chore: update docs and template (#62)

MarioCadenas · web-flow · commit d339e9193197 · 2026-01-22T09:16:34.000-08:00
diff --git a/docs/docs/app-management.mdx b/docs/docs/app-management.mdx
@@ -12,7 +12,7 @@ Manage your AppKit application throughout its lifecycle using the Databricks CLI
 
 ## Create app
 
-See the [Quick start](./index.md#quick-start-options) section to create a new Databricks app with AppKit installed.
+See the [Quick start](./index.md) section to create a new Databricks app with AppKit installed.
 
 ## Configuration
 
diff --git a/docs/docs/development/_ai-assisted-development.mdx b/docs/docs/development/_ai-assisted-development.mdx
diff --git a/docs/docs/development/_prerequisites_app.mdx b/docs/docs/development/_prerequisites_app.mdx
@@ -2,4 +2,4 @@
 
 - [Node.js](https://nodejs.org) environment
 - Databricks CLI: install and configure it according to the [official tutorial](https://docs.databricks.com/aws/en/dev-tools/cli/tutorial).
-- A new Databricks app with AppKit installed. See [Bootstrap a new Databricks app](../index.md#quick-start-options) for more details.
+- A new Databricks app with AppKit installed. See [Bootstrap a new Databricks app](../index.md) for more details.
diff --git a/docs/docs/development/index.mdx b/docs/docs/development/index.mdx
@@ -15,8 +15,7 @@ AppKit provides multiple development workflows to suit different needs: local de
 There are multiple supported development flows available with AppKit:
 
 1. **[Local development](./local-development.mdx)**: Run the development server with hot reload for both UI and backend code. This is the default development flow and is suitable for most use cases.
-2. **[AI-assisted development](./ai-assisted-development.mdx)**: Use an AI coding assistant connected via the Databricks MCP server to explore data, run CLI commands, and scaffold your app interactively.
-3. **[Remote Bridge](./remote-bridge.mdx)**: Create a remote bridge to a deployed backend while keeping your queries and UI local. This is useful for testing against production data or debugging deployed backend code without redeploying your app.
+1. **[Remote Bridge](./remote-bridge.mdx)**: Create a remote bridge to a deployed backend while keeping your queries and UI local. This is useful for testing against production data or debugging deployed backend code without redeploying your app.
 
 ## See also
  
diff --git a/docs/docs/development/local-development.mdx b/docs/docs/development/local-development.mdx
@@ -8,7 +8,7 @@ import Prerequisites from './_prerequisites_app.mdx';
 
 <Prerequisites />
 
-Once your app is bootstrapped according to the [Manual quick start](../index.md#manual-quick-start) guide, you can start the development server with hot reload for both UI and backend code.
+Once your app is bootstrapped according to the [Manual quick start](../index.md) guide, you can start the development server with hot reload for both UI and backend code.
 
 In the application root directory, run the following command to start the development server:
 
diff --git a/docs/docs/index.md b/docs/docs/index.md
@@ -20,36 +20,7 @@ AppKit simplifies building data applications on Databricks by providing:
 
 <Prerequisites />
 
-## Quick start options
-
-There are two ways to get started with AppKit:
-
-- **AI-assisted** (recommended): Use an AI coding assistant connected via the Databricks MCP server to explore data, run CLI commands, and scaffold your app interactively.
-- **Manual**: Use the Databricks CLI directly to create, bootstrap, and deploy your app.
-
-Choose the path that best fits your workflow; both approaches produce the same kind of AppKit-based Databricks application.
-
-## AI-first quick start
-
-Databricks AppKit is designed to work with AI coding assistants through the Databricks MCP server.
-
-Install the Databricks MCP server and configure it for use with your preferred AI assistant:
-
-```bash
-databricks experimental apps-mcp install
-```
-
-Once configured for your development environment, you can use your AI assistant to create and deploy new Databricks applications, as well as to iteratively evolve your app's codebase.
-
-Just prompt your AI assistant to create a new Databricks app, such as:
-
-```
-Create a new Databricks app that displays a dashboard of the nyc taxi trips dataset.
-```
-
-To learn more about the MCP server, see the [AI-assisted development](./development/ai-assisted-development.mdx) documentation.
-
-## Manual quick start
+## Quick start
 
 Learn how to create and deploy a sample Databricks application that uses AppKit with the Databricks CLI.
 
diff --git a/template/package.json b/template/package.json
@@ -28,8 +28,8 @@
   "license": "Unlicensed",
   "description": "{{.app_description}}",
   "dependencies": {
-    "@databricks/appkit": "0.2.0",
-    "@databricks/appkit-ui": "0.2.0",
+    "@databricks/appkit": "0.3.0",
+    "@databricks/appkit-ui": "0.3.0",
     "@databricks/sdk-experimental": "^0.14.2",
     "clsx": "^2.1.1",
     "embla-carousel-react": "^8.6.0",
