feat: turbostarter boilerplate

Production-ready Next.js boilerplate with:
- Runtime env validation (fail-fast on missing vars)
- Feature-gated config (S3, Stripe, email, OAuth)
- Docker + Coolify deployment pipeline
- PostgreSQL + pgvector, MinIO S3, Better Auth
- TypeScript strict mode (no ignoreBuildErrors)
- i18n (en/es), AI modules, billing, monitoring

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.context/turbostarter-framework-context/sections/mobile/ai.md

@@ -0,0 +1,59 @@
+---
+title: AI
+description: Learn how to use AI integration in your mobile app.
+url: /docs/mobile/ai
+---
+
+# AI
+
+As AI integration for [web](/docs/web/ai/overview), [extension](/docs/extension/ai), and mobile is based on the same battle-tested [Vercel AI SDK](https://sdk.vercel.ai/docs/introduction), the implementation is very similar across platforms.
+
+In this section, we'll focus on how to consume AI responses in the mobile app. For server-side implementation details, please refer to the [web documentation](/docs/web/ai/overview).
+
+## Features
+
+The most common AI integration features are also supported in the mobile app:
+
+* **Chat**: Build chat interfaces inside native mobile apps.
+* **Streaming**: Receive AI responses as soon as the model starts generating them, without waiting for the full response to be completed.
+* **Image generation**: Generate images based on a given prompt.
+
+You can easily compose your application using these building blocks or extend them to suit your specific needs.
+
+## Usage
+
+The usage of AI integration in the mobile app is the same as for [web app](/docs/web/ai/configuration#client-side) and [browser extension](/docs/extension/ai#server--client). We use the exact same [API endpoint](/docs/web/ai/configuration#api-endpoint), and since TurboStarter ships with built-in support for streaming on mobile, we can leverage it to display answers incrementally to the user as they're generated.
+
+```tsx title="ai.tsx"
+import { useChat } from "@ai-sdk/react";
+import { DefaultChatTransport } from "ai";
+
+const AI = () => {
+  const { messages } = useChat({
+    transport: new DefaultChatTransport({
+      api: "/api/ai/chat",
+    }),
+  });
+
+  return (
+    <View>
+      {messages.map((message) => (
+        <Text key={message.id}>
+          {message.parts.map((part, i) => {
+            switch (part.type) {
+              case "text":
+                return <Text key={`${message.id}-${i}`}>{part.text}</Text>;
+            }
+          })}
+        </Text>
+      ))}
+    </View>
+  );
+};
+
+export default AI;
+```
+
+By leveraging this integration, we can easily manage the state of the AI request and update the UI as soon as the response is ready.
+
+TurboStarter ships with a ready-to-use implementation of AI chat, allowing you to see this solution in action. Feel free to reuse or modify it according to your needs.