/website/public/gifs/jan-nano-demo.gif and /dev/null differ diff --git a/website/public/jan.png b/website/public/jan.png deleted file mode 100644 index 21ec5b15f..000000000 Binary files a/website/public/jan.png and /dev/null differ diff --git a/website/public/jan2.png b/website/public/jan2.png deleted file mode 100644 index ef2abe4d4..000000000 Binary files a/website/public/jan2.png and /dev/null differ diff --git a/website/public/openapi/openapi.json b/website/public/openapi/openapi.json deleted file mode 100644 index 75a60d208..000000000 --- a/website/public/openapi/openapi.json +++ /dev/null @@ -1,1459 +0,0 @@ -{ - "openapi": "3.1.0", - "info": { "title": "👋Jan API", "version": "0.3.14" }, - "paths": { - "/v1/completions": { - "post": { - "tags": ["OpenAI V1"], - "summary": "Completion", - "operationId": "create_completion_v1_completions_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/CreateCompletionRequest" - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "anyOf": [ - { "$ref": "#/components/schemas/CreateCompletionResponse" }, - { "type": "string" }, - { "$ref": "#/components/schemas/CreateCompletionResponse" } - ], - "title": "Completion response, when stream=False" - } - }, - "text/event-stream": { - "schema": { - "type": "string", - "title": "Server Side Streaming response, when stream=True. See SSE format: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format", - "example": "data: {... see CreateCompletionResponse ...} \\n\\n data: ... \\n\\n ... data: [DONE]" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/v1/embeddings": { - "post": { - "tags": ["OpenAI V1"], - "summary": "Embedding", - "operationId": "create_embedding_v1_embeddings_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/CreateEmbeddingRequest" - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { "application/json": { "schema": {} } } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/v1/chat/completions": { - "post": { - "tags": ["OpenAI V1"], - "summary": "Chat", - "operationId": "create_chat_completion_v1_chat_completions_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/CreateChatCompletionRequest" - }, - "examples": { - "normal": { - "summary": "Chat Completion", - "value": { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "What is the capital of France?" - } - ] - } - }, - "json_mode": { - "summary": "JSON Mode", - "value": { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "Who won the world series in 2020" - } - ], - "response_format": { "type": "json_object" } - } - }, - "tool_calling": { - "summary": "Tool Calling", - "value": { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "Extract Jason is 30 years old." - } - ], - "tools": [ - { - "type": "function", - "function": { - "name": "User", - "description": "User record", - "parameters": { - "type": "object", - "properties": { - "name": { "type": "string" }, - "age": { "type": "number" } - }, - "required": ["name", "age"] - } - } - } - ], - "tool_choice": { - "type": "function", - "function": { "name": "User" } - } - } - }, - "logprobs": { - "summary": "Logprobs", - "value": { - "model": "gpt-3.5-turbo", - "messages": [ - { - "role": "system", - "content": "You are a helpful assistant." - }, - { - "role": "user", - "content": "What is the capital of France?" - } - ], - "logprobs": true, - "top_logprobs": 10 - } - } - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "anyOf": [ - { - "$ref": "#/components/schemas/CreateChatCompletionResponse" - }, - { "type": "string" }, - { - "$ref": "#/components/schemas/CreateChatCompletionResponse" - } - ], - "title": "Completion response, when stream=False" - } - }, - "text/event-stream": { - "schema": { - "type": "string", - "title": "Server Side Streaming response, when stream=TrueSee SSE format: https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format", - "example": "data: {... see CreateChatCompletionResponse ...} \\n\\n data: ... \\n\\n ... data: [DONE]" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/v1/models": { - "get": { - "tags": ["OpenAI V1"], - "summary": "Models", - "operationId": "get_models_v1_models_get", - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/ModelList" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/extras/tokenize": { - "post": { - "tags": ["Extras"], - "summary": "Tokenize", - "operationId": "tokenize_extras_tokenize_post", - "requestBody": { - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/TokenizeInputRequest" } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/TokenizeInputResponse" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/extras/tokenize/count": { - "post": { - "tags": ["Extras"], - "summary": "Tokenize Count", - "operationId": "count_query_tokens_extras_tokenize_count_post", - "requestBody": { - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/TokenizeInputRequest" } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/TokenizeInputCountResponse" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - }, - "/extras/detokenize": { - "post": { - "tags": ["Extras"], - "summary": "Detokenize", - "operationId": "detokenize_extras_detokenize_post", - "requestBody": { - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/DetokenizeInputRequest" - } - } - }, - "required": true - }, - "responses": { - "200": { - "description": "Successful Response", - "content": { - "application/json": { - "schema": { - "$ref": "#/components/schemas/DetokenizeInputResponse" - } - } - } - }, - "422": { - "description": "Validation Error", - "content": { - "application/json": { - "schema": { "$ref": "#/components/schemas/HTTPValidationError" } - } - } - } - }, - "security": [{ "HTTPBearer": [] }] - } - } - }, - "components": { - "schemas": { - "ChatCompletionFunction": { - "properties": { - "name": { "type": "string", "title": "Name" }, - "description": { "type": "string", "title": "Description" }, - "parameters": { - "additionalProperties": { - "anyOf": [ - { "type": "integer" }, - { "type": "string" }, - { "type": "boolean" }, - { "items": {}, "type": "array" }, - { "additionalProperties": true, "type": "object" }, - { "type": "null" } - ] - }, - "type": "object", - "title": "Parameters" - } - }, - "type": "object", - "required": ["name", "parameters"], - "title": "ChatCompletionFunction" - }, - "ChatCompletionLogprobToken": { - "properties": { - "token": { "type": "string", "title": "Token" }, - "logprob": { "type": "number", "title": "Logprob" }, - "bytes": { - "anyOf": [ - { "items": { "type": "integer" }, "type": "array" }, - { "type": "null" } - ], - "title": "Bytes" - }, - "top_logprobs": { - "items": { - "$ref": "#/components/schemas/ChatCompletionTopLogprobToken" - }, - "type": "array", - "title": "Top Logprobs" - } - }, - "type": "object", - "required": ["token", "logprob", "bytes", "top_logprobs"], - "title": "ChatCompletionLogprobToken" - }, - "ChatCompletionLogprobs": { - "properties": { - "content": { - "anyOf": [ - { - "items": { - "$ref": "#/components/schemas/ChatCompletionLogprobToken" - }, - "type": "array" - }, - { "type": "null" } - ], - "title": "Content" - }, - "refusal": { - "anyOf": [ - { - "items": { - "$ref": "#/components/schemas/ChatCompletionLogprobToken" - }, - "type": "array" - }, - { "type": "null" } - ], - "title": "Refusal" - } - }, - "type": "object", - "required": ["content", "refusal"], - "title": "ChatCompletionLogprobs" - }, - "ChatCompletionMessageToolCall": { - "properties": { - "id": { "type": "string", "title": "Id" }, - "type": { "type": "string", "const": "function", "title": "Type" }, - "function": { - "$ref": "#/components/schemas/ChatCompletionMessageToolCallFunction" - } - }, - "type": "object", - "required": ["id", "type", "function"], - "title": "ChatCompletionMessageToolCall" - }, - "ChatCompletionMessageToolCallFunction": { - "properties": { - "name": { "type": "string", "title": "Name" }, - "arguments": { "type": "string", "title": "Arguments" } - }, - "type": "object", - "required": ["name", "arguments"], - "title": "ChatCompletionMessageToolCallFunction" - }, - "ChatCompletionNamedToolChoice": { - "properties": { - "type": { "type": "string", "const": "function", "title": "Type" }, - "function": { - "$ref": "#/components/schemas/ChatCompletionNamedToolChoiceFunction" - } - }, - "type": "object", - "required": ["type", "function"], - "title": "ChatCompletionNamedToolChoice" - }, - "ChatCompletionNamedToolChoiceFunction": { - "properties": { "name": { "type": "string", "title": "Name" } }, - "type": "object", - "required": ["name"], - "title": "ChatCompletionNamedToolChoiceFunction" - }, - "ChatCompletionRequestAssistantMessage": { - "properties": { - "role": { "type": "string", "const": "assistant", "title": "Role" }, - "content": { "type": "string", "title": "Content" }, - "tool_calls": { - "items": { - "$ref": "#/components/schemas/ChatCompletionMessageToolCall" - }, - "type": "array", - "title": "Tool Calls" - }, - "function_call": { - "$ref": "#/components/schemas/ChatCompletionRequestAssistantMessageFunctionCall" - } - }, - "type": "object", - "required": ["role"], - "title": "ChatCompletionRequestAssistantMessage" - }, - "ChatCompletionRequestAssistantMessageFunctionCall": { - "properties": { - "name": { "type": "string", "title": "Name" }, - "arguments": { "type": "string", "title": "Arguments" } - }, - "type": "object", - "required": ["name", "arguments"], - "title": "ChatCompletionRequestAssistantMessageFunctionCall" - }, - "ChatCompletionRequestFunctionCallOption": { - "properties": { "name": { "type": "string", "title": "Name" } }, - "type": "object", - "required": ["name"], - "title": "ChatCompletionRequestFunctionCallOption" - }, - "ChatCompletionRequestFunctionMessage": { - "properties": { - "role": { "type": "string", "const": "function", "title": "Role" }, - "content": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Content" - }, - "name": { "type": "string", "title": "Name" } - }, - "type": "object", - "required": ["role", "content", "name"], - "title": "ChatCompletionRequestFunctionMessage" - }, - "ChatCompletionRequestMessageContentPartImage": { - "properties": { - "type": { "type": "string", "const": "image_url", "title": "Type" }, - "image_url": { - "anyOf": [ - { "type": "string" }, - { - "$ref": "#/components/schemas/ChatCompletionRequestMessageContentPartImageImageUrl" - } - ], - "title": "Image Url" - } - }, - "type": "object", - "required": ["type", "image_url"], - "title": "ChatCompletionRequestMessageContentPartImage" - }, - "ChatCompletionRequestMessageContentPartImageImageUrl": { - "properties": { - "url": { "type": "string", "title": "Url" }, - "detail": { - "type": "string", - "enum": ["auto", "low", "high"], - "title": "Detail" - } - }, - "type": "object", - "required": ["url"], - "title": "ChatCompletionRequestMessageContentPartImageImageUrl" - }, - "ChatCompletionRequestMessageContentPartText": { - "properties": { - "type": { "type": "string", "const": "text", "title": "Type" }, - "text": { "type": "string", "title": "Text" } - }, - "type": "object", - "required": ["type", "text"], - "title": "ChatCompletionRequestMessageContentPartText" - }, - "ChatCompletionRequestResponseFormat": { - "properties": { - "type": { - "type": "string", - "enum": ["text", "json_object"], - "title": "Type" - }, - "schema": { - "anyOf": [ - { "type": "integer" }, - { "type": "string" }, - { "type": "boolean" }, - { "items": {}, "type": "array" }, - { "additionalProperties": true, "type": "object" }, - { "type": "null" } - ], - "title": "Schema" - } - }, - "type": "object", - "required": ["type"], - "title": "ChatCompletionRequestResponseFormat" - }, - "ChatCompletionRequestSystemMessage": { - "properties": { - "role": { "type": "string", "const": "system", "title": "Role" }, - "content": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Content" - } - }, - "type": "object", - "required": ["role", "content"], - "title": "ChatCompletionRequestSystemMessage" - }, - "ChatCompletionRequestToolMessage": { - "properties": { - "role": { "type": "string", "const": "tool", "title": "Role" }, - "content": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Content" - }, - "tool_call_id": { "type": "string", "title": "Tool Call Id" } - }, - "type": "object", - "required": ["role", "content", "tool_call_id"], - "title": "ChatCompletionRequestToolMessage" - }, - "ChatCompletionRequestUserMessage": { - "properties": { - "role": { "type": "string", "const": "user", "title": "Role" }, - "content": { - "anyOf": [ - { "type": "string" }, - { - "items": { - "anyOf": [ - { - "$ref": "#/components/schemas/ChatCompletionRequestMessageContentPartText" - }, - { - "$ref": "#/components/schemas/ChatCompletionRequestMessageContentPartImage" - } - ] - }, - "type": "array" - }, - { "type": "null" } - ], - "title": "Content" - } - }, - "type": "object", - "required": ["role", "content"], - "title": "ChatCompletionRequestUserMessage" - }, - "ChatCompletionResponseChoice": { - "properties": { - "index": { "type": "integer", "title": "Index" }, - "message": { - "$ref": "#/components/schemas/ChatCompletionResponseMessage" - }, - "logprobs": { - "anyOf": [ - { "$ref": "#/components/schemas/ChatCompletionLogprobs" }, - { "type": "null" } - ] - }, - "finish_reason": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Finish Reason" - } - }, - "type": "object", - "required": ["index", "message", "logprobs", "finish_reason"], - "title": "ChatCompletionResponseChoice" - }, - "ChatCompletionResponseFunctionCall": { - "properties": { - "name": { "type": "string", "title": "Name" }, - "arguments": { "type": "string", "title": "Arguments" } - }, - "type": "object", - "required": ["name", "arguments"], - "title": "ChatCompletionResponseFunctionCall" - }, - "ChatCompletionResponseMessage": { - "properties": { - "content": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Content" - }, - "tool_calls": { - "items": { - "$ref": "#/components/schemas/ChatCompletionMessageToolCall" - }, - "type": "array", - "title": "Tool Calls" - }, - "role": { - "type": "string", - "enum": ["assistant", "function"], - "title": "Role" - }, - "function_call": { - "$ref": "#/components/schemas/ChatCompletionResponseFunctionCall" - } - }, - "type": "object", - "required": ["content", "role"], - "title": "ChatCompletionResponseMessage" - }, - "ChatCompletionTool": { - "properties": { - "type": { "type": "string", "const": "function", "title": "Type" }, - "function": { - "$ref": "#/components/schemas/ChatCompletionToolFunction" - } - }, - "type": "object", - "required": ["type", "function"], - "title": "ChatCompletionTool" - }, - "ChatCompletionToolFunction": { - "properties": { - "name": { "type": "string", "title": "Name" }, - "description": { "type": "string", "title": "Description" }, - "parameters": { - "additionalProperties": { - "anyOf": [ - { "type": "integer" }, - { "type": "string" }, - { "type": "boolean" }, - { "items": {}, "type": "array" }, - { "additionalProperties": true, "type": "object" }, - { "type": "null" } - ] - }, - "type": "object", - "title": "Parameters" - } - }, - "type": "object", - "required": ["name", "parameters"], - "title": "ChatCompletionToolFunction" - }, - "ChatCompletionTopLogprobToken": { - "properties": { - "token": { "type": "string", "title": "Token" }, - "logprob": { "type": "number", "title": "Logprob" }, - "bytes": { - "anyOf": [ - { "items": { "type": "integer" }, "type": "array" }, - { "type": "null" } - ], - "title": "Bytes" - } - }, - "type": "object", - "required": ["token", "logprob", "bytes"], - "title": "ChatCompletionTopLogprobToken" - }, - "CompletionChoice": { - "properties": { - "text": { "type": "string", "title": "Text" }, - "index": { "type": "integer", "title": "Index" }, - "logprobs": { - "anyOf": [ - { "$ref": "#/components/schemas/CompletionLogprobs" }, - { "type": "null" } - ] - }, - "finish_reason": { - "anyOf": [ - { "type": "string", "enum": ["stop", "length"] }, - { "type": "null" } - ], - "title": "Finish Reason" - } - }, - "type": "object", - "required": ["text", "index", "logprobs", "finish_reason"], - "title": "CompletionChoice" - }, - "CompletionLogprobs": { - "properties": { - "text_offset": { - "items": { "type": "integer" }, - "type": "array", - "title": "Text Offset" - }, - "token_logprobs": { - "items": { "anyOf": [{ "type": "number" }, { "type": "null" }] }, - "type": "array", - "title": "Token Logprobs" - }, - "tokens": { - "items": { "type": "string" }, - "type": "array", - "title": "Tokens" - }, - "top_logprobs": { - "items": { - "anyOf": [ - { - "additionalProperties": { "type": "number" }, - "type": "object" - }, - { "type": "null" } - ] - }, - "type": "array", - "title": "Top Logprobs" - } - }, - "type": "object", - "required": ["text_offset", "token_logprobs", "tokens", "top_logprobs"], - "title": "CompletionLogprobs" - }, - "CompletionUsage": { - "properties": { - "prompt_tokens": { "type": "integer", "title": "Prompt Tokens" }, - "completion_tokens": { - "type": "integer", - "title": "Completion Tokens" - }, - "total_tokens": { "type": "integer", "title": "Total Tokens" } - }, - "type": "object", - "required": ["prompt_tokens", "completion_tokens", "total_tokens"], - "title": "CompletionUsage" - }, - "CreateChatCompletionRequest": { - "properties": { - "messages": { - "items": { - "anyOf": [ - { - "$ref": "#/components/schemas/ChatCompletionRequestSystemMessage" - }, - { - "$ref": "#/components/schemas/ChatCompletionRequestUserMessage" - }, - { - "$ref": "#/components/schemas/ChatCompletionRequestAssistantMessage" - }, - { - "$ref": "#/components/schemas/ChatCompletionRequestToolMessage" - }, - { - "$ref": "#/components/schemas/ChatCompletionRequestFunctionMessage" - } - ] - }, - "type": "array", - "title": "Messages", - "description": "A list of messages to generate completions for.", - "default": [] - }, - "functions": { - "anyOf": [ - { - "items": { - "$ref": "#/components/schemas/ChatCompletionFunction" - }, - "type": "array" - }, - { "type": "null" } - ], - "title": "Functions", - "description": "A list of functions to apply to the generated completions." - }, - "function_call": { - "anyOf": [ - { "type": "string", "enum": ["none", "auto"] }, - { - "$ref": "#/components/schemas/ChatCompletionRequestFunctionCallOption" - }, - { "type": "null" } - ], - "title": "Function Call", - "description": "A function to apply to the generated completions." - }, - "tools": { - "anyOf": [ - { - "items": { "$ref": "#/components/schemas/ChatCompletionTool" }, - "type": "array" - }, - { "type": "null" } - ], - "title": "Tools", - "description": "A list of tools to apply to the generated completions." - }, - "tool_choice": { - "anyOf": [ - { "type": "string", "enum": ["none", "auto", "required"] }, - { "$ref": "#/components/schemas/ChatCompletionNamedToolChoice" }, - { "type": "null" } - ], - "title": "Tool Choice", - "description": "A tool to apply to the generated completions." - }, - "max_tokens": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "Max Tokens", - "description": "The maximum number of tokens to generate. Defaults to inf" - }, - "min_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Min Tokens", - "description": "The minimum number of tokens to generate. It may return fewer tokens if another condition is met (e.g. max_tokens, stop).", - "default": 0 - }, - "logprobs": { - "anyOf": [{ "type": "boolean" }, { "type": "null" }], - "title": "Logprobs", - "description": "Whether to output the logprobs or not. Default is True", - "default": false - }, - "top_logprobs": { - "anyOf": [ - { "type": "integer", "minimum": 0.0 }, - { "type": "null" } - ], - "title": "Top Logprobs", - "description": "The number of logprobs to generate. If None, no logprobs are generated. logprobs need to set to True." - }, - "temperature": { - "type": "number", - "title": "Temperature", - "description": "Adjust the randomness of the generated text.\n\nTemperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.", - "default": 0.8 - }, - "top_p": { - "type": "number", - "maximum": 1.0, - "minimum": 0.0, - "title": "Top P", - "description": "Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\nTop-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.", - "default": 0.95 - }, - "min_p": { - "type": "number", - "maximum": 1.0, - "minimum": 0.0, - "title": "Min P", - "description": "Sets a minimum base probability threshold for token selection.\n\nThe Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.", - "default": 0.05 - }, - "stop": { - "anyOf": [ - { "type": "string" }, - { "items": { "type": "string" }, "type": "array" }, - { "type": "null" } - ], - "title": "Stop", - "description": "A list of tokens at which to stop generation. If None, no stop tokens are used." - }, - "stream": { - "type": "boolean", - "title": "Stream", - "description": "Whether to stream the results as they are generated. Useful for chatbots.", - "default": false - }, - "presence_penalty": { - "anyOf": [ - { "type": "number", "maximum": 2.0, "minimum": -2.0 }, - { "type": "null" } - ], - "title": "Presence Penalty", - "description": "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.", - "default": 0.0 - }, - "frequency_penalty": { - "anyOf": [ - { "type": "number", "maximum": 2.0, "minimum": -2.0 }, - { "type": "null" } - ], - "title": "Frequency Penalty", - "description": "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.", - "default": 0.0 - }, - "logit_bias": { - "anyOf": [ - { - "additionalProperties": { "type": "number" }, - "type": "object" - }, - { "type": "null" } - ], - "title": "Logit Bias" - }, - "seed": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "Seed" - }, - "response_format": { - "anyOf": [ - { - "$ref": "#/components/schemas/ChatCompletionRequestResponseFormat" - }, - { "type": "null" } - ] - }, - "model": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Model", - "description": "The model to use for generating completions." - }, - "n": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "N", - "default": 1 - }, - "user": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "User" - }, - "top_k": { - "type": "integer", - "minimum": 0.0, - "title": "Top K", - "description": "Limit the next token selection to the K most probable tokens.\n\nTop-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.", - "default": 40 - }, - "repeat_penalty": { - "type": "number", - "minimum": 0.0, - "title": "Repeat Penalty", - "description": "A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\nRepeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.", - "default": 1.1 - }, - "logit_bias_type": { - "anyOf": [ - { "type": "string", "enum": ["input_ids", "tokens"] }, - { "type": "null" } - ], - "title": "Logit Bias Type" - }, - "mirostat_mode": { - "type": "integer", - "maximum": 2.0, - "minimum": 0.0, - "title": "Mirostat Mode", - "description": "Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)", - "default": 0 - }, - "mirostat_tau": { - "type": "number", - "maximum": 10.0, - "minimum": 0.0, - "title": "Mirostat Tau", - "description": "Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text", - "default": 5.0 - }, - "mirostat_eta": { - "type": "number", - "maximum": 1.0, - "minimum": 0.001, - "title": "Mirostat Eta", - "description": "Mirostat learning rate", - "default": 0.1 - }, - "grammar": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Grammar" - } - }, - "type": "object", - "title": "CreateChatCompletionRequest", - "examples": [ - { - "messages": [ - { "content": "You are a helpful assistant.", "role": "system" }, - { "content": "What is the capital of France?", "role": "user" } - ] - } - ] - }, - "CreateChatCompletionResponse": { - "properties": { - "id": { "type": "string", "title": "Id" }, - "object": { - "type": "string", - "const": "chat.completion", - "title": "Object" - }, - "created": { "type": "integer", "title": "Created" }, - "model": { "type": "string", "title": "Model" }, - "choices": { - "items": { - "$ref": "#/components/schemas/ChatCompletionResponseChoice" - }, - "type": "array", - "title": "Choices" - }, - "usage": { "$ref": "#/components/schemas/CompletionUsage" } - }, - "type": "object", - "required": ["id", "object", "created", "model", "choices", "usage"], - "title": "CreateChatCompletionResponse" - }, - "CreateCompletionRequest": { - "properties": { - "prompt": { - "anyOf": [ - { "type": "string" }, - { "items": { "type": "string" }, "type": "array" } - ], - "title": "Prompt", - "description": "The prompt to generate completions for.", - "default": "" - }, - "suffix": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Suffix", - "description": "A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots." - }, - "max_tokens": { - "anyOf": [ - { "type": "integer", "minimum": 0.0 }, - { "type": "null" } - ], - "title": "Max Tokens", - "description": "The maximum number of tokens to generate.", - "default": 16 - }, - "min_tokens": { - "type": "integer", - "minimum": 0.0, - "title": "Min Tokens", - "description": "The minimum number of tokens to generate. It may return fewer tokens if another condition is met (e.g. max_tokens, stop).", - "default": 0 - }, - "temperature": { - "type": "number", - "title": "Temperature", - "description": "Adjust the randomness of the generated text.\n\nTemperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.", - "default": 0.8 - }, - "top_p": { - "type": "number", - "maximum": 1.0, - "minimum": 0.0, - "title": "Top P", - "description": "Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\nTop-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.", - "default": 0.95 - }, - "min_p": { - "type": "number", - "maximum": 1.0, - "minimum": 0.0, - "title": "Min P", - "description": "Sets a minimum base probability threshold for token selection.\n\nThe Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter min_p represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with min_p=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.", - "default": 0.05 - }, - "echo": { - "type": "boolean", - "title": "Echo", - "description": "Whether to echo the prompt in the generated text. Useful for chatbots.", - "default": false - }, - "stop": { - "anyOf": [ - { "type": "string" }, - { "items": { "type": "string" }, "type": "array" }, - { "type": "null" } - ], - "title": "Stop", - "description": "A list of tokens at which to stop generation. If None, no stop tokens are used." - }, - "stream": { - "type": "boolean", - "title": "Stream", - "description": "Whether to stream the results as they are generated. Useful for chatbots.", - "default": false - }, - "logprobs": { - "anyOf": [ - { "type": "integer", "minimum": 0.0 }, - { "type": "null" } - ], - "title": "Logprobs", - "description": "The number of logprobs to generate. If None, no logprobs are generated." - }, - "presence_penalty": { - "anyOf": [ - { "type": "number", "maximum": 2.0, "minimum": -2.0 }, - { "type": "null" } - ], - "title": "Presence Penalty", - "description": "Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.", - "default": 0.0 - }, - "frequency_penalty": { - "anyOf": [ - { "type": "number", "maximum": 2.0, "minimum": -2.0 }, - { "type": "null" } - ], - "title": "Frequency Penalty", - "description": "Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.", - "default": 0.0 - }, - "logit_bias": { - "anyOf": [ - { - "additionalProperties": { "type": "number" }, - "type": "object" - }, - { "type": "null" } - ], - "title": "Logit Bias" - }, - "seed": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "Seed" - }, - "model": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Model", - "description": "The model to use for generating completions." - }, - "n": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "N", - "default": 1 - }, - "best_of": { - "anyOf": [{ "type": "integer" }, { "type": "null" }], - "title": "Best Of", - "default": 1 - }, - "user": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "User" - }, - "top_k": { - "type": "integer", - "minimum": 0.0, - "title": "Top K", - "description": "Limit the next token selection to the K most probable tokens.\n\nTop-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.", - "default": 40 - }, - "repeat_penalty": { - "type": "number", - "minimum": 0.0, - "title": "Repeat Penalty", - "description": "A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\nRepeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.", - "default": 1.1 - }, - "logit_bias_type": { - "anyOf": [ - { "type": "string", "enum": ["input_ids", "tokens"] }, - { "type": "null" } - ], - "title": "Logit Bias Type" - }, - "mirostat_mode": { - "type": "integer", - "maximum": 2.0, - "minimum": 0.0, - "title": "Mirostat Mode", - "description": "Enable Mirostat constant-perplexity algorithm of the specified version (1 or 2; 0 = disabled)", - "default": 0 - }, - "mirostat_tau": { - "type": "number", - "maximum": 10.0, - "minimum": 0.0, - "title": "Mirostat Tau", - "description": "Mirostat target entropy, i.e. the target perplexity - lower values produce focused and coherent text, larger values produce more diverse and less coherent text", - "default": 5.0 - }, - "mirostat_eta": { - "type": "number", - "maximum": 1.0, - "minimum": 0.001, - "title": "Mirostat Eta", - "description": "Mirostat learning rate", - "default": 0.1 - }, - "grammar": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Grammar" - } - }, - "type": "object", - "title": "CreateCompletionRequest", - "examples": [ - { - "prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n", - "stop": ["\n", "###"] - } - ] - }, - "CreateCompletionResponse": { - "properties": { - "id": { "type": "string", "title": "Id" }, - "object": { - "type": "string", - "const": "text_completion", - "title": "Object" - }, - "created": { "type": "integer", "title": "Created" }, - "model": { "type": "string", "title": "Model" }, - "choices": { - "items": { "$ref": "#/components/schemas/CompletionChoice" }, - "type": "array", - "title": "Choices" - }, - "usage": { "$ref": "#/components/schemas/CompletionUsage" } - }, - "type": "object", - "required": ["id", "object", "created", "model", "choices"], - "title": "CreateCompletionResponse" - }, - "CreateEmbeddingRequest": { - "properties": { - "model": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Model", - "description": "The model to use for generating completions." - }, - "input": { - "anyOf": [ - { "type": "string" }, - { "items": { "type": "string" }, "type": "array" } - ], - "title": "Input", - "description": "The input to embed." - }, - "user": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "User" - } - }, - "type": "object", - "required": ["input"], - "title": "CreateEmbeddingRequest", - "examples": [{ "input": "The food was delicious and the waiter..." }] - }, - "DetokenizeInputRequest": { - "properties": { - "model": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Model", - "description": "The model to use for generating completions." - }, - "tokens": { - "items": { "type": "integer" }, - "type": "array", - "title": "Tokens", - "description": "A list of toekns to detokenize." - } - }, - "type": "object", - "required": ["tokens"], - "title": "DetokenizeInputRequest", - "example": [{ "tokens": [123, 321, 222] }] - }, - "DetokenizeInputResponse": { - "properties": { - "text": { - "type": "string", - "title": "Text", - "description": "The detokenized text." - } - }, - "type": "object", - "required": ["text"], - "title": "DetokenizeInputResponse", - "example": { "text": "How many tokens in this query?" } - }, - "HTTPValidationError": { - "properties": { - "detail": { - "items": { "$ref": "#/components/schemas/ValidationError" }, - "type": "array", - "title": "Detail" - } - }, - "type": "object", - "title": "HTTPValidationError" - }, - "ModelData": { - "properties": { - "id": { "type": "string", "title": "Id" }, - "object": { "type": "string", "const": "model", "title": "Object" }, - "owned_by": { "type": "string", "title": "Owned By" }, - "permissions": { - "items": { "type": "string" }, - "type": "array", - "title": "Permissions" - } - }, - "type": "object", - "required": ["id", "object", "owned_by", "permissions"], - "title": "ModelData" - }, - "ModelList": { - "properties": { - "object": { "type": "string", "const": "list", "title": "Object" }, - "data": { - "items": { "$ref": "#/components/schemas/ModelData" }, - "type": "array", - "title": "Data" - } - }, - "type": "object", - "required": ["object", "data"], - "title": "ModelList" - }, - "TokenizeInputCountResponse": { - "properties": { - "count": { - "type": "integer", - "title": "Count", - "description": "The number of tokens in the input." - } - }, - "type": "object", - "required": ["count"], - "title": "TokenizeInputCountResponse", - "example": { "count": 5 } - }, - "TokenizeInputRequest": { - "properties": { - "model": { - "anyOf": [{ "type": "string" }, { "type": "null" }], - "title": "Model", - "description": "The model to use for generating completions." - }, - "input": { - "type": "string", - "title": "Input", - "description": "The input to tokenize." - } - }, - "type": "object", - "required": ["input"], - "title": "TokenizeInputRequest", - "examples": [{ "input": "How many tokens in this query?" }] - }, - "TokenizeInputResponse": { - "properties": { - "tokens": { - "items": { "type": "integer" }, - "type": "array", - "title": "Tokens", - "description": "A list of tokens." - } - }, - "type": "object", - "required": ["tokens"], - "title": "TokenizeInputResponse", - "example": { "tokens": [123, 321, 222] } - }, - "ValidationError": { - "properties": { - "loc": { - "items": { "anyOf": [{ "type": "string" }, { "type": "integer" }] }, - "type": "array", - "title": "Location" - }, - "msg": { "type": "string", "title": "Message" }, - "type": { "type": "string", "title": "Error Type" } - }, - "type": "object", - "required": ["loc", "msg", "type"], - "title": "ValidationError" - } - }, - "securitySchemes": { "HTTPBearer": { "type": "http", "scheme": "bearer" } } - } -} diff --git a/website/public/videos/jan-nano-demo.mp4 b/website/public/videos/jan-nano-demo.mp4 deleted file mode 100644 index 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fee336449..000000000 --- a/website/src/components/CustomNav.astro +++ /dev/null @@ -1,322 +0,0 @@ ---- -// Custom navigation component to add Products and API Reference links -// This overrides the default Starlight Header component -import Search from '@astrojs/starlight/components/Search.astro'; -import ThemeSelect from '@astrojs/starlight/components/ThemeSelect.astro'; -import { Icon } from '@astrojs/starlight/components'; ---- - -
- -
- - - - - diff --git a/website/src/components/ReleaseDatabase.astro b/website/src/components/ReleaseDatabase.astro deleted file mode 100644 index 13f5c5a1d..000000000 --- a/website/src/components/ReleaseDatabase.astro +++ /dev/null @@ -1,671 +0,0 @@ ---- -export interface Props { - className?: string; -} - -const { className = '' } = Astro.props; ---- - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Release Database
Release VersionTarget DateStatus
- v0.6.7 - Web Search - ReleasedLive
-
-
-
User Stories
-
    -
  • As a user, I want to search the web directly from Jan chat for real-time information
    • -
  • As a developer, I want better API documentation for integration
    • -
  • As a researcher, I want source attribution for all search results
    • -
-
-
-
New Features
-
    -
  • Privacy-respecting web search integration
    • -
  • Multiple search engine support
    • -
  • Improved model downloading experience
    • -
  • Enhanced error handling and user feedback
    • -
-
-
-
Documentation
-
    -
  • Web Search API documentation
    • -
  • Privacy policy updates
    • -
  • Search configuration guide
    • -
-
-
-
- v0.6.5 - Desktop Stability - ReleasedLive
-
-
-
Improvements
-
    -
  • Enhanced memory management for large models
    • -
  • Improved GPU utilization
    • -
  • Better cross-platform compatibility
    • -
-
-
-
Bug Fixes
-
    -
  • Fixed model loading crashes on Windows
    • -
  • Resolved memory leaks in long conversations
    • -
  • Fixed UI freezing during model downloads
    • -
-
-
-
Performance
-
    -
  • 30% faster model loading times
    • -
  • Reduced memory footprint by 25%
    • -
  • Improved response streaming
    • -
-
-
-
- v0.7.0 - Deep Research - January 2025Building
-
-
-
User Stories
-
    -
  • As a researcher, I want to conduct deep research with multiple sources and citations
    • -
  • As a user, I want Jan Nano to work seamlessly on mobile devices
    • -
  • As a team lead, I want to preview jan.ai for my organization
    • -
-
-
-
New Features
-
    -
  • Deep research capabilities with multi-source analysis
    • -
  • jan.ai web platform beta launch
    • -
  • Improved Jan Nano performance and mobile optimization
    • -
  • Citation management and research reports
    • -
-
-
-
Documentation
-
    -
  • Research methodology guide
    • -
  • jan.ai platform documentation
    • -
  • Mobile optimization best practices
    • -
-
-
-
- v0.8.0 - Browser Automation - March 2025Building
-
-
-
User Stories
-
    -
  • As a user, I want Jan to automate web tasks for me safely and efficiently
    • -
  • As a business user, I want to automate repetitive web workflows
    • -
  • As a developer, I want browser automation APIs for custom integrations
    • -
-
-
-
New Features
-
    -
  • Browser automation capabilities (Beta)
    • -
  • Form filling and data extraction
    • -
  • Website navigation and interaction
    • -
  • Safety policies and user confirmation flows
    • -
-
-
-
Documentation
-
    -
  • Browser automation safety guide
    • -
  • Web scraping best practices
    • -
  • API reference for automation tools
    • -
-
-
-
- v0.9.0 - Mobile Launch - Q1 2025Planned
-
-
-
User Stories
-
    -
  • As a mobile user, I want Jan AI available on my iOS/Android device
    • -
  • As a commuter, I want to seamlessly switch between desktop and mobile
    • -
  • As a privacy-conscious user, I want local AI on my phone
    • -
-
-
-
New Features
-
    -
  • iOS and Android mobile applications
    • -
  • Three adaptive modes (Desktop, Server, Local)
    • -
  • Voice-first interface
    • -
  • Cross-device synchronization
    • -
  • Jan Nano on-device processing
    • -
-
-
-
Documentation
-
    -
  • Mobile setup and configuration guide
    • -
  • Voice interaction best practices
    • -
  • Device syncing tutorial
    • -
-
-
-
- v1.0.0 - Server Edition - Q2 2025Planned
-
-
-
User Stories
-
    -
  • As an IT admin, I want to deploy Jan for my entire organization
    • -
  • As a team lead, I want centralized AI with team collaboration features
    • -
  • As a CTO, I want enterprise-grade security and compliance
    • -
-
-
-
New Features
-
    -
  • Multi-user server deployment
    • -
  • Enterprise authentication (SSO, LDAP)
    • -
  • Team collaboration and shared conversations
    • -
  • Docker and Kubernetes deployment options
    • -
  • Admin dashboard and user management
    • -
-
-
-
Documentation
-
    -
  • Enterprise deployment guide
    • -
  • Security and compliance documentation
    • -
  • Admin management tutorials
    • -
-
-
-
- v1.1.0 - AI Agents - Q1 2026Planned
-
-
-
User Stories
-
    -
  • As a user, I want autonomous agents to handle complex multi-step tasks
    • -
  • As a business owner, I want AI that can coordinate multiple tools automatically
    • -
  • As a power user, I want to create custom agent workflows
    • -
-
-
-
New Features
-
    -
  • Autonomous AI agents
    • -
  • Multi-step task planning and execution
    • -
  • Tool orchestration and coordination
    • -
  • Goal-oriented intelligent reasoning
    • -
  • Custom agent workflow builder
    • -
-
-
-
Documentation
-
    -
  • Agent development guide
    • -
  • Workflow automation tutorials
    • -
  • Safety and oversight best practices
    • -
-
-
-
- v1.2.0 - Lucy Multimodal - Q2 2025Planned
-
-
-
User Stories
-
    -
  • As a content creator, I want AI that can understand and process images
    • -
  • As a researcher, I want to analyze documents with both text and visuals
    • -
  • As a designer, I want AI that can help with visual content creation
    • -
-
-
-
New Features
-
    -
  • Lucy multimodal model release
    • -
  • Image understanding and analysis
    • -
  • Document processing with OCR
    • -
  • Visual reasoning capabilities
    • -
  • Audio processing (planned)
    • -
-
-
-
Documentation
-
    -
  • Multimodal AI usage guide
    • -
  • Image processing tutorials
    • -
  • Visual reasoning examples
    • -
-
-
-
-
