React Native Roadmap 2026-W29

Implement the following React Native chunk for your mobile app: Integrate react-native-delta (https://github.com/zepto-labs/react-native-delta) for bandwidth-efficient OTA updates using binary diff patching.

Goal: Replace full-bundle OTA delivery with delta (diff-only) updates to reduce CDN bandwidth and user data usage.

Files to create or modify:

• package.json — add react-native-delta dependency
• scripts/generate-delta.sh — CI script to generate diff between old and new Hermes bundle
• src/bootstrap/applyOtaUpdate.ts — client-side logic to fetch and apply a delta patch before JS starts

Step-by-step implementation:

1. npm install react-native-delta
2. In your CI pipeline, after building the new bundle, run npx delta-gen --old build/old.bundle --new build/new.bundle --out build/delta.patch
3. Upload delta.patch to your CDN/OTA server alongside the full bundle
4. In applyOtaUpdate.ts, on app startup: check if a delta is available → download the delta patch → apply it using applyDelta(currentBundlePath, deltaPath, newBundlePath) → swap bundles
5. Fall back to full bundle download if no delta is available

Acceptance criteria checklist:

• delta.patch is generated in CI and at least 80% smaller than the full bundle for a one-line change
• App boots successfully after delta application on iOS Simulator
• App boots successfully after delta application on Android Emulator
• Full bundle fallback works when no delta is available

Implement the following React Native chunk for your mobile app: Set up Maestro MCP so your AI coding agent can run E2E tests on an iOS Simulator or Android Emulator (https://docs.maestro.dev/get-started/maestro-mcp).

Goal: Let an AI agent autonomously verify features it just built by driving the app UI.

Files to create or modify:

• e2e/flows/login.yaml — first Maestro test flow

Step-by-step implementation:

1. Install Maestro CLI: curl -Ls "https://get.maestro.mobile.dev" | bash
2. Register the MCP server in Claude Code: mcp add maestro -- maestro mcp
3. Boot iOS Simulator: xcrun simctl boot "iPhone 16" (or start Android Emulator from Android Studio)
4. Create e2e/flows/login.yaml:

appId: com.yourapp
---
- launchApp
- tapOn: 'Email'
- inputText: 'test@example.com'
- tapOn: 'Password'
- inputText: 'secret123'
- tapOn: 'Sign In'
- assertVisible: 'Welcome'

5. Run via the agent: instruct Claude Code "use Maestro MCP to run the login flow and screenshot the result"
6. Save the screenshot to e2e/screenshots/

Acceptance criteria checklist:

• Agent launches the app without manual simulator boot
• Agent runs the .yaml flow and reports PASS
• Screenshot of the passing test is saved
• Re-running maestro test e2e/flows/login.yaml works without the agent

Implement the following React Native chunk for your mobile app: Replace KeyboardAvoidingView with react-native-keyboard-controller (https://github.com/kirillzyusko/react-native-keyboard-controller), which is now available in Expo Go.

Goal: Smooth, animated keyboard avoidance without a custom dev client build.

Files to create or modify:

• app/_layout.tsx — wrap root with <KeyboardProvider>
• app/(auth)/login.tsx — replace KeyboardAvoidingView with KeyboardAwareScrollView
• app/(auth)/signup.tsx — same replacement
• app/(chat)/index.tsx — same replacement

Step-by-step implementation:

1. npx expo install react-native-keyboard-controller
2. In app/_layout.tsx, import and wrap the root Stack with <KeyboardProvider>:

import { KeyboardProvider } from 'react-native-keyboard-controller';
// wrap: <KeyboardProvider><Stack /></KeyboardProvider>

3. In each form screen, replace:

// Before
import { KeyboardAvoidingView } from 'react-native';
// After
import { KeyboardAwareScrollView } from 'react-native-keyboard-controller';

4. Test in Expo Go on iOS and Android

Acceptance criteria checklist:

• No keyboard flash or jump on iOS Simulator
• No keyboard flash or jump on Android Emulator
• All form inputs visible when keyboard is open on login screen
• All form inputs visible when keyboard is open on sign-up screen
• Test passes in Expo Go without a custom dev client

Implement the following React Native chunk for your mobile app: Integrate @react-native-ai/apple (https://github.com/deveix/react-native-apple-llm) to add on-device Apple Foundation Model inference.

Goal: Enable private, offline AI text generation using Apple Intelligence (iOS 18.1+).

Files to create or modify:

• package.json — add @react-native-ai/apple
• ios/[AppName]/[AppName].entitlements — add Foundation Models entitlement
• src/components/AppleLLMSummarizer.tsx — new UI component
• app/(demo)/apple-llm.tsx — demo screen

Step-by-step implementation:

1. npm install @react-native-ai/apple && cd ios && pod install
2. Add to .entitlements:

<key>com.apple.developer.foundation-models.inference</key>
<true/>

3. Create AppleLLMSummarizer.tsx:

import { useAppleLLM } from '@react-native-ai/apple';

export function AppleLLMSummarizer({ text }: { text: string }) {
  const { generate, response, isLoading } = useAppleLLM();
  return (
    <View>
      <Button title="Summarize" onPress={() => generate(`Summarize: ${text}`)} />
      {isLoading && <ActivityIndicator />}
      {response && <Text>{response}</Text>}
    </View>
  );
}

4. Add the screen to the router and test on a physical iPhone in Airplane Mode

Acceptance criteria checklist:

• App builds and runs on iPhone with iOS 18.1+
• Model responds in Airplane Mode (no network)
• Response appears within 5 seconds
• Loading indicator shown while processing
• No crash on unsupported devices (< iOS 18.1)

Implement the following React Native chunk for your mobile app: Build a ChatGPT-style AI chat screen with Retrieval-Augmented Generation (RAG) (inspired by https://react.statuscode.com/issues/483).

Goal: Domain-specific AI chat that answers questions grounded in your own knowledge base, with streaming responses.

Files to create or modify:

• src/data/knowledgeBase.ts — 5–10 FAQ entries as strings
• src/utils/rag.ts — embed texts, cosine similarity search, retrieve top-K chunks
• src/components/ChatBubble.tsx — user and assistant message bubble
• app/(chat)/ai-chat.tsx — chat screen with FlatList

Step-by-step implementation:

1. Install dependencies: npm install openai
2. Create knowledgeBase.ts with 5–10 FAQ strings about your app domain
3. In rag.ts, implement:
   • embedText(text) → calls openai.embeddings.create
   • findRelevantChunks(query, k=3) → cosine similarity on cached embeddings
   • buildSystemPrompt(chunks) → injects chunks as context
4. In ai-chat.tsx:
   • Maintain messages: Message[] state
   • On send: call findRelevantChunks(userMessage) → build prompt → stream from OpenAI
   • Append streaming tokens to the last assistant bubble
5. In ChatBubble.tsx, style user (right, blue) and assistant (left, gray) bubbles

Acceptance criteria checklist:

• User message appears immediately in the chat list
• Streaming tokens appear progressively in the assistant bubble
• Answer references content from the knowledge base (not generic)
• App handles API errors gracefully (shows error message in bubble)
• FlatList scrolls to bottom on new message