Tag Archives: Android

Announcing Kotlin Multiplatform Shared Module Template

Posted by Ben Trengrove - Developer Relations Engineer, Matt Dyor - Product Manager

To empower Android developers, we’re excited to announce Android Studio’s new Kotlin Multiplatform (KMP) Shared Module Template. This template was specifically designed to allow developers to use a single codebase and apply business logic across platforms. More specifically, developers will be able to add shared modules to existing Android apps and share the business logic across their Android and iOS applications.

This makes it easier for Android developers to craft, maintain, and most importantly, own the business logic. The KMP Shared Module Template is available within Android Studio when you create a new module within a project.

a screen shot of the new module tab in Android Studio
Shared Module Templates are found under the New Module tab

A single code base for business logic

Most developers have grown accustomed to maintaining different code bases, platform to platform. In the past, whenever there’s an update to the business logic, it must be carefully updated in each codebase. But with the KMP Shared Module Template:

    • Developers can write once and publish the business logic to wherever they need it.
    • Engineering teams can do more faster.
    • User experiences are more consistent across the entire audience, regardless of platform or form factor.
    • Releases are better coordinated and launched with fewer errors.

Customers and developer teams who adopt KMP Shared Module Templates should expect to achieve greater ROI from mobile teams who can turn their attention towards delighting their users more and worrying about inconsistent code less.

KMP enthusiasm

The Android developer community remains very excited about KMP, especially after Google I/O 2024 where Google announced official support for shared logic across Android and iOS. We have seen continued momentum and enthusiasm from the community. For example, there are now over 1,500 KMP libraries listed on JetBrains' klibs.io.

Our customers are excited because KMP has made Android developers more productive. Consistently, Android developers have said that they want solutions that allow them to share code more easily and they want tools which boost productivity. This is why we recommend KMP; KMP simultaneously delivers a great experience for Android users while boosting ROI for the app makers. The KMP Shared Module Template is the latest step towards a developer ecosystem where user experience is consistent and applications are updated seamlessly.

Large scale KMP adoptions

This KMP Shared Module Template is new, but KMP more broadly is a maturing technology with several large-scale migrations underway. In fact, KMP has matured enough to support mission critical applications at Google. Google Docs, for example, is now running KMP in production on iOS with runtime performance on par or better than before. Beyond Google, Stone’s 130 mobile developers are sharing over 50% of their code, allowing existing mobile teams to ship features approximately 40% faster to both Android and iOS.

KMP was designed for Android development

As always, we've designed the Shared Module Template with the needs of Android developer teams in mind. Making the KMP Shared Module Template part of the native Android Studio experience allows developers to efficiently add a shared module to an existing Android application and immediately start building shared business logic that leverages several KMP-ready Jetpack libraries including Room, SQLite, and DataStore to name just a few.

Come check it out at KotlinConf

Releasing Android Studio’s KMP Shared Module Template marks a significant step toward empowering Android development teams to innovate faster, to efficiently manage business logic, and to build high-quality applications with greater confidence. It means that Android developers can be responsible for the code that drives the business logic for every app across Android and iOS. We’re excited to bring Shared Module Template to KotlinConf in Copenhagen, May 21 - 23.

KotlinConf 2025 Copenhagen Denmark, May 21 Workshops May 22-23 Conference

Get started with KMP Shared Module Template

To get started, you'll need the latest edition of Android Studio. In your Android project, the Shared Module Template is available within Android Studio when you create a new module. Click on “File” then “New” then “New Module” and finally “Kotlin Multiplatform Shared Module” and you are ready to add a KMP Shared Module to your Android app.

We appreciate any feedback on things you like or features you would like to see. If you find a bug, please report the issue. Remember to also follow us on X, LinkedIn, Blog, or YouTube for more Android development updates!

Peacock built adaptively on Android to deliver great experiences across screens

Posted by Sa-ryong Kang and Miguel Montemayor - Developer Relations Engineers

Peacock is NBCUniversal’s streaming service app available in the US, offering culture-defining entertainment including live sports, exclusive original content, TV shows, and blockbuster movies. The app continues to evolve, becoming more than just a platform to watch content, but a hub of entertainment.

Today’s users are consuming entertainment on an increasingly wider array of device sizes and types, and in particular are moving towards mobile devices. Peacock has adopted Jetpack Compose to help with its journey in adapting to more screens and meeting users where they are.

Disclaimer: Peacock is available in the US only. This video will only be viewable to US viewers.

Adapting to more flexible form factors

The Peacock development team is focused on bringing the best experience to users, no matter what device they’re using or when they want to consume content. With an emerging trend from app users to watch more on mobile devices and large screens like foldables, the Peacock app needs to be able to adapt to different screen sizes. As more devices are introduced, the team needed to explore new solutions that make the most out of each unique display permutation.

The goal was to have the Peacock app to adapt to these new displays while continually offering high-quality entertainment without interruptions, like the stream reloading or visual errors. While thinking ahead, they also wanted to prepare and build a solution that was ready for Android XR as the entertainment landscape is shifting towards including more immersive experiences.

quote card featuring a headshot of Diego Valente, Head of Mobile, Peacock & Global Streaming, reads 'Thinking adaptively isn't just about supporting tablets or large screens - it's about future proofing your app. Investing in adaptability helps you meet user's expectations of having seamless experiencers across all their devices and sets you up for what's next.'

Building a future-proof experience with Jetpack Compose

In order to build a scalable solution that would help the Peacock app continue to evolve, the app was migrated to Jetpack Compose, Android’s toolkit for building scalable UI. One of the essential tools they used was the WindowSizeClass API, which helps developers create and test UI layouts for different size ranges. This API then allows the app to seamlessly switch between pre-set layouts as it reaches established viewport breakpoints for different window sizes.

The API was used in conjunction with Kotlin Coroutines and Flows to keep the UI state responsive as the window size changed. To test their work and fine tune edge case devices, Peacock used the Android Studio emulator to simulate a wide range of Android-based devices.

Jetpack Compose allowed the team to build adaptively, so now the Peacock app responds to a wide variety of screens while offering a seamless experience to Android users. “The app feels more native, more fluid, and more intuitive across all form factors,” said Diego Valente, Head of Mobile, Peacock and Global Streaming. “That means users can start watching on a smaller screen and continue instantly on a larger one when they unfold the device—no reloads, no friction. It just works.”

Preparing for immersive entertainment experiences

In building adaptive apps on Android, John Jelley, Senior Vice President, Product & UX, Peacock and Global Streaming, says Peacock has also laid the groundwork to quickly adapt to the Android XR platform: “Android XR builds on the same large screen principles, our investment here naturally extends to those emerging experiences with less developmental work.”

The team is excited about the prospect of features unlocked by Android XR, like Multiview for sports and TV, which enables users to watch multiple games or camera angles at once. By tailoring spatial windows to the user’s environment, the app could offer new ways for users to interact with contextual metadata like sports stats or actor information—all without ever interrupting their experience.

Build adaptive apps

Learn how to unlock your app's full potential on phones, tablets, foldables, and beyond.

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


16 things to know for Android developers at Google I/O 2025

Posted by Matthew McCullough – VP of Product Management, Android Developer

Today at Google I/O, we announced the many ways we’re helping you build excellent, adaptive experiences, and helping you stay more productive through updates to our tooling that put AI at your fingertips and throughout your development lifecycle. Here’s a recap of 16 of our favorite announcements for Android developers; you can also see what was announced last week in The Android Show: I/O Edition. And stay tuned over the next two days as we dive into all of the topics in more detail!

Building AI into your Apps

1: Building intelligent apps with Generative AI

Generative AI enhances apps' experience by making them intelligent, personalized and agentic. This year, we announced new ML Kit GenAI APIs using Gemini Nano for common on-device tasks like summarization, proofreading, rewrite, and image description. We also provided capabilities for developers to harness more powerful models such as Gemini Pro, Gemini Flash, and Imagen via Firebase AI Logic for more complex use cases like image generation and processing extensive data across modalities, including bringing AI to life in Android XR, and a new AI sample app, Androidify, that showcases how these APIs can transform your selfies into unique Android robots! To start building intelligent experiences by leveraging these new capabilities, explore the developer documentation, sample apps, and watch the overview session to choose the right solution for your app.