- - - - diff --git a/website/src/components/SimpleFloatingNav.astro b/website/src/components/SimpleFloatingNav.astro deleted file mode 100644 index 8c43fd6d1..000000000 --- a/website/src/components/SimpleFloatingNav.astro +++ /dev/null @@ -1,200 +0,0 @@ ---- -export interface Props { - currentPage?: string; -} - -const { currentPage = 'products' } = Astro.props; ---- - - - - - - diff --git a/website/src/components/SimpleTOC.astro b/website/src/components/SimpleTOC.astro deleted file mode 100644 index 94f0418dc..000000000 --- a/website/src/components/SimpleTOC.astro +++ /dev/null @@ -1,338 +0,0 @@ ---- -export interface Props { - sections?: Array<{ - id: string; - title: string; - level?: number; - }>; -} - -const { - sections = [ - { id: 'what-were-building', title: 'What We\'re Building' }, - { id: 'two-modes-one-experience', title: 'Two Modes, One Experience' }, - { id: 'our-product-principles', title: 'Our Product Principles' }, - { id: 'available-on-every-device', title: 'Available on Every Device' }, - { id: 'jan-desktop', title: 'Jan Desktop', level: 2 }, - { id: 'jan-web', title: 'Jan Web', level: 2 }, - { id: 'jan-mobile', title: 'Jan Mobile', level: 2 }, - { id: 'jan-server', title: 'Jan Server', level: 2 }, - { id: 'jan-mobile-three-modes-one-experience', title: 'Jan Mobile: Three Modes' }, - { id: 'what-makes-jan-different', title: 'What Makes Jan Different' }, - { id: 'development-timeline', title: 'Development Timeline' } - ] -} = Astro.props; ---- - - - - - - diff --git a/website/src/components/StatusIndicator.astro b/website/src/components/StatusIndicator.astro deleted file mode 100644 index f60b23454..000000000 --- a/website/src/components/StatusIndicator.astro +++ /dev/null @@ -1,147 +0,0 @@ ---- -export interface Props { - status: 'active' | 'warning' | 'success' | 'error' | 'idle'; - label: string; - pulse?: boolean; - size?: 'small' | 'medium' | 'large'; -} - -const { status, label, pulse = true, size = 'medium' } = Astro.props; - -const statusColors = { - active: '#00ff41', - warning: '#ffb000', - success: '#00ff41', - error: '#ff0040', - idle: '#888888' -}; - -const statusColor = statusColors[status]; ---- - -
-
- {label} -
- - diff --git a/website/src/content.config.ts b/website/src/content.config.ts deleted file mode 100644 index ee21da167..000000000 --- a/website/src/content.config.ts +++ /dev/null @@ -1,8 +0,0 @@ -import { defineCollection } from 'astro:content'; -import { docsLoader } from '@astrojs/starlight/loaders'; -import { docsSchema } from '@astrojs/starlight/schema'; -import { videosSchema } from 'starlight-videos/schemas'; - -export const collections = { - docs: defineCollection({ loader: docsLoader(), schema: docsSchema({ extend: videosSchema }) }), -}; diff --git a/website/src/content/docs/index.mdx b/website/src/content/docs/index.mdx deleted file mode 100644 index c4caf13d0..000000000 --- a/website/src/content/docs/index.mdx +++ /dev/null @@ -1,171 +0,0 @@ ---- -title: Jan -description: Jan is an open-source ChatGPT-alternative and self-hosted AI platform - build and run AI on your own desktop or server. -keywords: - [ - Jan, - Jan AI, - ChatGPT alternative, - OpenAI platform alternative, - local API, - local AI, - private AI, - conversational AI, - no-subscription fee, - large language model, - LLM, - ] ---- - -import { Aside } from '@astrojs/starlight/components'; - -![Jan's Cover Image](../../assets/jan-app-new.png) - - -Jan is a ChatGPT alternative that runs 100% offline on your desktop and (*soon*) on mobile. Our goal is to -make it easy for anyone, with or without coding skills, to download and use AI models with full control and -[privacy](https://www.reuters.com/legal/legalindustry/privacy-paradox-with-ai-2023-10-31/). - -Jan is powered by [Llama.cpp](https://github.com/ggerganov/llama.cpp), a local AI engine that provides an OpenAI-compatible -API that can run in the background by default at `https://localhost:1337` (or your custom port). This enables you to power all sorts of -applications with AI capabilities from your laptop/PC. For example, you can connect local tools like [Continue](https://jan.ai/docs/server-examples/continue-dev) -and [Cline](https://cline.bot/) to Jan and power them using your favorite models. - -Jan doesn't limit you to locally hosted models, meaning, you can create an API key from your favorite model provider, -add it to Jan via the configuration's page and start talking to your favorite models. - -### Features - -- Download popular open-source LLMs (Llama3, Gemma3, Qwen3, and more) from the HuggingFace [Model Hub](./jan/manage-models) -or import any GGUF files (the model format used by llama.cpp) available locally -- Connect to [cloud services](./jan/remote-models/openai) (OpenAI, Anthropic, Mistral, Groq, etc.) -- [Chat](./jan/threads) with AI models & [customize their parameters](./jan/explanation/model-parameters) via our -intuitive interface -- Use our [local API server](https://jan.ai/api-reference) with an OpenAI-equivalent API to power other apps. - -### Philosophy - -Jan is built to be [user-owned](about#-user-owned), this means that Jan is: -- Truly open source via the [Apache 2.0 license](https://github.com/menloresearch/jan/blob/dev/LICENSE) -- [Data is stored locally, following one of the many local-first principles](https://www.inkandswitch.com/local-first) -- Internet is optional, Jan can run 100% offline -- Free choice of AI models, both local and cloud-based -- We do not collect or sell user data. See our [Privacy Policy](./privacy). - - - -### Inspirations - -Jan is inspired by the concepts of [Calm Computing](https://en.wikipedia.org/wiki/Calm_technology), and the Disappearing Computer. - -## Acknowledgements - -Jan is built on the shoulders of many open-source projects like: - -- [Llama.cpp](https://github.com/ggerganov/llama.cpp/blob/master/LICENSE) -- [Scalar](https://github.com/scalar/scalar) - -## FAQs - -
-What is Jan? - -Jan is a customizable AI assistant that can run offline on your computer - a privacy-focused alternative to tools like ChatGPT, Anthropic's Claude, and Google Gemini, with optional cloud AI support. - -
- -
-How do I get started with Jan? - -Download Jan on your computer, download a model or add API key for a cloud-based one, and start chatting. For detailed setup instructions, see our installation guides. - -
- -
-Is Jan compatible with my system? - -Jan supports all major operating systems: -- [Mac](./jan/installation/mac#compatibility) -- [Windows](./jan/installation/windows#compatibility) -- [Linux](./jan/installation/linux) - -Hardware compatibility includes: -- NVIDIA GPUs (CUDA) -- AMD GPUs (Vulkan) -- Intel Arc GPUs (Vulkan) -- Any GPU with Vulkan support - -
- -
-How does Jan protect my privacy? - -Jan prioritizes privacy by: -- Running 100% offline with locally-stored data -- Using open-source models that keep your conversations private -- Storing all files and chat history on your device in the [Jan Data Folder](./jan/data-folder) -- Never collecting or selling your data - - - -You can optionally share anonymous usage statistics to help improve Jan, but your conversations are never shared. See our complete [Privacy Policy](./jan/privacy). - -
- -
-What models can I use with Jan? - -- Download optimized models from the [Jan Hub](./jan/manage-models) -- Import GGUF models from Hugging Face or your local files -- Connect to cloud providers like OpenAI, Anthropic, Mistral and Groq (requires your own API keys) - -
- -
-Is Jan really free? What's the catch? - -Jan is completely free and open-source with no subscription fees for local models and features. When using cloud-based models (like GPT-4o or Claude Sonnet 3.7), you'll only pay the standard rates to those providers—we add no markup. - -
- -
-Can I use Jan offline? - -Yes! Once you've downloaded a local model, Jan works completely offline with no internet connection needed. - -
- -
-How can I contribute or get community help? - -- Join our [Discord community](https://discord.gg/qSwXFx6Krr) to connect with other users -- Contribute through [GitHub](https://github.com/menloresearch/jan) (no permission needed!) -- Get troubleshooting help in our [Discord](https://discord.com/invite/FTk2MvZwJH) channel [#🆘|jan-help](https://discord.com/channels/1107178041848909847/1192090449725358130) -- Check our [Troubleshooting](./jan/troubleshooting) guide for common issues - -
- -
-Can I self-host Jan? - -Yes! We fully support the self-hosted movement. Either download Jan directly or fork it on [GitHub repository](https://github.com/menloresearch/jan) and build it from source. - -
- -
-What does Jan stand for? - -Jan stands for "Just a Name". We are, admittedly, bad at marketing 😂. - -
- -
-Are you hiring? - -Yes! We love hiring from our community. Check out our open positions at [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. - -![The Assistants management page, where you can view, add, edit, or delete assistants. Each assistant has a name, -description, and can be customized for different tasks.](../../../assets/assistants-ui-overview.png) - -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 (✏️) icon on any assistant card to update its name, description, or instructions. -- **Delete an Assistant**: Click the trash (🗑️) 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: - -![Assistant Dropdown](../../../assets/assistant-dropdown-updated.png) - -- 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: - -![Add Assistant Dialog](../../../assets/assistant-add-dialog.png) - -- To edit an existing assistant, click the gear (⚙️) icon next to its name in the dropdown. This opens the Edit Assistant dialog: - -![Edit Assistant Dialog](../../../assets/assistant-edit-dialog.png) - -### 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/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. - -![Open Jan Data Folder](../../../assets/settings-11.png) - - -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": "👋", - "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 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 step—each 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 names—use 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": "👋", - "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 step—each 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 names—use 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/llama-cpp.mdx b/website/src/content/docs/jan/explanation/llama-cpp.mdx deleted file mode 100644 index aa1cc3d02..000000000 --- a/website/src/content/docs/jan/explanation/llama-cpp.mdx +++ /dev/null @@ -1,207 +0,0 @@ ---- -title: Local AI Engine -description: Understand and configure Jan's local AI engine for running models on your hardware. -keywords: - [ - Jan, - Customizable Intelligence, LLM, - local AI, - privacy focus, - free and open source, - private and offline, - conversational AI, - no-subscription fee, - large language models, - Llama CPP integration, - llama.cpp Engine, - Intel CPU, - AMD CPU, - NVIDIA GPU, - AMD GPU Radeon, - Apple Silicon, - Intel Arc GPU, - ] ---- - -import { Aside, Tabs, TabItem } from '@astrojs/starlight/components'; - -## What is llama.cpp? - -llama.cpp is the engine that runs AI models locally on your computer. Think of it as the software -that takes an AI model file and makes it work on your hardware - whether that's your CPU, -graphics card, or Apple's M-series chips. - -Originally created by Georgi Gerganov, llama.cpp is designed to run large language models -efficiently on consumer hardware without requiring specialized AI accelerators or cloud connections. - -## Why This Matters - -- **Privacy**: Your conversations never leave your computer -- **Cost**: No monthly subscription fees or API costs -- **Speed**: No internet required once models are downloaded -- **Control**: Choose exactly which models to run and how they behave - -## Accessing Engine Settings - -Find llama.cpp settings at **Settings** > **Model Providers** > **Llama.cpp**: - -![llama.cpp](../../../../assets/llama.cpp-01-updated.png) - - - -## Engine Management - -| Feature | What It Does | When You Need It | -|---------|-------------|------------------| -| **Engine Version** | Shows which version of llama.cpp you're running | Check compatibility with newer models | -| **Check Updates** | Downloads newer engine versions | When new models require updated engine | -| **Backend Selection** | Choose the version optimized for your hardware | After installing new graphics cards or when performance is poor | - -## Hardware Backends - -Jan offers different backend versions optimized for your specific hardware. Think of these as different "drivers" - each one is tuned for particular processors or graphics cards. - - - - - - - -### NVIDIA Graphics Cards (Recommended for Speed) -Choose based on your CUDA version (check NVIDIA Control Panel): - -**For CUDA 12.0:** -- `llama.cpp-avx2-cuda-12-0` (most common) -- `llama.cpp-avx512-cuda-12-0` (newer Intel/AMD CPUs) - -**For CUDA 11.7:** -- `llama.cpp-avx2-cuda-11-7` (most common) -- `llama.cpp-avx512-cuda-11-7` (newer Intel/AMD CPUs) - -### CPU Only (No Graphics Card Acceleration) -- `llama.cpp-avx2` (most modern CPUs) -- `llama.cpp-avx512` (newer Intel/AMD CPUs) -- `llama.cpp-avx` (older CPUs) -- `llama.cpp-noavx` (very old CPUs) - -### Other Graphics Cards -- `llama.cpp-vulkan` (AMD, Intel Arc, some others) - - - - - - - -### NVIDIA Graphics Cards -Same CUDA options as Windows: -- `llama.cpp-avx2-cuda-12-0` (most common) -- `llama.cpp-avx2-cuda-11-7` (older drivers) - -### CPU Only -- `llama.cpp-avx2` (most modern CPUs) -- `llama.cpp-avx512` (newer Intel/AMD CPUs) -- `llama.cpp-arm64` (ARM processors like Raspberry Pi) - -### Other Graphics Cards -- `llama.cpp-vulkan` (AMD, Intel graphics) - - - - - -### Apple Silicon (M1/M2/M3/M4) -- `llama.cpp-mac-arm64` (recommended) - -### Intel Macs -- `llama.cpp-mac-amd64` - - - - - - - -## Performance Settings - -These control how efficiently models run: - -| Setting | What It Does | Recommended Value | Impact | -|---------|-------------|------------------|---------| -| **Continuous Batching** | Process multiple requests at once | Enabled | Faster when using multiple tools or having multiple conversations | -| **Parallel Operations** | How many requests to handle simultaneously | 4 | Higher = more multitasking, but uses more memory | -| **CPU Threads** | How many processor cores to use | Auto-detected | More threads can speed up CPU processing | - -## Memory Settings - -These control how models use your computer's memory: - -| Setting | What It Does | Recommended Value | When to Change | -|---------|-------------|------------------|----------------| -| **Flash Attention** | More efficient memory usage | Enabled | Leave enabled unless you have problems | -| **Caching** | Remember recent conversations | Enabled | Speeds up follow-up questions | -| **KV Cache Type** | Memory precision trade-off | f16 | Change to q8_0 or q4_0 if running out of memory | -| **mmap** | Load models more efficiently | Enabled | Helps with large models | -| **Context Shift** | Handle very long conversations | Disabled | Enable for very long chats or multiple tool calls | - -### KV Cache Types Explained -- **f16**: Most stable, uses more memory -- **q8_0**: Balanced memory usage and quality -- **q4_0**: Uses least memory, slight quality loss - -## Troubleshooting Common Issues - -**Models won't load:** -- Try a different backend (switch from CUDA to CPU or vice versa) -- Check if you have enough RAM/VRAM -- Update to latest engine version - -**Very slow performance:** -- Make sure you're using GPU acceleration (CUDA/Metal/Vulkan backend) -- Increase GPU Layers in model settings -- Close other memory-intensive programs - -**Out of memory errors:** -- Reduce Context Size in model settings -- Switch KV Cache Type to q8_0 or q4_0 -- Try a smaller model variant - -**Random crashes:** -- Switch to a more stable backend (try avx instead of avx2) -- Disable overclocking if you have it enabled -- Update graphics drivers - -## Quick Setup Guide - -**For most users:** -1. Use the default backend that Jan installs -2. Leave all performance settings at defaults -3. Only adjust if you experience problems - -**If you have an NVIDIA graphics card:** -1. Download the appropriate CUDA backend -2. Make sure GPU Layers is set high in model settings -3. Enable Flash Attention - -**If models are too slow:** -1. Check you're using GPU acceleration -2. Try enabling Continuous Batching -3. Close other applications using memory - -**If running out of memory:** -1. Change KV Cache Type to q8_0 -2. Reduce Context Size in model settings -3. Try a smaller model - - 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 4b60afd68..000000000 --- a/website/src/content/docs/jan/explanation/model-parameters.mdx +++ /dev/null @@ -1,109 +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 } 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 - -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 Parameters](../../../../assets/model-parameters.png) - -## Model Capabilities - -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 - -![Model Capabilities Edit 01](../../../../assets/model-capabilities-edit-01.png) -![Model Capabilities Edit 02](../../../../assets/model-capabilities-edit-02.png) - -## 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 238b663a3..000000000 --- a/website/src/content/docs/jan/installation/linux.mdx +++ /dev/null @@ -1,266 +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 → up to 3B parameter models (int4) - - 16GB → up to 7B parameter models (int4) - - 32GB → up to 13B parameter models (int4) - - - - - - - 6GB → up to 3B parameter models (int4) - - 8GB → up to 7B parameter models (int4) - - 12GB → 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 → up to 3B parameter models - - 16GB → up to 7B parameter models - - 32GB → 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 → 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 → **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 b50abf65f..000000000 --- a/website/src/content/docs/jan/installation/windows.mdx +++ /dev/null @@ -1,192 +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'; - - - -# Windows Installation - -## 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 → up to 3B parameter models (int4) -- 16GB → up to 7B parameter models (int4) -- 32GB → up to 13B parameter models (int4) - - - -**GPU**: -- 6GB → up to 3B parameter models -- 8GB → up to 7B parameter models -- 12GB → 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:** - -- 🔍 Deeper Research: Extended context allows for processing entire research papers, lengthy documents, and complex multi-turn conversations -- ⚡ Native 128k Window: Built to handle long contexts efficiently, maintaining performance across the full context range -- 📈 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 window—that'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: - -![Jan-Nano-128k Performance](../../../../assets/jan-nano-bench.png) - -**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 - -## 🖥️ 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 - -## 🤝 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 66d8a3ec1..000000000 --- a/website/src/content/docs/jan/jan-models/jan-nano-32.mdx +++ /dev/null @@ -1,136 +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'; - -![Jan Nano](../../../../assets/jan-nano0.png) - -## 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. - -![Jan Nano](../../../../assets/jan-nano1.png) - -**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. - -![Serper MCP](../../../../assets/serper-mcp.png) - -**Step 6** -Open up a new chat and ask Jan-Nano to search the web for you. - -![Jan Nano](/gifs/jan-nano-demo.gif) - -## 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 “thinking” 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/manage-models.mdx b/website/src/content/docs/jan/manage-models.mdx deleted file mode 100644 index 290d1452f..000000000 --- a/website/src/content/docs/jan/manage-models.mdx +++ /dev/null @@ -1,187 +0,0 @@ ---- -title: Managing Models -description: Manage your interaction with AI models locally. -keywords: - [ - Jan, - Customizable Intelligence, LLM, - local AI, - privacy focus, - free and open source, - private and offline, - conversational AI, - no-subscription fee, - large language models, - threads, - chat history, - thread history, - ] ---- - -import { Aside } from '@astrojs/starlight/components'; - -This guide shows you how to add, customize, and delete models within Jan. - -## Local Model - -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 Models - -#### 1. Download from Jan Hub (Recommended) - -The easiest way to get started is using Jan's built-in model hub (which is 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 about it -3. Choose a model that fits your needs & hardware specifications -4. Click **Download** on your chosen model - - - -![Download Model](../../../assets/model-management-01.png) - - -#### 2. Import from [Hugging Face](https://huggingface.co/) - -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 the [Hugging Face Models](https://huggingface.co/models) page. -2. Find the model you want to use, make sure it is a GGUF file that fits in 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 (if the option is available) - -**Copy the model ID.** -![Find HF Model](../../../assets/hf-unsloth.png) - -**Paste it in Jan's Hub Search Bar.** -![Import Model](../../../assets/model-management-02.png) - -#### 3. Import Local Files - -If you already have one or many GGUF model files on your computer: -1. In Jan, go to **Settings > Model Providers > Llama.cpp** -2. Click **Import** and select your GGUF file(s) -3. Choose how you want to import: - - **Link Files:** Creates symbolic links to your model files (saves space) - - **Duplicate:** Makes a copy of model files in Jan's directory -4. Click **Import** to complete (check the [Jan Data Folder](./data-folder) section for more info) - - - -![Download Model](../../../assets/model-management-04.png) - -![Download Model](../../../assets/model-import-04.png) - -![Download Model](../../../assets/model-import-05.png) - - -#### 4. Manual Setup - -For advanced users who want to add a specific model that is not available within the 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 your `model.json` file with your configuration. Here's an example with "TinyLlama Chat 1.1B Q4": - -```json -{ - "sources": [ - { - "filename": "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf", - "url": "https://huggingface.co/TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf" - } - ], - "id": "tinyllama-1.1b", - "object": "model", - "name": "TinyLlama Chat 1.1B Q4", - "version": "1.0", - "description": "TinyLlama is a tiny model with only 1.1B. It's a good model for less powerful computers.", - "format": "gguf", - "settings": { - "ctx_len": 4096, - "prompt_template": "<|system|>\n{system_message}<|user|>\n{prompt}<|assistant|>", - "llama_model_path": "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf" - }, - "parameters": { - "temperature": 0.7, - "top_p": 0.95, - "stream": true, - "max_tokens": 2048, - "stop": [], - "frequency_penalty": 0, - "presence_penalty": 0 - }, - "metadata": { - "author": "TinyLlama", - "tags": [ - "Tiny", - "Foundation Model" - ], - "size": 669000000 - }, - "engine": "nitro" -} -``` -##### Step 2: Modify Model Parameters - -Key fields to configure: -1. The **Settings** array is where you can set the path or location of your model in your computer, the context -length allowed, and the chat template expected by your model. -2. The [**Parameters**](./model-parameters) are the adjustable settings that affect how your model operates or -processes the data. The fields in the parameters array are typically general and can be used across different -models. Here is an example of model parameters: - -```json -"parameters":{ - "temperature": 0.7, - "top_p": 0.95, - "stream": true, - "max_tokens": 4096, - "frequency_penalty": 0, - "presence_penalty": 0, -} -``` - -### Delete Models -1. Go to **Settings > Model Providers > Llama.cpp** -2. Find the model you want to remove -3. Select the three dots icon next to it and select **Delete Model** - -![Delete Model](../../../assets/model-management-05.png) - -## Cloud Models - - - -Jan supports connecting to various AI cloud providers that are OpenAI API-compatible, including: OpenAI (GPT-4o, o3,...), -Anthropic (Claude), Groq, Mistral, and more. -1. Navigate to the **Settings** page -2. Under **Model Providers** section in the left sidebar, choose your preferred provider (OpenAI, Anthropic, etc.) -3. Enter your API key -4. The activated cloud models will be available in your model selector inside the **Chat** panel - -![Download Model](../../../assets/model-management-06.png) - -As soon as you add your key for a model provider like Anthropic or OpenAI, you will be able to pick one of their models to chat with. - -![Connect Remote APIs](../../../assets/quick-start-03.png) 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 ff4d919c9..000000000 --- a/website/src/content/docs/jan/mcp-examples/data-analysis/e2b.mdx +++ /dev/null @@ -1,288 +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 - -![Turn on MCP](../../../../../assets/mcp-on.png) - - - -2. **Get API Key**: Sign up at [e2b.dev](https://e2b.dev/), generate an API key - -![E2B API Key](../../../../../assets/e2b-key.png) - -Add a meaningful name to your key. - -![E2B MCP Server](../../../../../assets/e2b-key1.png) - -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` - -![E2B MCP Server](../../../../../assets/e2b-key2.png) - -4. **Verify**: Check server shows as active - - -![E2B MCP Server](../../../../../assets/e2b-key3.png) - -## 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. - - - -![E2B MCP Server](../../../../../assets/e2b-key4.png) - -### 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. - -![Chat and use E2B MCP ](../../../../../assets/e2b-key5.png) - - -``` -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 - - -![Chat and use E2B MCP ](../../../../../assets/e2b-key6.png) - -![Chat and use E2B MCP ](../../../../../assets/e2b-key7.png) - - - - - -### Statistical Computing - -``` -Run a Monte Carlo simulation with 10,000 iterations to estimate π. -``` - -Expected output: -- Numerical computation with numpy -- Convergence plot showing estimate improvement -- Final π 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/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 - -![MCP settings page with toggle enabled](../../../../../assets/mcp-on.png) - -### 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 - -![Exa API Key](../../../../../assets/exa.png) - -### 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` - -![Exa Jan MCP Server](../../../../../assets/exa1.png) - -### Verify Setup - -Check server status in the MCP Servers list. - -![Exa Jan MCP Server](../../../../../assets/exa2.png) - -### Model Configuration - -Use a compatible model provider: - -- **Jan Nano 32k** -- **Anthropic** -- **OpenAI** -- **OpenRouter** - -![E2B MCP Server](../../../../../assets/e2b-key4.png) - -## Usage - -Start a new chat with a tool-enabled model. Exa tools will appear in the available tools list. - -![Exa Tools Available](../../../../../assets/exa3.png) - -### Example Queries - -**Current Events & Activities:** - -``` -What is happening this week, mid July 2025, in Sydney, Australia? -``` - -![Exa Tools Available](../../../../../assets/exa4.png) - -**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.mdx b/website/src/content/docs/jan/mcp.mdx deleted file mode 100644 index 793424084..000000000 --- a/website/src/content/docs/jan/mcp.mdx +++ /dev/null @@ -1,200 +0,0 @@ ---- -title: Model Context Protocol -description: Manage your interaction with AI locally. -keywords: - [ - Jan, - Customizable Intelligence, LLM, - local AI, - privacy focus, - free and open source, - private and offline, - conversational AI, - no-subscription fee, - large language models, - threads, - chat history, - thread history, - ] ---- - -import { Aside } from '@astrojs/starlight/components'; - -```mermaid -graph TD - subgraph "What is MCP?" - You[You using Jan Desktop] - Claude[Jan AI Assistant] - - subgraph "Your Connected Tools" - Files[📁 Your Files
Documents, folders,
text files] - Database[📊 Your Data
Spreadsheets,
databases] - WebServices[🌐 Online Services
GitHub, Slack,
Google Drive] - Custom[🔧 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 -``` - - -Jan now supports the **Model Context Protocol (MCP)**, an open standard designed to allow language models to -interact with external tools and data sources. - -MCPs act as a common interface, standardizing the way an AI model can interact with external tools and data -sources. This enables a model to connect to any MCP-compliant tool without requiring custom -integration work. The way this works is via clients and servers. Clients are connected to an AI model and a host -where a user will describe the task needed to be done. These applications hosting client will want to connect -to different data sources to accomplish a task, for example, notion, google sheets, or even custom APIs. These -applications will be connected to a server with prompts, tools, and data sources which will be used to complete -the task. - -Jan is an MCP host that allows you to download different clients and servers and use them to accomplish a task. - -This document outlines the benefits, risks, and implementation of MCPs within Jan. - -## Core Benefits of MCP - -Integrating MCP provides a structured way to extend the capabilities of the models you use in Jan. Here are the three - -* **Standardization:** MCP aims to solve the "M x N" integration problem, where every model (M) needs a -unique connector for every tool (N). By adapting to a single standard, any compliant model can interface with any compliant tool. -* **Extensibility:** This allows you to augment your models with new abilities. For instance, an AI can be granted -access to search your local codebase, query a database, or interact with web APIs, all through the same protocol. -* **Flexibility:** Because the interface is standardized, you can swap out models or tools with minimal friction, -making your workflows more modular and adaptable over time. - - - - - -## Considerations and Risks - -While powerful, MCP is an evolving standard, and its use requires careful consideration of the following points: - -* **Security:** Granting a model access to external tools is a significant security consideration. A compromised -tool or a malicious prompt could potentially lead to unintended actions or data exposure. Jan's implementation -focuses on user-managed permissions to mitigate this risk, meaning, you have to turn on the permission for each -tool individually. -* **Standard Maturity:** As a relatively new protocol, best practices or sensible defaults are still being -established. Users should be aware of potential issues like prompt injection, where an input could be crafted to -misuse a tool's capabilities. -* **Resource Management:** Active MCP connections may consume a portion of a model's context window, which could -affect performance (i.e., the more tools the model and the larger the context of the conversation has the longer -you will need to wait for a response). Efficient management of tools and their outputs is important. - - -## Configure and Use MCPs within Jan - -To illustrate how MCPs can be used within Jan, we will walk through an example using the [Browser MCP](https://browsermcp.io/). - -Before we begin, you will need to enable experimental features at `General` > `Advanced`. Next, go to `Settings` > `MCP Servers`, and toggle -the `Allow All MCP Tool Permission` switch ON. - -![Turn on the MCP Host](../../../assets/mcp-on.png) - -Please note that you will also need to have **NodeJS** and/or **Python** installed on your machine. In case you don't -have either, you can download them from the official websites at the links below: -- [Node.js](https://nodejs.org/) -- [Python](https://www.python.org/) - - -### Browser MCP - -- Click on the `+` sign on the upper right-hand corner of the MCP box. - -![Add New Server](../../../assets/mcp-setup-1.png) - -- Enter the following details to configure the BrowserMCP: - - **Server Name**: `browsermcp` - - **Command**: `npx` - - **Arguments**: `@browsermcp/mcp` - - **Environment Variables**: You can leave this field empty. - -![Configure BrowserMCP](../../../assets/mcp-setup-2.png) - -- Check that the server has been activated successfully. - -![Server Confirmation](../../../assets/mcp-setup-3.png) - -- Open your favorite chrome-based browser (e.g., Google Chrome, Brave, Vivaldi, Microsoft Edge, etc...) and navigate to the -[Browser MCP Extension Page](https://chromewebstore.google.com/detail/browser-mcp-automate-your/bjfgambnhccakkhmkepdoekmckoijdlc). - -![Browser MCP Extension](../../../assets/mcp-setup-6.png) - -- Make sure to enable the extension to run on private windows. Since Browser Use will have access to all sites you've -already logged into in your regular browser session, it is best to give it a clean slate to start from. - -![Browser MCP Extension](../../../assets/mcp-setup-7.png) - -- Enable the extension to run on private windows by clicking on it and Connecting to the Browser MCP server. - -![Browser MCP Extension](../../../assets/mcp-setup-8.png) - -- Go back to Jan and pick a model with good tool use capabilities, for example, Claude 3.7 and 4 Sonnet, or Claude 4 Opus, -and make sure to enable tool calling via the UI by going to **Model Providers > Anthropic** and, after you have entered your -API key, enable tool from the **+** button. - -![Browser MCP Extension](../../../assets/mcp-setup-9.png) - -You can check and see if this was accurate below. - -![Browser MCP Extension](../../../assets/mcp-setup-10.png) - - -## Troubleshooting - -- The MCP server won't connect even though I've already added it to my list of MCP Servers? - - Make sure you have NodeJS and Python installed - - Make sure you typed the commands correctly in the MCP Server form - - Make sure the model you are using has tools enabled - - Restart Jan -- The open source model I picked won't use the MCPs I enabled. - - Make sure the model you are using has tools enabled - - Lots of open source models are not designed to use tools or simply don't work well with them, so you may need to try a different model - - The model you have selected might be good at tool calling but it is possible that it does not support images, effectively making it unsuitable for some tools that take screenshots of a website like the Browser MCP - -## Future Potential - -This integration is the foundation for creating more capable and context-aware AI assistants within Jan. The -long-term goal is to enable more sophisticated workflows that make use of your local environment securely as -well as your favorite tools. - -For example, an AI could cross-reference information between a local document and a remote API, or use a -local script toanalyze data and then summarize the findings, all orchestrated through Jan's interface. As -the MCP ecosystem grows, so will the potential applications within Jan. 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/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** - -![Anthropic](../../../../assets/model-management-06.png) - -### 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** - -![Cohere](../../../../assets/cohere.png) - - -### 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** - -![Google](../../../../assets/google.png) - - -### 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** - -![Groq](../../../../assets/groq.png) - - -### 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/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** - -![Mistral](../../../../assets/mistralai.png) - -### 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 - -![OpenAI](../../../../assets/openai.png) - - -### 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! 🙌 - -## 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** - -![OpenRouter](../../../../assets/openrouter.png) - -### 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) - -![Model Settings](../../../assets/trouble-shooting-04.png) - -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 - -![Hardware](../../../assets/hardware.png) - - -## 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. - -![Appearance](../../../assets/settings-04.png) - -### Spell Check - -Jan includes a built-in spell check feature to help catch typing errors in your messages. - -![Spell Check](../../../assets/settings-06.png) - -## 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 - - - -![Analytics](../../../assets/settings-07.png) - -### 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: - -![View Logs](../../../assets/settings-08.png) - -**2. Clear Logs** - -Jan retains your logs for only **24 hours**. To remove all logs from Jan, at **Clear Logs**, click the **Clear** button: - - - -![Clear Logs](../../../assets/settings-09.png) - - -### 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: - -![Open Jan Data Folder](../../../assets/settings-11.png) - -**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** - -![Edit Jan Data Folder](../../../assets/settings-12.png) - -### 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 - -![HTTPs Proxy](../../../assets/settings-13.png) - -**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 - - - -![Ignore SSL Certificates](../../../assets/settings-14.png) - -### 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 - -![Jan Quick Ask](../../../assets/settings-17.png) - -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 -![Jan Quick Ask](../../../assets/settings-18.png) diff --git a/website/src/content/docs/jan/threads.mdx b/website/src/content/docs/jan/threads.mdx deleted file mode 100644 index d7e2a2bd5..000000000 --- a/website/src/content/docs/jan/threads.mdx +++ /dev/null @@ -1,142 +0,0 @@ ---- -title: Start Chatting -description: Download models and manage your conversations with AI models locally. -keywords: - [ - Jan, - local AI, - LLM, - chat, - threads, - models, - download, - installation, - conversations, - ] ---- - -import { Aside } from '@astrojs/starlight/components'; - -
    - -### 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 a Model - -Jan requires a model to chat. Download one from the Hub: - -1. Go to the **Hub Tab** -2. Browse available models (must be GGUF format) -3. Select one matching your hardware specs -4. Click **Download** - -![Download a Model](../../../assets/model-management-01.png) - - - -**HuggingFace models:** Some require an access token. Add yours in **Settings > Model Providers > Llama.cpp > Hugging Face Access Token**. - -![Add HF Token](../../../assets/hf_token.png) - -### Step 3: Enable GPU Acceleration (Optional) - -For Windows/Linux with compatible graphics cards: - -1. Go to **Settings** > **Hardware** -2. Toggle **GPUs** to ON - -![Turn on GPU acceleration](../../../assets/gpu_accl.png) - - - -### Step 4: Start Chatting - -1. Click the **New Chat** icon -2. Select your model in the input field dropdown -3. Type your message and start chatting - -![Create New Thread](../../../assets/threads-new-chat-updated.png) - -
- -## 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 - -![Favorites and Recents](../../../assets/threads-favorites-and-recents-updated.png) - -### 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 - -![Context Menu](../../../assets/threads-context-menu-updated.png) - -### 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 - -![Assistant Instruction](../../../assets/assistant-dropdown.png) - -![Add an Assistant Instruction](../../../assets/assistant-edit-dialog.png) - -### 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** - -![Chat with a Model](../../../assets/model-parameters.png) - -### 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) - -![Connect Remote APIs](../../../assets/quick-start-03.png) - -For detailed setup, see [Remote APIs](/docs/remote-models/openai). 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** - -![App log](../../../assets/trouble-shooting-02.png) - -**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 $XDG_CONFIG_HOME/Jan -``` - -3. **Kill background processes** (for versions before 0.4.2): -```bash -ps aux | grep nitro -kill -9 -``` - -4. **Download fresh copy** from [jan.ai](/download) - - - - - - -### NVIDIA GPU Not Working - -If Jan isn't using your NVIDIA graphics card for acceleration: - -#### Step 1: Verify Hardware and System Requirements - -**Check GPU Detection:** - -*Windows:* Right-click desktop → NVIDIA Control Panel, or check Device Manager → Display Adapters - -*Linux:* Run `lspci | grep -i nvidia` - -**Install Required Software:** - -**NVIDIA Driver (470.63.01 or newer):** -1. Download from [nvidia.com/drivers](https://www.nvidia.com/drivers/) -2. Test: Run `nvidia-smi` in terminal - -**CUDA Toolkit (11.7 or newer):** -1. Download from [CUDA Downloads](https://developer.nvidia.com/cuda-downloads) -2. Test: Run `nvcc --version` - -**Linux Additional Requirements:** -```bash -# Install required packages -sudo apt update && sudo apt install gcc-11 g++-11 cpp-11 - -# Set CUDA environment -export LD_LIBRARY_PATH=$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 - -![Hardware](../../../assets/trouble-shooting-01.png) - -#### 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 **#🆘|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 88e60d145..000000000 --- a/website/src/content/docs/local-server/api-server.mdx +++ /dev/null @@ -1,136 +0,0 @@ ---- -title: Server Setup -description: Learn how to run Jan's local API server. -keywords: - [ - Jan, - Customizable Intelligence, LLM, - local AI, - privacy focus, - free and open source, - private and offline, - conversational AI, - no-subscription fee, - large language models, - Jan Extensions, - Extensions, - ] ---- - -import { Aside } from '@astrojs/starlight/components'; -import { Steps } from '@astrojs/starlight/components'; -import { Tabs, TabItem } from '@astrojs/starlight/components'; - - -Configure and start Jan's built-in API server. - -## Prerequisites - -- Jan installed and running -- At least one AI model downloaded or configured (see [Model Management](/docs/manage-models)) - -For an overview of Jan Local Server, see the [Local Server introduction](./index). - -
-![Local API Server](../../../assets/api-server.png) -
