Posted by Niharika Arora – Senior Developer Relations Engineer, Joseph Lewis – Staff Technical Writer, and Kumareshwaran Sreedharan – Product Manager, Zoho.

As an Android developer, you’re constantly looking for ways to enhance security, improve user experience, and streamline development. Zoho, a comprehensive cloud-based software suite focused on security and seamless experiences, achieved significant improvements by adopting passkeys in their OneAuth Android app.

Since integrating passkeys in 2024, Zoho achieved login speeds up to 6x faster than previous methods and a 31% month-over-month (MoM) growth in passkey adoption.

This case study examines Zoho’s adoption of passkeys and Android’s Credential Manager API to address authentication difficulties. It details the technical implementation process and highlights the impactful results.

Overcoming authentication challenges

Zoho utilizes a combination of authentication methods to protect user accounts. This included Zoho OneAuth, their own multi-factor authentication (MFA) solution, which supported both password-based and passwordless authentication using push notifications, QR codes, and time-based one-time passwords (TOTP). Zoho also supported federated logins, allowing authentication through Security Assertion Markup Language (SAML) and other third-party identity providers.

Challenges

Zoho, like many organizations, aimed to improve authentication security and user experience while reducing operational burdens. The primary challenges that led to the adoption of passkeys included:

• Security vulnerabilities: Traditional password-based methods left users susceptible to phishing attacks and password breaches.
• User friction: Password fatigue led to forgotten passwords, frustration, and increased reliance on cumbersome recovery processes.
• Operational inefficiencies: Handling password resets and MFA issues generated significant support overhead.
• Scalability concerns: A growing user base demanded a more secure and efficient authentication solution.

Why the shift to passkeys?

Passkeys were implemented in Zoho’s apps to address authentication challenges by offering a passwordless approach that significantly improves security and user experience. This solution leverages phishing-resistant authentication, cloud-synchronized credentials for effortless cross-device access, and biometrics (such as a fingerprint or facial recognition), PIN, or pattern for secure logins, thereby reducing the vulnerabilities and inconveniences associated with traditional passwords.

By adopting passkeys with Credential Manager, Zoho cut login times by up to 6x, slashed password-related support costs, and saw strong user adoption – doubling passkey sign-ins in 4 months with 31% MoM growth. Zoho users now enjoy faster, easier logins and phishing-resistant security.

Implementation with Credential Manager on Android

So, how did Zoho achieve these results? They used Android’s Credential Manager API, the recommended Jetpack library for implementing authentication on Android.

Credential Manager provides a unified API that simplifies handling of the various authentication methods. Instead of juggling different APIs for passwords, passkeys, and federated logins (like Sign in with Google), you use a single interface.

Implementing passkeys at Zoho required both client-side and server-side adjustments. Here’s a detailed breakdown of the passkey creation, sign-in, and server-side implementation process.

Passkey creation

To create a passkey, the app first retrieves configuration details from Zoho’s server. This process includes a unique verification, such as a fingerprint or facial recognition. This verification data, formatted as a requestJson string), is used by the app to build a CreatePublicKeyCredentialRequest. The app then calls the credentialManager.createCredential method, which prompts the user to authenticate using their device screen lock (biometrics, fingerprint, PIN, etc.).

Upon successful user confirmation, the app receives the new passkey credential data, sends it back to Zoho’s server for verification, and the server then stores the passkey information linked to the user’s account. Failures or user cancellations during the process are caught and handled by the app.

Sign-in

The Zoho Android app initiates the passkey sign-in process by requesting sign-in options, including a unique challenge, from Zoho’s backend server. The app then uses this data to construct a GetCredentialRequest, indicating it will authenticate with a passkey. It then invokes the Android CredentialManager.getCredential() API with this request. This action triggers a standardized Android system interface, prompting the user to choose their Zoho account (if multiple passkeys exist) and authenticate using their device’s configured screen lock (fingerprint, face scan, or PIN). After successful authentication, Credential Manager returns a signed assertion (proof of login) to the Zoho app. The app forwards this assertion to Zoho’s server, which verifies the signature against the user’s stored public key and validates the challenge, completing the secure sign-in process.

Server-side implementation

Zoho’s transition to supporting passkeys benefited from their backend systems already being FIDO WebAuthn compliant, which streamlined the server-side implementation process. However, specific modifications were still necessary to fully integrate passkey functionality.

The most significant challenge involved adapting the credential storage system. Zoho’s existing authentication methods, which primarily used passwords and FIDO security keys for multi-factor authentication, required different storage approaches than passkeys, which are based on cryptographic public keys. To address this, Zoho implemented a new database schema specifically designed to securely store passkey public keys and related data according to WebAuthn protocols. This new system was built alongside a lookup mechanism to validate and retrieve credentials based on user and device information, ensuring backward compatibility with older authentication methods.

Another server-side adjustment involved implementing the ability to handle requests from Android devices. Passkey requests originating from Android apps use a unique origin format (android:apk-key-hash:example) that is distinct from standard web origins that use a URI-based format (https://example.com/app). The server logic needed to be updated to correctly parse this format, extract the SHA-256 fingerprint hash of the app’s signing certificate, and validate it against a pre-registered list. This verification step ensures that authentication requests genuinely originate from Zoho’s Android app and protects against phishing attacks.

This code snippet demonstrates how the server checks for the Android-specific origin format and validates the certificate hash:

val origin: String = clientData.getString("origin")

if (origin.startsWith("android:apk-key-hash:")) { 
    val originSplit: List<String> = origin.split(":")
    if (originSplit.size > 3) {
               val androidOriginHashDecoded: ByteArray = Base64.getDecoder().decode(originSplit[3])

                if (!androidOriginHashDecoded.contentEquals(oneAuthSha256FingerPrint)) {
            throw IAMException(IAMErrorCode.WEBAUTH003)
        }
    } else {
        // Optional: Handle the case where the origin string is malformed    }
}

Error handling

Zoho implemented robust error handling mechanisms to manage both user-facing and developer-facing errors. A common error, CreateCredentialCancellationException, appeared when users manually canceled their passkey setup. Zoho tracked the frequency of this error to assess potential UX improvements. Based on Android’s UX recommendations, Zoho took steps to better educate their users about passkeys, ensure users were aware of passkey availability, and promote passkey adoption during subsequent sign-in attempts.