New experiences across devices

2: One app, every screen: think adaptive and unlock 500 million screens

Mobile Android apps form the foundation across phones, foldables, tablets and ChromeOS, and this year we’re helping you bring them to cars and XR and expanding usages with desktop windowing and connected displays. This expansion means tapping into an ecosystem of 500 million devices – a significant opportunity to engage more users when you think adaptive, building a single mobile app that works across form factors. Resources, including Compose Layouts library and Jetpack Navigation updates, help make building these dynamic experiences easier than before. You can see how Peacock, NBCUniveral’s streaming service (available in the US) is building adaptively to meet users where they are.

Disclaimer: Peacock is available in the US only. This video will only be viewable to US viewers.

3: Material 3 Expressive: design for intuition and emotion

The new Material 3 Expressive update provides tools to enhance your product's appeal by harnessing emotional UX, making it more engaging, intuitive, and desirable for users. Check out the I/O talk to learn more about expressive design and how it inspires emotion, clearly guides users toward their goals, and offers a flexible and personalized experience.

moving image of Material 3 Expressive demo

4: Smarter widgets, engaging live updates

Measure the return on investment of your widgets (available soon) and easily create personalized widget previews with Glance 1.2. Promoted Live Updates notify users of important ongoing notifications and come with a new Progress Style standardized template.

moving image of Material 3 Expressive demo

5: Enhanced Camera & Media: low light boost and battery savings

This year's I/O introduces several camera and media enhancements. These include a software low light boost for improved photography in dim lighting and native PCM offload, allowing the DSP to handle more audio playback processing, thus conserving user battery. Explore our detailed sessions on built-in effects within CameraX and Media3 for further information.

6: Build next-gen app experiences for Cars

We're launching expanded opportunities for developers to build in-car experiences, including new Gemini integrations, support for more app categories like Games and Video, and enhanced capabilities for media and communication apps via the Car App Library and new APIs. Alongside updated car app quality tiers and simplified distribution, we'll soon be providing improved testing tools like Android Automotive OS on Pixel Tablet and Firebase Test Lab access to help you bring your innovative apps to cars. Learn more from our technical session and blog post on new in-car app experiences.

7: Build for Android XR's expanding ecosystem with Developer Preview 2 of the SDK

We announced Android XR in December, and today at Google I/O we shared a bunch of updates coming to the platform including Developer Preview 2 of the Android XR SDK plus an expanding ecosystem of devices: in addition to the first Android XR headset, Samsung’s Project Moohan, you’ll also see more devices including a new portable Android XR device from our partners at XREAL. There’s lots more to cover for Android XR: Watch the Compose and AI on Android XR session, and the Building differentiated apps for Android XR with 3D content session, and learn more about building for Android XR.

product image of XREAL’s Project Aura against a nebulous black background
XREAL’s Project Aura

8: Express yourself on Wear OS: meet Material Expressive on Wear OS 6

This year we are launching Wear OS 6: the most powerful and expressive version of Wear OS. Wear OS 6 features Material 3 Expressive, a new UI design with personalized visuals and motion for user creativity, coming to Wear, Android, and Google apps later this year. Developers gain access to Material 3 Expressive on Wear OS by utilizing new Jetpack libraries: Wear Compose Material 3, which provides components for apps and Wear ProtoLayout Material 3 which provides components and layouts for tiles. Get started with Material 3 libraries and other updates on Wear.

moving image displays examples of Material 3 Expressive on Wear OS experiences
Some examples of Material 3 Expressive on Wear OS experiences

9: Engage users on Google TV with excellent TV apps

You can leverage more resources within Compose's core and Material libraries with the stable release of Compose for TV, empowering you to build excellent adaptive UIs across your apps. We're also thrilled to share exciting platform updates and developer tools designed to boost app engagement, including bringing Gemini capabilities to TV in the fall, opening enrollment for our Video Discovery API, and more.

Developer productivity

10: Build beautiful apps faster with Jetpack Compose

Compose is our big bet for UI development. The latest stable BOM release provides the features, performance, stability, and libraries that you need to build beautiful adaptive apps faster, so you can focus on what makes your app valuable to users.

moving image of compose adaptive layouts updates in the Google Play app
Compose Adaptive Layouts Updates in the Google Play app

11: Kotlin Multiplatform: new Shared Template lets you build across platforms, easily

Kotlin Multiplatform (KMP) enables teams to reach new audiences across Android and iOS with less development time. We’ve released a new Android Studio KMP shared module template, updated Jetpack libraries and new codelabs (Getting started with Kotlin Multiplatform and Migrating your Room database to KMP) to help developers who are looking to get started with KMP. Shared module templates make it easier for developers to craft, maintain, and own the business logic. Read more on what's new in Android's Kotlin Multiplatform.

12: Gemini in Android Studio: AI Agents to help you work

Gemini in Android Studio is the AI-powered coding companion that makes Android developers more productive at every stage of the dev lifecycle. In March, we introduced Image to Code to bridge the gap between UX teams and software engineers by intelligently converting design mockups into working Compose UI code. And today, we previewed new agentic AI experiences, Journeys for Android Studio and Version Upgrade Agent. These innovations make it easier to build and test code. You can read more about these updates in What’s new in Android development tools.

13: Android Studio: smarter with Gemini

In this latest release, we're empowering devs with AI-driven tools like Gemini in Android Studio, streamlining UI creation, making testing easier, and ensuring apps are future-proofed in our ever-evolving Android ecosystem. These innovations accelerate development cycles, improve app quality, and help you stay ahead in a dynamic mobile landscape. To take advantage, upgrade to the latest Studio release. You can read more about these innovations in What’s new in Android development tools.

moving image of Gemini in Android Studio Agentic Experiences including Journeys and Version Upgrade

And the latest on driving business growth

14: What’s new in Google Play

Get ready for exciting updates from Play designed to boost your discovery, engagement and revenue! Learn how we’re continuing to become a content-rich destination with enhanced personalization and fresh ways to showcase your apps and content. Plus, explore powerful new subscription features designed to streamline checkout and reduce churn. Read I/O 2025: What's new in Google Play to learn more.

a moving image of three mobile devices displaying how content is displayed on the Play Store

15: Start migrating to Play Games Services v2 today

Play Games Services (PGS) connects over 2 billion gamer profiles on Play, powering cross-device gameplay, personalized gaming content and rewards for your players throughout the gaming journey. We are moving PGS v1 features to v2 with more advanced features and an easier integration path. Learn more about the migration timeline and new features.

16: And of course, Android 16

We unpacked some of the latest features coming to users in Android 16, which we’ve been previewing with you for the last few months. If you haven’t already, make sure to test your apps with the latest Beta of Android 16. Android 16 includes Live Updates, professional media and camera features, desktop windowing and connected displays, major accessibility enhancements and much more.

Check out all of the Android and Play content at Google I/O

This was just a preview of some of the cool updates for Android developers at Google I/O, but stay tuned to Google I/O over the next two days as we dive into a range of Android developer topics in more detail. You can check out the What’s New in Android and the full Android track of sessions, and whether you’re joining in person or around the world, we can’t wait to engage with you!

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


Android’s Kotlin Multiplatform announcements at Google I/O and KotlinConf 25

Posted by Ben Trengrove - Developer Relations Engineer, Matt Dyor - Product Manager

Google I/O and KotlinConf 2025 bring a series of announcements on Android’s Kotlin and Kotlin Multiplatform efforts. Here’s what to watch out for:

Announcements from Google I/O 2025

Jetpack libraries

Our focus for Jetpack libraries and KMP is on sharing business logic across Android and iOS, but we have begun experimenting with web/WASM support.

We are adding KMP support to Jetpack libraries. Last year we started with Room, DataStore and Collection, which are now available in a stable release and recently we have added ViewModel, SavedState and Paging. The levels of support that our Jetpack libraries guarantee for each platform have been categorised into three tiers, with the top tier being for Android, iOS and JVM.

Tool improvements

We're developing new tools to help easily start using KMP in your app. With the KMP new module template in Android Studio Meerkat, you can add a new module to an existing app and share code to iOS and other supported KMP platforms.

In addition to KMP enhancements, Android Studio now supports Kotlin K2 mode for Android specific features requiring language support such as Live Edit, Compose Preview and many more.