- -## Start Server - -1. Navigate to **Local API Server** -2. Add an API Key (can be anything) -3. Configure settings (see [Server Configuration](#server-configuration) below) -4. Click **Start Server** -5. Wait for confirmation: `JAN API listening at: http://127.0.0.1:1337` - -![Local API Server](../../../assets/api-server2.png) - -## Test Server - -1. Click **API Playground** -2. Select a model -3. Send a test request - -## API Usage - -```jan/website/src/content/docs/local-server/api-server.mdx#L69-80 -curl http://127.0.0.1:1337/v1/chat/completions \ - -H "Content-Type: application/json" \ - -H "Authorization: Bearer testing-something" \ - -d '{ - "model": "jan-nano-gguf", - "messages": [ - { - "role": "user", - "content": "Write a one-sentence bedtime story about a unicorn." - } - ] - }' -``` - -Include your API key in the `Authorization` header for all requests. - -## Server Configuration - -### Host Address Options -- **127.0.0.1 (Recommended)**: - - Only accessible from your computer - - Most secure option for personal use -- **0.0.0.0**: - - Makes server accessible from other devices on your network - - Use with caution and only when necessary - -### Port Number -- Default: `1337` -- Can be any number between 1-65535 -- Avoid common ports (80, 443, 3000, 8080) that might be used by other applications - -### API Prefix -- Default: `/v1` -- Defines the base path for all API endpoints -- Example: http://127.0.0.1:1337/v1/chat/completions - -### Cross-Origin Resource Sharing (CORS) -CORS controls which websites can access your API, which is important for web applications running in browsers. - -**When to enable:** -- If you're building a web application that needs to access the API -- If you're using browser extensions - -**When to leave disabled:** -- If you're only using the API from your local applications -- If you're concerned about security - -### Verbose Server Logs -Enable to show: -- Detailed information about each API request -- Error messages and debugging information -- Server status updates - -## Troubleshooting - - - -### Common Server Issues -- Server not running -- Model not loaded in Jan -- Port already in use -- Check admin/sudo rights -- API endpoint doesn't match server settings -- Model name in request doesn't match Jan model name -- Invalid JSON format -- Firewall blocking connection -- Missing API key in request headers - -### CORS Errors -- Enable CORS in server settings -- Check request origin -- Verify URL matches server address -- Check browser console - -### Performance Issues -- Monitor CPU, RAM, GPU usage -- Reduce context length or GPU layers -- Close other resource-intensive applications diff --git a/website/src/content/docs/local-server/data-folder.mdx b/website/src/content/docs/local-server/data-folder.mdx deleted file mode 100644 index 59165137a..000000000 --- a/website/src/content/docs/local-server/data-folder.mdx +++ /dev/null @@ -1,179 +0,0 @@ ---- -title: Jan Data Folder -description: Understand where Jan stores your data and how to monitor server logs. -keywords: - [ - Jan, - local AI, - data folder, - logs, - server logs, - troubleshooting, - privacy, - local storage, - file structure, - ] ---- - -import { Aside, Tabs, TabItem } from '@astrojs/starlight/components'; - -Jan stores all your data locally on your computer. No cloud storage, no external servers - -everything stays on your machine. - -## Quick Access - -**Via Jan Interface:** -1. Go to Settings (⚙️) > Advanced Settings -2. Click the folder icon 📁 - -![Open Jan Data Folder](../../../assets/settings-11.png) - -**Via File Explorer:** - - - -```cmd -%APPDATA%\Jan\data -``` - - -```bash -~/Library/Application Support/Jan/data -``` - - - -```bash -# Default installation -~/.config/Jan/data - -# Custom installation -$XDG_CONFIG_HOME/Jan/data -``` - - - - - -## Monitoring Server Logs - -When Jan's local server is running, you can monitor real-time activity in the logs folder: - -![API Server Logs](../../../assets/api-server-logs.png) - -### Live Log Monitoring - -**Real-time logs show:** -- API requests and responses -- Model loading and inference activity -- Error messages and warnings -- Performance metrics -- Connection attempts from external applications - -**Accessing logs:** -- **In Jan**: System Monitor (footer) > App Log -- **File location**: `/logs/app.log` - -### Log Categories - -| Log Type | What It Shows | When It's Useful | -|----------|---------------|------------------| -| **[APP]** | Core application events | Startup issues, crashes, general errors | -| **[SERVER]** | API server activity | Connection problems, request failures | -| **[SPECS]** | Hardware information | Performance issues, compatibility problems | -| **[MODEL]** | Model operations | Loading failures, inference errors | - -## Data Structure - -``` -jan/ -├── assistants/ # AI personality settings -│ └── jan/ -│ └── assistant.json -├── engines/ # Engine configurations -│ └── llama.cpp/ -├── extensions/ # Add-on modules -│ └── extensions.json -├── logs/ # Server and application logs -│ └── app.log # Main log file -├── models/ # Downloaded AI models -│ └── huggingface.co/ -└── threads/ # Chat conversations - └── thread_id/ - ├── messages.jsonl - └── thread.json -``` - -## Key Folders Explained - -### `/logs/` - Server Activity Hub -Contains all application and server logs. Essential for troubleshooting and monitoring API activity. - -**What you'll find:** -- Real-time server requests -- Model loading status -- Error diagnostics -- Performance data - -### `/models/` - AI Model Storage -Where your downloaded models live. Each model includes: -- `model.gguf` - The actual AI model file -- `model.json` - Configuration and metadata - -### `/threads/` - Chat History -Every conversation gets its own folder with: -- `messages.jsonl` - Complete chat history -- `thread.json` - Thread metadata and settings - -### `/assistants/` - AI Personalities -Configuration files that define how your AI assistants behave, including their instructions and available tools. - -## Privacy & Security - -**Your data stays local:** -- No cloud backups or syncing -- Files stored in standard JSON/JSONL formats -- Complete control over your data -- Easy to backup or migrate - -**File permissions:** -- Only you and Jan can access these files -- Standard user-level permissions -- No elevated access required - - - -## Common Tasks - -### Backup Your Data -Copy the entire Jan data folder to backup: -- All chat history -- Model configurations -- Assistant settings -- Extension data - -### Clear Chat History -Delete individual thread folders in `/threads/` or use Jan's interface to delete conversations. - -### Export Conversations -Thread files are in standard JSON format - readable by any text editor or compatible with other applications. - -### Troubleshooting Data Issues -1. Check `/logs/app.log` for error messages -2. Verify folder permissions -3. Ensure sufficient disk space -4. Restart Jan if files appear corrupted - -## Uninstalling Jan - -If you need to completely remove Jan and all data: - -**Keep data (reinstall later):** Just uninstall the application -**Remove everything:** Delete the Jan data folder after uninstalling - -Detailed uninstall guides: -- [macOS](/docs/desktop/mac#step-2-clean-up-data-optional) -- [Windows](/docs/desktop/windows#step-2-handle-jan-data) -- [Linux](/docs/desktop/linux#uninstall-jan) 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 d81b48b90..000000000 --- a/website/src/content/docs/local-server/index.mdx +++ /dev/null @@ -1,195 +0,0 @@ ---- -title: Jan Local Server -description: Run Jan as a local AI server with OpenAI-compatible API for building AI applications. ---- - -import { Aside } from '@astrojs/starlight/components'; - -![Jan's Cover Image](../../../assets/ls.png) - -Jan Local Server provides an OpenAI-compatible API that runs entirely on your computer. Build AI applications using familiar API patterns while keeping complete control over your data and models. - -## How It Works - -Jan runs a local server powered by [llama.cpp](https://github.com/ggerganov/llama.cpp) that provides an OpenAI-compatible API. By default, it runs at `https://localhost:1337` and works completely offline. - -**What this enables:** -- Connect development tools like [Continue](./continue-dev) and [Cline](https://cline.bot/) to Jan -- Build AI applications without cloud dependencies -- Use both local and cloud models through the same API -- Maintain full privacy for local model interactions - -## Key Features - -**Local AI Models** -- Download popular open-source models (Llama, Gemma, Qwen) from Hugging Face -- Import any GGUF files from your computer -- Run models completely offline - -**Cloud Integration** -- Connect to cloud services (OpenAI, Anthropic, Mistral, Groq) -- Use your own API keys -- Switch between local and cloud models seamlessly - -**Developer-Friendly** -- OpenAI-compatible API for easy integration -- Chat interface for testing and configuration -- Model parameter customization - -**Complete Privacy** -- All data stored locally -- No cloud dependencies for local models -- You control what data leaves your machine - -## Why Choose Jan? - -**Truly Open Source** -- Apache 2.0 license - no restrictions -- Community-driven development -- Full transparency - -**Local-First Design** -- Works 100% offline with local models -- Data stays on your machine -- No vendor lock-in - -**Flexible Model Support** -- Your choice of AI models -- Both local and cloud options -- Easy model switching - -**No Data Collection** -- We don't collect or sell user data -- Local conversations stay local -- [Read our Privacy Policy](./privacy) - - - -## Philosophy - -Jan is built to be **user-owned**. This means: -- **True open source** - Apache 2.0 license with no hidden restrictions -- **Local data storage** - following [local-first principles](https://www.inkandswitch.com/local-first) -- **Internet optional** - works completely offline -- **Free choice** - use any AI models you want -- **No surveillance** - we don't collect or sell your data - -Read more about our [philosophy](/about#philosophy). - -## Inspiration - -Jan draws inspiration from [Calm Computing](https://en.wikipedia.org/wiki/Calm_technology) and the Disappearing Computer - technology that works quietly in the background without demanding constant attention. - -## Built With - -Jan stands on the shoulders of excellent open-source projects: -- [llama.cpp](https://github.com/ggerganov/llama.cpp) - Local AI model inference -- [Scalar](https://github.com/scalar/scalar) - API documentation - -## Frequently Asked Questions - -## What is Jan? - - Jan is a privacy-focused AI assistant that runs locally on your computer. It's an alternative to ChatGPT, Claude, and other cloud-based AI tools, with optional cloud AI support when you want it. - - -## How do I get started? - - Download Jan, add a model (either download locally or add a cloud API key), and start chatting. Check our [Quick Start guide](/docs/quickstart) for detailed setup instructions. - - -## What systems does Jan support? - - Jan works on all major operating systems: - - [macOS](/docs/desktop/mac#compatibility) - Intel and Apple Silicon - - [Windows](/docs/desktop/windows#compatibility) - x64 systems - - [Linux](/docs/desktop/linux) - Most distributions - - Jan supports various hardware: - - NVIDIA GPUs (CUDA acceleration) - - AMD GPUs (Vulkan support) - - Intel Arc GPUs (Vulkan support) - - Any GPU with Vulkan support - - CPU-only operation - - -## How does Jan protect my privacy? - - Jan prioritizes privacy through: - - **100% offline operation** with local models - - **Local data storage** - everything stays on your device - - **Open-source transparency** - you can verify what Jan does - - **No data collection** - we never see your conversations - - - - All your files and chat history are stored locally in the [Jan Data Folder](./data-folder). See our complete [Privacy Policy](./privacy). - - -## What AI models can I use? - - **Local models:** - - Download optimized models from the [Jan Hub](/docs/manage-models) - - Import GGUF models from Hugging Face - - Use any compatible local model files - - **Cloud models:** - - OpenAI (GPT-4, ChatGPT) - - Anthropic (Claude) - - Mistral, Groq, and others - - Bring your own API keys - - -## Is Jan really free? - - Yes! Jan is completely free and open-source with no subscription fees. - - **What's free:** - - Jan application and all features - - Local model usage (once downloaded) - - Local server and API - - **What costs money:** - - Cloud model usage (you pay providers directly) - - We add no markup to cloud service costs - - -## Can Jan work offline? - - Absolutely! Once you download a local model, Jan works completely offline with no internet connection needed. This is one of Jan's core features. - - -## How can I get help or contribute? - - **Get help:** - - Join our [Discord community](https://discord.gg/qSwXFx6Krr) - - Check the [Troubleshooting guide](./troubleshooting) - - Ask in [#🆘|jan-help](https://discord.com/channels/1107178041848909847/1192090449725358130) - - **Contribute:** - - Contribute on [GitHub](https://github.com/menloresearch/jan) - - No permission needed to submit improvements - - Help other users in Discord - - -## Can I self-host Jan? - - Yes! We fully support self-hosting. You can: - - Download Jan directly for personal use - - Fork the [GitHub repository](https://github.com/menloresearch/jan) - - Build from source - - Deploy on your own infrastructure - - -## What does 'Jan' stand for? - - "Just a Name" - we admit we're not great at marketing! 😄 - - -## Are you hiring? - - Yes! We love hiring from our community. Check our open positions at [Careers](https://menlo.bamboohr.com/careers). 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 `⚙️ 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 (⋮)** 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 8b7412faa..000000000 --- a/website/src/content/docs/local-server/llama-cpp.mdx +++ /dev/null @@ -1,164 +0,0 @@ ---- -title: llama.cpp Server -description: Configure Jan's local AI engine for optimal performance. -keywords: - [ - Jan, - local AI, - llama.cpp, - AI engine, - local models, - performance, - GPU acceleration, - CPU processing, - model optimization, - ] ---- - -import { Aside, Tabs, TabItem } from '@astrojs/starlight/components'; - -llama.cpp is the engine that runs AI models locally on your computer. It's what makes Jan work without -needing internet or cloud services. - -## Accessing Engine Settings - -Find llama.cpp settings at **Settings** (⚙️) > **Local Engine** > **llama.cpp**: - -![llama.cpp](../../../assets/llama.cpp-01-updated.png) - - - -## When to Adjust Settings - -You might need to modify these settings if: -- Models load slowly or don't work -- You've installed new hardware (like a graphics card) -- You want to optimize performance for your specific setup - -## Engine Management - -| Feature | What It Does | When You Need It | -|---------|-------------|------------------| -| **Engine Version** | Shows current llama.cpp version | Check compatibility with newer models | -| **Check Updates** | Downloads engine updates | When new models require updated engine | -| **Backend Selection** | Choose hardware-optimized version | After hardware changes or performance issues | - -## Hardware Backends - -Different backends are optimized for different hardware. Pick the one that matches your computer: - - - - -### NVIDIA Graphics Cards (Fastest) -**For CUDA 12.0:** -- `llama.cpp-avx2-cuda-12-0` (most common) -- `llama.cpp-avx512-cuda-12-0` (newer Intel/AMD CPUs) - -**For CUDA 11.7:** -- `llama.cpp-avx2-cuda-11-7` (older drivers) - -### CPU Only -- `llama.cpp-avx2` (modern CPUs) -- `llama.cpp-avx` (older CPUs) -- `llama.cpp-noavx` (very old CPUs) - -### Other Graphics Cards -- `llama.cpp-vulkan` (AMD, Intel Arc) - - - - -### NVIDIA Graphics Cards -- `llama.cpp-avx2-cuda-12-0` (recommended) -- `llama.cpp-avx2-cuda-11-7` (older drivers) - -### CPU Only -- `llama.cpp-avx2` (modern CPUs) -- `llama.cpp-arm64` (ARM processors) - -### Other Graphics Cards -- `llama.cpp-vulkan` (AMD, Intel graphics) - - - - -### Apple Silicon (M1/M2/M3/M4) -- `llama.cpp-mac-arm64` (recommended) - -### Intel Macs -- `llama.cpp-mac-amd64` - - - - - - -## Performance Settings - -| Setting | What It Does | Recommended | Impact | -|---------|-------------|-------------|---------| -| **Continuous Batching** | Handle multiple requests simultaneously | Enabled | Faster when using tools or multiple chats | -| **Parallel Operations** | Number of concurrent requests | 4 | Higher = more multitasking, uses more memory | -| **CPU Threads** | Processor cores to use | Auto | More threads can speed up CPU processing | - -## Memory Settings - -| Setting | What It Does | Recommended | When to Change | -|---------|-------------|-------------|----------------| -| **Flash Attention** | Efficient memory usage | Enabled | Leave enabled unless problems occur | -| **Caching** | Remember recent conversations | Enabled | Speeds up follow-up questions | -| **KV Cache Type** | Memory vs quality trade-off | f16 | Change to q8_0 if low on memory | -| **mmap** | Efficient model loading | Enabled | Helps with large models | -| **Context Shift** | Handle very long conversations | Disabled | Enable for very long chats | - -### Memory Options Explained -- **f16**: Best quality, uses more memory -- **q8_0**: Balanced memory and quality -- **q4_0**: Least memory, slight quality reduction - -## Quick Troubleshooting - -**Models won't load:** -- Try a different backend -- Check available RAM/VRAM -- Update engine version - -**Slow performance:** -- Verify GPU acceleration is active -- Close memory-intensive applications -- Increase GPU Layers in model settings - -**Out of memory:** -- Change KV Cache Type to q8_0 -- Reduce Context Size in model settings -- Try a smaller model - -**Crashes or errors:** -- Switch to a more stable backend (avx instead of avx2) -- Update graphics drivers -- Check system temperature - -## Quick Setup Guide - -**Most users:** -1. Use default settings -2. Only change if problems occur - -**NVIDIA GPU users:** -1. Download CUDA backend -2. Ensure GPU Layers is set high -3. Enable Flash Attention - -**Performance optimization:** -1. Enable Continuous Batching -2. Use appropriate backend for hardware -3. Monitor memory usage - - 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 8819442ba..000000000 --- a/website/src/content/docs/local-server/settings.mdx +++ /dev/null @@ -1,245 +0,0 @@ ---- -title: Settings -description: Configure Jan to work best for your needs and hardware. -keywords: - [ - Jan, - settings, - configuration, - model management, - privacy, - hardware settings, - local AI, - customization, - ] ---- - -import { Tabs, TabItem } from '@astrojs/starlight/components'; -import { Steps } from '@astrojs/starlight/components'; -import { Aside } from '@astrojs/starlight/components'; - -Access Jan's settings by clicking the ⚙️ icon in the bottom left corner. - -## Managing AI Models - -Find all model options at **Settings** > **Model Providers**: - -### Adding Models - -**From Hugging Face:** -- Enter a model's ID (like `microsoft/DialoGPT-medium`) in the search bar -- **Need authentication?** Some models require a Hugging Face token - add yours at **Settings > Model Providers > Hugging Face Access Token** - -**From Your Computer:** -- Click **Import Model** and select GGUF files from your computer -- Works with any compatible model files you've downloaded - -### Managing Existing Models - -**Start a model:** -1. Open a new chat and select the model you want -2. Or go to **Settings > Model Providers** and click the **Start** button - -**Remove a model:** -- Click the trash icon next to the **Start** button -- Confirm deletion when prompted - -### Hugging Face Token Setup - -For restricted models (like Meta's Llama models): -1. Get your token from [Hugging Face Tokens](https://huggingface.co/docs/hub/en/security-tokens) -2. Add it at **Settings > Model Providers > Hugging Face** - -## Model Configuration (Gear Icon) - -![Model Settings](../../../assets/trouble-shooting-04.png) - -Click the gear icon next to any model to adjust how it behaves: - -**Basic Settings:** -- **Context Size**: How much conversation history the model remembers -- **GPU Layers**: How much of the model runs on your graphics card (higher = faster, but uses more GPU memory) -- **Temperature**: Controls creativity (0.1 = focused, 1.0 = creative) - -**Advanced Controls:** -- **Top K & Top P**: Fine-tune how the model picks words (lower = more focused) -- **Min P**: Minimum probability threshold for word selection -- **Repeat Penalty**: Prevents the model from repeating itself too much -- **Presence Penalty**: Encourages the model to use varied vocabulary - - - -## Hardware Monitoring - -Check your computer's performance at **Settings** > **Hardware**: - -- **CPU, RAM, GPU**: Real-time usage and specifications -- **GPU Acceleration**: Turn GPU acceleration on/off -- **Temperature monitoring**: Keep an eye on system heat - -![Hardware](../../../assets/hardware.png) - - - -## Personalization - -### Visual Appearance - -Customize Jan's look at **Settings** > **Appearance**: -- **Theme**: Choose light or dark mode -- **Colors**: Pick your preferred color scheme -- **Code highlighting**: Adjust syntax colors for programming discussions - -![Appearance](../../../assets/settings-04.png) - -### Writing Assistance - -**Spell Check:** Jan can help catch typing mistakes in your messages. - -![Spell Check](../../../assets/settings-06.png) - -## Privacy & Data Control - -Access privacy settings at **Settings** > **Privacy**: - -### Usage Analytics - -**Default: No data collection.** Everything stays on your computer. - -**Optional: Help improve Jan** -- Toggle **Analytics** to share anonymous usage patterns -- No conversations or personal data ever shared -- Change this setting anytime - - - -![Analytics](../../../assets/settings-07.png) - -### Log Management - -**Viewing System Logs:** -- Logs help troubleshoot problems -- Click the folder icon to open App Logs and System Logs -- Logs are automatically deleted after 24 hours - -![View Logs](../../../assets/settings-08.png) - -**Clearing Logs:** -- Click **Clear** to remove all log files immediately -- Useful before sharing your computer or troubleshooting - - - -![Clear Logs](../../../assets/settings-09.png) - -### Data Folder Management - -Jan stores everything locally on your computer in standard file formats. - -**Access Your Data:** -- Click the folder icon to open Jan's data directory -- Find your chat history, models, and settings -- All files are yours to backup, move, or examine - -![Open Jan Data Folder](../../../assets/settings-11.png) - -**Change Storage Location:** -1. Click the pencil icon to edit the data folder location -2. Choose an empty directory -3. Confirm the move (original folder stays intact) -4. Restart Jan to complete the change - -![Edit Jan Data Folder](../../../assets/settings-12.png) - - - -## Network Settings - -### HTTPS Proxy Setup - -If you need to connect through a corporate network or want enhanced privacy: - -1. **Enable** the proxy toggle -2. Enter your proxy details: -``` -http://:@: -``` - -**Example:** -``` -http://user:pass@proxy.company.com:8080 -``` - -![HTTPS Proxy](../../../assets/settings-13.png) - - - -### SSL Certificate Handling - -**Ignore SSL Certificates:** Only enable this for: -- Corporate networks with internal certificates -- Development/testing environments -- Trusted network setups - -![Ignore SSL Certificates](../../../assets/settings-14.png) - - - -## Emergency Options - -### Factory Reset - -**When to use:** Only as a last resort for serious problems that other solutions can't fix. - -**What it does:** Returns Jan to its original state - deletes everything. - -**Steps:** -1. Click **Reset** under "Reset to Factory Settings" -2. Type **RESET** to confirm you understand this deletes everything -3. Optionally keep your current data folder location -4. Click **Reset Now** -5. Restart Jan - -![Factory Reset](../../../assets/settings-17.png) - -![Reset Confirmation](../../../assets/settings-18.png) - - - -**Try these first:** -- Restart Jan -- Check the [Troubleshooting Guide](./troubleshooting) -- Ask for help on [Discord](https://discord.gg/qSwXFx6Krr) - -## Quick Tips - -**For new users:** -- Start with default settings -- Try a few different models to find what works best -- Enable GPU acceleration if you have a graphics card - -**For performance:** -- Monitor hardware usage in real-time -- Adjust GPU layers based on your graphics card memory -- Use smaller models on older hardware - -**For privacy:** -- All data stays local by default -- Check the data folder to see exactly what's stored -- Analytics are opt-in only 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/content/products/index.mdx b/website/src/content/products/index.mdx deleted file mode 100644 index 1ac653817..000000000 --- a/website/src/content/products/index.mdx +++ /dev/null @@ -1,210 +0,0 @@ ---- -title: Products -description: AI that runs where you need it, how you need it -sidebar: - order: 0 ---- - -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -Jan is moving from a local AI application to a complete full-stack AI solution that you can self-host. This includes models, applications, and tools that delights users and help them solve their problems. - -## What We're Building - -**Jan Factory (or Agent)** = Jan Models + Jan Application + Jan Tools - -Unlike other AI assistants that do specific tasks with one model or have many models with a myriad of solutions, Jan provides: -- Its own specialised models that are optimised at specific tasks like web-search, creative writing, and translation -- Applications that work across all of your devices in an integrated way -- Tools that actually get things done - -## Two Modes, One Experience - -### Local (Incognito) Mode - -Run AI models entirely on your device, giving you complete privacy with no internet required. - -### Cloud Mode - -Connect to more powerful models when needed - either self-hosted or via jan.ai. - - - -## Our Product Principles - -### 1) It Just Works - -1. Open Jan, start chatting -2. Onboarding is fully available but optional -3. Setting up an API key is optional -4. Selecting a local model is optional -5. Become a power user at your own pace, if you want to - -We handle the complexity. - -### 2) Cloud When Needed - -Start completely locally and own your AI models. Add cloud capabilities only when you choose to. - -### 3) Solve Problems, Not Settings - -We help users get to answers quickly answers, not configuration options. Power users can dig deeper, but it's never required. - -## Available on Every Device - - - - **Available Now** - - Your personal AI workstation that helps with our use cases and powers other devices. Run models locally right away or bring an API key to connect to your favorite cloud-based models. - - - Runs models locally on your hardware - - GPU acceleration support - - Powers other devices via network connection - - Complete privacy and control - - Windows, macOS, and Linux support - - **Requirements:** - - Minimum 8GB RAM - - 10GB+ storage space - - Optional: NVIDIA GPU for acceleration - - - - **Beta Launch Soon** - - Web-based version of Jan with no setup required. Same default cloud mode for mobile and desktop users. - - - No installation needed - - Instant access from any browser - - Automatic updates and maintenance - - Default cloud backend for mobile apps - - Team collaboration features - - **Pricing:** - - Free for everyone - - Pro: Access our latest models - - Enterprise: Self-host or we host it for you - - - - **Coming Q4 2025** - - Connect to Desktop/Server, run local mode with Jan Nano or Lucy, same experience everywhere. - - Jan Mobile adapts to your situation: - - **At Home**: Connect to your Jan Desktop over WiFi - - **At Work**: Connect to your company Jan Server - - **On the Go**: Run Jan Nano on your phone or talk to cloud models - - - iOS and Android support - - Three adaptive modes (Desktop, Server, Local) - - Voice-first interface - - Seamless device switching - - Jan Nano for on-device AI - - - - **Coming Q2 2025** - - Self-hosted solution for teams and enterprises. Your own private AI cloud. - - - Support for 5-500+ concurrent users - - Enterprise authentication (SSO, LDAP) - - Docker and Kubernetes deployment - - Admin dashboard - - Team knowledge sharing - - **Deployment Options:** - - Docker: Single command setup - - Kubernetes: Enterprise scale - - Bare metal: Maximum control - - - -## Jan Mobile: Three Modes, One Experience - -Jan Mobile brings the same AI experience to your phone. Connect to your desktop, your server, or run models locally. - -### How It Works - -Jan Mobile adapts to your situation: - -**At Home** - Connect to your Jan Desktop over WiFi -Your Phone → WiFi → Your Desktop → Response - -**At Work** - Connect to your company Jan Server -Your Phone → Internet → Company Server → Response - -**On the Go** - Run Jan Nano on your phone or talk to your favorite cloud-based model -Your Phone → Jan Nano (6GB) → Response - -No configuration needed. It just works. - -### Key Features - -- **Seamless Switching**: Move from home to office to airplane. One-click and Jan adapts immediately. -- **Voice First**: Talk to Jan naturally. Responses can be spoken too. -- **Sync Everything**: Conversations, settings, and preferences follow you across devices. - -### Privacy & Security - -**Your Data, Your Control** -- Local Mode: Everything stays on your phone -- Desktop Mode: Direct encrypted connection -- Server Mode: Your organization's policies apply - -**No Compromises** -- Biometric app lock -- Encrypted storage -- No cloud backups without permission -- Clear data anytime - -## What Makes Jan Different - -| Feature | Other AI Assistants | Jan | -|---------|---------------------|-----| -| Models | Wrapper around Claude/GPT | Our own models + You can own them | -| Dual mode | Your data on their servers | Your data stays yours | -| Deployment | Cloud only | Local, self-hosted, or cloud | -| Cost | Subscription forever | Free locally, pay for cloud | - -## Development Timeline - -Jan is actively developed with regular releases. Our development follows these key milestones: - -### Current Focus -- **Jan Desktop**: Continuous improvements and model support -- **Jan Web**: Beta launch preparation -- **Model Development**: Jan Nano and Lucy optimization - -### Next 6 Months -- Jan Web public beta -- Mobile app development -- Server deployment tools - -### Future Vision -- Complete AI Agent platform -- Advanced tool integration -- Enterprise features - - - ---- - -**Ready to Get Started?** - -- [Download Jan Desktop](/download) - Available now for Windows, macOS, and Linux -- [Join Jan Web Beta](/beta) - Get early access to our web platform -- [Read the Documentation](/docs) - Learn how to use Jan effectively -- [Explore Our Models](/docs/models) - Discover Jan's specialized AI models - -For detailed information about each platform, explore the individual product pages: -- [Jan Desktop](/docs/products/platforms/desktop) -- [Jan Web](/docs/products/platforms/jan-ai) -- [Jan Mobile](/docs/products/platforms/mobile) -- [Jan Server](/docs/products/platforms/server) diff --git a/website/src/content/products/models/jan-nano.mdx b/website/src/content/products/models/jan-nano.mdx deleted file mode 100644 index 57e6ea3fe..000000000 --- a/website/src/content/products/models/jan-nano.mdx +++ /dev/null @@ -1,207 +0,0 @@ ---- -title: Jan Nano -description: Compact research model optimized for finding answers -sidebar: - order: 2 ---- - -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -Jan Nano is a 4-billion parameter model designed for research and information retrieval. Instead of trying to know everything, it excels at finding anything through deep integration with Model Context Protocol (MCP) tools. - -## Two Variants - -| Model | Context Window | Size | Use Case | -|-------|----------------|------|----------| -| Jan Nano 32k | 32,768 tokens | 4-8GB | Quick research, general queries | -| Jan Nano 128k | 131,072 tokens | 8-12GB | Deep research, document analysis | - - - -## What Makes Nano Different - -### Research-First Design -Jan Nano isn't trained to memorize facts. It's trained to: -- Find relevant information quickly -- Synthesize findings from multiple sources -- Maintain context across long research sessions -- Provide accurate citations - -### MCP Integration -Works seamlessly with: -- Web search (Serper, Exa) -- Document analysis -- Code repositories -- Custom data sources - -### Extended Context -The 128k variant can process: -- 50+ research papers simultaneously -- Entire codebases -- Book-length documents -- Thousand-page contracts - -## Performance - -### Hardware Requirements - - - - - 8GB RAM - - Any modern CPU - - Works on most devices - - - - - 16GB RAM - - GPU with 8GB+ VRAM - - CUDA-compatible - - - -### Speed Benchmarks - -| Device | Variant | Quantization | Speed | -|--------|---------|--------------|-------| -| M2 MacBook | 32k | Q4 | 80 tokens/s | -| RTX 4090 | 32k | Q8 | 200+ tokens/s | -| M2 MacBook | 128k | Q4 | 40 tokens/s | -| RTX 4090 | 128k | Q8 | 100+ tokens/s | - -## Getting Started - -### 1. Enable MCP -Go to **Settings** → **Model Providers** → **Llama.cpp** and enable tool use for Jan Nano. - -### 2. Add Search Tool -Add a search MCP like Serper: -- Get free API key from [serper.dev](https://serper.dev) -- Add to **Settings** → **MCP Servers** - -### 3. Start Researching -Open a chat and ask Jan Nano to search for information: - -``` -"What are the latest developments in quantum computing?" -"Search for recent breakthroughs in mRNA vaccine technology" -"Find and compare different approaches to carbon capture" -``` - -## Example Queries - -### Current Events -- Latest renewable energy developments in Europe -- Recent AI announcements from major tech companies -- Current economic indicators across G7 nations - -### Deep Research -- Analyze 20 papers on climate change impacts -- Compare different programming paradigms with examples -- Synthesize findings from multiple medical studies - -### Business Intelligence -- Market analysis across competitors -- Technology trends in specific sectors -- Regulatory changes affecting industries - - - -## Technical Details - -### Architecture -- **Base**: 4B parameter transformer -- **Training**: Optimized for tool use and retrieval -- **Context**: Native support (not retrofitted) -- **Quantization**: Q4, Q8, FP16 variants - -### Why Native Context Matters -Unlike models extended with YaRN or PI methods, Jan Nano 128k: -- Maintains consistent performance across full context -- Actually improves with more context (inverse scaling) -- No performance cliff at higher token counts - -### Deployment Options - -#### VLLM (Recommended for 128k) -```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 -``` - -#### llama.cpp -```bash -llama-server ... --rope-scaling yarn --rope-scale 3.2 --yarn-orig-ctx 40960 -``` - -## Limitations - -### Not Designed For -- Creative writing -- Complex reasoning without data -- Mathematical proofs -- Code generation from scratch - -### Best Used For -- Information retrieval -- Research synthesis -- Document analysis -- Fact-finding missions - -## Choosing Between Variants - -### Use 32k When -- Running on limited hardware -- Need quick responses -- Researching focused topics -- Battery life matters - -### Use 128k When -- Analyzing multiple documents -- Deep research projects -- Processing entire codebases -- Hardware isn't a constraint - -## Integration Examples - -### With Web Search -```python -# Jan Nano automatically uses search when needed -response = jan.chat( - model="jan-nano-32k", - message="Find the latest SpaceX launch details", - tools=["search"] -) -``` - -### With Document Analysis -```python -# Process long documents efficiently -response = jan.chat( - model="jan-nano-128k", - message="Summarize key findings from these papers", - attachments=["paper1.pdf", "paper2.pdf", "paper3.pdf"] -) -``` - -## The Philosophy - -Most models try to be encyclopedias. Jan Nano is a research assistant. It doesn't memorize the internet - it knows how to navigate it. - -This focused approach means: -- Smaller model size -- Better accuracy with sources -- More reliable information -- Efficient hardware usage - ---- - -[Download Jan Desktop](https://jan.ai/download) | [Model Details](https://huggingface.co/Menlo/Jan-nano) | [MCP Documentation](https://jan.ai/docs/mcp) diff --git a/website/src/content/products/models/jan-v1.mdx b/website/src/content/products/models/jan-v1.mdx deleted file mode 100644 index bdb7459eb..000000000 --- a/website/src/content/products/models/jan-v1.mdx +++ /dev/null @@ -1,44 +0,0 @@ ---- -title: Jan V1 -description: Our upcoming family of foundational models, built to compete with the best. -sidebar: - order: 1 -banner: - content: 'In Development: Jan V1 models are currently being trained and are not yet available.' ---- -import { Aside, Card, Tabs, TabItem } from '@astrojs/starlight/components'; - -## Our Foundational Model Family - -Jan V1 is our in-house family of models designed to compete directly with leading models like GPT-4 and Claude. We're not just fine-tuning; we're building powerful, general-purpose models from the ground up to solve real-world problems with a focus on efficiency and privacy. - - - -### The Vision - -Most AI applications today are simply wrappers around a few proprietary models. We believe in providing a powerful, open alternative that you can run on your own terms. Jan V1 is our commitment to that vision. - -### Planned Model Lineup - -| Model | Target Size | Intended Use Case | Availability | -|-------------|-------------|------------------------------|---------------| -| Jan V1-7B | 4-8GB | Fast, efficient daily tasks | Coming Soon | -| Jan V1-13B | 8-16GB | Balanced power and performance | Coming Soon | -| Jan V1-70B | 40-64GB | Deep analysis, professional work | Coming Soon | -| Jan V1-180B | 100GB+ | Frontier research, complex tasks | Planned 2026 | - -### What to Expect -- **Competitive Performance**: Aiming for results on par with leading closed-source models. -- **Optimized for Local Use**: Efficient quantized versions for running on your own hardware. -- **Privacy-Centric**: Trainable and runnable in your own environment, ensuring your data stays yours. -- **Seamless Integration**: Designed to work perfectly within the Jan ecosystem, from Desktop to Server. - ---- - -### Stay Updated - -Be the first to know about our training progress, benchmark results, and when the first Jan V1 models are released. - -[**Follow our development →**](https://jan.ai/v1-updates) \ No newline at end of file diff --git a/website/src/content/products/platforms/desktop.mdx b/website/src/content/products/platforms/desktop.mdx deleted file mode 100644 index 55347a9f0..000000000 --- a/website/src/content/products/platforms/desktop.mdx +++ /dev/null @@ -1,239 +0,0 @@ ---- -title: Jan Desktop -description: AI that runs on your computer, not someone else's -sidebar: - order: 2 ---- - -import { Aside, Card, CardGrid, Tabs, TabItem } from '@astrojs/starlight/components'; - -This is how Jan started and it has been available since day 1. Jan Desktop strives to be: - -> Your personal AI workstation that helps with our use cases and powers other devices. Run models locally right away or bring an API key to connect to your favorite cloud-based models. - -Jan Desktop is where it all starts. Download it, open it, start chatting. Your AI runs on your computer with zero setup required. - -## Two Modes, Zero Complexity - -### Local Mode (Default) -Your conversations stay on your computer. No internet needed. Complete privacy. - -### Cloud Mode -Connect to more powerful models when you need them. Your choice of provider. - - - -## What You Get - - - - Download once, use forever. Internet is optional. - - - - Everything stored in `~/jan`. No cloud backups unless you want them. - - - - Your desktop becomes an AI server for your phone and other computers via network connection. - - - - Local API at `localhost:1337`. Works with any OpenAI-compatible tool. - - - - Automatically detects and uses NVIDIA GPUs for faster performance. - - - - Windows, macOS, and Linux support with native performance. - - - -## System Requirements - -### Minimum Requirements -- **RAM:** 8GB (models use less than 80% of available memory) -- **Storage:** 10GB+ free space -- **OS:** Windows 10, macOS 12, Ubuntu 20.04 or newer - -### Recommended -- **RAM:** 16GB+ for larger models -- **Storage:** 20GB+ for multiple models -- **GPU:** NVIDIA GPU with 6GB+ VRAM for acceleration -- **OS:** Latest versions for best performance - -### GPU Acceleration (Optional) -- **NVIDIA Driver:** 470.63.01 or higher -- **CUDA Toolkit:** 11.7 or higher -- **Linux:** Additional GCC-11 packages required - - - -## Getting Started - -1. **Download Jan** from [jan.ai/download](https://jan.ai/download) -2. **Open the app** - it loads with everything ready -3. **Start chatting** - that's it - -## Local Mode Features - -### Built-in Model -Jan comes with a model that works immediately. No downloading, no waiting. - -### Smart Defaults -- Automatically uses your GPU if available -- Adjusts to your system's capabilities -- Works on older hardware too - -### Complete Privacy -- No telemetry by default -- No account required -- No data leaves your machine -- Chat history stored locally only -- Full control over your data - -## Cloud Mode (Optional) - -Connect to external AI providers when you need more power: - - - - Our cloud service (coming soon). One click to enable. - - - - Use your OpenAI API key for GPT-4 access. - - - - Connect to your own Jan Server. - - - -## Desktop as Your AI Hub - -Your desktop can power AI across all your devices. Jan Desktop automatically becomes a local server that other devices can connect to: - -**Network Sharing:** -- Mobile apps connect over WiFi -- Other computers can access your models -- API available at `localhost:1337` -- No internet required for local network - -**Supported Connections:** -- Jan Mobile (coming Q4 2025) -- Other Jan Desktop instances -- Any OpenAI-compatible application -- Custom scripts and tools - -## For Developers - -### Local API Server -```bash -# Always running at localhost:1337 -curl http://localhost:1337/v1/chat/completions \ - -H "Content-Type: application/json" \ - -d '{"model": "default", "messages": [{"role": "user", "content": "Hello"}]}' -``` - -### Works With -- Continue (AI coding assistant) -- Cursor (AI editor) -- Any OpenAI SDK -- Your custom scripts - -## Common Questions - -### Do I need to download models? -No. Jan comes with a default model that works immediately. - -### Can I use it offline? -Yes. Local Mode works completely offline once installed. - -### How do I switch models? -Most users don't need to. Power users can explore the Model Hub. - -### Is it really private? -Yes. In Local Mode, nothing leaves your computer. - -## Performance Expectations - -| Task | Local Mode | Cloud Mode | -|------|------------|------------| -| Simple questions | Instant | Instant | -| Complex analysis | 2-5 seconds | 1-2 seconds | -| Code generation | Good | Excellent | -| Privacy | Complete | Depends on provider | - -## Performance Optimization - -### Automatic GPU Detection -Jan automatically detects and configures: -- NVIDIA graphics cards -- Available VRAM -- Optimal GPU layers (ngl) settings -- Fallback to CPU if needed - -### Memory Management -- Smart model loading based on available RAM -- Automatic offloading when memory is low -- Background process cleanup -- Efficient resource usage - -## Tested Configurations - -Jan Desktop has been verified to work on these systems: - -**Windows Systems:** -- Windows 11 Pro + RTX 4070Ti + CUDA 12.2 + Driver 531.18 -- Windows 10 + GTX 1660Ti + Various driver versions - -**Linux Systems:** -- Ubuntu 22.04 + RTX 4070Ti + CUDA 12.2 + Driver 545 -- Ubuntu 20.04 on Proxmox VM + GTX 1660Ti + CUDA 12.1 - -**macOS Systems:** -- macOS Monterey and newer -- Both Intel and Apple Silicon (M1/M2) processors -- Automatic Metal acceleration on Apple Silicon - -## Coming Soon - -### Enhanced User Experience -- One-click model switching -- Improved onboarding flow -- Better hardware detection -- Voice input support - -### Advanced Features -- Model fine-tuning interface -- Custom model deployment -- Team collaboration features -- Enhanced mobile connectivity - - - -## Why Desktop? - -Your computer is powerful. It has GPUs, CPUs, and memory that can run AI locally. Jan Desktop uses that power to give you: - -- **Privacy**: Your data never leaves your machine -- **Control**: Update when you want, use what you want -- **Reliability**: Works offline, no API limits -- **Speed**: No network latency for local models - -## The Bottom Line - -Jan Desktop is AI that respects your computer is YOUR computer. Not a terminal to someone else's server. Just software that works for you. - ---- - -[Download Jan Desktop](https://jan.ai/download) | [Documentation](/docs) | [Discord Community](https://discord.gg/jan) diff --git a/website/src/content/products/platforms/jan-ai.mdx b/website/src/content/products/platforms/jan-ai.mdx deleted file mode 100644 index 83d3e841a..000000000 --- a/website/src/content/products/platforms/jan-ai.mdx +++ /dev/null @@ -1,189 +0,0 @@ ---- -title: jan.ai -description: Cloud AI that respects your privacy -sidebar: - order: 1 ---- - -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -**Status:** Beta Launch Soon - -Web-based version of Jan with no setup required. Same default cloud mode for mobile and desktop users. - -## What is Jan Web? - -Jan Web is the cloud-hosted version of Jan that runs in your browser. No installation needed, instant access from any device, with the same AI experience you get locally. - - - -## How It Works - - - - When you switch to Cloud Mode in Jan Desktop, it connects to Jan Web automatically. No configuration needed. - - - - Jan Web serves as the default cloud backend for mobile apps when not connected to your desktop. - - - - Visit jan.ai directly in your browser for instant access to AI without downloading anything. - - - - Share prompts, workflows, and collaborate on threads with your team members. - - - -## Key Features - -### No Installation Needed -- No installation needed -- Instant access from any browser -- Automatic updates and maintenance -- Works on any device with internet - -### Team Collaboration Features -- Share prompts across team members -- Share workflows and templates -- Collaborate on conversation threads -- Real-time team coordination - -### Cloud Backend for Mobile -- Default cloud backend for mobile apps -- Seamless sync across devices -- Consistent experience everywhere -- No setup required for mobile users - -### Enterprise Ready -- Team management features -- Admin controls and oversight -- Usage analytics and reporting -- Integration with existing tools - -## Pricing - -| Tier | Features | Price | -|------|----------|--------| -| **Free** | Free for everyone | $0 | -| **Pro** | Access our latest models