This code example demonstrates Zoho’s approach for how they handled their most common passkey creation errors:

private fun handleFailure(e: CreateCredentialException) {
    val msg = when (e) {
        is CreateCredentialCancellationException -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_SETUP_CANCELLED", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "The operation was canceled by the user."
        }
        is CreateCredentialInterruptedException -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_SETUP_INTERRUPTED", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "Passkey setup was interrupted. Please try again."
        }
        is CreateCredentialProviderConfigurationException -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_PROVIDER_MISCONFIGURED", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "Credential provider misconfigured. Contact support."
        }
        is CreateCredentialUnknownException -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_SETUP_UNKNOWN_ERROR", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "An unknown error occurred during Passkey setup."
        }
        is CreatePublicKeyCredentialDomException -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_WEB_AUTHN_ERROR", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "Passkey creation failed: ${e.domError}"
        }
        else -> {
            Analytics.addAnalyticsEvent(eventProtocol: "PASSKEY_SETUP_FAILED", GROUP_NAME)
            Analytics.addNonFatalException(e)
            "An unexpected error occurred. Please try again."
        }
    }
}

Testing passkeys in intranet environments

Zoho faced an initial challenge in testing passkeys within a closed intranet environment. The Google Password Manager verification process for passkeys requires public domain access to validate the relying party (RP) domain. However, Zoho’s internal testing environment lacked this public Internet access, causing the verification process to fail and hindering successful passkey authentication testing. To overcome this, Zoho created a publicly accessible test environment, which included hosting a temporary server with an asset link file and domain validation.

This example from the assetlinks.json file used in Zoho’s public test environment demonstrates how to associate the relying party domain with the specified Android app for passkey validation.

[
    {
        "relation": [
            "delegate_permission/common.handle_all_urls",
            "delegate_permission/common.get_login_creds"
        ],
        "target": {
            "namespace": "android_app",
            "package_name": "com.zoho.accounts.oneauth",
            "sha256_cert_fingerprints": [
                "SHA_HEX_VALUE" 
            ]
        }
    }
]

Integrate with an existing FIDO server

Android’s passkey system utilizes the modern FIDO2 WebAuthn standard. This standard requires requests in a specific JSON format, which helps maintain consistency between native applications and web platforms. To enable Android passkey support, Zoho did minor compatibility and structural changes to correctly generate and process requests that adhere to the required FIDO2 JSON structure.

This server update involved several specific technical adjustments:

1. Encoding conversion: The server converts the Base64 URL encoding (commonly used in WebAuthn for fields like credential IDs) to standard Base64 encoding before it stores the relevant data. The snippet below shows how a rawId might be encoded to standard Base64:

// Convert rawId bytes to a standard Base64 encoded string for storage
val base64RawId: String = Base64.getEncoder().encodeToString(rawId.toByteArray())

2. Transport list format: To ensure consistent data processing, the server logic handles lists of transport mechanisms (such as USB, NFC, and Bluetooth, which specify how the authenticator communicated) as JSON arrays.

3. Client data alignment: The Zoho team adjusted how the server encodes and decodes the clientDataJson field. This ensures the data structure aligns precisely with the expectations of Zoho’s existing internal APIs. The example below illustrates part of the conversion logic applied to client data before the server processes it:

private fun convertForServer(type: String): String {
    val clientDataBytes = BaseEncoding.base64().decode(type)
    val clientDataJson = JSONObject(String(clientDataBytes, StandardCharsets.UTF_8))
    val clientJson = JSONObject()
    val challengeFromJson = clientDataJson.getString("challenge")
    // 'challenge' is a technical identifier/token, not localizable text.
    clientJson.put("challenge", BaseEncoding.base64Url()
        .encode(challengeFromJson.toByteArray(StandardCharsets.UTF_8))) 

    clientJson.put("origin", clientDataJson.getString("origin"))
    clientJson.put("type", clientDataJson.getString("type"))
    clientJson.put("androidPackageName", clientDataJson.getString("androidPackageName"))
    return BaseEncoding.base64().encode(clientJson.toString().toByteArray())
}

User guidance and authentication preferences

A central part of Zoho’s passkey strategy involved encouraging user adoption while providing flexibility to align with different organizational requirements. This was achieved through careful UI design and policy controls.

Zoho recognized that organizations have varying security needs. To accommodate this, Zoho implemented:

• Admin enforcement: Through the Zoho Directory admin panel, administrators can designate passkeys as the mandatory, default authentication method for their entire organization. When this policy is enabled, employees are required to set up a passkey upon their next login and use it going forward.
• User choice: If an organization does not enforce a specific policy, individual users maintain control. They can choose their preferred authentication method during login, selecting from passkeys or other configured options via their authentication settings.

To make adopting passkeys appealing and straightforward for end-users, Zoho implemented:

• Easy setup: Zoho integrated passkey setup directly into the Zoho OneAuth mobile app (available for both Android and iOS). Users can conveniently configure their passkeys within the app at any time, smoothing the transition.
• Consistent access: Passkey support was implemented across key user touchpoints, ensuring users can register and authenticate using passkeys via:
• The Zoho OneAuth mobile app (Android & iOS);
• Their Zoho web accounts page.

This method ensured that the process of setting up and using passkeys was accessible and integrated into the platforms they already use, regardless of whether it was mandated by an admin or chosen by the user. You can learn more about how to create smooth user flows for passkey authentication by exploring our comprehensive passkeys user experience guide.

Impact on developer velocity and integration efficiency

Credential Manager, as a unified API, also helped improve developer productivity compared to older sign-in flows. It reduced the complexity of handling multiple authentication methods and APIs separately, leading to faster integration, from months to weeks, and fewer implementation errors. This collectively streamlined the sign-in process and improved overall reliability.

By implementing passkeys with Credential Manager, Zoho achieved significant, measurable improvements across the board:

• Dramatic speed improvements
• 2x faster login compared to traditional password authentication.
• 4x faster login compared to username or mobile number with email or SMS OTP authentication.
• 6x faster login compared to username, password, and SMS or authenticator OTP authentication.
• Reduced support costs
• Reduced password-related support requests, especially for forgotten passwords.
• Lower costs associated with SMS-based 2FA, as existing users can onboard directly with passkeys.
• Strong user adoption & enhanced security:
• Passkey sign-ins doubled in just 4 months, showing high user acceptance.
• Users migrating to passkeys are fully protected from common phishing and password breach threats.
• With 31% MoM adoption growth, more users are benefiting daily from enhanced security against vulnerabilities like phishing and SIM swaps.

Recommendations and best practices

To successfully implement passkeys on Android, developers should consider the following best practices:

• Leverage Android’s Credential Manager API:
• Credential Manager simplifies credential retrieval, reducing developer effort and ensuring a unified authentication experience.
• Handles passwords, passkeys, and federated login flows in a single interface.
• Ensure data encoding consistency while migrating from other FIDO authentication solutions:
• Make sure you handle consistent formatting for all inputs/outputs while migrating from other FIDO authentication solutions such as FIDO security keys.
• Optimize error handling and logging:
  • Implement robust error handling for a seamless user experience.
  • Provide localized error messages and use detailed logs to debug and resolve unexpected failures.
• Educate users on passkey recovery options:
• Prevent lockout scenarios by proactively guiding users on recovery options.
• Monitor adoption metrics and user feedback:
  • Track user engagement, passkey adoption rates, and login success rates to keep optimizing user experience.
  • Conduct A/B testing on different authentication flows to improve conversion and retention.