How Google is using KMP

Last year, Google Workspace began experimenting with KMP, and this is now running in production in the Google Docs app on iOS. The app’s runtime performance is on par or better than before1.

It’s been helpful to have an app at this scale test KMP out, because we’re able to identify issues and fix issues that benefit the KMP developer community.

For example, we've upgraded the Kotlin Native compiler to LLVM 16 and contributed a more efficient garbage collector and string implementation. We're also bringing the static analysis power of Android Lint to Kotlin targets and ensuring a unified Gradle DSL for both AGP and KGP to improve the plugin management experience.

New guidance

We're providing comprehensive guidance in the form of two new codelabs: Getting started with Kotlin Multiplatform and Migrating your Room database to KMP, to help you get from standalone Android and iOS apps to shared business logic.

Kotlin Improvements

Kotlin Symbol Processing (KSP2) is stable to better support new Kotlin language features and deliver better performance. It is easier to integrate with build systems, is thread-safe, and has better support for debugging annotation processors. In contrast to KSP1, KSP2 has much better compatibility across different Kotlin versions. The rewritten command line interface also becomes significantly easier to use as it is now a standalone program instead of a compiler plugin.

KotlinConf 2025

Google team members are presenting a number of talks at KotlinConf spanning multiple topics:

Talks

    • Deploying KMP at Google Workspace by Jason Parachoniak, Troels Lund, and Johan Bay from the Workspace team discusses the challenges and solutions, including bugs and performance optimizations, encountered when launching Kotlin Multiplatform at Google Workspace, offering comparisons to ObjectiveC and a Q&A. (Technical Session)

    • The Life and Death of a Kotlin/Native Object by Troels Lund offers a high-level explanation of the Kotlin/Native runtime's inner workings concerning object instantiation, memory management, and disposal. (Technical Session)

    • APIs: How Hard Can They Be? presented by Aurimas Liutikas and Alan Viverette from the Jetpack team delves into the lifecycle of API design, review processes, and evolution within AndroidX libraries, particularly considering KMP and related tools. (Technical Session)

    • Project Sparkles: How Compose for Desktop is changing Android Studio and IntelliJ with Chris Sinco and Sebastiano Poggi from the Android Studio team introduces the initiative ('Project Sparkles') aiming to modernize Android Studio and IntelliJ UIs using Compose for Desktop, covering goals, examples, and collaborations. (Technical Session)

    • JSpecify: Java Nullness Annotations and Kotlin presented by David Baker explains the significance and workings of JSpecify's standard Java nullness annotations for enhancing Kotlin's interoperability with Java libraries. (Lightning Session)

    • Lessons learned decoupling Architecture Components from platform specific code features Jeremy Woods and Marcello Galhardo from the Jetpack team sharing insights from the Android team on decoupling core components like SavedState and System Back from platform specifics to create common APIs. (Technical Session)

    • KotlinConf’s Closing Panel, a regular staple of the conference, returns, featuring Jeffrey van Gogh as Google’s representative on the panel. (Panel)

Live Workshops

If you are at KotlinConf in person, we will have guided live workshops with our new codelabs from above.


    • The codelab Migrating Room to Room KMP, also led by Matt Dyor, and Dustin Lam, Tomáš Mlynarič, demonstrates the process of migrating an existing Room database implementation to Room KMP within a shared module.

We love engaging with the Kotlin community. If you are attending KotlinConf, we hope you get a chance to check out our booth, with opportunities to chat with our engineers, get your questions answered, and learn more about how you can leverage Kotlin and KMP.

Learn more about Kotlin Multiplatform

To learn more about KMP and start sharing your business logic across platforms, check out our documentation and the sample.

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


1 Google Internal Data, March 2025

What’s new in Wear OS 6

Posted by Chiara Chiappini – Developer Relations Engineer

This year, we’re excited to introduce Wear OS 6: the most power-efficient and expressive version of Wear OS yet.

Wear OS 6 introduces the new design system we call Material 3 Expressive. It features a major refresh with visual and motion components designed to give users an experience with more personalization. The new design offers a great level of expression to meet user demand for experiences that are modern, relevant, and distinct. Material 3 Expressive is coming to Wear OS, Android, and all your favorite Google apps on these devices later this year.

The good news is that you don’t need to compromise battery for beauty: thanks to Wear OS platform optimizations, watches updating from Wear OS 5 to Wear OS 6 can see up to 10% improvement in battery life.1

Wear OS 6 developer preview

Today we’re releasing the Developer Preview of Wear OS 6, the next version of Google’s smartwatch platform, based on Android 16.

Wear OS 6 brings a number of developer-facing changes, such as refining the always-on display experience. Check out what’s changed and try the new Wear OS 6 emulator to test your app for compatibility with the new platform version.

Material 3 Expressive on Wear OS

moving image displays examples of Material 3 Expressive on Wear OS experiences
Some examples of Material 3 Expressive on Wear OS experiences

Material 3 Expressive for the watch is fully optimized for the round display. We recommend developers embrace the new design system in their apps and tiles. To help you adopt Material 3 Expressive in your app, we have begun releasing new design guidance for Wear OS, along with corresponding Figma design kits.

As a developer, you can get access the Material 3 Expressive on Wear OS using new Jetpack libraries:

These two libraries provide implementations for the components catalog that adheres to the Material 3 Expressive design language.

Make it personal with richer color schemes using themes

moving image showing how dynamic color theme updates colors of apps and Tiles
Dynamic color theme updates colors of apps and Tiles

The Wear Compose Material 3 and Wear Protolayout Material 3 libraries provide updated and extended color schemes, typography, and shapes to bring both depth and variety to your designs. Additionally, your tiles now align with the system font by default (on Wear OS 6+ devices), offering a more cohesive experience on the watch.

Both libraries introduce dynamic color theming, which automatically generates a color theme for your app or tile to match the colors of the watch face of Pixel watches.

Make it more glanceable with new tile components

Tiles now support a new framework and a set of components that embrace the watch's circular form factor. These components make tiles more consistent and glanceable, so users can more easily take swift action on the information included in them.

We’ve introduced a 3-slot tile layout to improve visual consistency in the Tiles carousel. This layout includes a title slot, a main content slot, and a bottom slot, designed to work across a range of different screen sizes:

moving image showing some examples of Tiles with the 3-slot tile layout
Some examples of Tiles with the 3-slot tile layout.

Highlight user actions and key information with components optimized for round screen

The new Wear OS Material 3 components automatically adapt to larger screen sizes, building on the Large Display support added as part of Wear OS 5. Additionally, components such as Buttons and Lists support shape morphing on apps.

The following sections highlight some of the most exciting changes to these components.

Embrace the round screen with the Edge Hugging Button

We introduced a new EdgeButton for apps and tiles with an iconic design pattern that maximizes the space within the circular form factor, hugs the edge of the screen, and comes in 4 standard sizes.

moving image of a sreenshot representing an EdgeButton in a scrollable screen.
Screenshot representing an EdgeButton in a scrollable screen.

Fluid navigation through lists using new indicators

The new TransformingLazyColumn from the Foundation library makes expressive motion easy with motion that fluidly traces the edges of the display. Developers can customize the collapsing behavior of the list when scrolling to the top, bottom and both sides of the screen. For example, components like Cards can scale down as they are closer to the top of the screen.

moving image showing a TransformingLazyColumn with content that collapses and changes in size when approaching the edge of the screens.
.
TransformingLazyColumn allows content to collapse and change in size when approaching the edge of the screens

Material 3 Expressive also includes a ScrollIndicator that features a new visual and motion design to make it easier for users to visualize their progress through a list. The ScrollIndicator is displayed by default when you use a TransformingLazyColumn and ScreenScaffold.

moving image showing side by side examples of ScrollIndicator in action
ScrollIndicator

Lastly, you can now use segments with the new ProgressIndicator, which is now available as a full-screen component for apps and as a small-size component for both apps and tiles.

moving image  showing a full-screen ProgressIndicator
Example of a full-screen ProgressIndicator

To learn more about the new features and see the full list of updates, see the release notes of the latest beta release of the Wear Compose and Wear Protolayout libraries. Check out the migration guidance for apps and tiles on how to upgrade your existing apps, or try one of our codelabs if you want to start developing using Material 3 Expressive design.