Access other cloud providers without API keys | Coming Soon | -| **Enterprise** | Self-host or we host it for you
Active support and SLAs
SSO integration
Team features | Contact Sales | - - - -## Why Use Jan Web? - -### When You Need Cloud Power -- Access from any device with internet -- More powerful models than local hardware -- Faster responses for complex tasks -- Always up-to-date with latest features - -### Team Collaboration -- Share knowledge and workflows -- Collaborate on projects in real-time -- Consistent experience across team -- Admin controls for organizations - -### Zero Setup -- No downloads or installations -- No API keys to manage -- No complex configurations -- Just open browser and start chatting - -### Enterprise Features -- SSO integration for security -- Team management and controls -- Usage analytics and reporting -- Professional support and SLAs - -## For Developers - -### API Access -```javascript -// Same API as local Jan -const response = await fetch('https://api.jan.ai/v1/chat/completions', { - headers: { 'Authorization': 'Bearer YOUR_KEY' }, - body: JSON.stringify({ - model: 'jan-nano', - messages: [{ role: 'user', content: 'Hello' }] - }) -}); -``` - -### OpenAI Compatible -Drop-in replacement for OpenAI API: -```python -# Just change the base URL -client = OpenAI( - base_url="https://api.jan.ai/v1", - api_key="your-jan-key" -) -``` - -### Integration Options -- REST API for custom applications -- Webhook support for automation -- Team API for organization management -- Usage analytics API for monitoring - -## Common Questions - -### How is this different from ChatGPT? -- Open source and transparent -- Privacy-focused by design -- Same experience as local Jan -- Team collaboration features -- Enterprise self-hosting options - -### Can I use my own models? -Not directly on Jan Web. For custom models, use Jan Desktop locally or self-host Jan Server. - -### Is my data secure? -Yes, Jan Web follows enterprise security practices: -- Encrypted connections and storage -- No training on user data -- Optional anonymous usage -- Regular security audits -- GDPR compliance - -### When will it launch? -Beta launch is coming soon. Full public launch planned for 2025. - -## Coming Soon - -### Beta Launch Features -- Web-based chat interface -- Team collaboration tools -- API access for developers -- Jan Desktop/Mobile integration -- Basic admin controls - -### Full Launch Features -- Advanced team management -- SSO and enterprise authentication -- Custom workflows and automation -- Usage analytics and reporting -- Advanced model access - - - -## The Philosophy - -Cloud AI doesn't have to mean giving up control. Jan Web proves you can have: -- Convenience without surveillance -- Team collaboration without data mining -- Enterprise features without vendor lock-in -- Cloud power with privacy respect - ---- - -[Join Beta Waitlist](https://jan.ai/beta) | [Contact Sales](https://jan.ai/enterprise) | [API Documentation](/docs/api) diff --git a/website/src/content/products/platforms/mobile.mdx b/website/src/content/products/platforms/mobile.mdx deleted file mode 100644 index 9337dac58..000000000 --- a/website/src/content/products/platforms/mobile.mdx +++ /dev/null @@ -1,219 +0,0 @@ ---- -title: Jan Mobile -description: Your AI assistant on the go -sidebar: - order: 3 ---- - -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -**Status:** Coming Q4 2025 - -Connect to Desktop/Server, run local mode with Jan Nano or Lucy, same experience everywhere. - -Jan Mobile brings the same AI experience to your phone. Connect to your desktop, your server, or run models locally. - - - -## How It Works - -Jan Mobile adapts to your situation: - -At Home, you can connect to your Jan Desktop over WiFi - -![Jan Mobile Home](../../../assets/jan_mobile2.png) - -At Work, you can connect to your company Jan Server - -![Jan Mobile Home](../../../assets/jan_mobile3.png) - -On the Go, you can run Jan Nano on your phone or talk to your favourite cloud-based model - -![Jan Mobile Home](../../../assets/jan_mobile4.png) - -**No configuration needed. It just works.** - -## Three Modes, One Experience - -### Desktop Mode -When you're near your computer at home, you an toggle phone uses its models and processing power. - -``` -Your Phone → WiFi → Your Desktop → Response - (automatic) (powerful models) -``` - -**Benefits:** -- Access to larger models -- Faster processing -- Shared conversations -- No phone battery drain - -### Server Mode -Connect to your organization's Jan Server for team collaboration. - -``` -Your Phone → Internet → Company Server → Response - (secure) (shared models) -``` - -**Benefits:** -- Team knowledge base -- Consistent models -- Central management -- Work anywhere - -### Local Mode -No connection? No problem. Jan Nano runs directly on your phone. - -``` -Your Phone → Jan Nano (6GB) → Response - (private & offline) -``` - -**Benefits:** -- Complete privacy -- Works offline -- No data usage -- Always available - - - -## Key Features - - - - Move from home to office to airplane. One-click and Jan adapts immediately. - - - - Talk to Jan naturally. Responses can be spoken too. - - - - Conversations, settings, and preferences follow you across devices. - - - - Native apps for both platforms with consistent experience. - - - - Automatically switches between Desktop, Server, and Local modes. - - - - Run AI models directly on your phone for complete privacy. - - - -## Privacy & Security - -### Your Data, Your Control -- Local Mode: Everything stays on your phone -- Desktop Mode: Direct encrypted connection -- Server Mode: Your organization's policies apply - -### No Compromises -- Biometric app lock -- Encrypted storage -- No cloud backups without permission -- Clear data anytime - -## Why Mobile Matters - -Your phone is with you always. Your AI assistant should be too. But that doesn't mean sacrificing -privacy or control. - -Jan Mobile proves you can have: -- Powerful AI anywhere -- Complete privacy when needed -- Simple experience always -- No subscriptions or tracking - -## Technical Details - -### App Architecture -- Built with native performance in mind -- Small app size (~50MB download) -- Efficient battery usage -- Background processing support - -### Supported Platforms -- **iOS:** iPhone and iPad support -- **Android:** Phone and tablet support -- **Voice Interface:** Natural speech input/output -- **Seamless Device Switching:** Real-time sync across devices - -### Jan Nano for Mobile -- Optimized 6GB model for mobile devices -- Runs entirely on-device -- No internet required -- Complete privacy and offline functionality - -## Coming Features - -### Launch (Q4 2025) -- iOS and Android native apps -- Three adaptive connection modes -- Voice-first interface -- Basic chat and conversation management -- Automatic mode switching - -### Post-Launch Updates -- Advanced voice input with wake words -- Background conversation sync -- Widget support for quick access -- Enhanced team collaboration features -- Custom model support - - - -## The Vision - -Most mobile AI apps are just cloud wrappers. Jan Mobile is different: - -| Feature | Other AI Apps | Jan Mobile | -|---------|---------------|------------| -| Offline Mode | ❌ | ✅ Jan Nano | -| Desktop Connection | ❌ | ✅ Your models | -| Privacy | Your data in cloud | Your data stays yours | -| Cost | Monthly subscription | Free with your hardware | - -## Development Status - -### Current Progress -- Core architecture completed -- Desktop/Server connection protocols implemented -- Jan Nano mobile optimization in progress -- iOS and Android app development underway - -### Beta Testing -- Closed beta planned for Q3 2025 -- Open beta following successful internal testing -- Community feedback integration throughout development - -## While You Wait for Jan Mobile - -1. **Set up Jan Desktop** - It will power your mobile experience when at home -2. **Try Jan Web** - Get familiar with the cloud interface -3. **Join our newsletter** - Be first to know when beta launches -4. **Provide feedback** - Help shape the mobile experience through our Discord community -5. **Prepare your setup** - Ensure Jan Desktop is configured for network access - -## Get Notified - -Want to be among the first to try Jan Mobile? -- [Join our newsletter](https://jan.ai/mobile) for launch updates -- [Follow us on Discord](https://discord.gg/jan) for development progress -- [Check our GitHub](https://github.com/janhq/jan) for technical updates - ---- - -[Jan Desktop](/docs/products/platforms/desktop) | [Jan Web](/docs/products/platforms/jan-ai) | [Jan Server](/docs/products/platforms/server) diff --git a/website/src/content/products/platforms/server.mdx b/website/src/content/products/platforms/server.mdx deleted file mode 100644 index 2c11b01aa..000000000 --- a/website/src/content/products/platforms/server.mdx +++ /dev/null @@ -1,335 +0,0 @@ ---- -title: Jan Server -description: Your own private AI cloud -sidebar: - order: 4 ---- - -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -**Status:** Coming Q2 2025 - -Self-hosted solution for teams and enterprises. Your own private AI cloud. - -Jan Server is Jan Desktop with multi-user support. Deploy it on your hardware to create your own private AI cloud for your team or organization. - - - -## What is Jan Server? - -``` -Jan Server = Jan Desktop + Multi-user support + Real hardware -``` - -It's the same engine that powers Jan Desktop, scaled up for teams. Your data stays on your servers, your models run on your GPUs, your AI remains yours. - -## Why Organizations Need This - -### The Problem -Every API call to ChatGPT or Claude is: -- Your intellectual property leaving your network -- Potential training data for someone else's model -- A compliance nightmare waiting to happen -- A monthly bill that never ends - -### The Solution -Jan Server gives you: -- **Complete control**: Your hardware, your rules -- **Total privacy**: Nothing leaves your network -- **Predictable costs**: One-time hardware investment -- **Compliance ready**: GDPR, HIPAA, SOC2 friendly - -## Key Features - - - - Support for 5-500+ concurrent users with individual accounts and permissions. - - - - SSO, LDAP integration and enterprise-grade security controls. - - - - Docker, Kubernetes, or bare metal deployment options. - - - - Centralized management for users, models, and system monitoring. - - - - Shared conversations, templates, and collaborative workflows. - - - - Drop-in replacement for OpenAI API - no code changes needed. - - - -## Deployment Options - - - - Perfect for getting started quickly with containerized deployment. - - - - Auto-scaling, high availability, and enterprise orchestration. - - - - Direct hardware access for maximum performance and customization. - - - -## Simple Deployment - -### Docker (Single Command Setup) -```yaml -version: '3' -services: - jan-server: - image: jan.ai/server:latest - ports: - - "80:80" - - "1337:1337" - volumes: - - ./models:/models - - ./data:/data - deploy: - resources: - reservations: - devices: - - capabilities: [gpu] -``` - -### Kubernetes (Enterprise Scale) -```yaml -apiVersion: apps/v1 -kind: Deployment -metadata: - name: jan-server -spec: - replicas: 3 - selector: - matchLabels: - app: jan-server - template: - metadata: - labels: - app: jan-server - spec: - containers: - - name: jan-server - image: jan.ai/server:latest - resources: - limits: - nvidia.com/gpu: 1 -``` - -### Bare Metal (Maximum Control) -Direct installation on your hardware for maximum performance and custom configurations. - -## Detailed Features - -### Enterprise Authentication -- **SSO Integration**: SAML, OAuth, OpenID Connect -- **LDAP/Active Directory**: Existing user directory integration -- **Role-Based Access**: Granular permissions and model access -- **API Key Management**: Individual and service account keys - -### Multi-User Management -- **User Accounts**: Individual profiles and preferences -- **Usage Tracking**: Per-user analytics and quotas -- **Team Collaboration**: Shared conversations and workflows -- **Admin Dashboard**: Centralized user and system management - -### Same API as Desktop -```python -# Your code doesn't change -client = OpenAI( - base_url="https://jan.company.internal/v1", - api_key="user-specific-key" -) -``` - -### Team Knowledge Sharing -- **Shared Conversations**: Collaborative chat threads -- **Template Library**: Reusable prompts and workflows -- **Knowledge Base**: Organizational AI knowledge -- **Team Analytics**: Usage patterns and insights - - - -## Scaling Guidelines - -### Small Teams (5-10 users) -- **Hardware**: Single RTX 6000 Ada (48GB VRAM) -- **RAM**: 128GB system memory -- **Models**: Up to 70B parameter models -- **Concurrent Users**: 5-10 active users -- **Estimated Cost**: ~$15,000 one-time hardware - -### Department Scale (10-50 users) -- **Hardware**: 2-4 GPU cluster nodes -- **RAM**: 256GB per node -- **Models**: Multiple concurrent model instances -- **Concurrent Users**: 10-50 active users -- **Estimated Cost**: ~$50,000-$100,000 one-time - -### Enterprise Scale (50+ users) -- **Hardware**: DGX cluster or custom configuration -- **RAM**: Scalable as needed -- **Models**: Full model library with redundancy -- **Concurrent Users**: 50-500+ active users -- **Estimated Cost**: Custom enterprise quote - -## Real-World Use Cases - -| Organization Type | Deployment | Benefits Achieved | -|------------------|------------|-------------------| -| Law Firm | 2x RTX 6000, 200 users | Client data never leaves network, GDPR compliance | -| Hospital System | DGX node, 500 users | HIPAA compliant AI assistant, medical data privacy | -| Tech Startup | 4x RTX 4090, 50 users | 90% cost reduction vs. OpenAI API calls | -| Research University | Multi-node cluster | Unrestricted research, no usage limits | -| Financial Services | Air-gapped deployment | Complete data isolation, regulatory compliance | - -## Hardware Requirements - -### Minimum Configuration -- **GPU**: RTX 3090 or better (24GB VRAM minimum) -- **CPU**: 16+ cores (Xeon, EPYC, or equivalent) -- **RAM**: 64GB system memory minimum -- **Storage**: 1TB NVMe SSD for models and data -- **Network**: Gigabit Ethernet minimum - -### Recommended Production Setup -- **GPU**: RTX 6000 Ada (48GB) or A100 (80GB) -- **CPU**: Dual socket Xeon/EPYC (32+ cores) -- **RAM**: 128-256GB system memory -- **Storage**: RAID NVMe array (2TB+ capacity) -- **Network**: 10Gbps for multiple concurrent users - -### Enterprise Scaling Guidelines -- **Users per GPU**: ~5-10 concurrent active users -- **70B Models**: Require 40-80GB VRAM depending on quantization -- **CPU Fallback**: Smaller models can run on CPU for cost optimization -- **High Availability**: Multi-node deployment with load balancing -- **Backup Strategy**: Regular model and data backups recommended - -## Why Self-Host? - -### For IT Teams -- No data leaves your network -- Complete audit trails -- Integrate with existing auth (LDAP/AD) -- Predictable resource usage - -### For Security Teams -- Air-gapped deployment options -- End-to-end encryption -- No third-party access -- Full compliance control - -### For Finance Teams -- One-time hardware cost -- No per-token pricing -- Predictable TCO -- Use existing infrastructure - -## Development Roadmap - -### Q2 2025 Launch Features -- Multi-user authentication and management -- Web-based admin dashboard -- OpenAI API compatibility -- Docker and Kubernetes deployment -- Basic usage monitoring and analytics -- Team collaboration features - -### Post-Launch Updates -- Advanced governance and compliance tools -- Model fine-tuning interface -- Automated scaling and load balancing -- Comprehensive backup and restore -- Enhanced security and audit logging - -### Future Vision -- Federated multi-site deployments -- Cross-region synchronization -- Advanced usage analytics and insights -- Custom model training and optimization -- Integration with enterprise workflow tools - - - -## Migration Path - -### From Cloud AI -1. Deploy Jan Server -2. Import your workflows -3. Update API endpoints -4. Migrate users gradually - -### From Jan Desktop -1. Same models work instantly -2. Add user management -3. Scale as needed - -## The Philosophy - -We believe organizations should own their AI infrastructure just like they own their data. Jan Server makes this possible without compromising on capabilities. - -This isn't about avoiding the cloud - it's about having a choice. Run your AI where it makes sense for your organization. - -## Support Options - -### Community Edition -- Full features -- Community support -- Perfect for small teams - -### Enterprise Edition -- Priority support -- Custom deployment help -- SLA guarantees -- Training included - -## Get Ready for Jan Server - -While Jan Server is in development (Q2 2025 launch): - -### Preparation Steps -1. **Assess Hardware Needs**: Review our scaling guidelines above -2. **Plan Network Architecture**: Consider security and access requirements -3. **Evaluate Authentication**: Determine SSO/LDAP integration needs -4. **Test with Jan Desktop**: Same models and API, perfect for preparation -5. **Join Early Access Program**: Get notified when beta testing begins - -### Early Access Benefits -- Beta testing opportunities -- Hardware optimization guidance -- Deployment planning assistance -- Priority technical support -- Input on feature development - -### Enterprise Consultation -For large deployments or custom requirements: -- Architecture planning sessions -- Hardware recommendation consultations -- Security and compliance reviews -- Migration planning from existing AI services -- Custom deployment assistance - ---- - -**Ready to Plan Your Deployment?** - -[Join Early Access](https://jan.ai/server) | [Enterprise Consultation](https://jan.ai/enterprise) | [Hardware Planning Guide](/docs/server/hardware) | [Jan Desktop Trial](/download) diff --git a/website/src/content/products/tools/browseruse.mdx b/website/src/content/products/tools/browseruse.mdx deleted file mode 100644 index 04f7c729f..000000000 --- a/website/src/content/products/tools/browseruse.mdx +++ /dev/null @@ -1,36 +0,0 @@ ---- -title: BrowserUse -description: Native browser automation for Jan, enabling AI to interact with the web on your behalf. -sidebar: - order: 3 -banner: - content: 'In Development: This tool is planned and not yet available.' ---- -import { Aside, Card, CardGrid } from '@astrojs-starlight/components'; - -## Let Jan Use Your Browser - -`BrowserUse` is a native tool being developed for the Jan ecosystem that will allow the AI to securely control a browser to perform tasks, automate workflows, and interact with websites just like a human would. - -Think of it as an integrated, privacy-first automation layer that turns Jan from a conversational AI into a powerful agent for action. - - - -### Inspired by the Best - -While tools like [Browser Use](https://docs.browser-use.com/introduction) exist for developers, Jan's `BrowserUse` will be a built-in, user-friendly feature. No complex setup required—just grant permission and let Jan handle the rest. - -### Built on MCP - -The tool will be implemented as a native **Model Context Protocol (MCP)** server within Jan, ensuring secure and standardized communication between the AI model and the browser. This allows for powerful, auditable, and secure web interactions. - ---- - -### Planned Core Features: -- **Secure Sessions**: All browsing activity happens in an isolated, sandboxed environment. -- **Natural Language Control**: Simply tell Jan what you want to do (e.g., "Book a flight," "Research this topic and summarize the top 5 findings," "Apply for this job for me"). -- **Visual Understanding**: Jan will be able to see and interpret the content on pages, not just the underlying code. -- **User in the Loop**: Always ask for permission before taking critical actions like submitting forms or making purchases. - diff --git a/website/src/content/products/tools/deepresearch.mdx b/website/src/content/products/tools/deepresearch.mdx deleted file mode 100644 index 2496b8c11..000000000 --- a/website/src/content/products/tools/deepresearch.mdx +++ /dev/null @@ -1,31 +0,0 @@ ---- -title: DeepResearch -description: Go beyond simple search with an AI agent that performs comprehensive, multi-step research for you. -sidebar: - order: 1 -banner: - content: 'In Development: This tool is planned and not yet available.' ---- -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -## Your Personal Research Analyst - -`DeepResearch` is a planned native tool for Jan that transforms it into a powerful research agent. Give it a complex question, and it will autonomously browse, analyze, and synthesize information from numerous sources to deliver a comprehensive, structured report. - -Think of it as Jan's answer to the advanced research capabilities seen in [OpenAI's ChatGPT](https://openai.com/index/introducing-deep-research/) and [Google's Gemini](https://gemini.google/overview/deep-research/), but built with privacy and user control at its core. - - - -### How It Will Work - -Unlike a simple web search that returns a list of links, `DeepResearch` will understand your goal, create a research plan, execute it, and deliver a final, synthesized document with citations. - -### Planned Core Features: -- **Autonomous Multi-Step Research**: Deconstructs complex questions into logical steps and executes them. -- **Comprehensive Source Analysis**: Can read and synthesize information from web pages, PDFs, and other documents. -- **Structured Report Generation**: Delivers well-organized reports, not just chat messages. -- **Full Transparency**: Cites all sources and shows its work, so you can verify the information. -- **Local-First Privacy**: Conduct research using local models to ensure your queries and findings remain private. - diff --git a/website/src/content/products/tools/search.mdx b/website/src/content/products/tools/search.mdx deleted file mode 100644 index 724496e3a..000000000 --- a/website/src/content/products/tools/search.mdx +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Search -description: A native search tool that gives you answers, not just links, with complete privacy. -sidebar: - order: 2 -banner: - content: 'In Development: This tool is planned and not yet available.' ---- -import { Aside, Card, CardGrid } from '@astrojs/starlight/components'; - -## Answers, Not Just Links - -`Search` is a planned native tool for Jan that rethinks web search. Instead of just giving you a list of links to sift through, it understands your question, scours the web, and provides a direct, synthesized answer with sources cited. - -Think of it as a private, self-hosted alternative to services like Perplexity.ai, integrated directly into your AI assistant. - - - -### How It's Different -- **Privacy-First**: Your search queries are processed locally and anonymized. What you search for is your business. -- **Direct Answers**: Get a concise, accurate answer compiled from the best sources, not just ten blue links. -- **Cited Sources**: Every piece of information is backed by a verifiable source, so you can trust the answer or dig deeper. -- **Conversational Follow-up**: Ask follow-up questions in a natural way, and Jan will maintain context. - -### Planned Core Features: -- **Real-Time Information**: Access up-to-the-minute news, data, and events. -- **Source Verification**: Prioritizes authoritative and reliable sources. -- **Customizable Focus**: Tailor your search to specific domains like academic papers, news, or code repositories. -- **Seamless Integration**: Works hand-in-hand with other tools like `DeepResearch` and `BrowserUse` for more complex tasks. - - ---- \ No newline at end of file diff --git a/website/src/layouts/BaseLayout.astro b/website/src/layouts/BaseLayout.astro deleted file mode 100644 index 70b6691f5..000000000 --- a/website/src/layouts/BaseLayout.astro +++ /dev/null @@ -1,238 +0,0 @@ ---- -export interface Props { - title?: string; - description?: string; -} - -const { title = 'Jan', description = 'AI that runs where you need it, how you need it' } = Astro.props; ---- - - - - - - - - {title} - - - - -
- -
- - diff --git a/website/src/layouts/Layout.astro b/website/src/layouts/Layout.astro deleted file mode 100644 index 591effc34..000000000 --- a/website/src/layouts/Layout.astro +++ /dev/null @@ -1,64 +0,0 @@ ---- -export interface Props { - title: string; -} - -const { title } = Astro.props; ---- - - - - - - - - - {title} - - - - - - - - - - - - - - - - - - - - diff --git a/website/src/pages/api-reference.astro b/website/src/pages/api-reference.astro deleted file mode 100644 index ac3d9b4f5..000000000 --- a/website/src/pages/api-reference.astro +++ /dev/null @@ -1,58 +0,0 @@ ---- -const title = '👋 Jan API Reference'; -const description = 'Interactive Jan API documentation powered by Scalar'; ---- - - - - - - - - {title} - - - -
- - - - - - \ No newline at end of file diff --git a/website/src/pages/prods.mdx b/website/src/pages/prods.mdx deleted file mode 100644 index 0c8fc7ba3..000000000 --- a/website/src/pages/prods.mdx +++ /dev/null @@ -1,534 +0,0 @@ ---- -layout: ../layouts/BaseLayout.astro -title: Product -description: AI that runs where you need it, how you need it ---- - -import SimpleFloatingNav from '../components/SimpleFloatingNav.astro'; -import SimpleTOC from '../components/SimpleTOC.astro'; -import ReleaseDatabase from '../components/ReleaseDatabase.astro'; - - - - - -
- -# Products - -

-Jan is moving from a local AI application to a complete full-stack AI solution that you can self-host. This includes models, -applications, and tools that delights users and help them solve their problems. -

- -## What We're Building - -**Jan Factory (or Agent)** = Jan Models + Jan Application + Jan Tools - -Unlike other AI assistants that do specific tasks with one model or have many models with a myriad of solutions, Jan provides: -- Its own specialised models that are optimised at specific tasks like web-search, creative writing, and translation -- Applications that work across all of your devices in an integrated way -- Tools that actually get things done - -## Two Modes, One Experience - -### Local (Incognito) Mode - -Run AI models entirely on your device, giving you complete privacy with no internet required. - -![Jan Desktop](../assets/jan_desktop.png) - -### Cloud Mode - -Connect to more powerful models when needed - either self-hosted or via jan.ai. - -![Jan Everywhere](../assets/jan_everywhere.png) - -Users shouldn't need to understand models, APIs, or technical details. Just choose Local for privacy or Cloud for power. - -## Our Product Principles - -### 1) It Just Works - -1. Open Jan, start chatting -2. Onboarding is fully available but optional -3. Setting up an API key is optional -4. Selecting a local model is optional -5. Become a power user at your own pace, if you want to - -We handle the complexity. - -### 2) Cloud When Needed - -Start completely locally and own your AI models. Add cloud capabilities only when you choose to. - -### 3) Solve Problems, Not Settings - -We help users get to answers quickly answers, not configuration options. Power users can dig deeper, but it's never required. - -## Available on Every Device - -### Jan Desktop - -This is how Jan started and it has been available since day 1. Jan Desktop stives to be: - -> Your personal AI workstation that helps with our use cases and powers other devices. Run models locally right away -or bring an API key to connect to your favorite cloud-based models. - -**Key Features:** -- Runs models locally on your hardware -- GPU acceleration support -- Powers other devices via network connection -- Complete privacy and control -- Windows, macOS, and Linux support - -**Requirements:** -- Minimum 8GB RAM -- 10GB+ storage space -- Optional: NVIDIA GPU for acceleration - -### Jan Web - -**Status:** Beta Launch Soon - -![Jan Web](../assets/jan_web.png) - -Web-based version of 👋 Jan with no setup required. Same default cloud mode for mobile and desktop users. - -**Key Features:** -- No installation needed -- Instant access from any browser -- Automatic updates and maintenance -- Default cloud backend for mobile apps -- Team collaboration features - - Share prompts - - Share workflows - - Collaborate on threads - -**Pricing:** -- Free for everyone -- Pro: - - Access our latest models - - Access other cloud providers, no need to bring their API keys -- Enterprise: - - Self-host or we host it for you - - Active support and SLAs - - SSO - - Team featues - -### Jan Mobile - -**Status:** Coming Q4 2025 - -Connect to Desktop/Server, run local mode with Jan Nano or Lucy, same experience everywhere. - -Jan Mobile adapts to your situation: - -At Home, you can connect to your Jan Desktop over WiFi - -![Jan Mobile Home](../assets/jan_mobile2.png) - -At Work, you can connect to your company Jan Server - -![Jan Mobile Home](../assets/jan_mobile3.png) - -On the Go, you can run Jan Nano on your phone or talk to your favourite cloud-based model - -![Jan Mobile Home](../assets/jan_mobile4.png) - -**Key Features:** -- iOS and Android support -- Three adaptive modes (Desktop, Server, Local) -- Voice-first interface -- Seamless device switching -- Jan Nano for on-device AI - -### Jan Server - -**Status:** Coming Q2 2025 - -Self-hosted solution for teams and enterprises. Your own private AI cloud. - -**Key Features:** -- Support for 5-500+ concurrent users -- Enterprise authentication (SSO, LDAP) -- Docker and Kubernetes deployment -- Admin dashboard -- Team knowledge sharing - -**Deployment Options:** -- Docker: Single command setup -- Kubernetes: Enterprise scale -- Bare metal: Maximum control - -## Jan Mobile: Three Modes, One Experience - -Jan Mobile brings the same AI experience to your phone. Connect to your desktop, your server, or run models locally. - -### How It Works - -Jan Mobile adapts to your situation: - -**At Home** - Connect to your Jan Desktop over WiFi -Your Phone → WiFi → Your Desktop → Response - -**At Work** - Connect to your company Jan Server -Your Phone → Internet → Company Server → Response - -**On the Go** - Run Jan Nano on your phone or talk to your favorite cloud-based model -Your Phone → Jan Nano (6GB) → Response - -No configuration needed. It just works. - -### Key Features - -- **Seamless Switching**: Move from home to office to airplane. One-click and Jan adapts immediately. -- **Voice First**: Talk to Jan naturally. Responses can be spoken too. -- **Sync Everything**: Conversations, settings, and preferences follow you across devices. - -### Privacy & Security - -**Your Data, Your Control** -- Local Mode: Everything stays on your phone -- Desktop Mode: Direct encrypted connection -- Server Mode: Your organization's policies apply - -**No Compromises** -- Biometric app lock -- Encrypted storage -- No cloud backups without permission -- Clear data anytime - -## What Makes Jan Different - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
FeatureOther AI AssistantsJan
ModelsWrapper around Claude/GPTOur own models + You can own them
Dual modeYour data on their serversYour data stays yours
DeploymentCloud onlyLocal, self-hosted, or cloud
CostSubscription foreverFree locally, pay for cloud
- -## Development Timeline - - - -
- - - - diff --git a/website/src/pages/products/index.astro b/website/src/pages/products/index.astro deleted file mode 100644 index 16323b3b6..000000000 --- a/website/src/pages/products/index.astro +++ /dev/null @@ -1,47 +0,0 @@ ---- -// Products index page with custom sidebar for root deployment -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../content/products/index.mdx'; ---- - - - - diff --git a/website/src/pages/products/models/jan-nano.astro b/website/src/pages/products/models/jan-nano.astro deleted file mode 100644 index 566397102..000000000 --- a/website/src/pages/products/models/jan-nano.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/models/jan-nano.mdx'; ---- - - - - diff --git a/website/src/pages/products/models/jan-v1.astro b/website/src/pages/products/models/jan-v1.astro deleted file mode 100644 index 786ea0845..000000000 --- a/website/src/pages/products/models/jan-v1.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/models/jan-v1.mdx'; ---- - - - - diff --git a/website/src/pages/products/platforms/desktop.astro b/website/src/pages/products/platforms/desktop.astro deleted file mode 100644 index 36bae5a97..000000000 --- a/website/src/pages/products/platforms/desktop.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/platforms/desktop.mdx'; ---- - - - - diff --git a/website/src/pages/products/platforms/jan-ai.astro b/website/src/pages/products/platforms/jan-ai.astro deleted file mode 100644 index 3a80fea75..000000000 --- a/website/src/pages/products/platforms/jan-ai.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/platforms/jan-ai.mdx'; ---- - - - - diff --git a/website/src/pages/products/platforms/mobile.astro b/website/src/pages/products/platforms/mobile.astro deleted file mode 100644 index 8f615fc2d..000000000 --- a/website/src/pages/products/platforms/mobile.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/platforms/mobile.mdx'; ---- - - - - diff --git a/website/src/pages/products/platforms/server.astro b/website/src/pages/products/platforms/server.astro deleted file mode 100644 index 54799ea6a..000000000 --- a/website/src/pages/products/platforms/server.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/platforms/server.mdx'; ---- - - - - diff --git a/website/src/pages/products/tools/browseruse.astro b/website/src/pages/products/tools/browseruse.astro deleted file mode 100644 index e1a6ce10e..000000000 --- a/website/src/pages/products/tools/browseruse.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/tools/browseruse.mdx'; ---- - - - - diff --git a/website/src/pages/products/tools/deepresearch.astro b/website/src/pages/products/tools/deepresearch.astro deleted file mode 100644 index a722a8da6..000000000 --- a/website/src/pages/products/tools/deepresearch.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/tools/deepresearch.mdx'; ---- - - - - diff --git a/website/src/pages/products/tools/search.astro b/website/src/pages/products/tools/search.astro deleted file mode 100644 index 295d6a94b..000000000 --- a/website/src/pages/products/tools/search.astro +++ /dev/null @@ -1,46 +0,0 @@ ---- -import StarlightPage from '@astrojs/starlight/components/StarlightPage.astro'; -import { Content } from '../../../content/products/tools/search.mdx'; ---- - - - - diff --git a/website/src/scripts/robot-interactions.js b/website/src/scripts/robot-interactions.js deleted file mode 100644 index 143cb2697..000000000 --- a/website/src/scripts/robot-interactions.js +++ /dev/null @@ -1,697 +0,0 @@ -// Robot Interactions - Make robots come alive with personality! - -class RobotInteractions { - constructor() { - this.robots = []; - this.mousePosition = { x: 0, y: 0 }; - this.isInitialized = false; - - // Robot moods and states - this.moods = ['happy', 'curious', 'excited', 'sleepy', 'thinking']; - this.currentMood = 'happy'; - - // Easter egg: secret robot dance - this.konami = []; - this.konamiCode = ['ArrowUp', 'ArrowUp', 'ArrowDown', 'ArrowDown', 'ArrowLeft', 'ArrowRight', 'ArrowLeft', 'ArrowRight', 'b', 'a']; - } - - init() { - if (this.isInitialized) return; - - this.findRobots(); - this.setupEventListeners(); - this.startIdleAnimations(); - this.setupEasterEggs(); - - this.isInitialized = true; - } - - findRobots() { - // Find all robot elements - const robotElements = document.querySelectorAll('.robot-head, .robot-container, .model-robot, .platform-robot, .tool-robot'); - - robotElements.forEach(robot => { - const eyes = robot.querySelectorAll('.robot-eye'); - if (eyes.length > 0) { - this.robots.push({ - element: robot, - eyes: Array.from(eyes), - originalPositions: Array.from(eyes).map(eye => { - const rect = eye.getBoundingClientRect(); - return { - x: rect.left + rect.width / 2, - y: rect.top + rect.height / 2 - }; - }), - isBlinking: false, - mood: 'happy' - }); - } - }); - } - - setupEventListeners() { - // Track mouse movement - document.addEventListener('mousemove', (e) => { - this.mousePosition = { x: e.clientX, y: e.clientY }; - this.updateEyes(); - }); - - // Robot hover interactions - this.robots.forEach(robot => { - robot.element.addEventListener('mouseenter', () => this.onRobotHover(robot)); - robot.element.addEventListener('mouseleave', () => this.onRobotLeave(robot)); - robot.element.addEventListener('click', () => this.onRobotClick(robot)); - }); - - // Keyboard interactions - document.addEventListener('keydown', (e) => this.handleKeyPress(e)); - } - - updateEyes() { - this.robots.forEach(robot => { - if (robot.isBlinking) return; - - robot.eyes.forEach((eye, index) => { - // Skip sleeping or closed eyes - if (eye.classList.contains('sleeping') || eye.classList.contains('closed')) return; - - const eyeRect = eye.getBoundingClientRect(); - const eyeCenter = { - x: eyeRect.left + eyeRect.width / 2, - y: eyeRect.top + eyeRect.height / 2 - }; - - // Calculate angle between eye and mouse - const angle = Math.atan2( - this.mousePosition.y - eyeCenter.y, - this.mousePosition.x - eyeCenter.x - ); - - // Calculate distance (capped for natural movement) - const distance = Math.min( - Math.hypot( - this.mousePosition.x - eyeCenter.x, - this.mousePosition.y - eyeCenter.y - ) / 10, - 3 // Maximum pupil movement - ); - - // Move the pupil (eye shine) - const pupil = eye.querySelector('::after') || eye; - const offsetX = Math.cos(angle) * distance; - const offsetY = Math.sin(angle) * distance; - - // Apply transform to eye or create inner pupil - if (!eye.querySelector('.pupil')) { - const pupilElement = document.createElement('div'); - pupilElement.className = 'pupil'; - eye.appendChild(pupilElement); - } - - const pupilElement = eye.querySelector('.pupil'); - if (pupilElement) { - pupilElement.style.transform = `translate(${offsetX}px, ${offsetY}px)`; - } - }); - }); - } - - onRobotHover(robot) { - // Add excited animation - robot.element.classList.add('hover-active'); - - // Make eyes bigger - robot.eyes.forEach(eye => { - eye.style.transform = 'scale(1.2)'; - eye.classList.add('excited'); - }); - - // Add blush effect - this.addBlush(robot); - - // Random reaction - const reactions = ['happy', 'surprised', 'love']; - const reaction = reactions[Math.floor(Math.random() * reactions.length)]; - - if (reaction === 'love') { - robot.eyes.forEach(eye => eye.classList.add('love')); - } - } - - onRobotLeave(robot) { - robot.element.classList.remove('hover-active'); - - // Reset eyes - robot.eyes.forEach(eye => { - eye.style.transform = ''; - eye.classList.remove('excited', 'love'); - }); - - // Remove blush - this.removeBlush(robot); - } - - onRobotClick(robot) { - // Trigger a fun animation - this.makeRobotJump(robot); - this.makeRobotSpeak(robot); - - // Easter egg: clicking 5 times makes robot do a dance - if (!robot.clickCount) robot.clickCount = 0; - robot.clickCount++; - - if (robot.clickCount >= 5) { - this.robotDance(robot); - robot.clickCount = 0; - } - } - - makeRobotJump(robot) { - robot.element.style.animation = 'robotJump 0.6s ease-out'; - setTimeout(() => { - robot.element.style.animation = ''; - }, 600); - } - - makeRobotSpeak(robot) { - const messages = [ - "Hello there! 👋", - "Beep boop! 🤖", - "AI at your service!", - "Let's build something cool!", - "Privacy first! 🔒", - "Running locally! 💪", - "*happy robot noises*", - "01001000 01101001! (Hi in binary!)" - ]; - - const message = messages[Math.floor(Math.random() * messages.length)]; - this.showSpeechBubble(robot, message); - } - - showSpeechBubble(robot, message) { - // Remove existing bubble - const existingBubble = robot.element.querySelector('.robot-speech-bubble'); - if (existingBubble) existingBubble.remove(); - - // Create new bubble - const bubble = document.createElement('div'); - bubble.className = 'robot-speech-bubble'; - bubble.innerHTML = ` - ${message} -