Passkeys, combined with the Android Credential Manager API, offer a powerful, unified authentication solution that enhances security while simplifying user experience. Passkeys significantly reduce phishing risks, credential theft, and unauthorized access. We encourage developers to try out the experience in their app and bring the most secure authentication to their users.

Get started with passkeys and Credential Manager

Get hands on with passkeys and Credential Manager on Android using our public sample code.

If you have any questions or issues, you can share with us through the Android Credentials issues tracker.

Posted by Dan Brown – Product Manager, Google Play

Google Play empowers you to manage and distribute your innovative and trusted apps and games to billions of users around the world across the entire breadth of Android devices, and historically, all Android devices have managed memory in 4 KB pages.

As device manufacturers equip devices with more RAM to optimize performance, many will adopt larger page sizes like 16 KB. Android 15 introduces support for the increased page size, ensuring your app can run on these evolving devices and benefit from the associated performance gains.

Starting November 1st, 2025, all new apps and updates to existing apps submitted to Google Play and targeting Android 15+ devices must support 16 KB page sizes.

This is a key technical requirement to ensure your users can benefit from the performance enhancements on newer devices and prepares your apps for the platform’s future direction of improved performance on newer hardware. Without recompiling to support 16 KB pages, your app might not function correctly on these devices when they become more widely available in future Android releases.

We’ve seen that 16 KB can help with:

• Faster app launches: See improvements ranging from 3% to 30% for various apps.
• Improved battery usage: Experience an average gain of 4.5%.
• Quicker camera starts: Launch the camera 4.5% to 6.6% faster.
• Speedier system boot-ups: Boot Android devices approximately 8% faster.

We recommend checking your apps early especially for dependencies that might not yet be 16 KB compatible. Many popular SDK providers, like React Native and Flutter, already offer compatible versions. For game developers, several leading game engines, such as Unity, support 16 KB, with support for Unreal Engine coming soon.

Reaching 16 KB compatibility

A substantial number of apps are already compatible, so your app may already work seamlessly with this requirement. For most of those that need to make adjustments, we expect the changes to be minimal.

• Apps with no native code should be compatible without any changes at all.
• Apps using libraries or SDKs that contain native code may need to update these to a compatible version.
• Apps with native code may need to recompile with a more recent toolchain and check for any code with incompatible low level memory management.

Our December blog post, Get your apps ready for 16 KB page size devices, provides a more detailed technical explanation and guidance on how to prepare your apps.

Check your app’s compatibility now

It’s easy to see if your app bundle already supports 16 KB memory page sizes. Visit the app bundle explorer page in Play Console to check your app’s build compliance and get guidance on where your app may need updating.

Beyond the app bundle explorer, make sure to also test your app in a 16 KB environment. This will help you ensure users don’t experience any issues and that your app delivers its best performance.

For more information, check out the full documentation.

Thank you for your continued support in bringing delightful, fast, and high-performance experiences to users across the breadth of devices Play supports. We look forward to seeing the enhanced experiences you’ll deliver with 16 KB support.

Posted by Donovan McMurray, Mayuri Khinvasara Khabya, Mozart Louis, and Nevin Mital – Developer Relations Engineers

Hello Android Developers!

We are the Android Developer Relations Camera & Media team, and we’re excited to bring you something a little different today. Over the past several months, we’ve been hard at work writing sample code and building demos that showcase how to take advantage of all the great potential Android offers for building delightful user experiences.

Some of these efforts are available for you to explore now, and some you’ll see later throughout the year, but for this blog post we thought we’d share some of the learnings we gathered while going through this exercise.

Grab your favorite Android plush or rubber duck, and read on to see what we’ve been up to!

Future-proof your app with Jetpack

Nevin Mital

One of our focuses for the past several years has been improving the developer tools available for video editing on Android. This led to the creation of the Jetpack Media3 Transformer APIs, which offer solutions for both single-asset and multi-asset video editing preview and export. Today, I’d like to focus on the Composition demo app, a sample app that showcases some of the multi-asset editing experiences that Transformer enables.

I started by adding a custom video compositor to demonstrate how you can arrange input video sequences into different layouts for your final composition, such as a 2×2 grid or a picture-in-picture overlay. You can customize this by implementing a VideoCompositorSettings and overriding the getOverlaySettings method. This object can then be set when building your Composition with setVideoCompositorSettings.

Here is an example for the 2×2 grid layout:

object : VideoCompositorSettings {
  ...

  override fun getOverlaySettings(inputId: Int, presentationTimeUs: Long): OverlaySettings {
    return when (inputId) {
      0 -> { // First sequence is placed in the top left
        StaticOverlaySettings.Builder()
          .setScale(0.5f, 0.5f)
          .setOverlayFrameAnchor(0f, 0f) // Middle of overlay
          .setBackgroundFrameAnchor(-0.5f, 0.5f) // Top-left section of background
          .build()
      }

      1 -> { // Second sequence is placed in the top right
        StaticOverlaySettings.Builder()
          .setScale(0.5f, 0.5f)
          .setOverlayFrameAnchor(0f, 0f) // Middle of overlay
          .setBackgroundFrameAnchor(0.5f, 0.5f) // Top-right section of background
          .build()
      }

      2 -> { // Third sequence is placed in the bottom left
        StaticOverlaySettings.Builder()
          .setScale(0.5f, 0.5f)
          .setOverlayFrameAnchor(0f, 0f) // Middle of overlay
          .setBackgroundFrameAnchor(-0.5f, -0.5f) // Bottom-left section of background
          .build()
      }

      3 -> { // Fourth sequence is placed in the bottom right
        StaticOverlaySettings.Builder()
          .setScale(0.5f, 0.5f)
          .setOverlayFrameAnchor(0f, 0f) // Middle of overlay
          .setBackgroundFrameAnchor(0.5f, -0.5f) // Bottom-right section of background
          .build()
      }

      else -> {
        StaticOverlaySettings.Builder().build()
      }
    }
  }
}

Since getOverlaySettings also provides a presentation time, we can even animate the layout, such as in this picture-in-picture example:

Next, I spent some time migrating the Composition demo app to use Jetpack Compose. With complicated editing flows, it can help to take advantage of as much screen space as is available, so I decided to use the supporting pane adaptive layout. This way, the user can fine-tune their video creation on the preview screen, and export options are only shown at the same time on a larger display. Below, you can see how the UI dynamically adapts to the screen size on a foldable device, when switching from the outer screen to the inner screen and vice versa.

What’s great is that by using Jetpack Media3 and Jetpack Compose, these features also carry over seamlessly to other devices and form factors, such as the new Android XR platform. Right out-of-the-box, I was able to run the demo app in Home Space with the 2D UI I already had. And with some small updates, I was even able to adapt the UI specifically for XR with features such as multiple panels, and to take further advantage of the extra space, an Orbiter with playback controls for the editing preview.