Watch Faces

With Wear OS 6 we are launching updates for watch face developers:

    • New options for customizing the appearance of your watch face using version 4 of Watch Face Format, such as animated state transitions from ambient to interactive and photo watch faces.
    • A new API for building watch face marketplaces.

Learn more about what's new in Watch Face updates.

Look for more information about the general availability of Wear OS 6 later this year.

Library updates

ProtoLayout

Since our last major release, we've improved capabilities and the developer experience of the Tiles and ProtoLayout libraries to address feedback we received from developers. Some of these enhancements include:

The example below shows how to display a layout with a text on a Tile using new enhancements:

// returns a LayoutElement for use in onTileRequest()
materialScope(context, requestParams.deviceConfiguration) {
    primaryLayout(
        mainSlot = {
            text(
                text = "Hello, World!".layoutString,
                typography = BODY_LARGE,
            )
        }
    )
}

For more information, see the migration instructions.

Credential Manager for Wear OS

The CredentialManager API is now available on Wear OS, starting with Google Pixel Watch devices running Wear OS 5.1. It introduces passkeys to Wear OS with a platform-standard authentication UI that is consistent with the experience on mobile.

The Credential Manager Jetpack library provides developers with a unified API that simplifies and centralizes their authentication implementation. Developers with an existing implementation on another form factor can use the same CredentialManager code, and most of the same supporting code to fulfill their Wear OS authentication workflow.

Credential Manager provides integration points for passkeys, passwords, and Sign in With Google, while also allowing you to keep your other authentication solutions as backups.

Users will benefit from a consistent, platform-standard authentication UI; the introduction of passkeys and other passwordless authentication methods, and the ability to authenticate without their phone nearby.

Check out the Authentication on Wear OS guidance to learn more.

Richer Wear Media Controls

New media controls for a Podcast
New media controls for a Podcast

Devices that run Wear OS 5.1 or later support enhanced media controls. Users who listen to media content on phones and watches can now benefit from the following new media control features on their watch:

    • They can fast-forward and rewind while listening to podcasts.
    • They can access the playlist and controls such as shuffle, like, and repeat through a new menu.

Developers with an existing implementation of action buttons and playlist can benefit from this feature without additional effort. Check out how users will get more controls from your media app on a Google Pixel Watch device.

Start building for Wear OS 6 now

With these updates, there’s never been a better time to develop an app on Wear OS. These technical resources are a great place to learn more how to get started:

Earlier this year, we expanded our smartwatch offerings with Galaxy Watch for Kids, a unique, phone-free experience designed specifically for children. This launch gives families a new way to stay connected, allowing children to explore Wear OS independently with a dedicated smartwatch. Consult our developer guidance to create a Wear OS app for kids.

We’re looking forward to seeing the experiences that you build on Wear OS!

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


1 Actual battery performance varies.

Androidify: Building powerful AI-driven experiences with Jetpack Compose, Gemini and CameraX

Posted by Rebecca Franks – Developer Relations Engineer

The Android bot is a beloved mascot for Android users and developers, with previous versions of the bot builder being very popular - we decided that this year we’d rebuild the bot maker from the ground up, using the latest technology backed by Gemini. Today we are releasing a new open source app, Androidify, for learning how to build powerful AI driven experiences on Android using the latest technologies such as Jetpack Compose, Gemini through Firebase, CameraX, and Navigation 3.

a moving image of various droid bots dancing individually

Androidify app demo

Here’s an example of the app running on the device, showcasing converting a photo to an Android bot that represents my likeness:

moving image showing the conversion of an image of a woman in a pink dress holding na umbrella into a 3D image of a droid bot wearing a pink dress holding an umbrella

Under the hood

The app combines a variety of different Google technologies, such as:

    • Gemini API - through Firebase AI Logic SDK, for accessing the underlying Imagen and Gemini models.
    • Jetpack Compose - for building the UI with delightful animations and making the app adapt to different screen sizes.
    • Navigation 3 - the latest navigation library for building up Navigation graphs with Compose.
    • CameraX Compose and Media3 Compose - for building up a custom camera with custom UI controls (rear camera support, zoom support, tap-to-focus) and playing the promotional video.

This sample app is currently using a standard Imagen model, but we've been working on a fine-tuned model that's trained specifically on all of the pieces that make the Android bot cute and fun; we'll share that version later this year. In the meantime, don't be surprised if the sample app puts out some interesting looking examples!

How does the Androidify app work?

The app leverages our best practices for Architecture, Testing, and UI to showcase a real world, modern AI application on device.

Flow chart describing Androidify app flow
Androidify app flow chart detailing how the app works with AI

AI in Androidify with Gemini and ML Kit

The Androidify app uses the Gemini models in a multitude of ways to enrich the app experience, all powered by the Firebase AI Logic SDK. The app uses Gemini 2.5 Flash and Imagen 3 under the hood:

    • Image validation: We ensure that the captured image contains sufficient information, such as a clearly focused person, and assessing for safety. This feature uses the multi-modal capabilities of Gemini API, by giving it a prompt and image at the same time:

val response = generativeModel.generateContent(
   content {
       text(prompt)
       image(image)
   },
)

    • Text prompt validation: If the user opts for text input instead of image, we use Gemini 2.5 Flash to ensure the text contains a sufficiently descriptive prompt to generate a bot.

    • Image captioning: Once we’re sure the image has enough information, we use Gemini 2.5 Flash to perform image captioning., We ask Gemini to be as descriptive as possible,focusing on the clothing and its colors.

    • “Help me write” feature: Similar to an “I’m feeling lucky” type feature, “Help me write” uses Gemini 2.5 Flash to create a random description of the clothing and hairstyle of a bot.

    • Image generation from the generated prompt: As the final step, Imagen generates the image, providing the prompt and the selected skin tone of the bot.

The app also uses the ML Kit pose detection to detect a person in the viewfinder and enable the capture button when a person is detected, as well as adding fun indicators around the content to indicate detection.

Explore more detailed information about AI usage in Androidify.

Jetpack Compose

The user interface of Androidify is built using Jetpack Compose, the modern UI toolkit that simplifies and accelerates UI development on Android.

Delightful details with the UI

The app uses Material 3 Expressive, the latest alpha release that makes your apps more premium, desirable, and engaging. It provides delightful bits of UI out-of-the-box, like new shapes, componentry, and using the MotionScheme variables wherever a motion spec is needed.

MaterialShapes are used in various locations. These are a preset list of shapes that allow for easy morphing between each other—for example, the cute cookie shape for the camera capture button:


Androidify app UI showing camera button
Camera button with a MaterialShapes.Cookie9Sided shape

Beyond using the standard Material components, Androidify also features custom composables and delightful transitions tailored to the specific needs of the app:

    • There are plenty of shared element transitions across the app—for example, a morphing shape shared element transition is performed between the “take a photo” button and the camera surface.

      moving example of expressive button shapes in slow motion

    • Custom enter transitions for the ResultsScreen with the usage of marquee modifiers.

      animated marquee example

    • Fun color splash animation as a transition between screens.

      moving image of a blue color splash transition between Androidify demo screens

    • Animating gradient buttons for the AI-powered actions.

      animated gradient button for AI powered actions example

To learn more about the unique details of the UI, read Androidify: Building delightful UIs with Compose

Adapting to different devices

Androidify is designed to look great and function seamlessly across candy bar phones, foldables, and tablets. The general goal of developing adaptive apps is to avoid reimplementing the same app multiple times on each form factor by extracting out reusable composables, and leveraging APIs like WindowSizeClass to determine what kind of layout to display.

a collage of different adaptive layouts for the Androidify app across small and large screens
Various adaptive layouts in the app

For Androidify, we only needed to leverage the width window size class. Combining this with different layout mechanisms, we were able to reuse or extend the composables to cater to the multitude of different device sizes and capabilities.

    • Responsive layouts: The CreationScreen demonstrates adaptive design. It uses helper functions like isAtLeastMedium() to detect window size categories and adjust its layout accordingly. On larger windows, the image/prompt area and color picker might sit side-by-side in a Row, while on smaller windows, the color picker is accessed via a ModalBottomSheet. This pattern, called “supporting pane”, highlights the supporting dependencies between the main content and the color picker.