- `; - - robot.element.appendChild(bubble); - - // Remove after 3 seconds - setTimeout(() => { - bubble.classList.add('fade-out'); - setTimeout(() => bubble.remove(), 300); - }, 3000); - } - - startIdleAnimations() { - // Random blinking - setInterval(() => { - this.robots.forEach(robot => { - if (Math.random() < 0.1 && !robot.isBlinking) { - this.makeRobotBlink(robot); - } - }); - }, 2000); - - // Random mood changes - setInterval(() => { - const robot = this.robots[Math.floor(Math.random() * this.robots.length)]; - if (robot && Math.random() < 0.3) { - this.changeRobotMood(robot); - } - }, 5000); - } - - makeRobotBlink(robot) { - robot.isBlinking = true; - - robot.eyes.forEach(eye => { - eye.style.transition = 'height 0.1s ease'; - eye.style.height = '2px'; - - setTimeout(() => { - eye.style.height = ''; - robot.isBlinking = false; - }, 150); - }); - } - - changeRobotMood(robot) { - const moods = ['thinking', 'happy', 'curious']; - const mood = moods[Math.floor(Math.random() * moods.length)]; - - switch(mood) { - case 'thinking': - this.showThinkingBubbles(robot); - break; - case 'happy': - this.makeRobotSmile(robot); - break; - case 'curious': - this.makeRobotLookAround(robot); - break; - } - } - - showThinkingBubbles(robot) { - const bubbles = document.createElement('div'); - bubbles.className = 'thinking-bubbles'; - bubbles.innerHTML = ` -
-
-
💭
- `; - - robot.element.appendChild(bubbles); - - setTimeout(() => { - bubbles.classList.add('fade-out'); - setTimeout(() => bubbles.remove(), 300); - }, 3000); - } - - makeRobotSmile(robot) { - const smile = robot.element.querySelector('.robot-smile'); - if (smile) { - smile.classList.add('big-smile'); - setTimeout(() => smile.classList.remove('big-smile'), 2000); - } - } - - makeRobotLookAround(robot) { - let direction = -1; - const lookInterval = setInterval(() => { - robot.eyes.forEach(eye => { - eye.style.transform = `translateX(${direction * 3}px)`; - }); - direction *= -1; - }, 500); - - setTimeout(() => { - clearInterval(lookInterval); - robot.eyes.forEach(eye => { - eye.style.transform = ''; - }); - }, 2000); - } - - addBlush(robot) { - const head = robot.element.querySelector('.robot-head') || robot.element; - - ['left', 'right'].forEach(side => { - const blush = document.createElement('div'); - blush.className = `robot-blush ${side}`; - head.appendChild(blush); - }); - } - - removeBlush(robot) { - const blushes = robot.element.querySelectorAll('.robot-blush'); - blushes.forEach(blush => blush.remove()); - } - - setupEasterEggs() { - // Konami code for robot dance party - document.addEventListener('keydown', (e) => { - this.konami.push(e.key); - this.konami = this.konami.slice(-10); - - if (this.konami.join(',') === this.konamiCode.join(',')) { - this.robotDanceParty(); - } - }); - - // Secret robot activation phrase - let secretPhrase = ''; - document.addEventListener('keypress', (e) => { - secretPhrase += e.key; - secretPhrase = secretPhrase.slice(-10); - - if (secretPhrase.includes('robot')) { - this.activateSecretRobotMode(); - } - }); - } - - robotDanceParty() { - document.body.classList.add('robot-dance-party'); - - // Make all robots dance - this.robots.forEach((robot, index) => { - setTimeout(() => { - this.robotDance(robot); - }, index * 200); - }); - - // Add disco lights - this.addDiscoLights(); - - // Stop after 10 seconds - setTimeout(() => { - document.body.classList.remove('robot-dance-party'); - this.removeDiscoLights(); - }, 10000); - } - - robotDance(robot) { - robot.element.classList.add('dancing'); - - // Random dance moves - const dances = ['wiggle', 'spin', 'bounce', 'shake']; - const dance = dances[Math.floor(Math.random() * dances.length)]; - - robot.element.style.animation = `robot-${dance} 1s ease-in-out infinite`; - - setTimeout(() => { - robot.element.style.animation = ''; - robot.element.classList.remove('dancing'); - }, 5000); - } - - addDiscoLights() { - const disco = document.createElement('div'); - disco.className = 'disco-lights'; - disco.innerHTML = ` -
🪩
-
-
-
-
- `; - document.body.appendChild(disco); - } - - removeDiscoLights() { - const disco = document.querySelector('.disco-lights'); - if (disco) disco.remove(); - } - - activateSecretRobotMode() { - // Make all robots super happy - this.robots.forEach(robot => { - robot.eyes.forEach(eye => { - eye.classList.add('sparkle', 'rainbow'); - }); - - this.showSpeechBubble(robot, "Secret mode activated! 🎉"); - }); - - // Remove after 5 seconds - setTimeout(() => { - this.robots.forEach(robot => { - robot.eyes.forEach(eye => { - eye.classList.remove('sparkle', 'rainbow'); - }); - }); - }, 5000); - } - - handleKeyPress(e) { - // Number keys change robot expressions - if (e.key >= '1' && e.key <= '9') { - const expressions = ['happy', 'sad', 'surprised', 'angry', 'love', 'sleepy', 'wink', 'dizzy', 'cool']; - const expression = expressions[parseInt(e.key) - 1]; - - if (expression) { - this.robots.forEach(robot => { - this.setRobotExpression(robot, expression); - }); - } - } - } - - setRobotExpression(robot, expression) { - // Clear previous expressions - const allExpressions = ['happy', 'sad', 'surprised', 'angry', 'love', 'sleepy', 'wink', 'dizzy', 'cool']; - robot.eyes.forEach(eye => { - allExpressions.forEach(exp => eye.classList.remove(exp)); - eye.classList.add(expression); - }); - - // Remove after 3 seconds - setTimeout(() => { - robot.eyes.forEach(eye => { - eye.classList.remove(expression); - }); - }, 3000); - } -} - -// Initialize when DOM is ready -if (document.readyState === 'loading') { - document.addEventListener('DOMContentLoaded', () => { - window.robotInteractions = new RobotInteractions(); - window.robotInteractions.init(); - }); -} else { - window.robotInteractions = new RobotInteractions(); - window.robotInteractions.init(); -} - -// Add required styles dynamically -const style = document.createElement('style'); -style.textContent = ` - /* Pupil for eye tracking */ - .robot-eye .pupil { - position: absolute; - top: 50%; - left: 50%; - width: 40%; - height: 40%; - background: white; - border-radius: 50%; - transform: translate(-50%, -50%); - transition: transform 0.1s ease-out; - pointer-events: none; - } - - /* Speech bubble */ - .robot-speech-bubble { - position: absolute; - bottom: 110%; - left: 50%; - transform: translateX(-50%); - background: white; - color: #1A1A2E; - padding: 0.75rem 1rem; - border-radius: 20px; - border: 3px solid #1A1A2E; - font-size: 0.9rem; - font-weight: 600; - white-space: nowrap; - box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2); - animation: bubbleIn 0.3s ease-out; - z-index: 100; - } - - .robot-speech-bubble.fade-out { - animation: bubbleOut 0.3s ease-in forwards; - } - - .bubble-tail { - position: absolute; - bottom: -8px; - left: 50%; - transform: translateX(-50%); - width: 0; - height: 0; - border-left: 8px solid transparent; - border-right: 8px solid transparent; - border-top: 10px solid white; - } - - .bubble-tail::before { - content: ''; - position: absolute; - bottom: 2px; - left: -10px; - width: 0; - height: 0; - border-left: 10px solid transparent; - border-right: 10px solid transparent; - border-top: 12px solid #1A1A2E; - } - - /* Thinking bubbles */ - .thinking-bubbles { - position: absolute; - top: -40px; - right: -20px; - animation: bubbleFloat 3s ease-in-out; - } - - .thinking-bubbles.fade-out { - animation: fadeOut 0.3s ease-in forwards; - } - - .think-bubble { - position: absolute; - background: white; - border: 2px solid #1A1A2E; - border-radius: 50%; - } - - .think-bubble.small { - width: 8px; - height: 8px; - bottom: 0; - left: 0; - } - - .think-bubble.medium { - width: 12px; - height: 12px; - bottom: 10px; - left: -5px; - } - - .think-bubble.large { - width: 30px; - height: 30px; - bottom: 25px; - left: -15px; - display: flex; - align-items: center; - justify-content: center; - font-size: 16px; - } - - /* Big smile animation */ - .robot-smile.big-smile { - width: 30px !important; - height: 15px !important; - border-width: 4px !important; - } - - /* Dance animations */ - @keyframes robot-wiggle { - 0%, 100% { transform: rotate(-5deg); } - 50% { transform: rotate(5deg); } - } - - @keyframes robot-spin { - 0% { transform: rotate(0deg); } - 100% { transform: rotate(360deg); } - } - - @keyframes robot-bounce { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-20px); } - } - - @keyframes robot-shake { - 0%, 100% { transform: translateX(0); } - 25% { transform: translateX(-5px); } - 75% { transform: translateX(5px); } - } - - @keyframes robotJump { - 0%, 100% { transform: translateY(0) scale(1); } - 50% { transform: translateY(-30px) scale(1.1); } - } - - @keyframes bubbleIn { - 0% { opacity: 0; transform: translateX(-50%) scale(0.8); } - 100% { opacity: 1; transform: translateX(-50%) scale(1); } - } - - @keyframes bubbleOut { - 0% { opacity: 1; transform: translateX(-50%) scale(1); } - 100% { opacity: 0; transform: translateX(-50%) scale(0.8); } - } - - @keyframes fadeOut { - 0% { opacity: 1; } - 100% { opacity: 0; } - } - - @keyframes bubbleFloat { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-5px); } - } - - /* Disco mode */ - .disco-lights { - position: fixed; - top: 0; - left: 0; - right: 0; - bottom: 0; - pointer-events: none; - z-index: 9999; - } - - .disco-ball { - position: absolute; - top: 50px; - left: 50%; - transform: translateX(-50%); - font-size: 60px; - animation: discoSpin 2s linear infinite; - } - - .light-beam { - position: absolute; - top: 100px; - left: 50%; - width: 100px; - height: 500px; - opacity: 0.3; - transform-origin: top center; - animation: lightSweep 4s ease-in-out infinite; - } - - .light-beam.red { background: linear-gradient(transparent, #FF006E); } - .light-beam.blue { background: linear-gradient(transparent, #3A86FF); animation-delay: 1s; } - .light-beam.green { background: linear-gradient(transparent, #06FFA5); animation-delay: 2s; } - .light-beam.yellow { background: linear-gradient(transparent, #FFB700); animation-delay: 3s; } - - @keyframes discoSpin { - 0% { transform: translateX(-50%) rotate(0deg); } - 100% { transform: translateX(-50%) rotate(360deg); } - } - - @keyframes lightSweep { - 0%, 100% { transform: translateX(-50%) rotate(-30deg); } - 50% { transform: translateX(-50%) rotate(30deg); } - } - - /* Rainbow eyes */ - .robot-eye.rainbow { - animation: rainbowEyes 2s linear infinite; - } - - @keyframes rainbowEyes { - 0% { background: #FF006E; } - 25% { background: #3A86FF; } - 50% { background: #06FFA5; } - 75% { background: #FFB700; } - 100% { background: #FF006E; } - } - - /* Sparkle effect */ - .robot-eye.sparkle::before { - content: '✨'; - position: absolute; - top: -10px; - right: -10px; - font-size: 12px; - animation: sparkleFloat 1s ease-in-out infinite; - } - - @keyframes sparkleFloat { - 0%, 100% { transform: translateY(0) rotate(0deg); } - 50% { transform: translateY(-5px) rotate(180deg); } - } -`; - -document.head.appendChild(style); diff --git a/website/src/styles/hand-drawn.css b/website/src/styles/hand-drawn.css deleted file mode 100644 index fe64e358e..000000000 --- a/website/src/styles/hand-drawn.css +++ /dev/null @@ -1,667 +0,0 @@ -/* Hand-Drawn UI Styles - PostHog-inspired playful design */ - -/* ===== HAND-DRAWN BORDERS ===== */ -.hand-drawn-border { - position: relative; - border: none !important; -} - -.hand-drawn-border::before { - content: ''; - position: absolute; - inset: -2px; - background: transparent; - border: 3px solid currentColor; - border-radius: 255px 15px 225px 15px/15px 225px 15px 255px; - pointer-events: none; -} - -.hand-drawn-border.thick::before { - border-width: 4px; - inset: -3px; -} - -.hand-drawn-border.animated::before { - animation: borderWobble 8s ease-in-out infinite; -} - -@keyframes borderWobble { - 0%, 100% { - border-radius: 255px 15px 225px 15px/15px 225px 15px 255px; - } - 25% { - border-radius: 15px 255px 15px 225px/225px 15px 255px 15px; - } - 50% { - border-radius: 225px 15px 255px 15px/15px 255px 15px 225px; - } - 75% { - border-radius: 15px 225px 15px 255px/255px 15px 225px 15px; - } -} - -/* ===== SKETCHY BUTTONS ===== */ -.btn-sketchy { - position: relative; - background: var(--robot-yellow); - color: var(--robot-dark); - padding: 1rem 2rem; - border: none; - font-weight: 700; - font-size: 1rem; - cursor: pointer; - transition: all 0.3s ease; - overflow: visible; -} - -.btn-sketchy::before { - content: ''; - position: absolute; - inset: -3px; - background: var(--robot-yellow); - transform: rotate(-1deg); - border-radius: 255px 15px 225px 15px/15px 225px 15px 255px; - z-index: -1; - transition: all 0.3s ease; -} - -.btn-sketchy::after { - content: ''; - position: absolute; - inset: 0; - border: 3px solid var(--robot-dark); - border-radius: 225px 15px 255px 15px/15px 255px 15px 225px; - transform: rotate(0.5deg); -} - -.btn-sketchy:hover { - transform: translateY(-2px) rotate(-0.5deg); -} - -.btn-sketchy:hover::before { - transform: rotate(-2deg) scale(1.05); - background: var(--robot-orange); -} - -.btn-sketchy:hover::after { - transform: rotate(1deg); -} - -.btn-sketchy:active { - transform: translateY(0) rotate(0deg); -} - -/* ===== HAND-DRAWN ARROWS ===== */ -.arrow-doodle { - position: relative; - width: 100px; - height: 50px; -} - -.arrow-doodle svg { - width: 100%; - height: 100%; - overflow: visible; -} - -.arrow-doodle path { - fill: none; - stroke: var(--robot-dark); - stroke-width: 3; - stroke-linecap: round; - stroke-linejoin: round; -} - -.arrow-doodle.animated path { - stroke-dasharray: 100; - stroke-dashoffset: 100; - animation: drawArrow 2s ease-in-out infinite; -} - -@keyframes drawArrow { - 0% { stroke-dashoffset: 100; } - 50% { stroke-dashoffset: 0; } - 100% { stroke-dashoffset: -100; } -} - -/* ===== WOBBLY UNDERLINES ===== */ -.underline-wobble { - position: relative; - text-decoration: none; - display: inline-block; -} - -.underline-wobble::after { - content: ''; - position: absolute; - bottom: -4px; - left: 0; - right: 0; - height: 3px; - background: currentColor; - clip-path: polygon( - 0% 50%, - 2% 35%, - 4% 65%, - 6% 45%, - 8% 55%, - 10% 40%, - 12% 60%, - 14% 50%, - 16% 35%, - 18% 65%, - 20% 45%, - 22% 55%, - 24% 40%, - 26% 60%, - 28% 50%, - 30% 35%, - 32% 65%, - 34% 45%, - 36% 55%, - 38% 40%, - 40% 60%, - 42% 50%, - 44% 35%, - 46% 65%, - 48% 45%, - 50% 55%, - 52% 40%, - 54% 60%, - 56% 50%, - 58% 35%, - 60% 65%, - 62% 45%, - 64% 55%, - 66% 40%, - 68% 60%, - 70% 50%, - 72% 35%, - 74% 65%, - 76% 45%, - 78% 55%, - 80% 40%, - 82% 60%, - 84% 50%, - 86% 35%, - 88% 65%, - 90% 45%, - 92% 55%, - 94% 40%, - 96% 60%, - 98% 50%, - 100% 50% - ); -} - -.underline-wobble:hover::after { - animation: wobbleUnderline 0.5s ease-in-out; -} - -@keyframes wobbleUnderline { - 0%, 100% { transform: translateY(0); } - 25% { transform: translateY(-2px); } - 75% { transform: translateY(2px); } -} - -/* ===== SKETCH CIRCLES ===== */ -.circle-sketch { - position: relative; - width: 100px; - height: 100px; - background: var(--robot-blue); -} - -.circle-sketch::before { - content: ''; - position: absolute; - inset: -5px; - background: transparent; - border: 3px solid var(--robot-dark); - border-radius: 50%; - clip-path: polygon( - 50% 0%, - 52% 2%, - 54% 1%, - 56% 3%, - 58% 2%, - 60% 0%, - 62% 1%, - 64% 3%, - 66% 2%, - 68% 1%, - 70% 0%, - 72% 2%, - 74% 3%, - 76% 1%, - 78% 2%, - 80% 0%, - 82% 1%, - 84% 3%, - 86% 2%, - 88% 1%, - 90% 0%, - 92% 2%, - 94% 1%, - 96% 3%, - 98% 2%, - 100% 0%, - 100% 50%, - 98% 52%, - 99% 54%, - 97% 56%, - 98% 58%, - 100% 60%, - 99% 62%, - 97% 64%, - 98% 66%, - 99% 68%, - 100% 70%, - 98% 72%, - 97% 74%, - 99% 76%, - 98% 78%, - 100% 80%, - 99% 82%, - 97% 84%, - 98% 86%, - 99% 88%, - 100% 90%, - 98% 92%, - 99% 94%, - 97% 96%, - 98% 98%, - 100% 100%, - 50% 100%, - 48% 98%, - 46% 99%, - 44% 97%, - 42% 98%, - 40% 100%, - 38% 99%, - 36% 97%, - 34% 98%, - 32% 99%, - 30% 100%, - 28% 98%, - 26% 97%, - 24% 99%, - 22% 98%, - 20% 100%, - 18% 99%, - 16% 97%, - 14% 98%, - 12% 99%, - 10% 100%, - 8% 98%, - 6% 99%, - 4% 97%, - 2% 98%, - 0% 100%, - 0% 50%, - 2% 48%, - 1% 46%, - 3% 44%, - 2% 42%, - 0% 40%, - 1% 38%, - 3% 36%, - 2% 34%, - 1% 32%, - 0% 30%, - 2% 28%, - 3% 26%, - 1% 24%, - 2% 22%, - 0% 20%, - 1% 18%, - 3% 16%, - 2% 14%, - 1% 12%, - 0% 10%, - 2% 8%, - 1% 6%, - 3% 4%, - 2% 2%, - 0% 0% - ); -} - -/* ===== ROUGH PAPER TEXTURE ===== */ -.paper-texture { - position: relative; - background: - repeating-linear-gradient( - 90deg, - transparent, - transparent 2px, - rgba(0, 0, 0, 0.03) 2px, - rgba(0, 0, 0, 0.03) 4px - ), - repeating-linear-gradient( - 0deg, - transparent, - transparent 2px, - rgba(0, 0, 0, 0.03) 2px, - rgba(0, 0, 0, 0.03) 4px - ), - linear-gradient( - 135deg, - #ffffff 0%, - #f8f8f8 100% - ); -} - -.paper-texture::before { - content: ''; - position: absolute; - inset: 0; - background: url('data:image/svg+xml;utf8,'); - pointer-events: none; -} - -/* ===== HANDWRITING FONTS ===== */ -@import url('https://fonts.googleapis.com/css2?family=Kalam:wght@400;700&family=Caveat:wght@400;700&display=swap'); - -.handwriting { - font-family: 'Kalam', cursive; - letter-spacing: 0.02em; -} - -.handwriting-alt { - font-family: 'Caveat', cursive; - font-size: 1.2em; -} - -/* ===== DOODLE CHECKBOX ===== */ -.checkbox-doodle { - position: relative; - width: 24px; - height: 24px; - cursor: pointer; -} - -.checkbox-doodle input { - position: absolute; - opacity: 0; - width: 100%; - height: 100%; - cursor: pointer; -} - -.checkbox-doodle .checkbox-box { - position: absolute; - inset: 0; - background: white; - border: 3px solid var(--robot-dark); - border-radius: 4px; - transform: rotate(-2deg); - transition: all 0.3s ease; -} - -.checkbox-doodle .checkbox-check { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%) scale(0) rotate(-45deg); - width: 12px; - height: 6px; - border-left: 3px solid var(--robot-green); - border-bottom: 3px solid var(--robot-green); - transition: all 0.3s cubic-bezier(0.68, -0.55, 0.265, 1.55); -} - -.checkbox-doodle input:checked ~ .checkbox-box { - background: var(--robot-yellow); - transform: rotate(2deg); -} - -.checkbox-doodle input:checked ~ .checkbox-check { - transform: translate(-50%, -50%) scale(1) rotate(-45deg); -} - -.checkbox-doodle:hover .checkbox-box { - transform: rotate(-4deg) scale(1.1); - border-color: var(--robot-blue); -} - -/* ===== TOOLTIP BUBBLE ===== */ -.tooltip-bubble { - position: relative; - display: inline-block; -} - -.tooltip-bubble .tooltip-content { - position: absolute; - bottom: 125%; - left: 50%; - transform: translateX(-50%) scale(0); - background: var(--robot-dark); - color: white; - padding: 0.75rem 1rem; - border-radius: 15px 15px 15px 2px; - font-size: 0.9rem; - white-space: nowrap; - box-shadow: 4px 4px 0 rgba(0, 0, 0, 0.2); - transform-origin: bottom center; - transition: all 0.3s cubic-bezier(0.68, -0.55, 0.265, 1.55); -} - -.tooltip-bubble .tooltip-content::after { - content: ''; - position: absolute; - top: 100%; - left: 50%; - transform: translateX(-50%); - width: 0; - height: 0; - border-left: 8px solid transparent; - border-right: 8px solid transparent; - border-top: 10px solid var(--robot-dark); - margin-left: -8px; -} - -.tooltip-bubble:hover .tooltip-content { - transform: translateX(-50%) scale(1); -} - -/* ===== WIGGLE TEXT ===== */ -.text-wiggle { - display: inline-block; - animation: textWiggle 2s ease-in-out infinite; -} - -.text-wiggle.subtle { - animation-duration: 4s; -} - -@keyframes textWiggle { - 0%, 100% { transform: rotate(-1deg); } - 50% { transform: rotate(1deg); } -} - -/* ===== HAND-DRAWN DIVIDER ===== */ -.divider-sketch { - width: 100%; - height: 20px; - margin: 2rem 0; - background: url('data:image/svg+xml;utf8,') center/contain no-repeat; -} - -/* ===== SCRIBBLE BACKGROUND ===== */ -.scribble-bg { - position: relative; - overflow: hidden; -} - -.scribble-bg::before { - content: ''; - position: absolute; - top: -50%; - left: -50%; - width: 200%; - height: 200%; - background: url('data:image/svg+xml;utf8,') 0 0/50px 50px; - transform: rotate(-5deg); - opacity: 0.1; - pointer-events: none; -} - -/* ===== STICKY NOTE ===== */ -.sticky-note { - position: relative; - background: #fff740; - padding: 1.5rem; - box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1); - transform: rotate(-2deg); - transition: all 0.3s ease; -} - -.sticky-note::before { - content: ''; - position: absolute; - top: -8px; - left: 50%; - transform: translateX(-50%); - width: 60px; - height: 16px; - background: rgba(0, 0, 0, 0.1); - border-radius: 8px; -} - -.sticky-note:hover { - transform: rotate(0deg) scale(1.02); - box-shadow: 4px 4px 20px rgba(0, 0, 0, 0.15); -} - -.sticky-note.pink { background: #ff9ff3; } -.sticky-note.blue { background: #54a0ff; } -.sticky-note.green { background: #1dd1a1; } - -/* ===== MARKER HIGHLIGHT ===== */ -.highlight-marker { - position: relative; - display: inline-block; - color: var(--robot-dark); - padding: 0 0.2em; -} - -.highlight-marker::before { - content: ''; - position: absolute; - bottom: 0; - left: -0.1em; - right: -0.1em; - height: 0.8em; - background: var(--robot-yellow); - opacity: 0.4; - transform: skew(-5deg); - z-index: -1; - transition: all 0.3s ease; -} - -.highlight-marker:hover::before { - height: 1.2em; - opacity: 0.6; -} - -.highlight-marker.pink::before { background: var(--robot-pink); } -.highlight-marker.blue::before { background: var(--robot-blue); } -.highlight-marker.green::before { background: var(--robot-green); } - -/* ===== ROUGH SHADOW ===== */ -.shadow-rough { - position: relative; -} - -.shadow-rough::after { - content: ''; - position: absolute; - inset: 4px -4px -4px 4px; - background: rgba(0, 0, 0, 0.2); - border-radius: inherit; - transform: rotate(-1deg); - z-index: -1; - filter: blur(2px); -} - -/* ===== HAND-DRAWN PROGRESS BAR ===== */ -.progress-sketch { - position: relative; - width: 100%; - height: 30px; - background: white; - border: 3px solid var(--robot-dark); - border-radius: 15px; - overflow: hidden; - transform: rotate(-1deg); -} - -.progress-sketch .progress-fill { - height: 100%; - background: repeating-linear-gradient( - 45deg, - var(--robot-green), - var(--robot-green) 10px, - transparent 10px, - transparent 20px - ); - border-right: 3px solid var(--robot-dark); - transition: width 0.5s ease; - animation: progressSlide 1s linear infinite; -} - -@keyframes progressSlide { - 0% { background-position: 0 0; } - 100% { background-position: 20px 0; } -} - -/* ===== UTILITY CLASSES ===== */ -.wiggle { animation: wiggle 2s ease-in-out infinite; } -.bounce { animation: bounce 3s ease-in-out infinite; } -.float { animation: float 4s ease-in-out infinite; } -.shake { animation: shake 0.5s ease-in-out infinite; } - -@keyframes wiggle { - 0%, 100% { transform: rotate(-2deg); } - 50% { transform: rotate(2deg); } -} - -@keyframes bounce { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-10px); } -} - -@keyframes float { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-20px); } -} - -@keyframes shake { - 0%, 100% { transform: translateX(0); } - 25% { transform: translateX(-5px); } - 75% { transform: translateX(5px); } -} - -/* ===== ACCESSIBILITY ===== */ -@media (prefers-reduced-motion: reduce) { - *, - *::before, - *::after { - animation-duration: 0.01ms !important; - animation-iteration-count: 1 !important; - transition-duration: 0.01ms !important; - } -} - -/* ===== RESPONSIVE ADJUSTMENTS ===== */ -@media (max-width: 768px) { - .btn-sketchy { - padding: 0.75rem 1.5rem; - font-size: 0.9rem; - } - - .sticky-note { - padding: 1rem; - } -} diff --git a/website/src/styles/products-architecture.css b/website/src/styles/products-architecture.css deleted file mode 100644 index 3ff9d0a3e..000000000 --- a/website/src/styles/products-architecture.css +++ /dev/null @@ -1,577 +0,0 @@ -/* ProductsArchitecture Component Styles */ - -.architecture-section { - background: var(--light-gray); - border-top: 1px solid var(--border-color); -} - -.architecture-section .badge-robot-head { - background: var(--pastel-blue); -} - -/* Architecture Stack */ -.architecture-stack { - margin: 4rem 0; - max-width: 900px; - margin-left: auto; - margin-right: auto; - position: relative; -} - -.stack-layer { - background: white; - border-radius: 20px; - border: 3px solid var(--border-color); - padding: 2rem; - margin-bottom: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; - position: relative; - display: flex; - align-items: center; - gap: 2rem; -} - -.stack-layer:hover { - transform: translateX(8px); - box-shadow: 0 12px 40px rgba(0, 0, 0, 0.12); -} - -.tools-layer { - border-color: var(--gentle-orange); - border-left-width: 8px; -} - -.models-layer { - border-color: var(--soft-green); - border-left-width: 8px; -} - -.platforms-layer { - border-color: var(--primary-blue); - border-left-width: 8px; - margin-bottom: 0; -} - -.layer-number { - position: absolute; - top: -15px; - left: 2rem; - width: 30px; - height: 30px; - background: var(--primary-blue); - color: white; - border-radius: 50%; - display: flex; - align-items: center; - justify-content: center; - font-weight: 700; - font-size: 1.1rem; - border: 3px solid white; - box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1); -} - -.layer-robot { - flex-shrink: 0; -} - -/* Robot Designs */ -.tools-robot, -.models-robot, -.platforms-robot { - position: relative; -} - -.tools-robot .robot-head, -.models-robot .robot-head, -.platforms-robot .robot-head { - width: 60px; - height: 60px; - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto 10px; - position: relative; -} - -.tools-robot .robot-body, -.models-robot .robot-body, -.platforms-robot .robot-body { - width: 70px; - height: 80px; - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.tools-robot .robot-head, -.tools-robot .robot-body { - background: var(--gentle-orange); -} - -.models-robot .robot-head, -.models-robot .robot-body { - background: var(--soft-green); -} - -.platforms-robot .robot-head, -.platforms-robot .robot-body { - background: var(--pastel-blue); -} - -.robot-eyes { - display: flex; - justify-content: space-between; - padding: 12px 8px; -} - -.robot-eye { - width: 12px; - height: 12px; - background: var(--dark-text); - border-radius: 50%; - position: relative; -} - -.robot-eye::after { - content: ''; - position: absolute; - top: 2px; - right: 2px; - width: 3px; - height: 3px; - background: white; - border-radius: 50%; -} - -.robot-eye.thinking { - animation: thinking 3s infinite ease-in-out; -} - -.robot-smile { - position: absolute; - bottom: 8px; - left: 50%; - transform: translateX(-50%); - width: 15px; - height: 8px; - border: 3px solid var(--dark-text); - border-top: none; - border-radius: 0 0 12px 12px; -} - -.robot-smile { - position: absolute; - bottom: 8px; - left: 50%; - transform: translateX(-50%); - width: 15px; - height: 8px; - border: 3px solid var(--dark-text); - border-top: none; - border-radius: 0 0 12px 12px; -} - -/* Robot-specific details */ -.robot-base { - margin-top: 5px; -} - -.base-foundation { - width: 50px; - height: 8px; - background: var(--medium-gray); - border-radius: 4px; - border: 2px solid var(--dark-text); - margin: 0 auto; -} - -.layer-content { - flex: 1; -} - -.layer-content h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.75rem; -} - -.layer-content p { - color: var(--medium-gray); - margin-bottom: 1.5rem; - line-height: 1.6; -} - -.layer-features { - display: flex; - flex-wrap: wrap; - gap: 0.75rem; -} - -.feature-item { - display: flex; - align-items: center; - gap: 0.5rem; - font-size: 0.9rem; - font-weight: 500; -} - -.feature-status { - width: 8px; - height: 8px; - border-radius: 50%; - border: 1px solid var(--dark-text); -} - -.feature-item.active .feature-status { - background: var(--success-green); - animation: pulse 2s infinite; -} - -.feature-item.beta .feature-status { - background: var(--warning-amber); - animation: pulse 2s infinite; -} - -.feature-item.coming .feature-status { - background: var(--medium-gray); -} - -/* Connection Lines */ -.layer-connection { - position: absolute; - bottom: -2rem; - left: 50%; - width: 4px; - height: 2rem; - background-color: var(--border-color); - z-index: -1; -} - -.platforms-layer .layer-connection { - display: none; -} - -.data-flow { - position: absolute; - top: 0; - left: -4px; - right: -4px; - height: 100%; -} - -.flow-packet { - position: absolute; - width: 6px; - height: 6px; - background: var(--primary-blue); - border-radius: 50%; - animation: flowData 3s infinite linear; - left: 50%; - transform: translateX(-50%); -} - -.flow-packet:nth-child(2) { - animation-delay: 1s; -} -.flow-packet:nth-child(3) { - animation-delay: 2s; -} - -/* Architecture Benefits */ -.architecture-benefits { - margin: 4rem 0; -} - -.architecture-benefits h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.benefits-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 2rem; -} - -.benefit-card { - background: white; - border-radius: 16px; - border: 2px solid var(--border-color); - padding: 2rem; - text-align: center; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; -} - -.benefit-card:hover { - transform: translateY(-4px); - box-shadow: 0 12px 40px rgba(0, 0, 0, 0.12); - border-color: var(--primary-blue); -} - -.benefit-robot { - margin-bottom: 1rem; -} - -.benefit-robot .robot-head { - width: 40px; - height: 40px; - background: var(--warm-yellow); - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 6px; - position: relative; -} - -.benefit-robot .robot-body { - width: 50px; - height: 60px; - background: var(--gentle-orange); - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto; - display: flex; - align-items: center; - justify-content: center; -} - -.benefit-card h4 { - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.benefit-card p { - color: var(--medium-gray); - line-height: 1.6; -} - -/* Interactive Demo */ -.architecture-demo { - margin: 4rem 0; - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - padding: 3rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.architecture-demo h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.demo-container { - max-width: 800px; - margin: 0 auto; -} - -.scenario-title { - text-align: center; - font-size: 1.2rem; - font-weight: 600; - color: var(--primary-blue); - margin-bottom: 2rem; - padding: 1rem; - background: var(--light-blue); - border-radius: 10px; - border: 2px solid var(--primary-blue); -} - -.demo-flow { - display: flex; - align-items: center; - justify-content: center; - gap: 1rem; - flex-wrap: wrap; -} - -.flow-step { - background: var(--cream-white); - border-radius: 12px; - border: 2px solid var(--border-color); - padding: 1.5rem; - text-align: center; - max-width: 200px; - transition: all 0.3s ease; -} - -.flow-step:hover { - border-color: var(--primary-blue); - transform: scale(1.05); -} - -.step-number { - width: 30px; - height: 30px; - background: var(--primary-blue); - color: white; - border-radius: 50%; - display: flex; - align-items: center; - justify-content: center; - font-weight: 700; - margin: 0 auto 1rem; -} - -.step-content { - font-size: 0.9rem; - line-height: 1.4; -} - -.step-content strong { - color: var(--primary-blue); - display: block; - margin-bottom: 0.5rem; -} - -.flow-arrow { - width: 30px; - height: 2px; - background: var(--primary-blue); - position: relative; -} - -.flow-arrow::after { - content: ''; - position: absolute; - right: -5px; - top: -3px; - width: 0; - height: 0; - border-top: 4px solid transparent; - border-bottom: 4px solid transparent; - border-left: 8px solid var(--primary-blue); -} - -/* Animations */ -@keyframes thinking { - 0%, - 100% { - transform: scaleY(1); - } - 50% { - transform: scaleY(0.8); - } -} - -@keyframes flowData { - 0% { - top: 0; - opacity: 0; - } - 10% { - opacity: 1; - } - 90% { - opacity: 1; - } - 100% { - top: 100%; - opacity: 0; - } -} - -@keyframes pulse { - 0%, - 100% { - transform: scale(1); - opacity: 1; - } - 50% { - transform: scale(1.2); - opacity: 0.7; - } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .stack-layer { - flex-direction: column; - text-align: center; - gap: 1.5rem; - } - - .layer-connection { - display: none; - } - - .benefits-grid { - grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); - } -} - -@media (max-width: 768px) { - .architecture-stack { - margin: 3rem 0; - } - - .stack-layer { - padding: 1.5rem; - margin-bottom: 1.5rem; - } - - .layer-content h3 { - font-size: 1.3rem; - } - - .demo-flow { - flex-direction: column; - } - - .flow-arrow { - transform: rotate(90deg); - } - - .architecture-demo { - padding: 2rem; - } -} - -@media (max-width: 480px) { - .stack-layer { - padding: 1.25rem; - } - - .benefits-grid { - grid-template-columns: 1fr; - } - - .benefit-card { - padding: 1.5rem; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - .robot-eye, - .flow-packet, - .feature-status, - .stack-layer, - .benefit-card, - .flow-step { - animation: none !important; - transition: none !