What’s great is that by using Jetpack Media3 and Jetpack Compose, these features also carry over seamlessly to other devices and form factors, such as the new Android XR platform. Right out-of-the-box, I was able to run the demo app in Home Space with the 2D UI I already had. And with some small updates, I was even able to adapt the UI specifically for XR with features such as multiple panels, and to take further advantage of the extra space, an Orbiter with playback controls for the editing preview.

Orbiter(
  position = OrbiterEdge.Bottom,
  offset = EdgeOffset.inner(offset = MaterialTheme.spacing.standard),
  alignment = Alignment.CenterHorizontally,
  shape = SpatialRoundedCornerShape(CornerSize(28.dp))
) {
  Row (horizontalArrangement = Arrangement.spacedBy(MaterialTheme.spacing.mini)) {
    // Playback control for rewinding by 10 seconds
    FilledTonalIconButton({ viewModel.seekBack(10_000L) }) {
      Icon(
        painter = painterResource(id = R.drawable.rewind_10),
        contentDescription = "Rewind by 10 seconds"
      )
    }
    // Playback control for play/pause
    FilledTonalIconButton({ viewModel.togglePlay() }) {
      Icon(
        painter = painterResource(id = R.drawable.rounded_play_pause_24),
        contentDescription = 
            if(viewModel.compositionPlayer.isPlaying) {
                "Pause preview playback"
            } else {
                "Resume preview playback"
            }
      )
    }
    // Playback control for forwarding by 10 seconds
    FilledTonalIconButton({ viewModel.seekForward(10_000L) }) {
      Icon(
        painter = painterResource(id = R.drawable.forward_10),
        contentDescription = "Forward by 10 seconds"
      )
    }
  }
}

Jetpack libraries unlock premium functionality incrementally

Donovan McMurray

Not only do our Jetpack libraries have you covered by working consistently across existing and future devices, but they also open the doors to advanced functionality and custom behaviors to support all types of app experiences. In a nutshell, our Jetpack libraries aim to make the common case very accessible and easy, and it has hooks for adding more custom features later.

We’ve worked with many apps who have switched to a Jetpack library, built the basics, added their critical custom features, and actually saved developer time over their estimates. Let’s take a look at CameraX and how this incremental development can supercharge your process.

// Set up CameraX app with preview and image capture.
// Note: setting the resolution selector is optional, and if not set,
// then a default 4:3 ratio will be used.
val aspectRatioStrategy = AspectRatioStrategy(
  AspectRatio.RATIO_16_9, AspectRatioStrategy.FALLBACK_RULE_NONE)
var resolutionSelector = ResolutionSelector.Builder()
  .setAspectRatioStrategy(aspectRatioStrategy)
  .build()

private val previewUseCase = Preview.Builder()
  .setResolutionSelector(resolutionSelector)
  .build()
private val imageCaptureUseCase = ImageCapture.Builder()
  .setResolutionSelector(resolutionSelector)
  .setCaptureMode(ImageCapture.CAPTURE_MODE_MINIMIZE_LATENCY)
  .build()

val useCaseGroupBuilder = UseCaseGroup.Builder()
  .addUseCase(previewUseCase)
  .addUseCase(imageCaptureUseCase)

cameraProvider.unbindAll()

camera = cameraProvider.bindToLifecycle(
  this,  // lifecycleOwner
  CameraSelector.DEFAULT_BACK_CAMERA,
  useCaseGroupBuilder.build(),
)

After setting up the basic structure for CameraX, you can set up a simple UI with a camera preview and a shutter button. You can use the CameraX Viewfinder composable which displays a Preview stream from a CameraX SurfaceRequest.

// Create preview
Box(
  Modifier
    .background(Color.Black)
    .fillMaxSize(),
  contentAlignment = Alignment.Center,
) {
  surfaceRequest?.let {
    CameraXViewfinder(
      modifier = Modifier.fillMaxSize(),
      implementationMode = ImplementationMode.EXTERNAL,
      surfaceRequest = surfaceRequest,
     )
  }
  Button(
    onClick = onPhotoCapture,
    shape = CircleShape,
    colors = ButtonDefaults.buttonColors(containerColor = Color.White),
    modifier = Modifier
      .height(75.dp)
      .width(75.dp),
  )
}

fun onPhotoCapture() {
  // Not shown: defining the ImageCapture.OutputFileOptions for
  // your saved images
  imageCaptureUseCase.takePicture(
    outputOptions,
    ContextCompat.getMainExecutor(context),
    object : ImageCapture.OnImageSavedCallback {
      override fun onError(exc: ImageCaptureException) {
        val msg = "Photo capture failed."
        Toast.makeText(context, msg, Toast.LENGTH_SHORT).show()
      }

      override fun onImageSaved(output: ImageCapture.OutputFileResults) {
        val savedUri = output.savedUri
        if (savedUri != null) {
          // Do something with the savedUri if needed
        } else {
          val msg = "Photo capture failed."
          Toast.makeText(context, msg, Toast.LENGTH_SHORT).show()
        }
      }
    },
  )
}

You’re already on track for a solid camera experience, but what if you wanted to add some extra features for your users? Adding filters and effects are easy with CameraX’s Media3 effect integration, which is one of the new features introduced in CameraX 1.4.0.

Here’s how simple it is to add a black and white filter from Media3’s built-in effects.

val media3Effect = Media3Effect(
  application,
  PREVIEW or IMAGE_CAPTURE,
  ContextCompat.getMainExecutor(application),
  {},
)
media3Effect.setEffects(listOf(RgbFilter.createGrayscaleFilter()))
useCaseGroupBuilder.addEffect(media3Effect)

The Media3Effect object takes a Context, a bitwise representation of the use case constants for targeted UseCases, an Executor, and an error listener. Then you set the list of effects you want to apply. Finally, you add the effect to the useCaseGroupBuilder we defined earlier.

There are many other built-in effects you can add, too! See the Media3 Effect documentation for more options, like brightness, color lookup tables (LUTs), contrast, blur, and many other effects.

To take your effects to yet another level, it’s also possible to define your own effects by implementing the GlEffect interface, which acts as a factory of GlShaderPrograms. You can implement a BaseGlShaderProgram’s drawFrame() method to implement a custom effect of your own. A minimal implementation should tell your graphics library to use its shader program, bind the shader program’s vertex attributes and uniforms, and issue a drawing command.

Jetpack libraries meet you where you are and your app’s needs. Whether that be a simple, fast-to-implement, and reliable implementation, or custom functionality that helps the critical user journeys in your app stand out from the rest, Jetpack has you covered!

Jetpack offers a foundation for innovative AI Features

Mayuri Khinvasara Khabya

Just as Donovan demonstrated with CameraX for capture, Jetpack Media3 provides a reliable, customizable, and feature-rich solution for playback with ExoPlayer. The AI Samples app builds on this foundation to delight users with helpful and enriching AI-driven additions.