    • Foldable support: The app actively checks for foldable device features. The camera screen uses WindowInfoTracker to get FoldingFeature information to adapt to different features by optimizing the layout for tabletop posture.

    • Rear display: Support for devices with multiple displays is included via the RearCameraUseCase, allowing for the device camera preview to be shown on the external screen when the device is unfolded (so the main content is usually displayed on the internal screen).

Using window size classes, coupled with creating a custom @LargeScreensPreview annotation, helps achieve unique and useful UIs across the spectrum of device sizes and window sizes.

CameraX and Media3 Compose

To allow users to base their bots on photos, Androidify integrates CameraX, the Jetpack library that makes camera app development easier.

The app uses a custom CameraLayout composable that supports the layout of the typical composables that a camera preview screen would include— for example, zoom buttons, a capture button, and a flip camera button. This layout adapts to different device sizes and more advanced use cases, like the tabletop mode and rear-camera display. For the actual rendering of the camera preview, it uses the new CameraXViewfinder that is part of the camerax-compose artifact.

CameraLayout in Compose
CameraLayout composable that takes care of different device configurations, such as table top mode

CameraLayout in Compose
CameraLayout composable that takes care of different device configurations, such as table top mode

The app also integrates with Media3 APIs to load an instructional video for showing how to get the best bot from a prompt or image. Using the new media3-ui-compose artifact, we can easily add a VideoPlayer into the app:

@Composable
private fun VideoPlayer(modifier: Modifier = Modifier) {
    val context = LocalContext.current
    var player by remember { mutableStateOf<Player?>(null) }
    LifecycleStartEffect(Unit) {
        player = ExoPlayer.Builder(context).build().apply {
            setMediaItem(MediaItem.fromUri(Constants.PROMO_VIDEO))
            repeatMode = Player.REPEAT_MODE_ONE
            prepare()
        }
        onStopOrDispose {
            player?.release()
            player = null
        }
    }
    Box(
        modifier
            .background(MaterialTheme.colorScheme.surfaceContainerLowest),
    ) {
        player?.let { currentPlayer ->
            PlayerSurface(currentPlayer, surfaceType = SURFACE_TYPE_TEXTURE_VIEW)
        }
    }
}

Using the new onLayoutRectChanged modifier, we also listen for whether the composable is completely visible or not, and play or pause the video based on this information:

var videoFullyOnScreen by remember { mutableStateOf(false) }     

LaunchedEffect(videoFullyOnScreen) {
     if (videoFullyOnScreen) currentPlayer.play() else currentPlayer.pause()
} 

// We add this onto the player composable to determine if the video composable is visible, and mutate the videoFullyOnScreen variable, that then toggles the player state. 
Modifier.onVisibilityChanged(
                containerWidth = LocalView.current.width,
                containerHeight = LocalView.current.height,
) { fullyVisible -> videoFullyOnScreen = fullyVisible }

// A simple version of visibility changed detection
fun Modifier.onVisibilityChanged(
    containerWidth: Int,
    containerHeight: Int,
    onChanged: (visible: Boolean) -> Unit,
) = this then Modifier.onLayoutRectChanged(100, 0) { layoutBounds ->
    onChanged(
        layoutBounds.boundsInRoot.top > 0 &&
            layoutBounds.boundsInRoot.bottom < containerHeight &&
            layoutBounds.boundsInRoot.left > 0 &&
            layoutBounds.boundsInRoot.right < containerWidth,
    )
}

Additionally, using rememberPlayPauseButtonState, we add on a layer on top of the player to offer a play/pause button on the video itself:

val playPauseButtonState = rememberPlayPauseButtonState(currentPlayer)
            OutlinedIconButton(
                onClick = playPauseButtonState::onClick,
                enabled = playPauseButtonState.isEnabled,
            ) {
                val icon =
                    if (playPauseButtonState.showPlay) R.drawable.play else R.drawable.pause
                val contentDescription =
                    if (playPauseButtonState.showPlay) R.string.play else R.string.pause
                Icon(
                    painterResource(icon),
                    stringResource(contentDescription),
                )
            }

Check out the code for more details on how CameraX and Media3 were used in Androidify.

Navigation 3

Screen transitions are handled using the new Jetpack Navigation 3 library androidx.navigation3. The MainNavigation composable defines the different destinations (Home, Camera, Creation, About) and displays the content associated with each destination using NavDisplay. You get full control over your back stack, and navigating to and from destinations is as simple as adding and removing items from a list.

@Composable
fun MainNavigation() {
   val backStack = rememberMutableStateListOf<NavigationRoute>(Home)
   NavDisplay(
       backStack = backStack,
       onBack = { backStack.removeLastOrNull() },
       entryProvider = entryProvider {
           entry<Home> { entry ->
               HomeScreen(
                   onAboutClicked = {
                       backStack.add(About)
                   },
               )
           }
           entry<Camera> {
               CameraPreviewScreen(
                   onImageCaptured = { uri ->
                       backStack.add(Create(uri.toString()))
                   },
               )
           }
           // etc
       },
   )
}

Notably, Navigation 3 exposes a new composition local, LocalNavAnimatedContentScope, to easily integrate your shared element transitions without needing to keep track of the scope yourself. By default, Navigation 3 also integrates with predictive back, providing delightful back experiences when navigating between screens, as seen in this prior shared element transition:

CameraLayout in Compose

Learn more about Jetpack Navigation 3, currently in alpha.

Learn more

By combining the declarative power of Jetpack Compose, the camera capabilities of CameraX, the intelligent features of Gemini, and thoughtful adaptive design, Androidify is a personalized avatar creation experience that feels right at home on any Android device. You can find the full code sample at github.com/android/androidify where you can see the app in action and be inspired to build your own AI-powered app experiences.

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


Engage users on Google TV with excellent TV apps

Posted by Shobana Radhakrishnan - Senior Director of Engineering, Google TV, and Paul Lammertsma - Developer Relations Engineer, Android

Over the past year, Google TV and Android TV achieved over 270 million monthly active devices, establishing one of the largest smart TV OS footprints. Building on this momentum, we are excited to share new platform features and developer tools designed to help you increase app engagement with our expanding user base.

Google TV with Gemini capabilities

Earlier this year, we announced that we’ll bring Gemini capabilities to Google TV, so users can speak more naturally and conversationally to find what to watch and get answers to complex questions.

A user pulls up Gemini on a TV asking for kid-friendly movie recommendations similar to Jurassic Park. Gemini responds with several movie recommendations

After each movie or show search, our new voice assistant will suggest relevant content from your apps, significantly increasing the discoverability of your content.

A user pulls up Gemini on a TV asking for help explaining the solar system to a first grader. Gemini responds with YouTube videos to help explain the solar system

Plus, users can easily ask questions about topics they're curious about and receive insightful answers with supporting videos.

We’re so excited to bring this helpful and delightful experience to users this fall.

Video Discovery API

Today, we’ve also opened partner enrollment for our Video Discovery API.

Video Discovery optimizes Resumption, Entitlements, and Recommendations across all Google TV form factors to enhance the end-user experience and boost app engagement.

    • Resumption: Partners can now easily display a user's paused video within the 'Continue Watching' row from the home screen. This row is a prime location that drives 60% of all user interactions on Google TV.
    • Entitlements: Video Discovery streamlines entitlement management, which matches app content to user eligibility. Users appreciate this because they can enjoy personalized recommendations without needing to manually update all their subscription details. This allows partners to connect with users across multiple discovery points on Google TV.
    • Recommendations: Video Discovery even highlights personalized content recommendations based on content that users watched inside apps.

Partners can begin incorporating the Video Discovery API today, starting with resumption and entitlement integrations. Check out g.co/tv/vda to learn more.

Jetpack Compose for TV

Compose for TV 1.0 expands on the core and Material Compose libraries

Last year, we launched Compose for TV 1.0 beta, which lets you build beautiful, adaptive UIs across Android, including Android TV OS.

Now, Compose for TV 1.0 is stable, and expands on the core and Material Compose libraries. We’ve even seen how the latest release of Compose significantly improves app startup within our internal benchmarking mobile sample, with roughly a 20% improvement compared with the March 2024 release. Because Compose for TV builds upon these libraries, apps built with Compose for TV should also see better app startup times.

