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Android 16 Developer Preview 2: What’s New and What to Expect

Category App Development, Product engineering

Google has released the Second Developer Preview of Android 16, bringing new capabilities and refinements for developers to test. This preview focuses on enhanced performance, security, AI integration, and user experience improvements. As the stable release is expected in mid-2025, now is the perfect time for developers to explore these changes and optimize their apps accordingly.

1. Improved Background Task Management

Why it matters: Apps running in the background can drain battery and impact performance. Google is introducing stricter limits on background services to improve efficiency while ensuring essential tasks continue running smoothly.

  • New execution limits prevent unnecessary background activity, reducing power consumption.
  • Optimized JobScheduler and WorkManager APIs help developers manage background tasks efficiently.
  • Foreground Service Task Manager ensures only critical services stay active when needed.

What developers should do: Review background task usage in apps and migrate to JobScheduler or WorkManager to comply with the new system limits.

2. Enhanced Privacy and Security Features

Why it matters: With every Android update, Google tightens app permissions to ensure better user data protection. Android 16 introduces new security measures that give users more control over their data.

  • Stronger data-sharing policies: Apps now require explicit user consent before accessing sensitive data.
  • Temporary permissions: Users can grant apps temporary access to certain data types, which are automatically revoked after a set time.
  • Private Compute Core improvements: On-device AI processing is now more secure, preventing unauthorized data exposure.

What developers should do: Audit app permissions and modify them to comply with Android 16’s stricter data-sharing requirements.

3. UI & UX Upgrades

Why it matters: Android 16 brings visual and usability improvements that make interactions more intuitive and fluid.

  • Adaptive refresh rates: The system dynamically adjusts refresh rates to balance smoothness and battery efficiency.
  • Improved gesture navigation: Refinements make gestures more precise, reducing accidental swipes or unintended actions.
  • Dynamic color theming: Apps can now adapt UI colors based on content, creating a more personalized experience for users.

What developers should do: Test app interfaces on Android 16 preview and optimize UI.

4. AI-Powered Developer Tools

Why it matters: AI-powered development tools can significantly boost productivity by automating repetitive tasks and providing smarter code suggestions.

  • AI-assisted code completion speeds up development by predicting code structures.
  • Automated bug detection identifies potential issues before they cause crashes.
  • Context-aware documentation suggestions generate relevant information to assist developers while coding.

What developers should do: Start using AI-assisted features in Android Studio to speed up coding, debugging, and documentation.

5. Expanded App Archiving Features

Why it matters: Many users uninstall apps to free up storage, leading to lost engagement. Android 16 introduces advanced app archiving, allowing users to temporarily remove parts of an app without losing data.

  • Partial app removal lets users reclaim space without deleting essential app data.
  • One-click app restoration allows quick reactivation when needed.
  • Better retention rates help developers reduce app uninstall rates.

What developers should do: Integrate archiving support into apps, ensuring a seamless experience when users restore archived apps.

How Developers Should Prepare

With Android 16 Developer Preview 2 available, developers should begin optimizing their apps to ensure full compatibility.

Key Action Items:

  1. Test your app on Android 16 preview builds to identify any potential issues.
  2. Optimize background tasks to align with the new execution limits.
  3. Review security policies and update how your app handles permissions.
  4. Experiment with AI-powered tools in Android Studio for faster development.
  5. Implement app archiving support to improve user retention.

 

What’s Next?

The public beta of Android 16 is expected soon, meaning developers should start testing, refining, and preparing their apps now. The final stable release is anticipated in mid-2025, and being ahead of these changes will ensure a smoother transition for both developers and users.

Are you ready for Android 16?

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As the data landscape continues to evolve rapidly, businesses are compelled to stay abreast of emerging trends to maintain competitiveness. In the year 2024, two prominent trends are poised to redefine data analytics: the proliferation of Generative AI and the adoption of modern data contracts. These trends not only reshape how organizations utilize data but also underscore the importance of ethical considerations and robust governance in data management. This article explores these trends in-depth, providing insights into effective strategies for implementation and the implications for businesses navigating the data landscape.Trend #1: The Ascendancy of Generative AIGenerative AI, characterized by its ability to create new content autonomously, has gained significant traction across industries. The advent of large language models (LLMs) has propelled Generative AI into the mainstream, with tech giants like Microsoft, Google, and Meta integrating Generative AI capabilities into their products. As businesses increasingly rely on AI-driven insights, Generative AI is poised to become an indispensable tool for enhancing productivity and driving innovation.Strategy for Effective Implementation:To leverage Generative AI effectively, businesses must develop a comprehensive strategy tailored to their specific needs and objectives. This strategy should encompass several key components:Identifying suitable use cases:Organizations should identify areas where Generative AI can augment existing processes and generate tangible value. 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This includes addressing issues related to data privacy, algorithmic bias, and the potential societal impact of AI-driven decisions.Trend #2: Adoption of Modern Data ContractsModern data contracts have emerged as a solution to streamline data usage and sharing, effectively addressing the challenges associated with broken data integrations and communication gaps between application and analytics teams.Structured Data Interactions:Modern data contracts represent a paradigm shift in how organizations manage data interactions. Unlike traditional contracts, which are static and cumbersome to maintain, modern data contracts are dynamic agreements that evolve with changing data requirements and business needs.Integration into workflows:By integrating data contracts into existing workflows and development processes, organizations can ensure seamless data interactions across disparate systems and applications. 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By developing comprehensive strategies for AI adoption and data governance, organizations can harness the transformative power of Generative AI while ensuring ethical and responsible data usage. Likewise, embracing modern data contracts enables organizations to streamline data interactions, improve collaboration, and enhance data quality and consistency. By embracing these trends and implementing best practices, businesses can unlock new opportunities for growth and success in the digital age.

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This approach emphasizes the need for system components to work together harmoniously while assessing their performance individually. The integration of cloud-based environments has become crucial, as cloud computing is an integral part of modern technology infrastructure. Cloud-based environments provide a flexible and reliable platform that enables seamless integration and coordination of various components, ultimately leading to enhanced system performance. It is crucial to prioritize comprehensive performance testing, which involves evaluating individual component performance, managing loads, monitoring in real-time, and debugging, to ensure optimal system performance.Self Service:When adopting the aforementioned trends, it’s essential to consider practical implementation tips for successful outcomes. For instance, performance engineers can use AI-powered tools to analyze performance data more effectively, leading to more accurate and actionable insights. 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