In today’s rapidly evolving digital landscape, users expect more from their media applications. Simply playing videos is no longer enough. Developers are constantly seeking ways to enhance user experiences and provide deeper engagement. Leveraging the power of Artificial Intelligence (AI), particularly when built upon robust media frameworks like Media3, offers exciting opportunities. Let’s take a look at some of the ways we can transform the way users interact with video content:

• Empowering Video Understanding: The core idea is to use AI, specifically multimodal models like the Gemini Flash and Pro models, to analyze video content and extract meaningful information. This goes beyond simply playing a video; it’s about understanding what’s in the video and making that information readily accessible to the user.
• Actionable Insights: The goal is to transform raw video into summaries, insights, and interactive experiences. This allows users to quickly grasp the content of a video and find specific information they need or learn something new!
• Accessibility and Engagement: AI helps make videos more accessible by providing features like summaries, translations, and descriptions. It also aims to increase user engagement through interactive features.

A Glimpse into AI-Powered Video Journeys

The following example demonstrates potential video journies enhanced by artificial intelligence. This sample integrates several components, such as ExoPlayer and Transformer from Media3; the Firebase SDK (leveraging Vertex AI on Android); and Jetpack Compose, ViewModel, and StateFlow. The code will be available soon on Github.

There are two experiences in particular that I’d like to highlight:

• HDR Thumbnails: AI can help identify key moments in the video that could make for good thumbnails. With those timestamps, you can use the new ExperimentalFrameExtractor API from Media3 to extract HDR thumbnails from videos, providing richer visual previews.
• Text-to-Speech: AI can be used to convert textual information derived from the video into spoken audio, enhancing accessibility. On Android you can also choose to play audio in different languages and dialects thus enhancing personalization for a wider audience.

Using the right AI solution

Currently, only cloud models support video inputs, so we went ahead with a cloud-based solution.Iintegrating Firebase in our sample empowers the app to:

• Generate real-time, concise video summaries automatically.
• Produce comprehensive content metadata, including chapter markers and relevant hashtags.
• Facilitate seamless multilingual content translation.

So how do you actually interact with a video and work with Gemini to process it? First, send your video as an input parameter to your prompt:

val promptData =
"Summarize this video in the form of top 3-4 takeaways only. Write in the form of bullet points. Don't assume if you don't know"

val generativeModel = Firebase.vertexAI.generativeModel("gemini-2.0-flash")
_outputText.value = OutputTextState.Loading

viewModelScope.launch(Dispatchers.IO) {
    try {
        val requestContent = content {
            fileData(videoSource.toString(), "video/mp4")
            text(prompt)
        }
        val outputStringBuilder = StringBuilder()

        generativeModel.generateContentStream(requestContent).collect { response ->
            outputStringBuilder.append(response.text)
            _outputText.value = OutputTextState.Success(outputStringBuilder.toString())
        }

        _outputText.value = OutputTextState.Success(outputStringBuilder.toString())

    } catch (error: Exception) {
        _outputText.value = error.localizedMessage?.let { OutputTextState.Error(it) }
    }
}

Notice there are two key components here:

• FileData: This component integrates a video into the query.
• Prompt: This asks the user what specific assistance they need from AI in relation to the provided video.

Of course, you can finetune your prompt as per your requirements and get the responses accordingly.

In conclusion, by harnessing the capabilities of Jetpack Media3 and integrating AI solutions like Gemini through Firebase, you can significantly elevate video experiences on Android. This combination enables advanced features like video summaries, enriched metadata, and seamless multilingual translations, ultimately enhancing accessibility and engagement for users. As these technologies continue to evolve, the potential for creating even more dynamic and intelligent video applications is vast.

Go above-and-beyond with specialized APIs

Mozart Louis

Android 16 introduces the new audio PCM Offload mode which can reduce the power consumption of audio playback in your app, leading to longer playback time and increased user engagement. Eliminating the power anxiety greatly enhances the user experience.

Oboe is Android’s premiere audio api that developers are able to use to create high performance, low latency audio apps. A new feature is being added to the Android NDK and Android 16 called Native PCM Offload playback.

Offload playback helps save battery life when playing audio. It works by sending a large chunk of audio to a special part of the device’s hardware (a DSP). This allows the CPU of the device to go into a low-power state while the DSP handles playing the sound. This works with uncompressed audio (like PCM) and compressed audio (like MP3 or AAC), where the DSP also takes care of decoding.

This can result in significant power saving while playing back audio and is perfect for applications that play audio in the background or while the screen is off (think audiobooks, podcasts, music etc).

We created the sample app PowerPlay to demonstrate how to implement these features using the latest NDK version, C++ and Jetpack Compose.

Here are the most important parts!

First order of business is to assure the device supports audio offload of the file attributes you need. In the example below, we are checking if the device support audio offload of stereo, float PCM file with a sample rate of 48000Hz.

       val format = AudioFormat.Builder()
            .setEncoding(AudioFormat.ENCODING_PCM_FLOAT)
            .setSampleRate(48000)
            .setChannelMask(AudioFormat.CHANNEL_OUT_STEREO)
            .build()

        val attributes =
            AudioAttributes.Builder()
                .setContentType(AudioAttributes.CONTENT_TYPE_MUSIC)
                .setUsage(AudioAttributes.USAGE_MEDIA)
                .build()
       
        val isOffloadSupported = 
            if (Build.VERSION.SDK_INT >= Build.VERSION_CODES.Q) {
                AudioManager.isOffloadedPlaybackSupported(format, attributes)
            } else {
                false
            }

        if (isOffloadSupported) {
            player.initializeAudio(PerformanceMode::POWER_SAVING_OFFLOADED)
        }

Once we know the device supports audio offload, we can confidently set the Oboe audio streams’ performance mode to the new performance mode option, PerformanceMode::POWER_SAVING_OFFLOADED.

Player::initializeAudio(bool isOffloadSupported) {
    // Create an audio stream
    AudioStreamBuilder builder;
    builder.setChannelCount(mChannelCount);
    builder.setDataCallback(mDataCallback);
    builder.setFormat(AudioFormat::Float);
    builder.setSampleRate(48000);
    builder.setErrorCallback(mErrorCallback);
    builder.setPresentationCallback(mPresentationCallback);

    if (isOffloadSupported) {
      builder.setPerformanceMode(oboe::PerformanceMode::POWER_SAVING_OFFLOADED);
      builder.setFramesPerDataCallback(128); // set a low frame buffer amount
    } else {
      builder.setPerformanceMode(oboe::PerformanceMode::LowLatency
    }
      builder.setSharingMode(SharingMode::Exclusive);
      builder.setSampleRateConversionQuality(SampleRateConversionQuality::Medium);
      Result result = builder.openStream(mAudioStream);
}

Now when audio is played back, it will be offloading audio to the DSP, helping save power when playing back audio.

There is more to this feature that will be covered in a future blog post, fully detailing out all of the new available APIs that will help you optimize your audio playback experience!