New to building with Compose, and not sure where to start? Our updated Jetcaster audio streaming app sample demonstrates how to use Compose across form factors. It includes a dedicated module for playing podcasts on TV by combining separate view models with shared business logic.

Focus Management Codelab

We understand that focus management can be challenging at times. That’s why we’ve published a codelab that reviews how to set initial focus, prepare for unexpected focus traversal, and efficiently restore focus.

Memory Optimization Guide

We’ve released a comprehensive guide on memory optimization, including memory targets for low RAM devices as well. Combined with Android Studio's powerful memory profiler, this helps you understand when your app exceeds those limits and why.

In-App Ratings and Reviews

Ratings and reviews entry point forJetStream sample app on TV

Moreover, app ratings and reviews are essential for developers, offering quantitative and qualitative feedback on user experiences. Now, we're extending the In-App Ratings and Reviews API to TV to allow developers to prompt users for ratings and reviews directly from Google TV. Check out our recent blog post detailing how to easily integrate the In-App Ratings and Reviews API.

Android 16 for TV

Android 16 for TV

We're excited to announce the upcoming release of Android 16 for TV. Developers can begin using the latest beta today. With Android 16, TV developers can access several great features:

    • Platform support for the Eclipsa Audio codec enables creators to use the IAMF spatial audio format. For ExoPlayer support that includes previous platform versions, see ExoPlayer's IAMF decoder module.
    • There are various improvements to media playback speed, consistency and efficiency, as well as HDMI-CEC reliability and performance optimizations for 64-bit kernels.
    • Additional APIs and user experiences from Android 16 are also available. We invite you to explore the complete list from the Android 16 for TV release notes.

What's next

We're incredibly excited to see how these announcements will optimize your development journey, and look forward to seeing the fantastic apps you'll launch on the platform!

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.

Androidify: How Androidify leverages Gemini, Firebase and ML Kit

Posted by Thomas Ezan – Developer Relations Engineer, Rebecca Franks – Developer Relations Engineer, and Avneet Singh – Product Manager

We’re bringing back Androidify later this year, this time powered by Google AI, so you can customize your very own Android bot and share your creativity with the world. Today, we’re releasing a new open source demo app for Androidify as a great example of how Google is using its Gemini AI models to enhance app experiences.

In this post, we'll dive into how the Androidify app uses Gemini models and Imagen via the Firebase AI Logic SDK, and we'll provide some insights learned along the way to help you incorporate Gemini and AI into your own projects. Read more about the Androidify demo app.

App flow

The overall app functions as follows, with various parts of it using Gemini and Firebase along the way:

flow chart demonstrating Androidify app flow

Gemini and image validation

To get started with Androidify, take a photo or choose an image on your device. The app needs to make sure that the image you upload is suitable for creating an avatar.

Gemini 2.5 Flash via Firebase helps with this by verifying that the image contains a person, that the person is in focus, and assessing image safety, including whether the image contains abusive content.

val jsonSchema = Schema.obj(
   properties = mapOf("success" to Schema.boolean(), "error" to Schema.string()),
   optionalProperties = listOf("error"),
   )
   
val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
   .generativeModel(
            modelName = "gemini-2.5-flash-preview-04-17",
   	     generationConfig = generationConfig {
                responseMimeType = "application/json"
                responseSchema = jsonSchema
            },
            safetySettings = listOf(
                SafetySetting(HarmCategory.HARASSMENT, HarmBlockThreshold.LOW_AND_ABOVE),
                SafetySetting(HarmCategory.HATE_SPEECH, HarmBlockThreshold.LOW_AND_ABOVE),
                SafetySetting(HarmCategory.SEXUALLY_EXPLICIT, HarmBlockThreshold.LOW_AND_ABOVE),
                SafetySetting(HarmCategory.DANGEROUS_CONTENT, HarmBlockThreshold.LOW_AND_ABOVE),
                SafetySetting(HarmCategory.CIVIC_INTEGRITY, HarmBlockThreshold.LOW_AND_ABOVE),
    	),
    )

 val response = generativeModel.generateContent(
            content {
                text("You are to analyze the provided image and determine if it is acceptable and appropriate based on specific criteria.... (more details see the full sample)")
                image(image)
            },
        )

val jsonResponse = Json.parseToJsonElement(response.text)
val isSuccess = jsonResponse.jsonObject["success"]?.jsonPrimitive?.booleanOrNull == true
val error = jsonResponse.jsonObject["error"]?.jsonPrimitive?.content

In the snippet above, we’re leveraging structured output capabilities of the model by defining the schema of the response. We’re passing a Schema object via the responseSchema param in the generationConfig.

We want to validate that the image has enough information to generate a nice Android avatar. So we ask the model to return a json object with success = true/false and an optional error message explaining why the image doesn't have enough information.

Structured output is a powerful feature enabling a smoother integration of LLMs to your app by controlling the format of their output, similar to an API response.

Image captioning with Gemini Flash

Once it's established that the image contains sufficient information to generate an Android avatar, it is captioned using Gemini 2.5 Flash with structured output.

val jsonSchema = Schema.obj(
            properties = mapOf(
                "success" to Schema.boolean(),
                "user_description" to Schema.string(),
            ),
            optionalProperties = listOf("user_description"),
        )
val generativeModel = createGenerativeTextModel(jsonSchema)

val prompt = "You are to create a VERY detailed description of the main person in the given image. This description will be translated into a prompt for a generative image model..."

val response = generativeModel.generateContent(
content { 
       	text(prompt) 
             	image(image) 
	})
        
val jsonResponse = Json.parseToJsonElement(response.text!!) 
val isSuccess = jsonResponse.jsonObject["success"]?.jsonPrimitive?.booleanOrNull == true

val userDescription = jsonResponse.jsonObject["user_description"]?.jsonPrimitive?.content

The other option in the app is to start with a text prompt. You can enter in details about your accessories, hairstyle, and clothing, and let Imagen be a bit more creative.

Android generation via Imagen

We’ll use this detailed description of your image to enrich the prompt used for image generation. We’ll add extra details around what we would like to generate and include the bot color selection as part of this too, including the skin tone selected by the user.

val imagenPrompt = "A 3D rendered cartoonish Android mascot in a photorealistic style, the pose is relaxed and straightforward, facing directly forward [...] The bot looks as follows $userDescription [...]"

We then call the Imagen model to create the bot. Using this new prompt, we create a model and call generateImages:

// we supply our own fine-tuned model here but you can use "imagen-3.0-generate-002" 
val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI()).imagenModel(
            "imagen-3.0-generate-002",
            safetySettings =
            ImagenSafetySettings(
                ImagenSafetyFilterLevel.BLOCK_LOW_AND_ABOVE,
                personFilterLevel = ImagenPersonFilterLevel.ALLOW_ALL,
            ),
)

val response = generativeModel.generateImages(imagenPrompt)

val image = response.images.first().asBitmap()

And that’s it! The Imagen model generates a bitmap that we can display on the user’s screen.

Finetuning the Imagen model

The Imagen 3 model was finetuned using Low-Rank Adaptation (LoRA). LoRA is a fine-tuning technique designed to reduce the computational burden of training large models. Instead of updating the entire model, LoRA adds smaller, trainable "adapters" that make small changes to the model's performance. We ran a fine tuning pipeline on the Imagen 3 model generally available with Android bot assets of different color combinations and different assets for enhanced cuteness and fun. We generated text captions for the training images and the image-text pairs were used to finetune the model effectively.

The current sample app uses a standard Imagen model, so the results may look a bit different from the visuals in this post. However, the app using the fine-tuned model and a custom version of Firebase AI Logic SDK was demoed at Google I/O. This app will be released later this year and we are also planning on adding support for fine-tuned models to Firebase AI Logic SDK later in the year.

moving image of Androidify app demo turning a selfie image of a bearded man wearing a black tshirt and sunglasses, with a blue back pack into a green 3D bearded droid wearing a black tshirt and sunglasses with a blue backpack
The original image... and Androidifi-ed image

ML Kit

The app also uses the ML Kit Pose Detection SDK to detect a person in the camera view, which triggers the capture button and adds visual indicators.