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .stack-layer, - .benefit-card, - .architecture-demo { - border-width: 3px; - } -} diff --git a/website/src/styles/products-models.css b/website/src/styles/products-models.css deleted file mode 100644 index d691759f7..000000000 --- a/website/src/styles/products-models.css +++ /dev/null @@ -1,758 +0,0 @@ -/* ProductsModels Component Styles */ - -.models-section { - background: var(--cream-white); - border-top: 1px solid var(--border-color); -} - -.models-section .badge-robot-head { - background: var(--soft-pink); -} - -/* Models Grid */ -.models-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 2rem; - margin: 4rem 0; -} - -.model-card { - position: relative; - overflow: visible; -} - -.model-robot-section { - position: relative; - text-align: center; - margin-bottom: 2rem; -} - -.model-robot { - display: inline-block; - position: relative; - margin-bottom: 1rem; -} - -.model-robot .robot-head { - width: 70px; - height: 70px; - border-radius: 16px; - border: 3px solid var(--dark-text); - margin: 0 auto 12px; - position: relative; -} - -.model-robot .robot-body { - width: 80px; - height: 90px; - border-radius: 16px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; - flex-direction: column; -} - -/* Robot variations */ -.v1-robot .robot-head { background: var(--success-green); } -.v1-robot .robot-body { background: var(--success-green); } - -.nano-robot .robot-head { background: var(--primary-blue); } -.nano-robot .robot-body { background: var(--primary-blue); } - -.lucy-robot .robot-head { background: var(--warning-amber); } -.lucy-robot .robot-body { background: var(--warning-amber); } - -.robot-eyes { - display: flex; - justify-content: space-between; - padding: 12px 10px; -} - -.robot-eye { - width: 14px; - height: 14px; - background: var(--dark-text); - border-radius: 50%; - position: relative; -} - -.robot-eye::after { - content: ''; - position: absolute; - top: 3px; - right: 3px; - width: 4px; - height: 4px; - background: white; - border-radius: 50%; -} - -.robot-eye.research { - background: var(--primary-blue); - animation: researchBlink 3s infinite ease-in-out; -} - -.robot-eye.sleeping { - height: 4px; - border-radius: 2px; - background: var(--dark-text); -} - -.robot-eye.sleeping::after { - display: none; -} - -.robot-antenna { - position: absolute; - top: -12px; - left: 50%; - transform: translateX(-50%); - width: 3px; - height: 15px; - background: var(--dark-text); - border-radius: 2px; -} - -.antenna-tip { - position: absolute; - top: -6px; - left: 50%; - transform: translateX(-50%); - width: 8px; - height: 8px; - border-radius: 50%; - border: 2px solid var(--dark-text); -} - -.antenna-tip.active { - background: var(--success-green); - animation: pulse 2s infinite; -} - -.robot-chest { - margin-bottom: 8px; -} - -.chest-panel { - display: flex; - flex-direction: column; - gap: 2px; -} - -.panel-line { - width: 20px; - height: 2px; - background: var(--dark-text); - border-radius: 1px; -} - -.panel-line.active { - background: white; - animation: dataFlow 2s infinite ease-in-out; -} - -.panel-line:nth-child(2) { animation-delay: 0.5s; } -.panel-line:nth-child(3) { animation-delay: 1s; } - -.robot-badge { - background: white; - color: var(--dark-text); - padding: 2px 8px; - border-radius: 8px; - font-size: 0.7rem; - font-weight: 700; - border: 2px solid var(--dark-text); -} - -.robot-scanner { - position: absolute; - top: 8px; - right: 8px; - width: 12px; - height: 12px; -} - -.scanner-beam { - width: 100%; - height: 2px; - background: var(--primary-blue); - border-radius: 1px; - animation: scan 3s infinite ease-in-out; -} - -.research-display { - display: flex; - gap: 3px; -} - -.display-dot { - width: 4px; - height: 4px; - background: white; - border-radius: 50%; - animation: pulse 2s infinite; -} - -.display-dot:nth-child(2) { animation-delay: 0.5s; } -.display-dot:nth-child(3) { animation-delay: 1s; } - -.robot-vision-sensor { - position: absolute; - top: 5px; - left: 50%; - transform: translateX(-50%); - width: 16px; - height: 16px; -} - -.vision-ring { - width: 100%; - height: 100%; - border: 2px solid var(--dark-text); - border-radius: 50%; - position: relative; -} - -.vision-center { - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - width: 6px; - height: 6px; - background: var(--error-red); - border-radius: 50%; -} - -.multimodal-display { - display: flex; - gap: 2px; -} - -.modal-icon { - width: 6px; - height: 6px; - border-radius: 2px; - border: 1px solid var(--dark-text); -} - -.modal-icon.vision { background: var(--primary-blue); } -.modal-icon.text { background: var(--success-green); } -.modal-icon.audio { background: var(--medium-gray); } - -.robot-progress { - margin-top: 8px; - text-align: center; -} - -.progress-bar { - width: 40px; - height: 4px; - background: var(--border-color); - border-radius: 2px; - margin: 0 auto 4px; - overflow: hidden; -} - -.progress-fill { - height: 100%; - background: var(--warning-amber); - border-radius: 2px; - width: 60%; - animation: training 3s infinite ease-in-out; -} - -.progress-text { - font-size: 0.7rem; - color: var(--medium-gray); - font-weight: 500; -} - -.robot-arms { - display: flex; - justify-content: space-between; - margin-top: -30px; - padding: 0 15px; - position: relative; - z-index: -1; -} - -.arm { - width: 10px; - height: 30px; - background: var(--gentle-orange); - border-radius: 5px; - border: 2px solid var(--dark-text); - transform-origin: top center; -} - -.robot-tools { - display: flex; - gap: 8px; - margin-top: 8px; -} - -.tool { - width: 8px; - height: 8px; - border-radius: 2px; - border: 1px solid var(--dark-text); -} - -.search-tool { background: var(--primary-blue); } -.analysis-tool { background: var(--success-green); } - -.model-status-bubble { - position: absolute; - top: -10px; - right: 1rem; -} - -/* Model Content */ -.model-content { - padding: 0; -} - -.model-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 1rem; -} - -.model-header h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); -} - -.model-version { - background: var(--light-gray); - color: var(--medium-gray); - padding: 0.25rem 0.5rem; - border-radius: 8px; - font-size: 0.7rem; - font-weight: 600; - border: 1px solid var(--border-color); -} - -.model-description { - color: var(--medium-gray); - margin-bottom: 1.5rem; - line-height: 1.6; -} - -.model-specs { - margin-bottom: 1.5rem; -} - -.spec-section { - margin-bottom: 1rem; -} - -.spec-section h4 { - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - margin-bottom: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.spec-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(100px, 1fr)); - gap: 0.75rem; -} - -.spec-item { - background: var(--light-gray); - border-radius: 8px; - padding: 0.75rem; - text-align: center; - border: 1px solid var(--border-color); -} - -.spec-label { - font-size: 0.8rem; - font-weight: 600; - color: var(--primary-blue); - margin-bottom: 0.25rem; -} - -.spec-value { - font-size: 0.9rem; - color: var(--dark-text); - font-weight: 500; -} - -.spec-highlight { - background: var(--light-blue); - border: 2px solid var(--primary-blue); - border-radius: 10px; - padding: 1rem; - text-align: center; -} - -.highlight-label { - font-size: 0.8rem; - font-weight: 600; - color: var(--primary-blue); - margin-bottom: 0.25rem; - text-transform: uppercase; -} - -.highlight-value { - font-size: 1.2rem; - font-weight: 700; - color: var(--dark-text); -} - -.model-capabilities { - margin-bottom: 1.5rem; -} - -.model-capabilities h4 { - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - margin-bottom: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.capabilities-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(120px, 1fr)); - gap: 0.75rem; -} - -.capability { - display: flex; - flex-direction: column; - align-items: center; - gap: 0.5rem; - padding: 0.75rem; - border-radius: 10px; - border: 2px solid var(--border-color); - text-align: center; - font-size: 0.8rem; - font-weight: 500; - transition: all 0.3s ease; -} - -.capability:hover { - transform: translateY(-2px); - box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1); -} - -.capability.active { - border-color: var(--success-green); - background: var(--soft-green); -} - -.capability.coming { - border-color: var(--medium-gray); - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} - -.capability-icon { - width: 20px; - height: 20px; - border-radius: 4px; - border: 2px solid var(--dark-text); - position: relative; -} - -/* Capability icon colors */ -.capability-icon.reasoning { background: var(--primary-blue); } -.capability-icon.coding { background: var(--success-green); } -.capability-icon.multilingual { background: var(--warning-amber); } -.capability-icon.tools { background: var(--light-purple); } -.capability-icon.search { background: var(--primary-blue); } -.capability-icon.document { background: var(--warning-amber); } -.capability-icon.mobile { background: var(--success-green); } -.capability-icon.mcp { background: var(--light-purple); } -.capability-icon.image { background: var(--medium-gray); } -.capability-icon.ocr { background: var(--medium-gray); } -.capability-icon.visual { background: var(--medium-gray); } -.capability-icon.processing { background: var(--medium-gray); } - -/* Add small symbols to capability icons */ -.capability-icon::after { - content: ''; - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - width: 6px; - height: 6px; - background: white; - border-radius: 2px; -} - -.capability-icon.reasoning::after { border-radius: 50%; } -.capability-icon.coding::after { width: 8px; height: 4px; } -.capability-icon.search::after { border-radius: 50%; width: 4px; height: 4px; } - -.model-metrics { - display: flex; - justify-content: space-around; - padding-top: 1rem; - border-top: 1px solid var(--border-color); -} - -.metric { - text-align: center; -} - -.metric-value { - font-size: 1.1rem; - font-weight: 700; - color: var(--primary-blue); - margin-bottom: 0.25rem; -} - -.metric-label { - font-size: 0.8rem; - color: var(--medium-gray); - text-transform: uppercase; - letter-spacing: 0.05em; -} - -/* Models Comparison */ -.models-comparison { - margin: 4rem 0; - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - padding: 3rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.models-comparison h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.comparison-table { - display: grid; - grid-template-columns: 1fr 1fr 1fr 1fr; - gap: 1px; - background: var(--border-color); - border-radius: 12px; - overflow: hidden; -} - -.table-header { - display: contents; -} - -.header-cell { - background: var(--primary-blue); - color: white; - padding: 1rem; - font-weight: 700; - text-align: center; -} - -.table-row { - display: contents; -} - -.table-cell { - background: white; - padding: 1rem; - text-align: center; - font-size: 0.9rem; -} - -.feature-col { - font-weight: 600; - color: var(--dark-text); - text-align: left; -} - -.model-col { - color: var(--medium-gray); -} - -.status-indicator { - display: inline-flex; - align-items: center; - gap: 0.25rem; - padding: 0.25rem 0.5rem; - border-radius: 12px; - font-size: 0.7rem; - font-weight: 600; - text-transform: uppercase; -} - -.status-indicator::before { - content: ''; - width: 6px; - height: 6px; - border-radius: 50%; -} - -.status-indicator.active { - background: var(--soft-green); - color: var(--success-green); -} - -.status-indicator.active::before { - background: var(--success-green); -} - -.status-indicator.beta { - background: var(--warm-yellow); - color: var(--warning-amber); -} - -.status-indicator.beta::before { - background: var(--warning-amber); -} - -.status-indicator.coming { - background: var(--light-gray); - color: var(--medium-gray); -} - -.status-indicator.coming::before { - background: var(--medium-gray); -} - -/* Animations */ -@keyframes researchBlink { - 0%, 100% { opacity: 1; } - 50% { opacity: 0.3; } -} - -@keyframes scan { - 0% { transform: translateY(0) scaleX(1); } - 50% { transform: translateY(4px) scaleX(1.2); } - 100% { transform: translateY(8px) scaleX(1); } -} - -@keyframes training { - 0% { width: 0%; } - 50% { width: 80%; } - 100% { width: 60%; } -} - -@keyframes dataFlow { - 0%, 100% { opacity: 0.5; transform: scaleX(1); } - 50% { opacity: 1; transform: scaleX(1.2); } -} - -@keyframes pulse { - 0%, 100% { transform: scale(1); opacity: 1; } - 50% { transform: scale(1.1); opacity: 0.7; } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .models-grid { - grid-template-columns: repeat(auto-fit, minmax(350px, 1fr)); - } - - .models-comparison { - padding: 2rem; - } - - .comparison-table { - font-size: 0.8rem; - } -} - -@media (max-width: 768px) { - .models-grid { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .model-robot .robot-head { - width: 60px; - height: 60px; - } - - .model-robot .robot-body { - width: 70px; - height: 80px; - } - - .spec-grid { - grid-template-columns: repeat(auto-fit, minmax(80px, 1fr)); - } - - .capabilities-grid { - grid-template-columns: repeat(auto-fit, minmax(100px, 1fr)); - } - - .comparison-table { - grid-template-columns: 1fr; - gap: 0; - } - - .table-row { - display: grid; - grid-template-columns: 1fr 1fr 1fr 1fr; - gap: 1px; - margin-bottom: 1px; - } - - .models-comparison { - padding: 1.5rem; - } -} - -@media (max-width: 480px) { - .model-content { - padding: 1rem; - } - - .model-specs { - margin-bottom: 1rem; - } - - .spec-grid { - grid-template-columns: 1fr 1fr; - } - - .capabilities-grid { - grid-template-columns: 1fr 1fr; - } - - .model-metrics { - flex-direction: column; - gap: 1rem; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - .robot-eye, .scanner-beam, .display-dot, .panel-line, .progress-fill, .antenna-tip, .capability { - animation: none !important; - transition: none !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .model-card, .comparison-table { - border-width: 3px; - } - - .capability, .spec-item { - border-width: 2px; - } -} diff --git a/website/src/styles/products-platforms.css b/website/src/styles/products-platforms.css deleted file mode 100644 index a5eaf7c23..000000000 --- a/website/src/styles/products-platforms.css +++ /dev/null @@ -1,1009 +0,0 @@ -/* Products Platforms Section Styles */ - -.platforms-section { - background: var(--light-gray); - position: relative; - overflow: hidden; -} - -/* Platforms Timeline */ -.platforms-timeline { - position: relative; - max-width: 1000px; - margin: 4rem auto; - padding: 0 2rem; -} - -.timeline-path { - position: absolute; - left: 50px; - top: 0; - bottom: 0; - width: 4px; - background: linear-gradient( - 180deg, - var(--primary-blue) 0%, - var(--soft-pink) 25%, - var(--warm-yellow) 50%, - var(--soft-green) 75%, - var(--primary-blue) 100% - ); - border-radius: 2px; -} - -.timeline-path::before { - content: ''; - position: absolute; - top: -20px; - left: 50%; - transform: translateX(-50%); - width: 12px; - height: 12px; - background: var(--primary-blue); - border-radius: 50%; - border: 3px solid white; -} - -/* Platform Items */ -.platform-item { - position: relative; - display: flex; - align-items: center; - gap: 3rem; - margin-bottom: 4rem; - opacity: 0; - transform: translateX(-50px); - animation: slideInFromLeft 0.6s ease-out forwards; -} - -.platform-item:nth-child(2) { animation-delay: 0.2s; } -.platform-item:nth-child(3) { animation-delay: 0.4s; } -.platform-item:nth-child(4) { animation-delay: 0.6s; } - -@keyframes slideInFromLeft { - to { - opacity: 1; - transform: translateX(0); - } -} - -@keyframes slideInFromRight { - from { - opacity: 0; - transform: translateX(50px); - } - to { - opacity: 1; - transform: translateX(0); - } -} - -/* Platform Robots Container */ -.platform-robot { - position: relative; - width: 150px; - flex-shrink: 0; - z-index: 2; -} - -.platform-robot .robot-head { - width: 60px; - height: 60px; - margin: 0 auto 10px; - border-radius: 15px; - border: 3px solid var(--dark-text); - position: relative; - transition: all 0.3s ease; -} - -.platform-robot .robot-body { - width: 80px; - height: 100px; - margin: 0 auto; - border-radius: 15px; - border: 3px solid var(--dark-text); - position: relative; - transition: all 0.3s ease; -} - -/* Desktop Robot Styles */ -.desktop-robot .robot-head { - background: linear-gradient(135deg, var(--primary-blue), var(--pastel-blue)); -} - -.desktop-robot .robot-body { - background: linear-gradient(135deg, var(--pastel-blue), var(--primary-blue)); -} - -.desktop-robot .robot-screen { - position: absolute; - top: 15px; - left: 50%; - transform: translateX(-50%); - width: 50px; - height: 30px; - background: #000; - border: 2px solid var(--primary-blue); - border-radius: 4px; - overflow: hidden; -} - -.screen-content { - padding: 4px; -} - -.screen-bar { - height: 2px; - background: var(--soft-green); - margin: 2px 0; - border-radius: 1px; - animation: codeFlow 2s ease-in-out infinite; -} - -.screen-bar:nth-child(2) { animation-delay: 0.5s; width: 80%; } -.screen-bar:nth-child(3) { animation-delay: 1s; width: 60%; } - -@keyframes codeFlow { - 0%, 100% { opacity: 0.3; transform: translateX(-10px); } - 50% { opacity: 1; transform: translateX(0); } -} - -.robot-keyboard { - position: absolute; - bottom: 20px; - left: 50%; - transform: translateX(-50%); - display: flex; - gap: 2px; -} - -.key { - width: 6px; - height: 6px; - background: var(--dark-text); - border-radius: 1px; -} - -.robot-base { - margin-top: 10px; -} - -.base-stand { - width: 40px; - height: 8px; - background: var(--dark-text); - margin: 0 auto; - border-radius: 4px; -} - -/* Web Robot Styles */ -.web-robot .robot-head { - background: linear-gradient(135deg, var(--light-blue), var(--soft-green)); -} - -.web-robot .robot-body { - background: linear-gradient(135deg, var(--soft-green), var(--light-blue)); -} - -.robot-browser { - position: absolute; - top: 10px; - left: 50%; - transform: translateX(-50%); - width: 45px; - height: 35px; - background: white; - border: 2px solid var(--dark-text); - border-radius: 4px; - overflow: hidden; -} - -.browser-bar { - display: flex; - gap: 2px; - padding: 2px; - background: var(--light-gray); - border-bottom: 1px solid var(--border-color); -} - -.browser-dot { - width: 4px; - height: 4px; - border-radius: 50%; -} - -.browser-dot.red { background: #ff5f56; } -.browser-dot.yellow { background: #ffbd2e; } -.browser-dot.green { background: #27ca3f; } - -.browser-content { - padding: 4px; -} - -.content-line { - height: 2px; - background: var(--primary-blue); - margin: 2px 0; - border-radius: 1px; - width: 100%; -} - -.content-line.short { width: 70%; } - -.robot-cloud { - position: absolute; - top: 20px; - left: 50%; - transform: translateX(-50%); - width: 40px; - height: 40px; - display: flex; - flex-direction: column; - align-items: center; - justify-content: center; -} - -.cloud-icon { - font-size: 24px; -} - -.cloud-data { - display: flex; - gap: 2px; - margin-top: 4px; -} - -.data-bit { - width: 4px; - height: 4px; - background: var(--primary-blue); - border-radius: 50%; - animation: dataFlow 1.5s ease-in-out infinite; -} - -.data-bit:nth-child(2) { animation-delay: 0.5s; } - -@keyframes dataFlow { - 0%, 100% { opacity: 0; transform: translateY(0); } - 50% { opacity: 1; transform: translateY(-5px); } -} - -/* Mobile Robot Styles */ -.mobile-robot .robot-head { - background: linear-gradient(135deg, var(--warm-yellow), var(--gentle-orange)); -} - -.mobile-robot .robot-body { - background: linear-gradient(135deg, var(--gentle-orange), var(--warm-yellow)); -} - -.robot-phone { - position: absolute; - top: 10px; - left: 50%; - transform: translateX(-50%); - width: 30px; - height: 40px; - background: #000; - border: 2px solid var(--dark-text); - border-radius: 6px; - overflow: hidden; -} - -.phone-screen { - margin: 2px; - height: calc(100% - 4px); - background: var(--primary-blue); - border-radius: 4px; - display: flex; - align-items: center; - justify-content: center; -} - -.phone-ui { - display: flex; - flex-direction: column; - gap: 4px; -} - -.ui-element { - width: 20px; - height: 4px; - background: white; - border-radius: 2px; - opacity: 0.8; -} - -.robot-signal { - position: absolute; - top: 20px; - right: 10px; - display: flex; - gap: 2px; -} - -.signal-bar { - width: 3px; - height: 8px; - background: var(--dark-text); - border-radius: 1px; - opacity: 0.3; -} - -.signal-bar:nth-child(2) { height: 12px; opacity: 0.6; } -.signal-bar:nth-child(3) { height: 16px; opacity: 1; } - -.development-indicator { - position: absolute; - bottom: -30px; - left: 50%; - transform: translateX(-50%); - text-align: center; - width: 100%; -} - -.dev-progress { - width: 80px; - height: 4px; - background: var(--light-gray); - border-radius: 2px; - margin: 0 auto 8px; - overflow: hidden; -} - -.progress-fill { - height: 100%; - width: 60%; - background: var(--warm-yellow); - border-radius: 2px; - animation: progressAnimation 2s ease-in-out infinite; -} - -@keyframes progressAnimation { - 0%, 100% { width: 60%; } - 50% { width: 80%; } -} - -.dev-text { - font-size: 0.8rem; - color: var(--medium-gray); - font-weight: 600; -} - -/* Server Robot Styles */ -.server-robot .robot-head { - background: linear-gradient(135deg, var(--dark-text), var(--medium-gray)); -} - -.server-robot .robot-body { - background: linear-gradient(135deg, var(--medium-gray), var(--dark-text)); -} - -.robot-server-display { - position: absolute; - top: 15px; - left: 50%; - transform: translateX(-50%); -} - -.server-lights { - display: flex; - gap: 4px; -} - -.light { - width: 6px; - height: 6px; - border-radius: 50%; - background: var(--success-green); - animation: serverBlink 2s ease-in-out infinite; -} - -.light:nth-child(2) { animation-delay: 0.5s; background: var(--warning-amber); } -.light:nth-child(3) { animation-delay: 1s; background: var(--error-red); } - -@keyframes serverBlink { - 0%, 100% { opacity: 0.3; } - 50% { opacity: 1; } -} - -.robot-rack { - padding: 20px 10px; -} - -.rack-unit { - width: 100%; - height: 8px; - background: var(--light-gray); - margin: 4px 0; - border-radius: 2px; - position: relative; - overflow: hidden; -} - -.rack-unit::after { - content: ''; - position: absolute; - top: 0; - left: -20px; - width: 20px; - height: 100%; - background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.4), transparent); - animation: rackScan 3s linear infinite; -} - -@keyframes rackScan { - to { left: 100%; } -} - -.robot-connections { - margin-top: 10px; - display: flex; - justify-content: center; - gap: 8px; -} - -.connection-port { - width: 12px; - height: 6px; - background: var(--dark-text); - border-radius: 2px; -} - -/* Platform Cards */ -.platform-card { - flex: 1; - background: white; - border-radius: 20px; - border: 3px solid var(--border-color); - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; - position: relative; -} - -.platform-card:hover { - transform: translateY(-4px); - box-shadow: 0 12px 40px rgba(0, 0, 0, 0.12); - border-color: var(--primary-blue); -} - -.platform-status { - display: inline-flex; - align-items: center; - gap: 0.5rem; - padding: 0.5rem 1rem; - border-radius: 20px; - font-size: 0.8rem; - font-weight: 600; - margin-bottom: 1rem; -} - -.platform-status.available { - background: var(--success-green); - color: white; -} - -.platform-status.beta { - background: var(--warning-amber); - color: white; -} - -.platform-status.coming { - background: var(--medium-gray); - color: white; - border: 2px dashed white; -} - -.platform-card h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.5rem; -} - -.platform-card p { - color: var(--medium-gray); - margin-bottom: 1.5rem; - line-height: 1.6; -} - -/* Platform Metrics */ -.platform-metrics { - display: flex; - gap: 2rem; - margin-bottom: 1.5rem; -} - -.metric { - display: flex; - align-items: center; - gap: 0.75rem; -} - -.metric-icon { - width: 32px; - height: 32px; - background: var(--light-blue); - border-radius: 8px; - display: flex; - align-items: center; - justify-content: center; -} - -.metric-text { - display: flex; - flex-direction: column; -} - -.metric-value { - font-size: 1.2rem; - font-weight: 700; - color: var(--dark-text); -} - -.metric-label { - font-size: 0.8rem; - color: var(--medium-gray); -} - -/* Platform Features */ -.platform-features { - display: grid; - grid-template-columns: repeat(2, 1fr); - gap: 1rem; - margin-bottom: 1.5rem; -} - -.feature-item { - display: flex; - align-items: center; - gap: 0.5rem; - font-size: 0.9rem; -} - -.feature-icon { - width: 20px; - height: 20px; - background: var(--light-purple); - border-radius: 4px; - flex-shrink: 0; -} - -/* Platform Specs */ -.platform-specs { - display: flex; - gap: 1rem; - padding: 1rem; - background: var(--light-gray); - border-radius: 12px; -} - -.spec-item { - flex: 1; - text-align: center; -} - -.spec-label { - font-size: 0.8rem; - color: var(--medium-gray); - margin-bottom: 0.25rem; -} - -.spec-value { - font-size: 1rem; - font-weight: 700; - color: var(--dark-text); -} - -/* Mobile Modes */ -.mobile-modes { - margin-top: 2rem; - padding: 2rem; - background: var(--light-blue); - border-radius: 16px; -} - -.mobile-modes h4 { - text-align: center; - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1.5rem; -} - -.modes-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); - gap: 1.5rem; -} - -.mode-card { - background: white; - border-radius: 12px; - padding: 1.5rem; - border: 2px solid transparent; - transition: all 0.3s ease; - cursor: pointer; -} - -.mode-card:hover { - border-color: var(--primary-blue); - transform: translateY(-2px); - box-shadow: 0 8px 20px rgba(0, 0, 0, 0.1); -} - -.mode-card.active { - border-color: var(--primary-blue); - background: var(--light-blue); -} - -.mode-icon { - display: flex; - justify-content: center; - margin-bottom: 1rem; -} - -.mode-robot-mini { - width: 40px; - height: 50px; - position: relative; -} - -.mini-head { - width: 20px; - height: 20px; - margin: 0 auto 5px; - border-radius: 6px; - border: 2px solid var(--dark-text); -} - -.mini-body { - width: 30px; - height: 25px; - margin: 0 auto; - border-radius: 6px; - border: 2px solid var(--dark-text); - display: flex; - align-items: center; - justify-content: center; -} - -.desktop-color { background: var(--primary-blue); } -.server-color { background: var(--medium-gray); } -.local-color { background: var(--soft-green); } - -.mode-content h5 { - font-size: 1.1rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.5rem; -} - -.mode-content p { - font-size: 0.9rem; - color: var(--medium-gray); - margin-bottom: 1rem; -} - -.mode-flow { - display: flex; - align-items: center; - gap: 0.5rem; - font-size: 0.8rem; - color: var(--primary-blue); - font-weight: 600; - margin-bottom: 1rem; -} - -.mode-benefits { - display: flex; - flex-direction: column; - gap: 0.5rem; -} - -.benefit { - font-size: 0.85rem; - color: var(--dark-text); - padding-left: 1rem; - position: relative; -} - -.benefit::before { - content: '✓'; - position: absolute; - left: 0; - color: var(--success-green); - font-weight: bold; -} - -/* Mobile Features */ -.mobile-features { - margin-top: 2rem; -} - -.mobile-features h4 { - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1.5rem; -} - -.feature-highlight-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); - gap: 1.5rem; -} - -.feature-highlight { - display: flex; - gap: 1rem; -} - -.highlight-icon { - width: 48px; - height: 48px; - background: var(--light-purple); - border-radius: 12px; - flex-shrink: 0; -} - -.highlight-content h5 { - font-size: 1rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.25rem; -} - -.highlight-content p { - font-size: 0.85rem; - color: var(--medium-gray); - line-height: 1.5; -} - -/* Deployment Options */ -.deployment-options { - display: flex; - gap: 1rem; - margin-top: 1rem; -} - -.deploy-option { - flex: 1; - display: flex; - align-items: center; - gap: 0.75rem; - padding: 1rem; - background: var(--light-gray); - border-radius: 8px; -} - -.deploy-icon { - width: 32px; - height: 32px; - background: var(--primary-blue); - border-radius: 8px; - display: flex; - align-items: center; - justify-content: center; - color: white; - font-weight: bold; -} - -.deploy-content { - display: flex; - flex-direction: column; -} - -.deploy-name { - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); -} - -.deploy-desc { - font-size: 0.8rem; - color: var(--medium-gray); -} - -/* Platform Comparison */ -.platform-comparison { - margin: 4rem 0; -} - -.platform-comparison h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.comparison-grid { - background: white; - border-radius: 16px; - overflow: hidden; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.comparison-header { - display: grid; - grid-template-columns: 2fr 2fr 1fr 2fr; - background: var(--light-gray); - font-weight: 700; - color: var(--dark-text); -} - -.header-item { - padding: 1rem; - border-right: 1px solid var(--border-color); -} - -.header-item:last-child { - border-right: none; -} - -.comparison-row { - display: grid; - grid-template-columns: 2fr 2fr 1fr 2fr; - border-bottom: 1px solid var(--border-color); - transition: all 0.3s ease; -} - -.comparison-row:last-child { - border-bottom: none; -} - -.comparison-row:hover { - background: var(--light-blue); -} - -.row-item { - padding: 1rem; - border-right: 1px solid var(--border-color); - display: flex; - align-items: center; -} - -.row-item:last-child { - border-right: none; -} - -.platform-name { - display: flex; - align-items: center; - gap: 0.75rem; - font-weight: 600; -} - -.platform-mini-icon { - width: 24px; - height: 24px; - border-radius: 6px; - border: 2px solid var(--dark-text); -} - -.desktop-mini { background: var(--primary-blue); } -.web-mini { background: var(--soft-green); } -.mobile-mini { background: var(--warm-yellow); } -.server-mini { background: var(--medium-gray); } - -.status-indicator { - display: inline-flex; - align-items: center; - gap: 0.5rem; - padding: 0.25rem 0.75rem; - border-radius: 20px; - font-size: 0.8rem; - font-weight: 600; -} - -.status-indicator.active { - background: var(--success-green); - color: white; -} - -.status-indicator.beta { - background: var(--warning-amber); - color: white; -} - -.status-indicator.coming { - background: var(--light-gray); - color: var(--medium-gray); - border: 2px dashed var(--medium-gray); -} - -/* Hover Animation for Platform Robots */ -.platform-robot.hover-animation { - animation: robotHoverBounce 0.8s ease-in-out; -} - -@keyframes robotHoverBounce { - 0%, 100% { transform: translateY(0) scale(1); } - 50% { transform: translateY(-10px) scale(1.05); } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .platforms-timeline { - padding: 0 1rem; - } - - .platform-item { - flex-direction: column; - text-align: center; - } - - .timeline-path { - display: none; - } - - .modes-grid { - grid-template-columns: 1fr; - } - - .comparison-grid { - overflow-x: auto; - } - - .comparison-header, - .comparison-row { - min-width: 600px; - } -} - -@media (max-width: 768px) { - .platform-robot { - transform: scale(0.8); - margin-bottom: 1rem; - } - - .platform-features { - grid-template-columns: 1fr; - } - - .platform-specs { - flex-direction: column; - gap: 0.5rem; - } - - .spec-item { - display: flex; - justify-content: space-between; - text-align: left; - } - - .mobile-features { - padding: 1rem; - } - - .feature-highlight-grid { - grid-template-columns: 1fr; - } -} - -@media (max-width: 480px) { - .platform-card { - padding: 1.5rem; - } - - .platform-metrics { - flex-direction: column; - gap: 1rem; - } - - .deployment-options { - flex-direction: column; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - *, - *::before, - *::after { - animation: none !important; - transition: none !important; - } -} diff --git a/website/src/styles/products-principles.css b/website/src/styles/products-principles.css deleted file mode 100644 index 221fdbbfd..000000000 --- a/website/src/styles/products-principles.css +++ /dev/null @@ -1,1303 +0,0 @@ -/* ProductsPrinciples Component Styles */ - -.principles-section { - background: var(--cream-white); - border-top: 1px solid var(--border-color); -} - -.principles-section .badge-robot-head { - background: var(--error-red); -} - -/* Main Comparison */ -.main-comparison { - margin: 4rem 0; -} - -.comparison-sides { - display: grid; - grid-template-columns: 1fr auto 1fr; - gap: 2rem; - align-items: start; - max-width: 1000px; - margin: 0 auto; -} - -.comparison-side { - background: white; - border-radius: 20px; - border: 3px solid var(--border-color); - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; -} - -.other-ai { - border-color: var(--error-red); - border-left-width: 6px; -} - -.jan-side { - border-color: var(--success-green); - border-left-width: 6px; -} - -.comparison-side:hover { - transform: translateY(-4px); - box-shadow: 0 12px 40px rgba(0, 0, 0, 0.12); -} - -.side-header { - text-align: center; - margin-bottom: 2rem; -} - -.side-robot { - display: inline-block; - position: relative; - margin-bottom: 1rem; -} - -/* Other AI Robot (Corporate) */ -.other-robot .robot-head { - width: 60px; - height: 60px; - background: var(--error-red); - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto 10px; - position: relative; -} - -.other-robot .robot-body { - width: 70px; - height: 80px; - background: var(--error-red); - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - flex-direction: column; - align-items: center; - justify-content: center; -} - -.robot-eyes { - display: flex; - justify-content: space-between; - padding: 12px 8px; -} - -.robot-eye { - width: 12px; - height: 12px; - background: var(--dark-text); - border-radius: 50%; - position: relative; -} - -.robot-eye::after { - content: ''; - position: absolute; - top: 2px; - right: 2px; - width: 3px; - height: 3px; - background: white; - border-radius: 50%; -} - -.robot-eye.corporate { - background: var(--dark-text); - animation: corporateGlare 3s infinite ease-in-out; -} - -.robot-corporate-hat { - position: absolute; - top: -8px; - left: 50%; - transform: translateX(-50%); - width: 25px; - height: 15px; - background: var(--dark-text); - border-radius: 8px 8px 0 0; - display: flex; - align-items: center; - justify-content: center; -} - -.hat-logo { - color: var(--warning-amber); - font-size: 0.7rem; - font-weight: 700; -} - -.robot-tie { - width: 8px; - height: 25px; - background: var(--dark-text); - margin-bottom: 5px; - position: relative; -} - -.tie-pattern { - position: absolute; - top: 3px; - left: 50%; - transform: translateX(-50%); - width: 4px; - height: 4px; - background: var(--warning-amber); - border-radius: 50%; -} - -.robot-briefcase { - width: 20px; - height: 12px; - background: var(--dark-text); - border-radius: 2px; - display: flex; - align-items: center; - justify-content: center; -} - -.briefcase-lock { - width: 6px; - height: 6px; - background: var(--warning-amber); - border-radius: 2px; -} - -.robot-chains { - display: flex; - gap: 2px; - margin-top: 8px; -} - -.chain-link { - width: 8px; - height: 4px; - border: 1px solid var(--dark-text); - border-radius: 2px; - background: var(--medium-gray); -} - -/* Jan Robot (Friendly) */ -.jan-robot .robot-head { - width: 60px; - height: 60px; - background: var(--success-green); - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto 10px; - position: relative; -} - -.jan-robot .robot-body { - width: 70px; - height: 80px; - background: var(--success-green); - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - flex-direction: column; - align-items: center; - justify-content: center; -} - -.robot-eye.friendly { - background: var(--dark-text); - animation: friendlyBlink 4s infinite ease-in-out; -} - -.robot-smile { - position: absolute; - bottom: 12px; - left: 50%; - transform: translateX(-50%); - width: 20px; - height: 10px; - border: 3px solid var(--dark-text); - border-top: none; - border-radius: 0 0 20px 20px; -} - -.robot-heart { - margin-bottom: 8px; - position: relative; -} - -.heart-beat { - width: 15px; - height: 15px; - background: var(--error-red); - border-radius: 50%; - position: relative; - animation: heartbeat 2s infinite; -} - -.heart-beat::before, -.heart-beat::after { - content: ''; - position: absolute; - width: 15px; - height: 15px; - background: var(--error-red); - border-radius: 50%; -} - -.heart-beat::before { - top: -7px; - left: 0; -} - -.heart-beat::after { - top: 0; - left: -7px; -} - -.robot-shield { - width: 18px; - height: 20px; - background: var(--primary-blue); - border-radius: 50% 50% 50% 50% / 60% 60% 40% 40%; - border: 2px solid var(--dark-text); - display: flex; - align-items: center; - justify-content: center; -} - -.shield-lock { - width: 6px; - height: 6px; - background: white; - border-radius: 2px; -} - -.robot-freedom { - margin-top: 8px; -} - -.freedom-wings { - display: flex; - gap: 4px; - align-items: center; -} - -.wing { - width: 12px; - height: 8px; - background: white; - border: 2px solid var(--dark-text); - border-radius: 50%; - animation: wingFlap 3s infinite ease-in-out; -} - -.left-wing { - animation-delay: 0s; -} - -.right-wing { - animation-delay: 0.5s; -} - -/* VS Divider */ -.vs-divider { - display: flex; - align-items: center; - justify-content: center; - padding: 2rem 0; -} - -.vs-robot { - position: relative; -} - -.vs-robot .robot-head { - width: 40px; - height: 40px; - background: var(--warning-amber); - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 8px; - position: relative; -} - -.vs-robot .robot-body { - width: 50px; - height: 60px; - background: var(--warning-amber); - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto; - display: flex; - align-items: center; - justify-content: center; -} - -.robot-eye.vs-eye { - animation: vsFlicker 1s infinite ease-in-out; -} - -.vs-text { - font-size: 1.2rem; - font-weight: 800; - color: var(--dark-text); -} - -.robot-lightning { - position: absolute; - top: -15px; - left: 50%; - transform: translateX(-50%); -} - -.lightning-bolt { - width: 0; - height: 0; - border-left: 6px solid transparent; - border-right: 6px solid transparent; - border-top: 12px solid var(--warning-amber); - position: relative; -} - -.lightning-bolt::after { - content: ''; - position: absolute; - top: 8px; - left: -3px; - width: 0; - height: 0; - border-left: 3px solid transparent; - border-right: 3px solid transparent; - border-top: 6px solid var(--warning-amber); -} - -.side-header h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.5rem; -} - -.side-header p { - color: var(--medium-gray); - font-size: 0.9rem; -} - -.side-features { - display: flex; - flex-direction: column; - gap: 1rem; -} - -.feature-item { - display: flex; - align-items: flex-start; - gap: 1rem; - padding: 1rem; - border-radius: 12px; - transition: all 0.3s ease; -} - -.feature-item.limitation { - background: var(--warm-yellow); - border: 2px solid var(--error-red); -} - -.feature-item.advantage { - background: var(--soft-green); - border: 2px solid var(--success-green); -} - -.feature-item:hover { - transform: translateX(4px); -} - -.feature-icon { - width: 30px; - height: 30px; - border-radius: 8px; - border: 2px solid var(--dark-text); - flex-shrink: 0; -} - -.feature-icon.wrapper { - background: var(--error-red); -} -.feature-icon.cloud-only { - background: var(--medium-gray); -} -.feature-icon.subscription { - background: var(--warning-amber); -} -.feature-icon.control { - background: var(--error-red); -} -.feature-icon.own-models { - background: var(--success-green); -} -.feature-icon.privacy-first { - background: var(--primary-blue); -} -.feature-icon.free-local { - background: var(--success-green); -} -.feature-icon.your-rules { - background: var(--primary-blue); -} - -.feature-content h4 { - font-size: 1rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.25rem; -} - -.feature-content p { - font-size: 0.9rem; - color: var(--medium-gray); - line-height: 1.4; -} - -/* Detailed Comparison */ -.detailed-comparison { - margin: 4rem 0; - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - padding: 3rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.detailed-comparison h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.comparison-table { - display: grid; - grid-template-columns: 1fr 1fr 1fr; - gap: 1px; - background: var(--border-color); - border-radius: 12px; - overflow: hidden; -} - -.table-header { - display: contents; -} - -.header-cell { - background: var(--primary-blue); - color: white; - padding: 1rem; - font-weight: 700; - text-align: center; -} - -.table-row { - display: contents; -} - -.table-cell { - background: white; - padding: 1rem; - text-align: center; - transition: all 0.3s ease; -} - -.feature-col { - display: flex; - align-items: center; - justify-content: center; - gap: 0.75rem; - font-weight: 600; - color: var(--dark-text); -} - -.feature-robot { - position: relative; -} - -.robot-mini-head { - width: 20px; - height: 20px; - border-radius: 6px; - border: 2px solid var(--dark-text); - margin: 0 auto 4px; -} - -.robot-mini-body { - width: 25px; - height: 30px; - border-radius: 6px; - border: 2px solid var(--dark-text); - margin: 0 auto; -} - -.models-feature .robot-mini-head { - background: var(--soft-pink); -} -.models-feature .robot-mini-body { - background: var(--soft-pink); -} - -.privacy-feature .robot-mini-head { - background: var(--primary-blue); -} -.privacy-feature .robot-mini-body { - background: var(--primary-blue); -} - -.deployment-feature .robot-mini-head { - background: var(--success-green); -} -.deployment-feature .robot-mini-body { - background: var(--success-green); -} - -.cost-feature .robot-mini-head { - background: var(--warning-amber); -} -.cost-feature .robot-mini-body { - background: var(--warning-amber); -} - -.control-feature .robot-mini-head { - background: var(--light-purple); -} -.control-feature .robot-mini-body { - background: var(--light-purple); -} - -.limitation-indicator { - background: var(--warm-yellow); - color: var(--error-red); - padding: 0.5rem 1rem; - border-radius: 8px; - font-weight: 600; - border: 2px solid var(--error-red); -} - -.advantage-indicator { - background: var(--soft-green); - color: var(--success-green); - padding: 0.5rem 1rem; - border-radius: 8px; - font-weight: 600; - border: 2px solid var(--success-green); -} - -/* Philosophy Cards */ -.philosophy-cards { - margin: 4rem 0; -} - -.philosophy-cards h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.cards-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 2rem; -} - -.philosophy-card { - background: white; - border-radius: 16px; - border: 2px solid var(--border-color); - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; - text-align: center; -} - -.philosophy-card:hover { - border-color: var(--primary-blue); -} - -.card-robot { - margin-bottom: 1.5rem; - display: inline-block; -} - -.card-robot .robot-head { - width: 50px; - height: 50px; - border-radius: 12px; - border: 3px solid var(--dark-text); - margin: 0 auto 8px; - position: relative; -} - -.card-robot .robot-body { - width: 60px; - height: 70px; - border-radius: 12px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.models-card .card-robot .robot-head { - background: var(--soft-pink); -} -.models-card .card-robot .robot-body { - background: var(--soft-pink); -} - -.infrastructure-card .card-robot .robot-head { - background: var(--success-green); -} -.infrastructure-card .card-robot .robot-body { - background: var(--success-green); -} - -.tools-card .card-robot .robot-head { - background: var(--warning-amber); -} -.tools-card .card-robot .robot-body { - background: var(--warning-amber); -} - -.robot-brain { - position: absolute; - top: 8px; - left: 50%; - transform: translateX(-50%); -} - -.brain-core { - width: 15px; - height: 10px; - background: var(--primary-blue); - border-radius: 8px; - border: 2px solid var(--dark-text); -} - -.model-display { - display: flex; - flex-direction: column; - gap: 3px; -} - -.model-layer { - width: 25px; - height: 3px; - background: var(--dark-text); - border-radius: 2px; -} - -.robot-home { - position: absolute; - top: 5px; - left: 50%; - transform: translateX(-50%); -} - -.home-roof { - width: 20px; - height: 10px; - background: var(--error-red); - border: 2px solid var(--dark-text); - border-radius: 2px 2px 0 0; -} - -.home-door { - width: 8px; - height: 8px; - background: var(--dark-text); - border-radius: 2px; - margin: 0 auto; -} - -.infrastructure-display { - display: flex; - flex-direction: column; - align-items: center; - gap: 4px; -} - -.server-rack { - width: 20px; - height: 15px; - background: var(--dark-text); - border-radius: 2px; -} - -.connection-lines { - display: flex; - gap: 2px; -} - -.line { - width: 8px; - height: 1px; - background: var(--dark-text); -} - -.robot-tools-display { - position: absolute; - top: 8px; - left: 50%; - transform: translateX(-50%); - display: flex; - gap: 4px; -} - -.tool-wrench { - width: 8px; - height: 8px; - background: var(--dark-text); - border-radius: 2px; - transform: rotate(45deg); -} - -.tool-gear { - width: 8px; - height: 8px; - background: var(--dark-text); - border-radius: 50%; -} - -.automation-display { - width: 30px; - height: 20px; -} - -.automation-flow { - display: flex; - justify-content: space-between; - align-items: center; -} - -.flow-dot { - width: 4px; - height: 4px; - background: var(--dark-text); - border-radius: 50%; - animation: flowPulse 2s infinite ease-in-out; -} - -.flow-dot:nth-child(2) { - animation-delay: 0.5s; -} -.flow-dot:nth-child(3) { - animation-delay: 1s; -} - -.card-content h4 { - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.card-content p { - color: var(--medium-gray); - line-height: 1.6; -} - -.card-icon { - width: 40px; - height: 40px; - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 1rem; -} - -.card-icon.our-models { - background: var(--soft-pink); -} -.card-icon.your-infrastructure { - background: var(--success-green); -} -.card-icon.real-tools { - background: var(--warning-amber); -} - -/* Principles CTA */ -.principles-cta { - margin: 4rem 0; - background: var(--light-blue); - border-radius: 20px; - border: 3px solid var(--primary-blue); - padding: 3rem; - display: flex; - align-items: center; - gap: 2rem; - box-shadow: 0 8px 30px rgba(59, 130, 246, 0.2); -} - -.cta-robot { - flex-shrink: 0; - position: relative; -} - -.cta-robot .robot-head { - width: 80px; - height: 80px; - background: var(--primary-blue); - border-radius: 20px; - border: 4px solid var(--dark-text); - margin: 0 auto 12px; - position: relative; -} - -.cta-robot .robot-body { - width: 90px; - height: 100px; - background: var(--primary-blue); - border-radius: 20px; - border: 4px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.robot-eye.confident { - background: var(--dark-text); - animation: confidentGlow 3s infinite ease-in-out; -} - -.robot-confident-smile { - position: absolute; - bottom: 15px; - left: 50%; - transform: translateX(-50%); - width: 30px; - height: 15px; - border: 4px solid var(--dark-text); - border-bottom: none; - border-radius: 30px 30px 0 0; -} - -.robot-badge { - background: white; - color: var(--primary-blue); - padding: 0.5rem 1rem; - border-radius: 12px; - font-size: 0.8rem; - font-weight: 700; - border: 2px solid var(--dark-text); - text-transform: uppercase; -} - -.robot-cape { - position: absolute; - top: 70px; - left: -10px; - right: -10px; - height: 40px; - background: var(--error-red); - border-radius: 0 0 15px 15px; - border: 3px solid var(--dark-text); - border-top: none; - z-index: -1; -} - -.cape-flow { - width: 100%; - height: 100%; - background: linear-gradient( - 45deg, - transparent 30%, - rgba(255, 255, 255, 0.2) 50%, - transparent 70% - ); - animation: capeFlow 3s infinite ease-in-out; -} - -.cta-content { - flex: 1; -} - -.cta-content h3 { - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.cta-content p { - font-size: 1.1rem; - color: var(--medium-gray); - margin-bottom: 2rem; - line-height: 1.6; -} - -.cta-buttons { - display: flex; - gap: 1rem; - flex-wrap: wrap; -} - -/* Animations */ -@keyframes corporateGlare { - 0%, - 100% { - background: var(--dark-text); - } - 50% { - background: var(--error-red); - } -} - -@keyframes friendlyBlink { - 0%, - 90%, - 100% { - transform: scaleY(1); - } - 95% { - transform: scaleY(0.1); - } -} - -@keyframes heartbeat { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.2); - } -} - -@keyframes wingFlap { - 0%, - 100% { - transform: translateY(0); - } - 50% { - transform: translateY(-3px); - } -} - -@keyframes vsFlicker { - 0%, - 100% { - opacity: 1; - } - 50% { - opacity: 0.5; - } -} - -@keyframes flowPulse { - 0%, - 100% { - transform: scale(1); - opacity: 1; - } - 50% { - transform: scale(1.3); - opacity: 0.7; - } -} - -@keyframes confidentGlow { - 0%, - 100% { - box-shadow: 0 0 5px var(--primary-blue); - } - 50% { - box-shadow: 0 0 15px var(--primary-blue); - } -} - -@keyframes capeFlow { - 0% { - transform: translateX(-100%); - } - 100% { - transform: translateX(100%); - } -} - -@keyframes corporateShake { - 0%, - 100% { - transform: translateX(0); - } - 25% { - transform: translateX(-2px) rotate(-1deg); - } - 75% { - transform: translateX(2px) rotate(1deg); - } -} - -@keyframes friendlyBounce { - 0%, - 100% { - transform: translateY(0); - } - 50% { - transform: translateY(-8px); - } -} - -@keyframes vsFlash { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.1); - box-shadow: 0 0 15px var(--warning-amber); - } -} - -@keyframes featureBounce { - 0%, - 100% { - transform: translateY(0); - } - 50% { - transform: translateY(-4px); - } -} - -@keyframes cardRobotFloat { - 0%, - 100% { - transform: translateY(0); - } - 50% { - transform: translateY(-6px); - } -} - -@keyframes slideInComparison { - 0% { - transform: translateX(-30px); - opacity: 0; - } - 100% { - transform: translateX(0); - opacity: 1; - } -} - -@keyframes fadeInUp { - 0% { - transform: translateY(30px); - opacity: 0; - } - 100% { - transform: translateY(0); - opacity: 1; - } -} - -@keyframes ctaRobotCelebrate { - 0%, - 100% { - transform: translateY(0) rotate(0deg); - } - 25% { - transform: translateY(-10px) rotate(-5deg); - } - 75% { - transform: translateY(-5px) rotate(5deg); - } -} - -@keyframes advantagePulse { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.05); - } -} - -@keyframes limitationShake { - 0%, - 100% { - transform: translateX(0); - } - 25% { - transform: translateX(-2px); - } - 75% { - transform: translateX(2px); - } -} - -@keyframes iconPulse { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.2); - } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .comparison-sides { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .vs-divider { - order: 2; - transform: rotate(90deg); - padding: 1rem 0; - } - - .other-ai { - order: 1; - } - - .jan-side { - order: 3; - } - - .principles-cta { - flex-direction: column; - text-align: center; - gap: 2rem; - } -} - -@media (max-width: 768px) { - .detailed-comparison { - padding: 2rem; - } - - .comparison-table { - grid-template-columns: 1fr; - gap: 0; - } - - .table-row { - display: grid; - grid-template-columns: 1fr 1fr 1fr; - gap: 1px; - margin-bottom: 1px; - } - - .cards-grid { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .philosophy-card { - padding: 1.5rem; - } - - .feature-item { - flex-direction: column; - text-align: center; - gap: 1rem; - } - - .side-robot .robot-head { - width: 50px; - height: 50px; - } - - .side-robot .robot-body { - width: 60px; - height: 70px; - } -} - -@media (max-width: 480px) { - .comparison-side { - padding: 1.5rem; - } - - .detailed-comparison { - padding: 1.5rem; - } - - .philosophy-cards h3, - .detailed-comparison h3 { - font-size: 1.8rem; - } - - .cta-content h3 { - font-size: 1.8rem; - } - - .cta-buttons { - flex-direction: column; - align-items: center; - } - - .table-row { - grid-template-columns: 1fr; - text-align: left; - } - - .header-cell, - .table-cell { - padding: 0.75rem; - font-size: 0.8rem; - } - - .feature-col { - justify-content: flex-start; - } - - .advantage-indicator, - .limitation-indicator { - padding: 0.5rem; - font-size: 0.8rem; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - .other-robot, - .jan-robot, - .vs-robot, - .feature-robot, - .card-robot, - .cta-robot, - .comparison-side, - .feature-item, - .philosophy-card, - .advantage-indicator, - .limitation-indicator { - animation: none !important; - transition: none !