What’s next

Of course, we were only able to share the tip of the iceberg with you here, so to dive deeper into the samples, check out the following links:

• Jetpack Media3 Composition Demo app
• SociaLite
• AI Samples
• PowerPlay

Hopefully these examples have inspired you to explore what new and fascinating experiences you can build on Android. Tune in to our session at Google I/O in a couple weeks to learn even more about use-cases supported by solutions like Jetpack CameraX and Jetpack Media3!

Posted by Matthew McCullough – Vice President, Product Management, Android Developer

We just dropped an I/O Edition of The Android Show, where we unpacked exciting new experiences coming to the Android ecosystem: a fresh and dynamic look and feel, smarts across your devices, and enhanced safety and security features. Join Sameer Samat, President of Android Ecosystem, and the Android team to learn about exciting new development in the episode below, and read about all of the updates for users.

Tune into Google I/O next week – including the Developer Keynote as well as the full Android track of sessions – where we’re covering these topics in more detail and how you can get started.

Start building with Material 3 Expressive

The world of UX design is constantly evolving, and you deserve the tools to create truly engaging and impactful experiences. That’s why Material Design’s latest evolution, Material 3 Expressive, provides new ways to make your product more engaging, easy to use, and desirable. Learn more, and try out the new Material 3 Expressive: an expansion pack designed to enhance your app’s appeal by harnessing emotional UX, making it more engaging, intuitive, and desirable for users. It comes with new components, motion-physics system, type styles, colors, shapes and more.

Material 3 Expressive will be coming to Android 16 later this year; check out the Google I/O talk next week where we’ll dive into this in more detail.

A fluid design built for your watch’s round display

Wear OS 6, arriving later this year, brings Material 3 Expressive design to Google’s smartwatch platform. New design language puts the round watch display at the heart of the experience, and is embraced in every single component and motion of the System, from buttons to notifications. You’ll be able to try new visual design and upgrade existing app experiences to a new level. Next week, tune in to the What’s New in Android session to learn more.

Plus some goodies in Android 16…

We also 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, you can try out the latest Beta of Android 16.

A few new features that Android 16 adds which developers should pay attention to are Live updates, professional media and camera features, desktop windowing for tablets, major accessibility enhancements and much more:

• Live Updates allow your app to show time-sensitive progress updates. Use the new ProgressStyle template for an improved experience around navigation, deliveries, and rideshares.
• Professional media and camera features include hybrid auto exposure, precise color temperature and tint adjustments, night mode scene detection, and support for the new Advanced Professional Video format.

Watch the What’s New in Android session and the Live updates talk to learn more.

Tune in next week to Google I/O

This was just a preview of some Android-related news, so remember to tune in next week to Google I/O, where we’ll be diving into a range of Android developer topics in a lot more detail. You can check out What’s New in Android and the full Android track of sessions to start planning your time.

We can’t wait to see you next week, whether you’re joining in person or virtually from anywhere around the world!

Posted by Robbie McLachlan – Developer Marketing

In our latest #WeArePlay film, which celebrates the people behind apps and games on Google Play, we meet Clément, the founder of Imagine Games. His game, My Lovely Planet, turns casual mobile gaming into tangible environmental action, planting real trees and supporting reforestation projects worldwide. Discover the inspiration behind My Lovely Planet and the impact it’s had so far.

What inspired you to combine gaming with positive environmental impact?

I’ve always loved gaming and believed in technology’s potential to tackle environmental challenges. But it was my time working with an NGO in Madagascar, where I witnessed firsthand the devastating effects of environmental changes that truly sparked my mission. Combining gaming and sustainability just made sense. Billions of people play games, so why not harness that entertainment to create real-world impact? So far, the results speak for themselves: we’ve built an engaged global community committed to protecting the environment.

How do players in My Lovely Planet make real-world differences through the game?

With My Lovely Planet, planting a tree in the game means planting a real tree in the world. Our community has already planted over 360,000 trees through partnerships with NGOs like Graines de Vie in Madagascar, Kenya, and France. We’ve also supported ocean-cleaning, bee-protection, and drone reforestation projects.

Balancing fun with impact was key. Players wouldn’t stay just for the mission, so we focused on creating a genuinely fun match-3 style game. Once gameplay was strong, we made real-world actions like tree planting core rewards in the game, helping players feel naturally connected to their impact. Our goal is to keep growing this model to protect biodiversity and fight climate change.

Can you tell us about your drone-led reforestation project in France?

Our latest initiative involves using drones to reforest areas severely impacted by insect infestations and other environmental issues. We’re dropping over one million specially-coated seeds by drone, which is a completely new and efficient way of reforesting large areas. It’s exciting because if this pilot succeeds, it could be replicated worldwide, significantly boosting global reforestation efforts.

How has Google Play helped your journey?

Google Play has been crucial for My Lovely Planet – it’s our main distribution channel, with about 70% of our players coming through the platform. It makes it incredibly easy and convenient for anyone to download and start playing immediately. which is essential for engaging a global community. Plus, from a developer’s standpoint, the flexibility, responsiveness, and powerful testing tools Google Play provides have made launching and scaling our game faster and smoother, allowing us to focus even more on our environmental impact.

What is next for My Lovely Planet?

Right now, we’re focused on expanding the game experience by adding more engaging levels, and introducing exciting new features like integrating our eco-friendly cryptocurrency, My Lovely Coin, into gameplay. Following the success of our first drone-led reforestation project in France, our next step is tracking its impact and expanding this approach to other regions. Ultimately, we aim to build the world’s largest gaming community dedicated to protecting the environment, empowering millions to make a difference while enjoying the game.

Discover other inspiring app and game founders featured in #WeArePlay.

Posted by Ivy Knight – Senior Design Advocate

Here’s your guide to the essential Android Design sessions, resources, and announcements for I/O ‘25:

Check out the latest Android updates

The Android Show: I/O Edition

The Android Show had a special I/O edition this year with some exciting announcements like Material Expressive!

Learn more about the new Live Update Notification templates in the Android Notifications & Live Updates for an in-depth look at what they are, when to use them, and why. You can also get the Live Update design template in the Android UI Kit, read more in the updated Notification guidance, and get hands-on with the Jetsnack Live Updates and Widget case study.

Make your apps more expressive

Get a jump on the future of Google’s UX design: Material 3 Expressive. Learn how to use new emotional design patterns to boost engagement, usability, and desire for your product in the Build Next-Level UX with Material 3 Expressive session and check out the expressive update on Material.io.

Stay up to date with Android Accessibility Updates, highlighting accessibility features launching with Android 16: enhanced dark themes, options for those with motion sickness, a new way to increase text contrast, and more.

Catch the Mastering text input in Compose session to learn more about how engaging robust text experiences are built with Jetpack Compose. It covers Autofill integration, dynamic text resizing, and custom input transformations. This is a great session to watch to see what’s possible when designing text inputs.