To do this, we add the SDK to the app, and use PoseDetection.getClient(). Then, using the poseDetector, we look at the detectedLandmarks that are in the streaming image coming from the Camera, and we set the _uiState.detectedPose to true if a nose and shoulders are visible:

private suspend fun runPoseDetection() {
    PoseDetection.getClient(
        PoseDetectorOptions.Builder()
            .setDetectorMode(PoseDetectorOptions.STREAM_MODE)
            .build(),
    ).use { poseDetector ->
        // Since image analysis is processed by ML Kit asynchronously in its own thread pool,
        // we can run this directly from the calling coroutine scope instead of pushing this
        // work to a background dispatcher.
        cameraImageAnalysisUseCase.analyze { imageProxy ->
            imageProxy.image?.let { image ->
                val poseDetected = poseDetector.detectPersonInFrame(image, imageProxy.imageInfo)
                _uiState.update { it.copy(detectedPose = poseDetected) }
            }
        }
    }
}

private suspend fun PoseDetector.detectPersonInFrame(
    image: Image,
    imageInfo: ImageInfo,
): Boolean {
    val results = process(InputImage.fromMediaImage(image, imageInfo.rotationDegrees)).await()
    val landmarkResults = results.allPoseLandmarks
    val detectedLandmarks = mutableListOf<Int>()
    for (landmark in landmarkResults) {
        if (landmark.inFrameLikelihood > 0.7) {
            detectedLandmarks.add(landmark.landmarkType)
        }
    }

    return detectedLandmarks.containsAll(
        listOf(PoseLandmark.NOSE, PoseLandmark.LEFT_SHOULDER, PoseLandmark.RIGHT_SHOULDER),
    )
}
moving image showing the camera shutter button activating when an orange droid figurine is held in the camera frame
The camera shutter button is activated when a person (or a bot!) enters the frame.

Get started with AI on Android

The Androidify app makes an extensive use of the Gemini 2.5 Flash to validate the image and generate a detailed description used to generate the image. It also leverages the specifically fine-tuned Imagen 3 model to generate images of Android bots. Gemini and Imagen models are easily integrated into the app via the Firebase AI Logic SDK. In addition, ML Kit Pose Detection SDK controls the capture button, enabling it only when a person is present in front of the camera.

To get started with AI on Android, go to the Gemini and Imagen documentation for Android.

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.

What’s New in Jetpack Compose

Posted by Nick Butcher – Product Manager

At Google I/O 2025, we announced a host of features, performance, stability, libraries, and tools updates for Jetpack Compose, our recommended Android UI toolkit. With Compose you can build excellent apps that work across devices. Compose has matured a lot since it was first announced (at Google I/O 2019!) and we're now seeing 60% of the top 1,000 apps in the Play Store such as MAX and Google Drive use and love it.

New Features

Since I/O last year, Compose Bill of Materials (BOM) version 2025.05.01 adds new features such as:

    • Autofill support that lets users automatically insert previously entered personal information into text fields.
    • Auto-sizing text to smoothly adapt text size to a parent container size.
    • Visibility tracking for when you need high-performance information on a composable's position in its root container, screen, or window.
    • Animate bounds modifier for beautiful automatic animations of a Composable's position and size within a LookaheadScope.
    • Accessibility checks in tests that let you build a more accessible app UI through automated a11y testing.

LookaheadScope {
    Box(
        Modifier
            .animateBounds(this@LookaheadScope)
            .width(if(inRow) 100.dp else 150.dp)
            .background(..)
            .border(..)
    )
}
moving image of animate bounds modifier in action

For more details on these features, read What’s new in the Jetpack Compose April ’25 release and check out these talks from Google I/O:

If you’re looking to try out new Compose functionality, the alpha BOM offers new features that we're working on including:

    • Pausable Composition (see below)
    • Updates to LazyLayout prefetch
    • Context Menus
    • New modifiers: onFirstVisible, onVisbilityChanged, contentType
    • New Lint checks for frequently changing values and elements that should be remembered in composition

Please try out the alpha features and provide feedback to help shape the future of Compose.

Material Expressive

At Google I/O, we unveiled Material Expressive, Material Design’s latest evolution that helps you make your products even more engaging and easier to use. It's a comprehensive addition of new components, styles, motion and customization options that help you to build beautiful rich UIs. The Material3 library in the latest alpha BOM contains many of the new expressive components for you to try out.

moving image of material expressive design example

Learn more to start building with Material Expressive.

Adaptive layouts library

Developing adaptive apps across form factors including phones, foldables, tablets, desktop, cars and Android XR is now easier with the latest enhancements to the Compose adaptive layouts library. The stable 1.1 release adds support for predictive back gestures for smoother transitions and pane expansion for more flexible two pane layouts on larger screens. Furthermore, the 1.2 (alpha) release adds more flexibility for how panes are displayed, adding strategies for reflowing and levitating.

moving image of compose adaptive layouts updates in the Google Play app
Compose Adaptive Layouts Updates in the Google Play app

Learn more about building adaptive android apps with Compose.

Performance

With each release of Jetpack Compose, we continue to prioritize performance improvements. The latest stable release includes significant rewrites and improvements to multiple sub-systems including semantics, focus and text optimizations. Best of all these are available to you simply by upgrading your Compose dependency; no code changes required.

bar chart of internal benchmarks for performance run on a Pixel 3a device from January to May 2023 measured by jank rate
Internal benchmark, run on a Pixel 3a

We continue to work on further performance improvements, notable changes in the latest alpha BOM include:

    • Pausable Composition allows compositions to be paused, and their work split up over several frames.
    • Background text prefetch enables text layout caches to be pre-warmed on a background thread, enabling faster text layout.
    • LazyLayout prefetch improvements enabling lazy layouts to be smarter about how much content to prefetch, taking advantage of pausable composition.

Together these improvements eliminate nearly all jank in an internal benchmark.

Stability

We've heard from you that upgrading your Compose dependency can be challenging, encountering bugs or behaviour changes that prevent you from staying on the latest version. We've invested significantly in improving the stability of Compose, working closely with the many Google app teams building with Compose to detect and prevent issues before they even make it to a release.

Google apps develop against and release with snapshot builds of Compose; as such, Compose is tested against the hundreds of thousands of Google app tests and any Compose issues are immediately actioned by our team. We have recently invested in increasing the cadence of updating these snapshots and now update them daily from Compose tip-of-tree, which means we’re receiving feedback faster, and are able to resolve issues long before they reach a public release of the library.

Jetpack Compose also relies on @Experimental annotations to mark APIs that are subject to change. We heard your feedback that some APIs have remained experimental for a long time, reducing your confidence in the stability of Compose. We have invested in stabilizing experimental APIs to provide you a more solid API surface, and reduced the number of experimental APIs by 32% in the last year.

We have also heard that it can be hard to debug Compose crashes when your own code does not appear in the stack trace. In the latest alpha BOM, we have added a new opt-in feature to provide more diagnostic information. Note that this does not currently work with minified builds and comes at a performance cost, so we recommend only using this feature in debug builds.

class App : Application() {
   override fun onCreate() {
        // Enable only for debug flavor to avoid perf impact in release
        Composer.setDiagnosticStackTraceEnabled(BuildConfig.DEBUG)
   }
}

Libraries

We know that to build great apps, you need Compose integration in the libraries that interact with your app's UI.

A core library that powers any Compose app is Navigation. You told us that you often encountered limitations when managing state hoisting and directly manipulating the back stack with the current Compose Navigation solution. We went back to the drawing-board and completely reimagined how a navigation library should integrate with the Compose mental model. We're excited to introduce Navigation 3, a new artifact designed to empower you with greater control and simplify complex navigation flows.

We're also investing in Compose support for CameraX and Media3, making it easier to integrate camera capture and video playback into your UI with Compose idiomatic components.

@Composable
private fun VideoPlayer(
    player: Player?, // from media3
    modifier: Modifier = Modifier
) {
    Box(modifier) {
        PlayerSurface(player) // from media3-ui-compose
        player?.let {
            // custom play-pause button UI
            val playPauseButtonState = rememberPlayPauseButtonState(it) // from media3-ui-compose
            MyPlayPauseButton(playPauseButtonState, Modifier.align(BottomEnd).padding(16.dp))
        }
    }
}
To learn more, see the media3 Compose documentation and the CameraX samples.