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .comparison-side, - .detailed-comparison, - .philosophy-card, - .principles-cta { - border-width: 3px; - } - - .feature-item { - border-width: 2px; - } -} diff --git a/website/src/styles/products-roadmap.css b/website/src/styles/products-roadmap.css deleted file mode 100644 index 1cc480dae..000000000 --- a/website/src/styles/products-roadmap.css +++ /dev/null @@ -1,837 +0,0 @@ -/* ProductsRoadmap Component Styles */ - -.roadmap-section { - background: var(--light-gray); - border-top: 1px solid var(--border-color); -} - -.roadmap-section .badge-robot-head { - background: var(--light-purple); -} - -/* Roadmap Overview */ -.roadmap-overview { - margin: 4rem 0; - max-width: 900px; - margin-left: auto; - margin-right: auto; -} - -.timeline-stats { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); - gap: 2rem; -} - -.stat-card { - background: white; - border-radius: 16px; - border: 2px solid var(--border-color); - padding: 1.5rem; - display: flex; - align-items: center; - gap: 1rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); - transition: all 0.3s ease; -} - -.stat-card:hover { - transform: translateY(-4px); - box-shadow: 0 12px 40px rgba(0, 0, 0, 0.12); -} - -.stat-robot { - flex-shrink: 0; -} - -.stat-robot .robot-head { - width: 40px; - height: 40px; - border-radius: 10px; - border: 3px solid var(--dark-text); - margin: 0 auto 8px; - position: relative; -} - -.stat-robot .robot-body { - width: 50px; - height: 60px; - border-radius: 10px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.released-robot .robot-head { - background: var(--success-green); -} -.released-robot .robot-body { - background: var(--success-green); -} - -.development-robot .robot-head { - background: var(--primary-blue); -} -.development-robot .robot-body { - background: var(--primary-blue); -} - -.planned-robot .robot-head { - background: var(--medium-gray); -} -.planned-robot .robot-body { - background: var(--medium-gray); -} - -.success-check { - width: 20px; - height: 10px; - border-left: 4px solid white; - border-bottom: 4px solid white; - transform: rotate(-45deg); -} - -.work-indicator { - display: flex; - gap: 3px; -} - -.work-dot { - width: 6px; - height: 6px; - background: white; - border-radius: 50%; - animation: workPulse 2s infinite ease-in-out; -} - -.work-dot:nth-child(2) { - animation-delay: 0.3s; -} -.work-dot:nth-child(3) { - animation-delay: 0.6s; -} - -.planning-board { - display: flex; - flex-direction: column; - gap: 3px; -} - -.plan-item { - width: 20px; - height: 3px; - background: white; - border-radius: 2px; -} - -.stat-content { - text-align: left; -} - -.stat-number { - font-size: 2rem; - font-weight: 800; - color: var(--dark-text); - line-height: 1; -} - -.stat-label { - font-size: 1rem; - color: var(--medium-gray); - font-weight: 500; -} - -/* Roadmap Database */ -.roadmap-database { - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.database-header { - display: flex; - justify-content: space-between; - align-items: center; - padding: 1.5rem; - border-bottom: 1px solid var(--border-color); -} - -.database-title { - display: flex; - align-items: center; - gap: 1rem; -} - -.title-robot .robot-head { - width: 30px; - height: 30px; - background: var(--light-purple); - border-radius: 8px; - border: 2px solid var(--dark-text); - margin-bottom: 4px; -} - -.title-robot .robot-body { - width: 35px; - height: 40px; - background: var(--light-purple); - border-radius: 8px; - border: 2px solid var(--dark-text); - display: flex; - align-items: center; - justify-content: center; - font-weight: 700; - font-size: 0.9rem; -} - -.database-title h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); -} - -.database-controls { - display: flex; - gap: 0.75rem; -} - -.control-btn { - background: var(--light-gray); - border: 1px solid var(--border-color); - border-radius: 8px; - padding: 0.5rem 1rem; - font-size: 0.8rem; - font-weight: 600; - color: var(--dark-text); - cursor: pointer; - transition: all 0.3s ease; -} - -.control-btn:hover { - background: var(--border-color); - transform: translateY(-2px); -} - -.database-table { - padding: 1.5rem; -} - -.table-header { - display: grid; - grid-template-columns: 2fr 1fr 1fr 80px; - gap: 1rem; - padding: 0 1rem 1rem; - border-bottom: 1px solid var(--border-color); - font-size: 0.8rem; - font-weight: 600; - color: var(--medium-gray); - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.expand-col { - text-align: right; -} - -/* Roadmap Row */ -.roadmap-row { - border-bottom: 1px solid var(--border-color); - transition: background-color 0.3s ease; -} - -.roadmap-row:last-child { - border-bottom: none; -} - -.roadmap-row.expanded { - background: var(--light-blue); -} - -.row-main { - display: grid; - grid-template-columns: 2fr 1fr 1fr 80px; - gap: 1rem; - align-items: center; - padding: 1rem; - cursor: pointer; -} - -.version-col { - display: flex; - align-items: center; - gap: 1rem; -} - -.version-robot { - flex-shrink: 0; -} - -.robot-mini-head { - width: 25px; - height: 25px; - border-radius: 6px; - border: 2px solid var(--dark-text); - margin: 0 auto 4px; -} - -.robot-mini-body { - width: 30px; - height: 35px; - border-radius: 6px; - border: 2px solid var(--dark-text); - margin: 0 auto; - display: flex; - align-items: center; - justify-content: center; - font-weight: 700; -} - -.released-version .robot-mini-head, -.released-version .robot-mini-body { - background: var(--success-green); -} - -.development-version .robot-mini-head, -.development-version .robot-mini-body { - background: var(--primary-blue); -} - -.planned-version .robot-mini-head, -.planned-version .robot-mini-body { - background: var(--medium-gray); -} - -.version-info { - display: flex; - flex-direction: column; -} - -.version-number { - font-size: 1rem; - font-weight: 700; - color: var(--dark-text); -} - -.version-name { - font-size: 0.9rem; - color: var(--medium-gray); -} - -.date-badge { - display: inline-block; - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; -} - -.date-badge.released { - background: var(--soft-green); - color: var(--success-green); -} -.date-badge.development { - background: var(--light-blue); - color: var(--primary-blue); -} -.date-badge.planned { - background: var(--light-gray); - color: var(--medium-gray); -} - -.status-indicator { - display: flex; - align-items: center; - gap: 0.5rem; - font-size: 0.9rem; - font-weight: 600; -} - -.status-dot { - width: 8px; - height: 8px; - border-radius: 50%; -} - -.status-indicator.released { - color: var(--success-green); -} -.status-indicator.released .status-dot { - background: var(--success-green); -} - -.status-indicator.development { - color: var(--primary-blue); -} -.status-indicator.development .status-dot { - background: var(--primary-blue); - animation: pulse 2s infinite; -} - -.status-indicator.planned { - color: var(--medium-gray); -} -.status-indicator.planned .status-dot { - background: var(--medium-gray); -} - -.expand-btn { - width: 30px; - height: 30px; - background: var(--light-gray); - border: 1px solid var(--border-color); - border-radius: 50%; - cursor: pointer; - display: flex; - align-items: center; - justify-content: center; - transition: all 0.3s ease; -} - -.expand-btn:hover { - background: var(--border-color); - transform: scale(1.1); -} - -.expand-icon { - font-size: 1.2rem; - font-weight: 700; - color: var(--medium-gray); - transition: transform 0.3s ease; -} - -/* Row Details */ -.row-details { - max-height: 0; - opacity: 0; - overflow: hidden; - transition: max-height 0.5s ease-out, opacity 0.5s ease-out; -} - -.details-content { - padding: 1rem 2rem 2rem; - display: grid; - grid-template-columns: repeat(auto-fit, minmax(250px, 1fr)); - gap: 2rem; - border-top: 1px solid var(--border-color); -} - -.detail-section h4 { - font-size: 1rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; - border-bottom: 2px solid var(--primary-blue); - padding-bottom: 0.5rem; - display: inline-block; -} - -.detail-section ul { - list-style: none; - padding: 0; - margin: 0; - display: flex; - flex-direction: column; - gap: 0.75rem; -} - -.detail-section li { - padding-left: 1.5rem; - position: relative; - font-size: 0.9rem; - color: var(--medium-gray); -} - -.detail-section li::before { - content: "→"; - position: absolute; - left: 0; - color: var(--primary-blue); - font-weight: 700; -} - -/* Roadmap CTA */ -.roadmap-cta { - margin: 4rem 0 0; - background: var(--light-blue); - border-radius: 20px; - border: 3px solid var(--primary-blue); - padding: 3rem; - display: flex; - align-items: center; - gap: 2rem; - box-shadow: 0 8px 30px rgba(59, 130, 246, 0.2); -} - -.cta-robot { - flex-shrink: 0; -} - -.cta-robot .robot-head { - width: 80px; - height: 80px; - background: var(--primary-blue); - border-radius: 20px; - border: 4px solid var(--dark-text); - margin: 0 auto 12px; - position: relative; -} - -.cta-robot .robot-body { - width: 90px; - height: 100px; - background: var(--primary-blue); - border-radius: 20px; - border: 4px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.robot-eye.excited { - animation: excitedWiggle 2s infinite ease-in-out; -} - -.robot-smile-big { - position: absolute; - bottom: 15px; - left: 50%; - transform: translateX(-50%); - width: 35px; - height: 20px; - border: 4px solid var(--dark-text); - border-bottom: none; - border-radius: 35px 35px 0 0; -} - -.robot-progress-wheel { - width: 40px; - height: 40px; - border-radius: 50%; - border: 3px solid var(--dark-text); - position: relative; - animation: spin 4s infinite linear; -} - -.progress-segment { - position: absolute; - width: 50%; - height: 50%; -} - -.progress-segment:nth-child(1) { - top: 0; - left: 0; - background: var(--soft-green); - border-radius: 100% 0 0 0; -} -.progress-segment:nth-child(2) { - top: 0; - right: 0; - background: var(--warm-yellow); - border-radius: 0 100% 0 0; -} -.progress-segment:nth-child(3) { - bottom: 0; - left: 0; - background: var(--light-purple); - border-radius: 0 0 0 100%; -} -.progress-segment:nth-child(4) { - bottom: 0; - right: 0; - background: var(--soft-pink); - border-radius: 0 0 100% 0; -} - -.robot-arms.celebrating { - display: flex; - justify-content: space-between; - margin-top: -80px; - padding: 0 10px; -} - -.arm.left-celebrate, -.arm.right-celebrate { - width: 15px; - height: 40px; - background: var(--gentle-orange); - border-radius: 8px; - border: 3px solid var(--dark-text); - transform-origin: bottom center; - animation: celebrate 1.5s infinite ease-in-out; -} - -.arm.right-celebrate { - animation-delay: 0.3s; -} - -.cta-content h3 { - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.cta-content p { - font-size: 1.1rem; - color: var(--medium-gray); - margin-bottom: 2rem; - line-height: 1.6; -} - -.cta-buttons { - display: flex; - gap: 1rem; - flex-wrap: wrap; -} - -/* Animations */ -@keyframes workPulse { - 0%, - 100% { - transform: scale(1); - opacity: 0.7; - } - 50% { - transform: scale(1.2); - opacity: 1; - } -} - -@keyframes pulse { - 0%, - 100% { - transform: scale(1); - opacity: 1; - } - 50% { - transform: scale(1.1); - opacity: 0.7; - } -} - -@keyframes versionCelebrate { - 0%, - 100% { - transform: translateY(0); - } - 25% { - transform: translateY(-8px) rotate(-5deg); - } - 75% { - transform: translateY(-4px) rotate(5deg); - } -} - -@keyframes versionHover { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.05); - } -} - -@keyframes slideInFromLeft { - 0% { - transform: translateX(-30px); - opacity: 0; - } - 100% { - transform: translateX(0); - opacity: 1; - } -} - -@keyframes fadeInUp { - 0% { - transform: translateY(20px); - opacity: 0; - } - 100% { - transform: translateY(0); - opacity: 1; - } -} - -@keyframes excitedWiggle { - 0%, - 100% { - transform: rotate(0); - } - 25% { - transform: rotate(-3deg); - } - 75% { - transform: rotate(3deg); - } -} - -@keyframes spin { - 0% { - transform: rotate(0deg); - } - 100% { - transform: rotate(360deg); - } -} - -@keyframes celebrate { - 0%, - 100% { - transform: rotate(0deg); - } - 50% { - transform: rotate(15deg); - } -} - -@keyframes ctaRobotCelebration { - 0%, - 100% { - transform: translateY(0) rotate(0); - } - 25% { - transform: translateY(-10px) rotate(-5deg); - } - 75% { - transform: translateY(-5px) rotate(5deg); - } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .database-table { - font-size: 0.9rem; - } - - .table-header { - grid-template-columns: 2fr 1.5fr 1fr 60px; - } - - .row-main { - grid-template-columns: 2fr 1.5fr 1fr 60px; - } -} - -@media (max-width: 768px) { - .timeline-stats { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .database-header { - flex-direction: column; - gap: 1rem; - align-items: flex-start; - } - - .table-header { - display: none; - } - - .row-main { - grid-template-columns: 1fr; - gap: 1rem; - padding: 1.5rem; - } - - .row-cell { - display: flex; - justify-content: space-between; - align-items: center; - border-bottom: 1px dashed var(--border-color); - padding-bottom: 0.75rem; - } - - .row-cell:last-child { - border-bottom: none; - } - - .row-cell::before { - content: attr(data-label); - font-weight: 600; - color: var(--medium-gray); - font-size: 0.8rem; - text-transform: uppercase; - } - - .expand-col { - justify-content: flex-end; - } - - .details-content { - grid-template-columns: 1fr; - padding: 1rem; - } - - .roadmap-cta { - flex-direction: column; - text-align: center; - } -} - -@media (max-width: 480px) { - .roadmap-database { - font-size: 0.8rem; - } - - .row-main { - padding: 1rem; - } - - .version-col { - gap: 0.75rem; - } - - .version-number { - font-size: 0.9rem; - } - - .cta-content h3 { - font-size: 1.8rem; - } - - .cta-buttons { - flex-direction: column; - align-items: center; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - .work-dot, - .status-indicator.development .status-dot, - .robot-eye, - .progress-wheel, - .arm, - .stat-card, - .roadmap-row, - .control-btn, - .expand-btn, - .row-details, - .version-robot { - animation: none !important; - transition: none !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .stat-card, - .roadmap-database, - .roadmap-cta { - border-width: 3px; - } - - .roadmap-row { - border-width: 2px; - } -} diff --git a/website/src/styles/products-tools.css b/website/src/styles/products-tools.css deleted file mode 100644 index 8603b491e..000000000 --- a/website/src/styles/products-tools.css +++ /dev/null @@ -1,597 +0,0 @@ -/* ProductsTools Component Styles */ - -.tools-section { - background: var(--cream-white); - border-top: 1px solid var(--border-color); -} - -.tools-section .badge-robot-head { - background: var(--warning-amber); -} - -/* Tools Grid */ -.tools-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(350px, 1fr)); - gap: 2rem; - margin: 4rem 0; -} - -.tool-card { - position: relative; - overflow: visible; -} - -.tool-robot-section { - position: relative; - text-align: center; - margin-bottom: 2rem; -} - -.tool-robot { - display: inline-block; - position: relative; - margin-bottom: 1rem; -} - -.tool-robot .robot-head { - width: 60px; - height: 60px; - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto 10px; - position: relative; -} - -.tool-robot .robot-body { - width: 70px; - height: 80px; - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -/* Robot Variations */ -.search-robot .robot-head, -.search-robot .robot-body { - background: var(--success-green); -} - -.research-robot .robot-head, -.research-robot .robot-body { - background: var(--primary-blue); -} - -.browser-robot .robot-head, -.browser-robot .robot-body { - background: var(--warning-amber); -} - -.agents-robot .robot-head, -.agents-robot .robot-body { - background: var(--light-purple); -} - -.agents-robot.sleeping { - opacity: 0.7; - animation: sleepBob 4s infinite ease-in-out; -} - -.tool-status-bubble { - position: absolute; - top: -10px; - right: 1rem; -} - -/* Tool Content */ -.tool-content { - padding: 0; -} - -.tool-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 1rem; -} - -.tool-header h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); -} - -.tool-version { - background: var(--light-gray); - color: var(--medium-gray); - padding: 0.25rem 0.5rem; - border-radius: 8px; - font-size: 0.7rem; - font-weight: 600; - border: 1px solid var(--border-color); -} - -.tool-description { - color: var(--medium-gray); - margin-bottom: 1.5rem; - line-height: 1.6; -} - -.tool-features { - margin-bottom: 1.5rem; -} - -.tool-features h4 { - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - margin-bottom: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.features-list { - display: flex; - flex-direction: column; - gap: 0.75rem; -} - -.feature-item { - display: flex; - align-items: center; - gap: 0.75rem; - font-size: 0.9rem; - color: var(--dark-text); - padding: 0.5rem; - border-radius: 8px; - transition: all 0.3s ease; -} - -.feature-item:hover { - background: var(--light-gray); -} - -.feature-item.coming { - color: var(--medium-gray); - font-style: italic; -} - -.feature-icon { - width: 20px; - height: 20px; - border-radius: 4px; - border: 2px solid var(--dark-text); - flex-shrink: 0; -} - -.tool-metrics { - display: flex; - justify-content: space-around; - padding-top: 1rem; - border-top: 1px solid var(--border-color); -} - -.metric { - text-align: center; -} - -.metric-value { - font-size: 1.1rem; - font-weight: 700; - color: var(--primary-blue); - margin-bottom: 0.25rem; -} - -.metric-label { - font-size: 0.8rem; - color: var(--medium-gray); - text-transform: uppercase; - letter-spacing: 0.05em; -} - -/* Tools Integration Flow */ -.tools-integration { - margin: 4rem 0; - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - padding: 3rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.tools-integration h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.integration-flow { - display: flex; - align-items: center; - justify-content: center; - gap: 1rem; - flex-wrap: wrap; -} - -.flow-step { - text-align: center; -} - -.step-robot { - margin-bottom: 0.5rem; -} - -.robot-mini { - width: 40px; - height: 50px; - border-radius: 8px; - border: 2px solid var(--dark-text); - margin: 0 auto; -} - -.search-mini { background: var(--success-green); } -.research-mini { background: var(--primary-blue); } -.browser-mini { background: var(--warning-amber); } -.agents-mini { background: var(--light-purple); } - -.step-content h4 { - font-size: 1rem; - font-weight: 600; - color: var(--dark-text); -} - -.step-content p { - font-size: 0.8rem; - color: var(--medium-gray); -} - -.flow-arrow { - width: 40px; - height: 2px; - background: var(--primary-blue); - position: relative; -} - -.flow-arrow.coming { - background: var(--medium-gray); - border-style: dashed; -} - -.flow-arrow .arrow-head { - position: absolute; - right: -5px; - top: -3px; - width: 0; - height: 0; - border-top: 4px solid transparent; - border-bottom: 4px solid transparent; - border-left: 8px solid var(--primary-blue); -} - -.flow-arrow.coming .arrow-head { - border-left-color: var(--medium-gray); -} - -/* Tools Comparison Table */ -.tools-comparison { - margin: 4rem 0; - background: white; - border-radius: 20px; - border: 2px solid var(--border-color); - padding: 3rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.08); -} - -.tools-comparison h3 { - text-align: center; - font-size: 2rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 2rem; -} - -.comparison-table { - display: grid; - grid-template-columns: 1fr 1fr 1fr 1fr; - gap: 1px; - background: var(--border-color); - border-radius: 12px; - overflow: hidden; -} - -.table-header { display: contents; } - -.header-cell { - background: var(--primary-blue); - color: white; - padding: 1rem; - font-weight: 700; - text-align: center; -} - -.table-row { display: contents; } - -.table-cell { - background: white; - padding: 1rem; - text-align: center; - font-size: 0.9rem; -} - -.tool-col { - display: flex; - align-items: center; - justify-content: center; - gap: 0.5rem; - font-weight: 600; -} - -.tool-mini-icon { - width: 20px; - height: 20px; - border-radius: 4px; - border: 2px solid var(--dark-text); -} - -.search-icon { background: var(--success-green); } -.research-icon { background: var(--primary-blue); } -.browser-icon { background: var(--warning-amber); } -.agents-icon { background: var(--light-purple); } - -/* Animations */ -@keyframes toolBounce { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-8px); } -} - -@keyframes sleepShift { - 0%, 100% { transform: translateX(0) rotate(0); } - 50% { transform: translateX(4px) rotate(2deg); } -} - -@keyframes slideInFromBottom { - 0% { transform: translateY(50px); opacity: 0; } - 100% { transform: translateY(0); opacity: 1; } -} - -@keyframes miniRobotSpin { - 0% { transform: rotate(0); } - 100% { transform: rotate(360deg); } -} - -@keyframes iconPulse { - 0%, 100% { transform: scale(1); } - 50% { transform: scale(1.1); } -} - -@keyframes featureIconBounce { - 0%, 100% { transform: scale(1); } - 50% { transform: scale(1.2) rotate(5deg); } -} - -@keyframes fadeInUp { - 0% { transform: translateY(20px); opacity: 0; } - 100% { transform: translateY(0); opacity: 1; } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .tools-grid { - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - } -} - -@media (max-width: 768px) { - .tools-grid { - grid-template-columns: 1fr; - gap: 1.5rem; - } - - .tools-comparison { - padding: 2rem; - } - - .comparison-table { - grid-template-columns: 1fr; - gap: 0; - } - - .table-row { - display: grid; - grid-template-columns: 1fr 1fr 1fr 1fr; - gap: 1px; - margin-bottom: 1px; - } -} - -@media (max-width: 480px) { - .flow-arrow { - transform: rotate(90deg); - } - - .table-row { - display: flex; - flex-direction: column; - gap: 0; - margin-bottom: 1px; - } - - .table-cell { - border-bottom: 1px solid var(--border-color); - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - .tool-robot, - .tool-card, - .flow-step, - .robot-mini, - .tool-mini-icon, - .feature-icon { - animation: none !important; - transition: none !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .tool-card, - .tools-integration, - .tools-comparison { - border-width: 3px; - } -} - -/* Robot-specific details and animations that were missing */ -.robot-eyes { - display: flex; - justify-content: space-between; - padding: 12px 10px; -} - -.robot-eye { - width: 12px; - height: 12px; - background: var(--dark-text); - border-radius: 50%; -} - -.robot-eye.active { - animation: eyeBlink 4s infinite ease-in-out; -} - -.robot-eye.thinking { - height: 3px; - border-radius: 2px; - animation: thinkingBlink 3s infinite ease-in-out; -} - -.robot-eye.beta { - background: var(--dark-text); - animation: betaGlow 2s infinite ease-in-out; -} - -.robot-eye.sleeping { - height: 3px; - border-radius: 2px; -} - -@keyframes eyeBlink { - 0%, 90%, 100% { transform: scaleY(1); } - 95% { transform: scaleY(0.1); } -} - -@keyframes thinkingBlink { - 0%, 100% { width: 12px; } - 50% { width: 4px; } -} - -@keyframes betaGlow { - 0%, 100% { box-shadow: none; } - 50% { box-shadow: 0 0 5px var(--warning-amber); } -} - -.robot-search-beam { - position: absolute; - bottom: 10px; - left: 50%; - transform: translateX(-50%); - width: 40px; - height: 20px; - perspective: 50px; -} - -.search-wave { - position: absolute; - width: 100%; - height: 1px; - background: white; - opacity: 0.7; - animation: searchWave 3s infinite ease-out; -} - -.search-wave:nth-child(2) { animation-delay: 1s; } -.search-wave:nth-child(3) { animation-delay: 2s; } - -@keyframes searchWave { - 0% { transform: translateY(0) scaleY(1); opacity: 0; } - 50% { opacity: 0.7; } - 100% { transform: translateY(20px) scaleY(3); opacity: 0; } -} - -.robot-screen { - width: 40px; - height: 30px; - background: var(--dark-text); - border-radius: 4px; - padding: 5px; -} - -.search-results .result-line { - height: 2px; - background: white; - border-radius: 1px; - margin-bottom: 3px; - animation: searchResults 4s infinite; -} - -.search-results .result-line.short { - width: 60%; -} - -@keyframes searchResults { - 0%, 100% { width: 100%; } - 50% { width: 40%; } -} - -.robot-magnifier { - position: absolute; - bottom: -10px; - right: -10px; -} - -.magnifier-glass { - width: 20px; - height: 20px; - border: 3px solid var(--dark-text); - border-radius: 50%; - background: var(--light-blue); -} - -.magnifier-handle { - width: 15px; - height: 4px; - background: var(--dark-text); - transform: rotate(45deg) translate(12px, -3px); -} - -.sleep-indicator { - position: absolute; - top: -5px; - right: -20px; - display: flex; - flex-direction: column; -} - -.sleep-z { - font-size: 0.8rem; - font-weight: 600; - color: var(--medium-gray); - animation: sleepZ 3s infinite ease-in-out; -} - -.sleep-z:nth-child(2) { animation-delay: 1s; } -.sleep-z:nth-child(3) { animation-delay: 2s; } - -@keyframes sleepZ { - 0% { transform: translate(0, 0); opacity: 0; } - 50% { transform: translate(5px, -5px); opacity: 1; } - 100% { transform: translate(10px, -10px); opacity: 0; } -} diff --git a/website/src/styles/products.css b/website/src/styles/products.css deleted file mode 100644 index 05b0fd54d..000000000 --- a/website/src/styles/products.css +++ /dev/null @@ -1,1644 +0,0 @@ -/* Products Page Styles - Blue Dominant Theme */ - -/* Color Palette - Blue Dominant */ -:root { - --primary-blue: #3b82f6; - --light-blue: #93c5fd; - --pale-blue: #dbeafe; - --sky-blue: #e0f2fe; - --soft-green: #bbf7d0; - --warm-yellow: #fef08a; - --gentle-orange: #fed7aa; - --light-purple: #e9d5ff; - --soft-pink: #fce7f3; - --cream-white: #fefce8; - --light-gray: #f8fafc; - --medium-gray: #64748b; - --dark-text: #1e293b; - --success-green: #22c55e; - --warning-amber: #f59e0b; - --error-red: #ef4444; - --white: #ffffff; -} - -/* Base Styles */ -* { - box-sizing: border-box; -} - -.products-page { - background: linear-gradient( - 135deg, - var(--pale-blue) 0%, - var(--light-gray) 100% - ); - min-height: 100vh; - font-family: - 'Inter', - -apple-system, - BlinkMacSystemFont, - sans-serif; - line-height: 1.6; - color: var(--dark-text); -} - -.container { - max-width: 1200px; - margin: 0 auto; - padding: 0 2rem; -} - -/* Hero Section */ -.hero-section { - position: relative; - padding: 4rem 0 6rem; - background: linear-gradient( - 135deg, - var(--primary-blue) 0%, - var(--light-blue) 100% - ); - overflow: hidden; -} - -.hero-content { - position: relative; - text-align: center; - z-index: 2; - max-width: 800px; - margin: 0 auto; - padding: 0 2rem; -} - -.hero-badge { - display: inline-flex; - align-items: center; - gap: 0.75rem; - background: rgba(255, 255, 255, 0.9); - padding: 0.75rem 1.5rem; - border-radius: 50px; - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - margin-bottom: 2rem; - box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1); - border: 2px solid var(--dark-text); -} - -.badge-robot { - display: flex; - flex-direction: column; - align-items: center; - gap: 2px; -} - -.badge-robot-head { - width: 16px; - height: 16px; - background: var(--primary-blue); - border-radius: 4px; - border: 2px solid var(--dark-text); -} - -.badge-robot-body { - width: 12px; - height: 12px; - background: var(--light-blue); - border-radius: 2px; - border: 1px solid var(--dark-text); -} - -.hero-title { - margin-bottom: 1.5rem; -} - -.title-main { - display: block; - font-size: clamp(2.5rem, 8vw, 3.5rem); - font-weight: 800; - color: var(--white); - margin-bottom: 0.5rem; - line-height: 1.1; - text-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); -} - -.title-sub { - display: block; - font-size: 1.2rem; - font-weight: 500; - color: rgba(255, 255, 255, 0.9); -} - -.hero-description { - font-size: 1.1rem; - color: rgba(255, 255, 255, 0.9); - margin-bottom: 3rem; - line-height: 1.6; -} - -/* Hero Robot */ -.hero-robot { - margin: 3rem 0; -} - -.robot-container { - position: relative; - display: inline-block; - animation: bounce 3s infinite ease-in-out; -} - -.robot-head { - width: 80px; - height: 80px; - background: var(--warm-yellow); - border-radius: 20px; - position: relative; - margin: 0 auto 10px; - border: 4px solid var(--dark-text); - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); -} - -.robot-eyes { - display: flex; - justify-content: space-between; - padding: 15px 12px; -} - -.eye { - width: 16px; - height: 16px; - background: var(--dark-text); - border-radius: 50%; - position: relative; -} - -.eye::after { - content: ''; - position: absolute; - top: 3px; - right: 3px; - width: 4px; - height: 4px; - background: white; - border-radius: 50%; -} - -.robot-mouth.happy { - position: absolute; - bottom: 15px; - left: 50%; - transform: translateX(-50%); - width: 20px; - height: 10px; - border: 3px solid var(--dark-text); - border-bottom: none; - border-radius: 20px 20px 0 0; -} - -.robot-body { - width: 100px; - height: 100px; - background: var(--soft-green); - border-radius: 15px; - margin: 0 auto; - border: 4px solid var(--dark-text); - position: relative; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); -} - -.robot-panel { - display: flex; - justify-content: center; - gap: 8px; - padding: 20px; -} - -.panel-light { - width: 12px; - height: 12px; - border-radius: 50%; - border: 2px solid var(--dark-text); -} - -.panel-light.blue { - background: var(--primary-blue); -} -.panel-light.green { - background: var(--success-green); -} -.panel-light.amber { - background: var(--warning-amber); - animation: blink 1.5s infinite; -} - -.speech-bubble { - position: absolute; - top: -40px; - left: 120%; - background: white; - padding: 0.5rem 1rem; - border-radius: 15px; - border: 2px solid var(--dark-text); - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - white-space: nowrap; - box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1); -} - -.speech-bubble::before { - content: ''; - position: absolute; - left: -10px; - top: 50%; - transform: translateY(-50%); - width: 0; - height: 0; - border-top: 8px solid transparent; - border-bottom: 8px solid transparent; - border-right: 10px solid white; -} - -.speech-bubble::after { - content: ''; - position: absolute; - left: -12px; - top: 50%; - transform: translateY(-50%); - width: 0; - height: 0; - border-top: 10px solid transparent; - border-bottom: 10px solid transparent; - border-right: 12px solid var(--dark-text); -} - -/* Table of Contents */ -.toc-section { - padding: 4rem 0; - background: var(--white); -} - -.toc-card { - background: var(--white); - border-radius: 20px; - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); -} - -.toc-card h2 { - text-align: center; - margin-bottom: 2rem; - font-size: 1.8rem; - font-weight: 700; - color: var(--dark-text); -} - -.toc-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); - gap: 1rem; -} - -.toc-item { - display: flex; - align-items: center; - gap: 1rem; - padding: 1rem; - border-radius: 15px; - background: var(--pale-blue); - border: 2px solid var(--primary-blue); - text-decoration: none; - color: var(--dark-text); - font-weight: 600; - transition: all 0.3s ease; -} - -.toc-item:hover { - transform: translateY(-2px); - box-shadow: 0 4px 15px rgba(59, 130, 246, 0.3); - background: var(--light-blue); -} - -.toc-icon { - width: 40px; - height: 40px; - border-radius: 10px; - border: 2px solid var(--dark-text); - display: flex; - align-items: center; - justify-content: center; - font-size: 1.2rem; - flex-shrink: 0; -} - -.toc-icon.vision { - background: var(--soft-pink); -} -.toc-icon.work { - background: var(--light-blue); -} -.toc-icon.platform { - background: var(--gentle-orange); -} -.toc-icon.model { - background: var(--soft-green); -} -.toc-icon.tool { - background: var(--light-purple); -} -.toc-icon.compare { - background: var(--warm-yellow); -} -.toc-icon.roadmap { - background: var(--primary-blue); -} - -/* Section Styles */ -.section-header { - text-align: center; - margin-bottom: 4rem; -} - -.section-badge { - display: inline-flex; - align-items: center; - gap: 0.75rem; - background: rgba(255, 255, 255, 0.9); - padding: 0.75rem 1.5rem; - border-radius: 50px; - font-size: 0.9rem; - font-weight: 600; - color: var(--dark-text); - margin-bottom: 1rem; - box-shadow: 0 4px 20px rgba(0, 0, 0, 0.1); - border: 2px solid var(--dark-text); -} - -.section-title { - font-size: 2.5rem; - font-weight: 800; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.section-description { - font-size: 1.2rem; - color: var(--medium-gray); - max-width: 600px; - margin: 0 auto; -} - -/* Vision Section */ -.vision-section { - padding: 6rem 0; - background: var(--sky-blue); -} - -.vision-formula { - display: flex; - align-items: center; - justify-content: center; - gap: 1rem; - flex-wrap: wrap; - margin: 3rem 0; -} - -.formula-item { - display: flex; - flex-direction: column; - align-items: center; - gap: 0.5rem; -} - -.formula-robot { - width: 60px; - height: 80px; - position: relative; -} - -.formula-robot .robot-head { - width: 40px; - height: 40px; - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 5px; - background: var(--primary-blue); -} - -.formula-robot .robot-body { - width: 50px; - height: 40px; - border-radius: 8px; - border: 2px solid var(--dark-text); - margin: 0 auto; - background: var(--light-blue); -} - -.formula-plus, -.formula-equals { - font-size: 1.5rem; - font-weight: 800; - color: var(--dark-text); -} - -.vision-principles { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 2rem; - margin-top: 4rem; -} - -.principle-card { - background: var(--white); - border-radius: 20px; - padding: 2rem; - text-align: center; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); -} - -.principle-robot { - margin: 0 auto 1rem; - width: 50px; - height: 70px; -} - -.principle-robot .robot-head { - width: 30px; - height: 30px; - border-radius: 8px; - border: 2px solid var(--dark-text); - margin: 0 auto 5px; - background: var(--warm-yellow); -} - -.principle-robot .robot-body { - width: 40px; - height: 35px; - border-radius: 6px; - border: 2px solid var(--dark-text); - margin: 0 auto; - background: var(--soft-green); -} - -.principle-card h3 { - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.principle-card p { - color: var(--medium-gray); - line-height: 1.6; -} - -/* Works Section */ -.works-section { - padding: 6rem 0; - background: var(--light-gray); -} - -.modes-comparison { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(400px, 1fr)); - gap: 2rem; - margin-top: 4rem; -} - -.mode-card { - background: var(--white); - border-radius: 20px; - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); - text-align: center; -} - -.mode-robot { - margin: 0 auto 1.5rem; - width: 60px; - height: 80px; -} - -.mode-robot .robot-head { - width: 40px; - height: 40px; - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 5px; -} - -.mode-robot .robot-body { - width: 50px; - height: 40px; - border-radius: 8px; - border: 2px solid var(--dark-text); - margin: 0 auto; -} - -.local-mode .mode-robot .robot-head { - background: var(--success-green); -} -.local-mode .mode-robot .robot-body { - background: var(--soft-green); -} - -.cloud-mode .mode-robot .robot-head { - background: var(--primary-blue); -} -.cloud-mode .mode-robot .robot-body { - background: var(--light-blue); -} - -.mode-card h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.mode-card p { - color: var(--medium-gray); - margin-bottom: 1.5rem; -} - -.mode-features { - display: flex; - flex-direction: column; - gap: 0.5rem; -} - -.feature-item { - padding: 0.5rem 1rem; - background: var(--pale-blue); - border-radius: 15px; - border: 2px solid var(--primary-blue); - font-size: 0.9rem; - font-weight: 600; -} - -/* Platforms Section */ -.platforms-section { - padding: 6rem 0; - background: var(--cream-white); -} - -.platforms-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 2rem; - margin-top: 4rem; -} - -.platform-card { - background: var(--white); - border-radius: 20px; - padding: 2rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); - transition: all 0.3s ease; - text-align: center; -} - -.platform-card:hover { - transform: translateY(-5px); - box-shadow: 0 15px 40px rgba(0, 0, 0, 0.15); -} - -.platform-status { - display: inline-block; - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - margin-bottom: 1rem; - border: 2px solid var(--dark-text); -} - -.platform-status:contains('Available') { - background: var(--success-green); - color: white; -} -.platform-status:contains('Beta') { - background: var(--warning-amber); - color: white; -} -.platform-status:contains('Q4') { - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} -.platform-status:contains('Coming') { - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} - -.platform-robot { - margin: 0 auto 1.5rem; - width: 60px; - height: 80px; -} - -.platform-robot .robot-head { - width: 40px; - height: 40px; - border-radius: 10px; - border: 2px solid var(--dark-text); - margin: 0 auto 5px; -} - -.platform-robot .robot-body { - width: 50px; - height: 40px; - border-radius: 8px; - border: 2px solid var(--dark-text); - margin: 0 auto; -} - -.desktop-robot .robot-head { - background: var(--success-green); -} -.desktop-robot .robot-body { - background: var(--soft-green); -} - -.web-robot .robot-head { - background: var(--primary-blue); -} -.web-robot .robot-body { - background: var(--light-blue); -} - -.mobile-robot .robot-head { - background: var(--warning-amber); -} -.mobile-robot .robot-body { - background: var(--warm-yellow); -} - -.server-robot .robot-head { - background: var(--light-purple); -} -.server-robot .robot-body { - background: var(--soft-pink); -} - -.platform-card h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.5rem; -} - -.platform-card p { - color: var(--medium-gray); - margin-bottom: 1rem; -} - -.platform-metrics { - margin-bottom: 1rem; -} - -.metric { - font-size: 0.9rem; - color: var(--primary-blue); - font-weight: 600; -} - -.platform-features { - display: flex; - flex-wrap: wrap; - gap: 0.5rem; - justify-content: center; -} - -.feature { - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - background: var(--pale-blue); - color: var(--dark-text); - border: 2px solid var(--primary-blue); -} - -.feature.coming { - border-style: dashed; - color: var(--medium-gray); - background: var(--light-gray); -} - -/* Models Section */ -.models-section { - padding: 6rem 0; - background: var(--sky-blue); -} - -.models-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(350px, 1fr)); - gap: 2rem; - margin-top: 4rem; -} - -.model-card { - background: var(--white); - border-radius: 20px; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); - overflow: hidden; - transition: all 0.3s ease; - position: relative; -} - -.model-card:hover { - transform: translateY(-5px); - box-shadow: 0 15px 40px rgba(0, 0, 0, 0.15); -} - -.model-robot { - display: inline-block; - position: relative; - margin: 2rem auto 1rem; - text-align: center; -} - -.model-robot .robot-head { - width: 60px; - height: 60px; - border-radius: 15px; - border: 3px solid var(--dark-text); - margin: 0 auto 8px; - position: relative; -} - -.model-robot .robot-body { - width: 70px; - height: 60px; - border-radius: 12px; - border: 3px solid var(--dark-text); - margin: 0 auto; - position: relative; - display: flex; - align-items: center; - justify-content: center; -} - -.v1-robot .robot-head { - background: var(--success-green); -} -.v1-robot .robot-body { - background: var(--soft-green); -} - -.nano-robot .robot-head { - background: var(--primary-blue); -} -.nano-robot .robot-body { - background: var(--light-blue); -} - -.lucy-robot .robot-head { - background: var(--warning-amber); -} -.lucy-robot .robot-body { - background: var(--warm-yellow); -} - -.status-light { - width: 12px; - height: 12px; - border-radius: 50%; - border: 2px solid var(--dark-text); - position: absolute; - top: 5px; - right: 5px; -} - -.status-light.active { - background: var(--success-green); - animation: pulse 2s infinite; -} -.status-light.beta { - background: var(--warning-amber); - animation: pulse 1.5s infinite; -} -.status-light.coming { - background: var(--medium-gray); -} - -.robot-speech { - position: absolute; - top: -25px; - left: 50%; - transform: translateX(-50%); - background: white; - padding: 0.25rem 0.75rem; - border-radius: 15px; - border: 2px solid var(--dark-text); - font-size: 0.8rem; - font-weight: 600; - white-space: nowrap; - box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1); -} - -.model-content { - padding: 0 2rem 2rem; -} - -.model-header { - display: flex; - justify-content: space-between; - align-items: center; - margin-bottom: 1.5rem; - text-align: left; -} - -.model-header h3 { - font-size: 1.5rem; - font-weight: 700; - color: var(--dark-text); -} - -.model-status { - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - border: 2px solid var(--dark-text); -} - -.model-status.active { - background: var(--success-green); - color: white; -} -.model-status.beta { - background: var(--warning-amber); - color: white; -} -.model-status.coming { - background: var(--light-gray); - color: var(--medium-gray); -} - -.model-specs { - display: grid; - grid-template-columns: 1fr 1fr; - gap: 1.5rem; - margin-bottom: 1.5rem; -} - -.spec-group h4 { - font-size: 0.9rem; - color: var(--medium-gray); - margin-bottom: 0.5rem; - font-weight: 600; - text-transform: uppercase; - letter-spacing: 0.05em; -} - -.spec-list { - display: flex; - flex-direction: column; - gap: 0.25rem; -} - -.spec-item { - font-size: 0.9rem; - color: var(--dark-text); - font-weight: 500; -} - -.spec-highlight { - font-size: 1.1rem; - font-weight: 700; - color: var(--primary-blue); -} - -.model-description { - color: var(--medium-gray); - margin-bottom: 1.5rem; - line-height: 1.6; -} - -.model-capabilities { - display: flex; - flex-wrap: wrap; - gap: 0.5rem; -} - -.capability { - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - background: var(--success-green); - color: white; - border: 2px solid var(--dark-text); -} - -.capability.coming { - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} - -/* Tools Section */ -.tools-section { - padding: 6rem 0; - background: var(--light-gray); -} - -.tools-grid { - display: grid; - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - gap: 2rem; - margin-top: 4rem; -} - -.tool-card { - background: var(--white); - border-radius: 20px; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); - border: 3px solid var(--dark-text); - overflow: hidden; - transition: all 0.3s ease; - position: relative; -} - -.tool-card:hover { - transform: translateY(-5px); - box-shadow: 0 15px 40px rgba(0, 0, 0, 0.15); -} - -.tool-robot { - padding: 2rem 2rem 1rem; - text-align: center; - position: relative; -} - -.tool-robot .robot-head { - width: 50px; - height: 50px; - border-radius: 12px; - border: 3px solid var(--dark-text); - margin: 0 auto 8px; - position: relative; -} - -.tool-robot .robot-body { - width: 60px; - height: 50px; - border-radius: 12px; - border: 3px solid var(--dark-text); - margin: 0 auto; - display: flex; - align-items: center; - justify-content: center; - font-size: 1.2rem; -} - -.search-tool .tool-robot .robot-head { - background: var(--success-green); -} -.search-tool .tool-robot .robot-body { - background: var(--soft-green); -} - -.research-tool .tool-robot .robot-head { - background: var(--primary-blue); -} -.research-tool .tool-robot .robot-body { - background: var(--light-blue); -} - -.browser-tool .tool-robot .robot-head { - background: var(--warning-amber); -} -.browser-tool .tool-robot .robot-body { - background: var(--warm-yellow); -} - -.agents-tool .tool-robot .robot-head { - background: var(--light-purple); -} -.agents-tool .tool-robot .robot-body { - background: var(--soft-pink); -} - -.tool-robot.sleeping { - opacity: 0.6; -} - -.sleep-indicator { - position: absolute; - top: -10px; - right: -10px; - font-size: 0.8rem; - color: var(--medium-gray); - animation: float 2s infinite ease-in-out; -} - -.tool-content { - padding: 0 2rem 2rem; -} - -.tool-status { - display: inline-block; - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - margin-bottom: 1rem; - border: 2px solid var(--dark-text); -} - -.tool-status.active { - background: var(--success-green); - color: white; -} -.tool-status.beta { - background: var(--warning-amber); - color: white; -} -.tool-status.coming { - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} - -.tool-content h3 { - font-size: 1.3rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 0.5rem; -} - -.tool-content p { - color: var(--medium-gray); - margin-bottom: 1rem; -} - -.tool-features { - display: flex; - flex-wrap: wrap; - gap: 0.5rem; -} - -.tool-features .feature { - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - background: var(--pale-blue); - color: var(--dark-text); - border: 2px solid var(--primary-blue); -} - -.tool-features .feature.coming { - border-style: dashed; - color: var(--medium-gray); - background: var(--light-gray); -} - -/* Comparison Section */ -.comparison-section { - padding: 6rem 0; - background: var(--cream-white); -} - -.comparison-table { - background: var(--white); - border-radius: 20px; - border: 3px solid var(--dark-text); - overflow: hidden; - margin-top: 4rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); -} - -.table-header { - display: grid; - grid-template-columns: 1fr 1fr 1fr; - background: var(--primary-blue); - color: white; -} - -.header-cell { - padding: 1rem; - font-weight: 700; - text-align: center; - border-right: 2px solid var(--dark-text); -} - -.header-cell:last-child { - border-right: none; -} - -.header-cell.jan-col { - background: var(--success-green); -} - -.table-row { - display: grid; - grid-template-columns: 1fr 1fr 1fr; - border-bottom: 2px solid var(--light-gray); -} - -.table-row:last-child { - border-bottom: none; -} - -.table-cell { - padding: 1rem; - text-align: center; - border-right: 2px solid var(--light-gray); -} - -.table-cell:last-child { - border-right: none; -} - -.table-cell.feature-col { - font-weight: 600; - background: var(--pale-blue); -} - -.table-cell.jan-col { - background: var(--soft-green); - font-weight: 600; - color: var(--dark-text); -} - -/* Roadmap Section */ -.roadmap-section { - padding: 6rem 0; - background: var(--sky-blue); -} - -.roadmap-database { - background: var(--white); - border-radius: 20px; - border: 3px solid var(--dark-text); - overflow: hidden; - margin-top: 4rem; - box-shadow: 0 8px 30px rgba(0, 0, 0, 0.1); -} - -.database-header { - display: grid; - grid-template-columns: 2fr 2fr 1fr 0.5fr; - background: var(--primary-blue); - color: white; -} - -.database-header .header-cell { - padding: 1rem; - font-weight: 700; - text-align: center; - border-right: 2px solid var(--dark-text); -} - -.roadmap-row { - border-bottom: 2px solid var(--light-gray); - position: relative; -} - -.roadmap-row:last-child { - border-bottom: none; -} - -.roadmap-row { - display: grid; - grid-template-columns: 2fr 2fr 1fr 0.5fr; -} - -.row-cell { - padding: 1rem; - text-align: center; - border-right: 2px solid var(--light-gray); - display: flex; - align-items: center; - justify-content: center; -} - -.row-cell:last-child { - border-right: none; -} - -.expand-btn { - background: var(--primary-blue); - color: white; - border: 2px solid var(--dark-text); - border-radius: 50%; - width: 30px; - height: 30px; - cursor: pointer; - font-weight: bold; - font-size: 1.2rem; - transition: all 0.3s ease; -} - -.expand-btn:hover { - background: var(--success-green); - transform: scale(1.1); -} - -.status-badge { - padding: 0.25rem 0.75rem; - border-radius: 15px; - font-size: 0.8rem; - font-weight: 600; - border: 2px solid var(--dark-text); -} - -.status-badge.released { - background: var(--success-green); - color: white; -} - -.status-badge.in-progress { - background: var(--warning-amber); - color: white; -} - -.status-badge.planned { - background: var(--light-gray); - color: var(--medium-gray); - border-style: dashed; -} - -.row-details { - grid-column: 1 / -1; - background: var(--pale-blue); - border-top: 2px solid var(--light-gray); - max-height: 0; - overflow: hidden; - transition: max-height 0.3s ease; -} - -.roadmap-row.expanded .row-details { - max-height: 500px; -} - -.details-content { - padding: 2rem; -} - -.detail-section { - margin-bottom: 2rem; -} - -.detail-section:last-child { - margin-bottom: 0; -} - -.detail-section h4 { - font-size: 1.1rem; - font-weight: 700; - color: var(--dark-text); - margin-bottom: 1rem; -} - -.detail-section ul { - list-style: none; - padding: 0; - margin: 0; -} - -.detail-section li { - padding: 0.5rem 0; - border-bottom: 1px solid var(--light-gray); - color: var(--medium-gray); -} - -.detail-section li:last-child { - border-bottom: none; -} - -/* CTA Section */ -.cta-section { - padding: 6rem 0; - background: var(--primary-blue); - text-align: center; -} - -.cta-content { - max-width: 600px; - margin: 0 auto; -} - -.cta-robot { - display: inline-block; - margin-bottom: 2rem; - animation: bounce 3s infinite ease-in-out; -} - -.cta-robot .robot-head { - width: 80px; - height: 80px; - background: var(--warm-yellow); - border-radius: 20px; - border: 4px solid var(--dark-text); - margin: 0 auto 10px; - position: relative; -} - -.cta-robot .robot-eyes { - display: flex; - justify-content: space-between; - padding: 15px 12px; -} - -.eye.winking { - height: 4px; - border-radius: 2px; - animation: wink 3s infinite; -} - -.cta-robot .robot-body { - width: 100px; - height: 80px; - background: var(--soft-green); - border-radius: 15px; - border: 4px solid var(--dark-text); - margin: 0 auto; - display: flex; - align-items: center; - justify-content: center; -} - -.heart { - font-size: 1.5rem; - animation: heartbeat 2s infinite; -} - -.cta-content h2 { - font-size: 2.5rem; - font-weight: 800; - color: white; - margin-bottom: 1rem; -} - -.cta-content p { - font-size: 1.2rem; - color: rgba(255, 255, 255, 0.9); - margin-bottom: 2rem; -} - -.cta-buttons { - display: flex; - justify-content: center; - gap: 1rem; - flex-wrap: wrap; -} - -.btn-primary, -.btn-secondary { - padding: 1rem 2rem; - border-radius: 50px; - font-size: 1rem; - font-weight: 600; - text-decoration: none; - transition: all 0.3s ease; - border: 3px solid var(--dark-text); - display: inline-block; -} - -.btn-primary { - background: var(--success-green); - color: white; -} - -.btn-primary:hover { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(34, 197, 94, 0.3); -} - -.btn-secondary { - background: white; - color: var(--dark-text); -} - -.btn-secondary:hover { - transform: translateY(-2px); - box-shadow: 0 8px 25px rgba(0, 0, 0, 0.1); -} - -/* Animations */ -@keyframes bounce { - 0%, - 100% { - transform: translateY(0px); - } - 50% { - transform: translateY(-10px); - } -} - -@keyframes blink { - 0%, - 90%, - 100% { - opacity: 1; - } - 95% { - opacity: 0.3; - } -} - -@keyframes pulse { - 0%, - 100% { - transform: scale(1); - opacity: 1; - } - 50% { - transform: scale(1.1); - opacity: 0.8; - } -} - -@keyframes wink { - 0%, - 90%, - 100% { - height: 16px; - border-radius: 50%; - } - 95% { - height: 4px; - border-radius: 2px; - } -} - -@keyframes heartbeat { - 0%, - 100% { - transform: scale(1); - } - 50% { - transform: scale(1.2); - } -} - -@keyframes float { - 0%, - 100% { - transform: translateY(0px); - } - 50% { - transform: translateY(-5px); - } -} - -/* Responsive Design */ -@media (max-width: 1024px) { - .vision-formula { - flex-direction: column; - align-items: center; - } - - .modes-comparison { - grid-template-columns: 1fr; - } - - .models-grid { - grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); - } - - .comparison-table, - .roadmap-database { - font-size: 0.9rem; - } - - .database-header, - .roadmap-row { - grid-template-columns: 1.5fr 1.5fr 1fr 0.5fr; - } -} - -@media (max-width: 768px) { - .container { - padding: 0 1rem; - } - - .title-main { - font-size: 2.5rem; - } - - .section-title { - font-size: 2rem; - } - - .toc-grid { - grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); - } - - .vision-principles { - grid-template-columns: 1fr; - } - - .platforms-grid { - grid-template-columns: 1fr; - } - - .model-specs { - grid-template-columns: 1fr; - } - - .tools-grid { - grid-template-columns: 1fr; - } - - .cta-buttons { - flex-direction: column; - align-items: center; - } - - .speech-bubble { - display: none; - } - - .comparison-table, - .roadmap-database { - font-size: 0.8rem; - } - - .table-header, - .table-row { - grid-template-columns: 1fr; - } - - .table-cell, - .header-cell { - text-align: left; - border-right: none; - border-bottom: 1px solid var(--light-gray); - } - - .database-header, - .roadmap-row { - grid-template-columns: 1fr; - } - - .row-cell { - text-align: left; - border-right: none; - border-bottom: 1px solid var(--light-gray); - } -} - -@media (max-width: 480px) { - .hero-content { - padding: 0 1rem; - } - - .section-title { - font-size: 1.8rem; - } - - .cta-content h2 { - font-size: 2rem; - } - - .platform-card, - .model-card, - .tool-card { - padding: 1.5rem; - } -} - -/* Accessibility */ -@media (prefers-reduced-motion: reduce) { - * { - animation-duration: 0.01ms !important; - animation-iteration-count: 1 !important; - transition-duration: 0.01ms !important; - } -} - -/* High contrast mode */ -@media (prefers-contrast: high) { - .model-card, - .platform-card, - .tool-card, - .toc-card { - border-width: 4px; - } - - .comparison-table, - .roadmap-database { - border-width: 4px; - } -} - -/* Focus states for accessibility */ -.toc-item:focus, -.expand-btn:focus, -.btn-primary:focus, -.btn-secondary:focus { - outline: 2px solid var(--primary-blue); - outline-offset: 2px; -} diff --git a/website/src/styles/retro-effects.css b/website/src/styles/retro-effects.css deleted file mode 100644 index 1e6f42294..000000000 --- a/website/src/styles/retro-effects.css +++ /dev/null @@ -1,822 +0,0 @@ -/* Retro-Tech Effects CSS */ -/* RadioShack meets PostHog aesthetic */ - -:root { - /* Retro Color Palette */ - --retro-green: #00ff41; - --retro-amber: #ffb000; - --retro-blue: #00bfff; - --retro-red: #ff0040; - --retro-cyan: #00ffff; - --retro-magenta: #ff00ff; - - /* Terminal Colors */ - --terminal-bg: #0a0a0a; - --terminal-fg: #00ff41; - --terminal-cursor: #ffffff; - - /* Glow Effects */ - --glow-green: rgba(0, 255, 65, 0.5); - --glow-amber: rgba(255, 176, 0, 0.5); - --glow-blue: rgba(0, 191, 255, 0.5); - --glow-red: rgba(255, 0, 64, 0.5); -} - -/* CRT Monitor Effect */ -.crt-effect { - position: relative; - background: radial-gradient(ellipse at center, #0a0a0a 0%, #000000 100%); -} - -.crt-effect::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: repeating-linear-gradient( - 0deg, - rgba(0, 255, 65, 0.05) 0px, - rgba(0, 255, 65, 0.05) 1px, - transparent 1px, - transparent 3px - ); - pointer-events: none; - z-index: 1000; -} - -.crt-effect::after { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: radial-gradient( - ellipse at center, - transparent 40%, - rgba(0, 0, 0, 0.8) 100% - ); - pointer-events: none; - z-index: 1001; -} - -/* Matrix Rain Effect */ -.matrix-rain { - position: fixed; - top: 0; - left: 0; - width: 100%; - height: 100%; - pointer-events: none; - z-index: -1; - overflow: hidden; -} - -.matrix-char { - position: absolute; - color: var(--retro-green); - font-family: 'JetBrains Mono', monospace; - font-size: 14px; - animation: matrixFall 3s linear infinite; - opacity: 0.7; -} - -@keyframes matrixFall { - 0% { - transform: translateY(-100vh); - opacity: 1; - } - 100% { - transform: translateY(100vh); - opacity: 0; - } -} - -/* Glitch Text Effects */ -.glitch-text { - position: relative; - color: var(--retro-green); - font-weight: bold; -} - -.glitch-text::before, -.glitch-text::after { - content: attr(data-text); - position: absolute; - top: 0; - left: 0; - width: 100%; - height: 100%; -} - -.glitch-text::before { - animation: glitch-anim-1 0.3s infinite; - color: var(--retro-red); - z-index: -1; -} - -.glitch-text::after { - animation: glitch-anim-2 0.3s infinite; - color: var(--retro-blue); - z-index: -2; -} - -@keyframes glitch-anim-1 { - 0% { - clip-path: inset(40% 0 61% 0); - transform: translate(-2px, -2px); - } - 20% { - clip-path: inset(92% 0 1% 0); - transform: translate(2px, 2px); - } - 40% { - clip-path: inset(43% 0 1% 0); - transform: translate(-2px, 2px); - } - 60% { - clip-path: inset(25% 0 58% 0); - transform: translate(2px, -2px); - } - 80% { - clip-path: inset(54% 0 7% 0); - transform: translate(-2px, -2px); - } - 100% { - clip-path: inset(58% 0 43% 0); - transform: translate(2px, 2px); - } -} - -@keyframes glitch-anim-2 { - 0% { - clip-path: inset(25% 0 58% 0); - transform: translate(2px, 1px); - } - 20% { - clip-path: inset(54% 0 7% 0); - transform: translate(-1px, 2px); - } - 40% { - clip-path: inset(58% 0 43% 0); - transform: translate(1px, -1px); - } - 60% { - clip-path: inset(40% 0 61% 0); - transform: translate(-1px, -2px); - } - 80% { - clip-path: inset(92% 0 1% 0); - transform: translate(2px, 1px); - } - 100% { - clip-path: inset(43% 0 1% 0); - transform: translate(-2px, -1px); - } -} - -/* Neon Glow Effects */ -.neon-glow { - text-shadow: - 0 0 5px currentColor, - 0 0 10px currentColor, - 0 0 15px currentColor, - 0 0 20px currentColor; - animation: neonFlicker 2s infinite alternate; -} - -@keyframes neonFlicker { - 0%, - 18%, - 22%, - 25%, - 53%, - 57%, - 100% { - text-shadow: - 0 0 5px currentColor, - 0 0 10px currentColor, - 0 0 15px currentColor, - 0 0 20px currentColor; - } - 20%, - 24%, - 55% { - text-shadow: none; - } -} - -/* Terminal Cursor */ -.terminal-cursor { - display: inline-block; - background-color: var(--terminal-cursor); - color: var(--terminal-bg); - animation: cursorBlink 1s infinite; - margin-left: 2px; - width: 8px; - height: 1em; -} - -@keyframes cursorBlink { - 0%, - 50% { - opacity: 1; - } - 51%, - 100% { - opacity: 0; - } -} - -/* Data Stream Animation */ -.data-stream { - position: relative; - overflow: hidden; -} - -.data-stream::before { - content: ''; - position: absolute; - top: 0; - left: -100%; - width: 100%; - height: 100%; - background: linear-gradient( - 90deg, - transparent, - rgba(0, 255, 65, 0.1) 10%, - rgba(0, 255, 65, 0.3) 50%, - rgba(0, 255, 65, 0.1) 90%, - transparent - ); - animation: dataStreamFlow 2s infinite; -} - -@keyframes dataStreamFlow { - 0% { - left: -100%; - opacity: 0; - } - 50% { - opacity: 1; - } - 100% { - left: 100%; - opacity: 0; - } -} - -/* Circuit Board Traces */ -.circuit-trace { - position: relative; - overflow: hidden; -} - -.circuit-trace::after { - content: ''; - position: absolute; - top: 50%; - left: 0; - right: 0; - height: 2px; - background: linear-gradient( - 90deg, - transparent, - var(--retro-green) 20%, - var(--retro-green) 80%, - transparent - ); - transform: translateY(-50%); - animation: circuitPulse 3s infinite; -} - -@keyframes circuitPulse { - 0%, - 100% { - opacity: 0.3; - box-shadow: 0 0 5px var(--retro-green); - } - 50% { - opacity: 1; - box-shadow: 0 0 15px var(--retro-green); - } -} - -/* Electronic Component Styles */ -.component-resistor { - display: inline-block; - width: 20px; - height: 6px; - background: linear-gradient( - 90deg, - transparent 0%, - var(--retro-amber) 20%, - var(--retro-red) 40%, - var(--retro-amber) 60%, - var(--retro-red) 80%, - transparent 100% - ); - border-radius: 1px; - position: relative; -} - -.component-resistor::before, -.component-resistor::after { - content: ''; - position: absolute; - top: 50%; - width: 5px; - height: 1px; - background: var(--retro-green); - transform: translateY(-50%); -} - -.component-resistor::before { - left: -5px; -} -.component-resistor::after { - right: -5px; -} - -.component-led { - display: inline-block; - width: 8px; - height: 8px; - border-radius: 50%; - background: radial-gradient(circle, var(--retro-red), transparent); - box-shadow: 0 0 10px var(--retro-red); - animation: ledBlink 2s infinite; -} - -@keyframes ledBlink { - 0%, - 80%, - 100% { - opacity: 0.3; - box-shadow: 0 0 5px var(--retro-red); - } - 85%, - 95% { - opacity: 1; - box-shadow: 0 0 15px var(--retro-red); - } -} - -/* Retro Button Styles */ -.retro-button { - background: linear-gradient(145deg, #2a2a2a, #1a1a1a); - border: 2px solid var(--retro-green); - color: var(--retro-green); - padding: 0.75rem 1.5rem; - font-family: 'JetBrains Mono', monospace; - font-weight: 600; - text-transform: uppercase; - letter-spacing: 0.1em; - cursor: pointer; - position: relative; - overflow: hidden; - transition: all 0.3s ease; -} - -.retro-button::before { - content: ''; - position: absolute; - top: 0; - left: -100%; - width: 100%; - height: 100%; - background: linear-gradient( - 90deg, - transparent, - rgba(0, 255, 65, 0.2), - transparent - ); - transition: left 0.5s; -} - -.retro-button:hover::before { - left: 100%; -} - -.retro-button:hover { - box-shadow: - 0 0 20px var(--glow-green), - inset 0 0 20px rgba(0, 255, 65, 0.1); - transform: translateY(-2px); -} - -.retro-button:active { - transform: translateY(0); - box-shadow: - 0 0 10px var(--glow-green), - inset 0 0 10px rgba(0, 255, 65, 0.2); -} - -/* Holographic Effect */ -.holographic { - background: linear-gradient( - 45deg, - var(--retro-cyan), - var(--retro-magenta), - var(--retro-amber), - var(--retro-green) - ); - background-size: 400% 400%; - animation: holographicShift 3s ease infinite; - -webkit-background-clip: text; - -webkit-text-fill-color: transparent; - background-clip: text; -} - -@keyframes holographicShift { - 0% { - background-position: 0% 50%; - } - 50% { - background-position: 100% 50%; - } - 100% { - background-position: 0% 50%; - } -} - -/* System Status Indicators */ -.system-status { - display: flex; - align-items: center; - gap: 0.5rem; - font-family: 'JetBrains Mono', monospace; - font-size: 0.8rem; - text-transform: uppercase; -} - -.status-bar { - width: 60px; - height: 4px; - background: #333; - border-radius: 2px; - overflow: hidden; - position: relative; -} - -.status-bar::after { - content: ''; - position: absolute; - top: 0; - left: 0; - height: 100%; - background: var(--retro-green); - border-radius: 2px; - animation: statusProgress 2s ease-in-out infinite; -} - -@keyframes statusProgress { - 0% { - width: 0%; - } - 50% { - width: 100%; - } - 100% { - width: 0%; - } -} - -/* Vintage Monitor Bezel */ -.monitor-bezel { - background: linear-gradient(135deg, #2a2a2a 0%, #1a1a1a 50%, #0a0a0a 100%); - border: 3px solid #333; - border-radius: 15px; - padding: 20px; - position: relative; - box-shadow: - inset 0 0 20px rgba(0, 0, 0, 0.5), - 0 0 30px rgba(0, 0, 0, 0.8); -} - -.monitor-bezel::before { - content: ''; - position: absolute; - top: 10px; - right: 10px; - width: 8px; - height: 8px; - border-radius: 50%; - background: var(--retro-red); - box-shadow: 0 0 8px var(--retro-red); - animation: powerIndicator 3s infinite; -} - -@keyframes powerIndicator { - 0%, - 90%, - 100% { - opacity: 1; - } - 95% { - opacity: 0.3; - } -} - -/* ASCII Art Enhancement */ -.ascii-art { - font-family: 'JetBrains Mono', monospace; - white-space: pre; - color: var(--retro-green); - text-shadow: 0 0 10px var(--glow-green); - animation: asciiGlow 4s ease-in-out infinite alternate; -} - -@keyframes asciiGlow { - 0% { - text-shadow: 0 0 5px var(--glow-green); - opacity: 0.8; - } - 100% { - text-shadow: 0 0 20px var(--glow-green); - opacity: 1; - } -} - -/* Retro Loading Animation */ -.retro-loader { - display: inline-block; - position: relative; - width: 40px; - height: 40px; -} - -.retro-loader::after { - content: ''; - position: absolute; - border: 3px solid #333; - border-top: 3px solid var(--retro-green); - border-radius: 50%; - width: 30px; - height: 30px; - animation: retroSpin 1s linear infinite; -} - -@keyframes retroSpin { - 0% { - transform: rotate(0deg); - } - 100% { - transform: rotate(360deg); - } -} - -/* Scanline Effect */ -.scanlines { - position: relative; -} - -.scanlines::after { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: repeating-linear-gradient( - 0deg, - transparent, - transparent 2px, - rgba(0, 255, 65, 0.03) 2px, - rgba(0, 255, 65, 0.03) 4px - ); - pointer-events: none; - animation: scanlineMove 8s linear infinite; -} - -@keyframes scanlineMove { - 0% { - transform: translateY(0); - } - 100% { - transform: translateY(4px); - } -} - -/* Phosphor Screen Effect */ -.phosphor-screen { - background: radial-gradient(ellipse at center, #001100 0%, #000000 100%); - color: var(--retro-green); - text-shadow: 0 0 2px var(--retro-green); -} - -/* Terminal Window */ -.terminal-window { - background: #000000; - border: 2px solid var(--retro-green); - border-radius: 4px; - padding: 1rem; - font-family: 'JetBrains Mono', monospace; - color: var(--retro-green); - box-shadow: - 0 0 20px rgba(0, 255, 65, 0.3), - inset 0 0 20px rgba(0, 255, 65, 0.1); -} - -.terminal-header { - display: flex; - align-items: center; - margin-bottom: 1rem; - padding-bottom: 0.5rem; - border-bottom: 1px solid var(--retro-green); -} - -.terminal-title { - color: var(--retro-amber); - font-weight: bold; - text-transform: uppercase; -} - -/* Retro Grid */ -.retro-grid { - background-image: - linear-gradient(rgba(0, 255, 65, 0.1) 1px, transparent 1px), - linear-gradient(90deg, rgba(0, 255, 65, 0.1) 1px, transparent 1px); - background-size: 20px 20px; -} - -/* Digital Clock Display */ -.digital-clock { - font-family: 'JetBrains Mono', monospace; - color: var(--retro-green); - background: #000000; - padding: 0.5rem 1rem; - border: 1px solid var(--retro-green); - font-weight: bold; - text-shadow: 0 0 10px var(--retro-green); - letter-spacing: 0.1em; -} - -/* Radar Sweep Effect */ -.radar-sweep { - position: relative; - border-radius: 50%; - background: radial-gradient( - circle, - transparent 30%, - rgba(0, 255, 65, 0.1) 100% - ); - overflow: hidden; -} - -.radar-sweep::before { - content: ''; - position: absolute; - top: 50%; - left: 50%; - width: 50%; - height: 2px; - background: linear-gradient(90deg, var(--retro-green), transparent); - transform-origin: left center; - animation: radarSweep 3s linear infinite; -} - -@keyframes radarSweep { - 0% { - transform: translate(-50%, -50%) rotate(0deg); - } - 100% { - transform: translate(-50%, -50%) rotate(360deg); - } -} - -/* VHS Static Effect */ -.vhs-static { - position: relative; - overflow: hidden; -} - -.vhs-static::before { - content: ''; - position: absolute; - top: 0; - left: 0; - right: 0; - bottom: 0; - background: - repeating-linear-gradient( - 90deg, - transparent, - transparent 2px, - rgba(255, 255, 255, 0.03) 2px, - rgba(255, 255, 255, 0.03) 4px - ), - repeating-linear-gradient( - 0deg, - transparent, - transparent 2px, - rgba(255, 255, 255, 0.03) 2px, - rgba(255, 255, 255, 0.03) 4px - ); - animation: vhsStatic 0.1s infinite; - pointer-events: none; -} - -@keyframes vhsStatic { - 0% { - opacity: 0.03; - } - 50% { - opacity: 0.05; - } - 100% { - opacity: 0.03; - } -} - -/* Accessibility and Reduced Motion */ -@media (prefers-reduced-motion: reduce) { - *, - *::before, - *::after { - animation-duration: 0.01ms !important; - animation-iteration-count: 1 !important; - transition-duration: 0.01ms !important; - } -} - -/* High Contrast Mode */ -@media (prefers-contrast: high) { - :root { - --retro-green: #00ff00; - --retro-amber: #ffff00; - --retro-red: #ff0000; - --retro-blue: #0080ff; - } - - .crt-effect::before { - display: none; - } - - .neon-glow { - text-shadow: none; - font-weight: bold; - } - - .scanlines::after { - display: none; - } -} - -/* Dark Mode Enhancements */ -@media (prefers-color-scheme: dark) { - .retro-button { - background: linear-gradient(145deg, #1a1a1a, #0a0a0a); - } - - .monitor-bezel { - background: linear-gradient(135deg, #1a1a1a 0%, #0a0a0a 50%, #000000 100%); - } -} - -/* Print Styles */ -@media print { - .matrix-rain, - .crt-effect::before, - .crt-effect::after, - .neon-glow, - .data-stream::before, - .circuit-trace::after, - .scanlines::after { - display: none !important; - } - - .retro-button, - .terminal-window { - border: 2px solid #000 !important; - background: #fff !important; - color: #000 !important; - } -} - -/* Mobile Optimizations */ -@media (max-width: 768px) { - .retro-button { - padding: 0.5rem 1rem; - font-size: 0.9rem; - } - - .terminal-window { - padding: 0.75rem; - font-size: 0.9rem; - } - - .digital-clock { - font-size: 0.8rem; - padding: 0.25rem 0.5rem; - } -} - -/* Focus States for Accessibility */ -.retro-button:focus, -.terminal-window:focus { - outline: 2px solid var(--retro-green); - outline-offset: 2px; -} diff --git a/website/src/styles/robot-personalities.css b/website/src/styles/robot-personalities.css deleted file mode 100644 index 71a1cac66..000000000 --- a/website/src/styles/robot-personalities.css +++ /dev/null @@ -1,393 +0,0 @@ -/* Robot Personalities - Fun, Playful, PostHog-inspired Style */ - -/* ===== VIBRANT COLOR PALETTE ===== */ -:root { - /* Primary Colors - Bold & Playful */ - --robot-pink: #FF006E; - --robot-blue: #3A86FF; - --robot-green: #06FFA5; - --robot-yellow: #FFB700; - --robot-purple: #8338EC; - --robot-orange: #FB5607; - --robot-teal: #00F5FF; - --robot-red: #FF4365; - - /* Background & UI Colors */ - --robot-dark: #1A1A2E; - --robot-light: #FFFFFF; - --robot-gray: #4A4E69; - --robot-bg-purple: #F3E8FF; - --robot-bg-blue: #E8F4FF; - --robot-bg-pink: #FFE8F0; - --robot-bg-yellow: #FFF8E8; - --robot-bg-green: #E8FFEA; - - /* Special Effects */ - --robot-glow-pink: rgba(255, 0, 110, 0.4); - --robot-glow-blue: rgba(58, 134, 255, 0.4); - --robot-glow-green: rgba(6, 255, 165, 0.4); - --robot-shadow: rgba(26, 26, 46, 0.15); -} - -/* ===== HAPPY ROBOT SMILES ===== */ -.robot-smile { - position: absolute; - bottom: 12px; - left: 50%; - transform: translateX(-50%); - width: 20px; - height: 10px; - border: 3px solid var(--robot-dark); - border-top: none !important; - border-radius: 0 0 20px 20px !important; - background: transparent; -} - -/* Big smile variant */ -.robot-smile-big { - width: 30px; - height: 15px; - border-radius: 0 0 30px 30px !important; -} - -/* Cheeky smile */ -.robot-smile.cheeky { - transform: translateX(-50%) rotate(-5deg); -} - -/* ===== ROBOT EYES WITH PERSONALITY ===== */ -.robot-eye { - width: 12px; - height: 12px; - background: var(--robot-dark); - border-radius: 50%; - position: relative; - transition: all 0.3s ease; -} - -/* Eye shine */ -.robot-eye::after { - content: ''; - position: absolute; - top: 2px; - right: 2px; - width: 4px; - height: 4px; - background: white; - border-radius: 50%; - animation: eyeShine 3s infinite; -} - -/* Winking eye */ -.robot-eye.wink { - height: 2px; - border-radius: 0; - animation: wink 4s infinite; -} - -/* Excited eyes */ -.robot-eye.excited { - transform: scale(1.2); -} - -.robot-eye.excited::before { - content: ''; - position: absolute; - top: -5px; - left: 50%; - transform: translateX(-50%); - width: 2px; - height: 4px; - background: var(--robot-dark); - border-radius: 1px; -} - -/* Heart eyes */ -.robot-eye.love { - background: var(--robot-pink); - clip-path: polygon(50% 25%, 15% 0%, 0% 15%, 0% 40%, 50% 90%, 100% 40%, 100% 15%, 85% 0%); - border-radius: 0; - width: 14px; - height: 14px; -} - -/* Sleepy eyes */ -.robot-eye.sleeping { - height: 2px; - border-radius: 0; - background: var(--robot-gray); -} - -/* ===== ROBOT BLUSH ===== */ -.robot-blush { - position: absolute; - width: 12px; - height: 8px; - background: var(--robot-pink); - opacity: 0.3; - border-radius: 50%; - top: 50%; -} - -.robot-blush.left { - left: -5px; -} - -.robot-blush.right { - right: -5px; -} - -/* ===== ROBOT HEAD STYLES ===== */ -.robot-head { - position: relative; - animation: robotBob 4s ease-in-out infinite; -} - -/* Add antenna with animation */ -.robot-antenna { - position: absolute; - top: -20px; - left: 50%; - transform: translateX(-50%); - width: 3px; - height: 15px; - background: var(--robot-dark); - border-radius: 2px; -} - -.robot-antenna::after { - content: ''; - position: absolute; - top: -8px; - left: 50%; - transform: translateX(-50%); - width: 12px; - height: 12px; - background: var(--robot-pink); - border: 2px solid var(--robot-dark); - border-radius: 50%; - animation: antennaGlow 2s ease-in-out infinite; -} - -/* ===== ROBOT BODY DECORATIONS ===== */ -.robot-chest-screen { - position: absolute; - top: 20px; - left: 50%; - transform: translateX(-50%); - width: 40px; - height: 30px; - background: var(--robot-dark); - border-radius: 8px; - overflow: hidden; -} - -.robot-chest-screen::after { - content: '♥'; - position: absolute; - top: 50%; - left: 50%; - transform: translate(-50%, -50%); - color: var(--robot-pink); - font-size: 20px; - animation: heartBeat 1.5s ease-in-out infinite; -} - -/* Robot buttons */ -.robot-button { - position: absolute; - width: 8px; - height: 8px; - background: var(--robot-yellow); - border: 2px solid var(--robot-dark); - border-radius: 50%; - cursor: pointer; - transition: all 0.2s ease; -} - -.robot-button:hover { - transform: scale(1.2); - background: var(--robot-green); -} - -/* ===== SPECIAL ROBOT VARIANTS ===== */ - -/* Hero Robot - Extra Playful */ -.hero-robot .robot-head { - background: linear-gradient(135deg, var(--robot-yellow) 0%, var(--robot-orange) 100%); - box-shadow: 0 8px 32px var(--robot-shadow); -} - -.hero-robot .robot-body { - background: linear-gradient(135deg, var(--robot-green) 0%, var(--robot-teal) 100%); - box-shadow: 0 8px 32px var(--robot-shadow); -} - -/* Models Robot - Smart & Analytical */ -.models-robot .robot-head { - background: linear-gradient(135deg, var(--robot-purple) 0%, var(--robot-pink) 100%); -} - -.models-robot::before { - content: ''; - position: absolute; - top: -30px; - left: 50%; - transform: translateX(-50%); - width: 60px; - height: 60px; - border: 2px dashed var(--robot-purple); - border-radius: 50%; - animation: thinkingCircle 6s linear infinite; -} - -/* Platform Robot - Connected */ -.platform-robot .robot-body::before, -.platform-robot .robot-body::after { - content: ''; - position: absolute; - width: 40px; - height: 2px; - background: var(--robot-blue); - top: 50%; - animation: connectionPulse 2s ease-in-out infinite; -} - -.platform-robot .robot-body::before { - left: -40px; -} - -.platform-robot .robot-body::after { - right: -40px; -} - -/* Tools Robot - Action-Oriented */ -.tools-robot { - animation: toolsWorking 3s ease-in-out infinite; -} - -.tools-robot .robot-arms { - animation: armSwing 2s ease-in-out infinite; -} - -/* ===== FUN ANIMATIONS ===== */ -@keyframes robotBob { - 0%, 100% { transform: translateY(0); } - 50% { transform: translateY(-5px); } -} - -@keyframes antennaGlow { - 0%, 100% { - background: var(--robot-pink); - box-shadow: 0 0 10px var(--robot-glow-pink); - } - 50% { - background: var(--robot-green); - box-shadow: 0 0 20px var(--robot-glow-green); - } -} - -@keyframes heartBeat { - 0%, 100% { transform: translate(-50%, -50%) scale(1); } - 25% { transform: translate(-50%, -50%) scale(1.1); } - 50% { transform: translate(-50%, -50%) scale(0.9); } - 75% { transform: translate(-50%, -50%) scale(1.05); } -} - -@keyframes eyeShine { - 0%, 90%, 100% { opacity: 1; } - 95% { opacity: 0; } -} - -@keyframes wink { - 0%, 40%, 50%, 100% { height: 12px; border-radius: 50%; } - 45% { height: 2px; border-radius: 0; } -} - -@keyframes thinkingCircle { - 0% { transform: translateX(-50%) rotate(0deg); } - 100% { transform: translateX(-50%) rotate(360deg); } -} - -@keyframes connectionPulse { - 0%, 100% { opacity: 0.3; width: 40px; } - 50% { opacity: 1; width: 60px; } -} - -@keyframes toolsWorking { - 0%, 100% { transform: rotate(0deg); } - 25% { transform: rotate(-2deg); } - 75% { transform: rotate(2deg); } -} - -@keyframes armSwing { - 0%, 100% { transform: rotate(0deg); } - 25% { transform: rotate(-10deg); } - 75% { transform: rotate(10deg); } -} - -/* ===== SPARKLE EFFECTS ===== */ -.robot-sparkles { - position: absolute; - top: -20px; - left: -20px; - right: -20px; - bottom: -20px; - pointer-events: none; -} - -.sparkle { - position: absolute; - width: 4px; - height: 4px; - background: var(--robot-yellow); - animation: sparkle 3s ease-in-out infinite; -} - -.sparkle:nth-child(1) { top: 10%; left: 20%; animation-delay: 0s; } -.sparkle:nth-child(2) { top: 30%; right: 15%; animation-delay: 0.5s; } -.sparkle:nth-child(3) { bottom: 20%; left: 10%; animation-delay: 1s; } -.sparkle:nth-child(4) { bottom: 10%; right: 25%; animation-delay: 1.5s; } - -@keyframes sparkle { - 0%, 100% { - opacity: 0; - transform: scale(0) rotate(0deg); - } - 50% { - opacity: 1; - transform: scale(1) rotate(180deg); - } -} - -/* ===== HOVER INTERACTIONS ===== */ -.robot-head:hover { - animation-play-state: paused; - transform: scale(1.05); - cursor: pointer; -} - -.robot-head:hover .robot-eye { - transform: scale(1.2); -} - -.robot-head:hover .robot-smile { - border-color: var(--robot-pink); - width: 24px; -} - -/* ===== ACCESSIBILITY ===== */ -@media (prefers-reduced-motion: reduce) { - * { - animation-duration: 0.01ms !important; - animation-iteration-count: 1 !important; - transition-duration: 0.01ms !important; - } -} - -/* ===== RESPONSIVE ADJUSTMENTS ===== */ -@media (max-width: 768px) { - .robot-head, .robot-body { - transform: scale(0.9); - } -} diff --git a/website/tsconfig.json b/website/tsconfig.json deleted file mode 100644 index 8bf91d3bb..000000000 --- a/website/tsconfig.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "extends": "astro/tsconfigs/strict", - "include": [".astro/types.d.ts", "**/*"], - "exclude": ["dist"] -}