Thinking across form factors

These design resources and sessions can help you design across more Android form factors or update your existing experiences.

Preview Gemini in-car, imagining seamless navigation and personalized entertainment in the New In-Car App Experiences session. Then explore the new Car UI Design Kit to bring your app to Android Car platforms and speed up your process with the latest Android form factor kit.

Engaging with users on Google TV with excellent TV apps session discusses new ways the Google TV experience is making it easier for users to find and engage with content, including improvement to out-of-box solutions and updates to Android TV OS.

Want a peek at how to bring immersive content, like 3D models, to Android XR with the Building differentiated apps for Android XR with 3D Content session.

Plus WearOS is releasing an updated design kit @AndroidDesign Figma and learning Pathway.

Tip top apps

We’ve also released the following new Android design guidance to help you design the best Android experiences:

In-app Settings

Read up on the latest suggested patterns to build out your app’s settings.

Help and Feedback

Along with settings, learn about adding help and feedback to your app.

Widget Configuration

Does your app need setup? New guidance to help guide in adding configuration to your app’s widgets.

Edge-to-edge design

Allow your apps to take full advantage of the entire screen with the latest guidance on designing for edge-to-edge.

Check out figma.com/@androiddesign for even more new and updated resources.

Visit the I/O 2025 website, build your schedule, and engage with the community. If you are at the Shoreline come say hello to us in the Android tent at our booths.

We can’t wait to see what you create with these new tools and insights. Happy I/O!

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

Posted by Fahd Imtiaz – Product Manager, Android Developer

If your app isn’t built to adapt, you’re missing out on the opportunity to reach a giant swath of users across 500 million devices! At Google I/O this year, we are exploring how adaptive development isn’t just a good idea, but essential to building apps that shine across the expanding Android device ecosystem. This is your guide to meeting users wherever they are, with experiences that are perfectly tailored to their needs.

The advantage of building adaptive

In today’s multi-device world, users expect their favorite applications to work flawlessly and intuitively, whether they’re on a smartphone, tablet, or Chromebook. This expectation for seamless experiences isn’t just about convenience; it’s an important factor for user engagement and retention.

For example, entertainment apps (including Prime Video, Netflix, and Hulu) users on both phone and tablet spend almost 200% more time in-app (nearly 3x engagement) than phone-only users in the US^*.

Peacock, NBCUniversal’s streaming service has seen a trend of users moving between mobile and large screens and building adaptively enables a single build to work across the different form factors.

“This allows Peacock to have more time to innovate faster and deliver more value to its customers.”

– Diego Valente, Head of Mobile, Peacock and Global Streaming

Adaptive Android development offers the strategic solution, enabling apps to perform effectively across an expanding array of devices and contexts through intelligent design choices that emphasize code reuse and scalability. With Android’s continuous growth into new form factors and upcoming enhancements such as desktop windowing and connected displays in Android 16, an app’s ability to seamlessly adapt to different screen sizes is becoming increasingly crucial for retaining users and staying competitive.

Beyond direct user benefits, designing adaptively also translates to increased visibility. The Google Play Store actively helps promote developers whose apps excel on different form factors. If your application delivers a great experience on tablets or is excellent on ChromeOS, users on those devices will have an easier time discovering your app. This creates a win-win situation: better quality apps for users and a broader audience for you.

Latest in adaptive Android development from Google I/O

To help you more effectively build compelling adaptive experiences, we shared several key updates at I/O this year.

Build for the expanding Android device ecosystem

Your mobile apps can now reach users beyond phones on over 500 million active devices, including foldables, tablets, Chromebooks, and even compatible cars, with minimal changes. Android 16 introduces significant advancements in desktop windowing for a true desktop-like experience on large screens and when devices are connected to external displays. And, Android XR is opening a new dimension, allowing your existing mobile apps to be available in immersive virtual environments.

The mindset shift to Adaptive

With the expanding Android device ecosystem, adaptive app development is a fundamental strategy. It’s about how the same mobile app runs well across phones, foldables, tablets, Chromebooks, connected displays, XR, and cars, laying a strong foundation for future devices and differentiating for specific form factors. You don’t need to rebuild your app for each form factor; but rather make small, iterative changes, as needed, when needed. Embracing this adaptive mindset today isn’t just about keeping pace; it’s about leading the charge in delivering exceptional user experiences across the entire Android ecosystem.

Leverage powerful tools and libraries to build adaptive apps:

• Compose Adaptive Layouts library: This library makes adaptive development easier by allowing your app code to fit into canonical layout patterns like list-detail and supporting pane, that automatically reflow as your app is resized, flipped or folded. In the 1.1 release, we introduced pane expansion, allowing users to resize panes. The Socialite demo app showcased how one codebase using this library can adapt across six form factors. New adaptation strategies like “Levitate” (elevating a pane, e.g., into a dialog or bottom sheet) and “Reflow” (reorganizing panes on the same level) were also announced in 1.2 (alpha). For XR, component overrides can automatically spatialize UI elements.
• Jetpack Navigation 3 (Alpha): This new navigation library simplifies defining user journeys across screens with less boilerplate code, especially for multi-pane layouts in Compose. It helps handle scenarios where list and detail panes might be separate destinations on smaller screens but shown together on larger ones. Check out the new Jetpack Navigation library in alpha.
• Jetpack Compose input enhancements: Compose’s layered architecture, strong input support, and single location for layout logic simplify creating adaptive UIs. Upcoming in Compose 1.9 are right-click context menus and enhanced trackpad/mouse functionality.
• Window Size Classes: Use window size classes for top-level layout decisions. AndroidX.window 1.5 introduces two new width size classes – “large” (1200dp to 1600dp) and “extra-large” (1600dp and larger) – providing more granular breakpoints for large screens. This helps in deciding when to expand navigation rails or show three panes of content. Support for these new breakpoints was also announced in the Compose adaptive layouts library 1.2 alpha, along with design guidance.
• Compose previews: Get quick feedback by visualizing your layouts across a wide variety of screen sizes and aspect ratios. You can also specify different devices by name to preview your UI on their respective sizes and with their inset values.
• Testing adaptive layouts: Validating your adaptive layouts is crucial and Android Studio offers various tools for testing – including previews for different sizes and aspect ratios, a resizable emulator to test across different screen sizes with a single AVD, screenshot tests, and instrumental behavior tests. And with Journeys with Gemini in Android Studio, you can define tests using natural language for even more robust testing across different window sizes.

Ensuring app availability across devices

Avoid unnecessarily declaring required features (like specific cameras or GPS) in your manifest, as this can prevent your app from appearing in the Play Store on devices that lack those specific hardware components but could otherwise run your app perfectly.

Handling different input methods

Remember to handle various input methods like touch, keyboard, and mouse, especially with Chromebook detachables and connected displays.