Tools

We continue to improve the Android Studio tools for creating Compose UIs. The latest Narwhal canary includes:

    • Resizable Previews instantly show you how your Compose UI adapts to different window sizes
    • Preview navigation improvements using clickable names and components
    • Studio Labs 🧪: Compose preview generation with Gemini quickly generate a preview
    • Studio Labs 🧪: Transform UI with Gemini change your UI with natural language, directly from preview.
    • Studio Labs 🧪: Image attachment in Gemini generate Compose code from images.

For more information read What's new in Android development tools.

moving image of resizable preview in Jetpack Compose
Resizable Preview

New Compose Lint checks

The Compose alpha BOM introduces two new annotations and associated lint checks to help you to write correct and performant Compose code. The @FrequentlyChangingValue annotation and FrequentlyChangedStateReadInComposition lint check warns in situations where function calls or property reads in composition might cause frequent recompositions. For example, frequent recompositions might happen when reading scroll position values or animating values. The @RememberInComposition annotation and RememberInCompositionDetector lint check warns in situations where constructors, functions, and property getters are called directly inside composition (e.g. the TextFieldState constructor) without being remembered.

Happy Composing

We continue to invest in providing the features, performance, stability, libraries and tools that you need to build excellent apps. We value your input so please share feedback on our latest updates or what you'd like to see next.

Explore this announcement and all Google I/O 2025 updates on io.google starting May 22.


On-device GenAI APIs as part of ML Kit help you easily build with Gemini Nano

Posted by Caren Chang - Developer Relations Engineer, Chengji Yan - Software Engineer, Taj Darra - Product Manager

We are excited to announce a set of on-device GenAI APIs, as part of ML Kit, to help you integrate Gemini Nano in your Android apps.

To start, we are releasing 4 new APIs:

    • Summarization: to summarize articles and conversations
    • Proofreading: to polish short text
    • Rewriting: to reword text in different styles
    • Image Description: to provide short description for images

Key benefits of GenAI APIs

GenAI APIs are high level APIs that allow for easy integration, similar to existing ML Kit APIs. This means you can expect quality results out of the box without extra effort for prompt engineering or fine tuning for specific use cases.

GenAI APIs run on-device and thus provide the following benefits:

    • Input, inference, and output data is processed locally
    • Functionality remains the same without reliable internet connection
    • No additional cost incurred for each API call

To prevent misuse, we also added safety protection in various layers, including base model training, safety-aware LoRA fine-tuning, input and output classifiers and safety evaluations.

How GenAI APIs are built

There are 4 main components that make up each of the GenAI APIs.

  1. Gemini Nano is the base model, as the foundation shared by all APIs.
  2. Small API-specific LoRA adapter models are trained and deployed on top of the base model to further improve the quality for each API.
  3. Optimized inference parameters (e.g. prompt, temperature, topK, batch size) are tuned for each API to guide the model in returning the best results.
  4. An evaluation pipeline ensures quality in various datasets and attributes. This pipeline consists of: LLM raters, statistical metrics and human raters.

Together, these components make up the high-level GenAI APIs that simplify the effort needed to integrate Gemini Nano in your Android app.

Evaluating quality of GenAI APIs

For each API, we formulate a benchmark score based on the evaluation pipeline mentioned above. This score is based on attributes specific to a task. For example, when evaluating the summarization task, one of the attributes we look at is “grounding” (ie: factual consistency of generated summary with source content).

To provide out-of-box quality for GenAI APIs, we applied feature specific fine-tuning on top of the Gemini Nano base model. This resulted in an increase for the benchmark score of each API as shown below:

Use case in English Gemini Nano Base Model ML Kit GenAI API
Summarization 77.2 92.1
Proofreading 84.3 90.2
Rewriting 79.5 84.1
Image Description 86.9 92.3

In addition, this is a quick reference of how the APIs perform on a Pixel 9 Pro:

Prefix Speed
(input processing rate)
Decode Speed
(output generation rate)
Text-to-text 510 tokens/second 11 tokens/second
Image-to-text 510 tokens/second + 0.8 seconds for image encoding 11 tokens/second

Sample usage

This is an example of implementing the GenAI Summarization API to get a one-bullet summary of an article:

val articleToSummarize = "We are excited to announce a set of on-device generative AI APIs..."

// Define task with desired input and output format
val summarizerOptions = SummarizerOptions.builder(context)
    .setInputType(InputType.ARTICLE)
    .setOutputType(OutputType.ONE_BULLET)
    .setLanguage(Language.ENGLISH)
    .build()
val summarizer = Summarization.getClient(summarizerOptions)

suspend fun prepareAndStartSummarization(context: Context) {
    // Check feature availability. Status will be one of the following: 
    // UNAVAILABLE, DOWNLOADABLE, DOWNLOADING, AVAILABLE
    val featureStatus = summarizer.checkFeatureStatus().await()

    if (featureStatus == FeatureStatus.DOWNLOADABLE) {
        // Download feature if necessary.
        // If downloadFeature is not called, the first inference request will 
        // also trigger the feature to be downloaded if it's not already
        // downloaded.
        summarizer.downloadFeature(object : DownloadCallback {
            override fun onDownloadStarted(bytesToDownload: Long) { }

            override fun onDownloadFailed(e: GenAiException) { }

            override fun onDownloadProgress(totalBytesDownloaded: Long) {}

            override fun onDownloadCompleted() {
                startSummarizationRequest(articleToSummarize, summarizer)
            }
        })    
    } else if (featureStatus == FeatureStatus.DOWNLOADING) {
        // Inference request will automatically run once feature is      
        // downloaded.
        // If Gemini Nano is already downloaded on the device, the   
        // feature-specific LoRA adapter model will be downloaded very  
        // quickly. However, if Gemini Nano is not already downloaded, 
        // the download process may take longer.
        startSummarizationRequest(articleToSummarize, summarizer)
    } else if (featureStatus == FeatureStatus.AVAILABLE) {
        startSummarizationRequest(articleToSummarize, summarizer)
    } 
}

fun startSummarizationRequest(text: String, summarizer: Summarizer) {
    // Create task request  
    val summarizationRequest = SummarizationRequest.builder(text).build()

    // Start summarization request with streaming response
    summarizer.runInference(summarizationRequest) { newText -> 
        // Show new text in UI
    }

    // You can also get a non-streaming response from the request
    // val summarizationResult = summarizer.runInference(summarizationRequest)
    // val summary = summarizationResult.get().summary
}

// Be sure to release the resource when no longer needed
// For example, on viewModel.onCleared() or activity.onDestroy()
summarizer.close()

For more examples of implementing the GenAI APIs, check out the official documentation and samples on GitHub:

Use cases

Here is some guidance on how to best use the current GenAI APIs:

For Summarization, consider:

    • Conversation messages or transcripts that involve 2 or more users
    • Articles or documents less than 4000 tokens (or about 3000 English words). Using the first few paragraphs for summarization is usually good enough to capture the most important information.

For Proofreading and Rewriting APIs, consider utilizing them during the content creation process for short content below 256 tokens to help with tasks such as:

    • Refining messages in a particular tone, such as more formal or more casual
    • Polishing personal notes for easier consumption later

For the Image Description API, consider it for:

    • Generating titles of images
    • Generating metadata for image search
    • Utilizing descriptions of images in use cases where the images themselves cannot be displayed, such as within a list of chat messages
    • Generating alternative text to help visually impaired users better understand content as a whole

GenAI API in production

Envision is an app that verbalizes the visual world to help people who are blind or have low vision lead more independent lives. A common use case in the app is for users to take a picture to have a document read out loud. Utilizing the GenAI Summarization API, Envision is now able to get a concise summary of a captured document. This significantly enhances the user experience by allowing them to quickly grasp the main points of documents and determine if a more detailed reading is desired, saving them time and effort.

side by side images of a mobile device showing a document on a table on the left, and the results of the scanned document on the right showing details providing the what, when, and where as written in the document

Supported devices

GenAI APIs are available on Android devices using optimized MediaTek Dimensity, Qualcomm Snapdragon, and Google Tensor platforms through AICore. For a comprehensive list of devices that support GenAI APIs, refer to our official documentation.

Learn more

Start implementing GenAI APIs in your Android apps today with guidance from our official documentation and samples on GitHub: AI Catalog GenAI API Samples with Compose, ML Kit GenAI APIs Quickstart.