Prepare for orientation and resizability API changes in Android 16

Beginning in Android 16, for apps targeting SDK 36, manifest and runtime restrictions on orientation, resizability, and aspect ratio will be ignored on displays that are at least 600dp in both dimensions. To meet user expectations, your apps will need layouts that work for both portrait and landscape windows, and support resizing at runtime. There’s a temporary opt-out manifest flag at both the application and activity level to delay these changes until targetSdk 37, and these changes currently do not apply to apps categorized as “Games”. Learn more about these API changes.

Adaptive considerations for games

Games need to be adaptive too and Unity 6 will add enhanced support for configuration handling, including APIs for screenshots, aspect ratio, and density. Success stories like Asphalt Legends Unite show significant user retention increases on foldables after implementing adaptive features.

Start building adaptive today

Now is the time to elevate your Android apps, making them intuitively responsive across form factors. With the latest tools and updates we’re introducing, you have the power to build experiences that seamlessly flow across all devices, from foldables to cars and beyond. Implementing these strategies will allow you to expand your reach and delight users across the Android ecosystem.

Get inspired by the “Adaptive Android development makes your app shine across devices” talk, and explore all the resources you’ll need to start your journey at developer.android.com/adaptive-apps!

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

^*Source: internal Google data

Posted by Rebecca Franks – Developer Relations Engineer

Androidify is a new sample app we built using the latest best practices for mobile apps. Previously, we covered all the different features of the app, from Gemini integration and CameraX functionality to adaptive layouts. In this post, we dive into the Jetpack Compose usage throughout the app, building upon our base knowledge of Compose to add delightful and expressive touches along the way!

Material 3 Expressive

Material 3 Expressive is an expansion of the Material 3 design system. It’s a set of new features, updated components, and design tactics for creating emotionally impactful UX.

It’s been released as part of the alpha version of the Material 3 artifact (androidx.compose.material3:material3:1.4.0-alpha10) and contains a wide range of new components you can use within your apps to build more personalized and delightful experiences. Learn more about Material 3 Expressive’s component and theme updates for more engaging and user-friendly products.

In addition to the new component updates, Material 3 Expressive introduces a new motion physics system that’s encompassed in the Material theme.

In Androidify, we’ve utilized Material 3 Expressive in a few different ways across the app. For example, we’ve explicitly opted-in to the new MaterialExpressiveTheme and chosen MotionScheme.expressive() (this is the default when using expressive) to add a bit of playfulness to the app:

@Composable
fun AndroidifyTheme(
   content: @Composable () -> Unit,
) {
   val colorScheme = LightColorScheme


   MaterialExpressiveTheme(
       colorScheme = colorScheme,
       typography = Typography,
       shapes = shapes,
       motionScheme = MotionScheme.expressive(),
       content = {
           SharedTransitionLayout {
               CompositionLocalProvider(LocalSharedTransitionScope provides this) {
                   content()
               }
           }
       },
   )
}

Some of the new componentry is used throughout the app, including the HorizontalFloatingToolbar for the Prompt type selection:

The app also uses MaterialShapes in various locations, which are a preset list of shapes that allow for easy morphing between each other. For example, check out the cute cookie shape for the camera capture button:

Animations

Wherever possible, the app leverages the Material 3 Expressive MotionScheme to obtain a themed motion token, creating a consistent motion feeling throughout the app. For example, the scale animation on the camera button press is powered by defaultSpatialSpec(), a specification used for animations that move something across a screen (such as x,y or rotation, scale animations):

val interactionSource = remember { MutableInteractionSource() }
val animationSpec = MaterialTheme.motionScheme.defaultSpatialSpec<Float>()
Spacer(
   modifier
       .indication(interactionSource, ScaleIndicationNodeFactory(animationSpec))
       .clip(MaterialShapes.Cookie9Sided.toShape())
       .size(size)
       .drawWithCache {
           //.. etc
       },
)

Shared element animations

The app uses shared element transitions between different screen states. Last year, we showcased how you can create shared elements in Jetpack Compose, and we’ve extended this in the Androidify sample to create a fun example. It combines the new Material 3 Expressive MaterialShapes, and performs a transition with a morphing shape animation:

To do this, we created a custom Modifier that takes in the target and resting shapes for the sharedBounds transition:

@Composable
fun Modifier.sharedBoundsRevealWithShapeMorph(
   sharedContentState: 
SharedTransitionScope.SharedContentState,
   sharedTransitionScope: SharedTransitionScope = 
LocalSharedTransitionScope.current,
   animatedVisibilityScope: AnimatedVisibilityScope = 
LocalNavAnimatedContentScope.current,
   boundsTransform: BoundsTransform = 
MaterialTheme.motionScheme.sharedElementTransitionSpec,
   resizeMode: SharedTransitionScope.ResizeMode = 
SharedTransitionScope.ResizeMode.RemeasureToBounds,
   restingShape: RoundedPolygon = RoundedPolygon.rectangle().normalized(),
   targetShape: RoundedPolygon = RoundedPolygon.circle().normalized(),
)

Then, we apply a custom OverlayClip to provide the morphing shape, by tying into the AnimatedVisibilityScope provided by the LocalNavAnimatedContentScope:

val animatedProgress =
   animatedVisibilityScope.transition.animateFloat(targetValueByState = targetValueByState)


val morph = remember {
   Morph(restingShape, targetShape)
}
val morphClip = MorphOverlayClip(morph, { animatedProgress.value })


return this@sharedBoundsRevealWithShapeMorph
   .sharedBounds(
       sharedContentState = sharedContentState,
       animatedVisibilityScope = animatedVisibilityScope,
       boundsTransform = boundsTransform,
       resizeMode = resizeMode,
       clipInOverlayDuringTransition = morphClip,
       renderInOverlayDuringTransition = renderInOverlayDuringTransition,
   )

View the full code snippet for this Modifer on GitHub.

Autosize text

With the latest release of Jetpack Compose 1.8, we added the ability to create text composables that automatically adjust the font size to fit the container’s available size with the new autoSize parameter:

BasicText(text,
style = MaterialTheme.typography.titleLarge,
autoSize = TextAutoSize.StepBased(maxFontSize = 220.sp),
)

This is used front and center for the “Customize your own Android Bot” text:

This text composable is interesting because it needed to have the fun dancing Android bot in the middle of the text. To do this, we use InlineContent, which allows us to append a composable in the middle of the text composable itself:

@Composable
private fun DancingBotHeadlineText(modifier: Modifier = Modifier) {
   Box(modifier = modifier) {
       val animatedBot = "animatedBot"
       val text = buildAnnotatedString {
           append(stringResource(R.string.customize))
           // Attach "animatedBot" annotation on the placeholder
           appendInlineContent(animatedBot)
           append(stringResource(R.string.android_bot))
       }
       var placeHolderSize by remember {
           mutableStateOf(220.sp)
       }
       val inlineContent = mapOf(
           Pair(
               animatedBot,
               InlineTextContent(
                   Placeholder(
                       width = placeHolderSize,
                       height = placeHolderSize,
                       placeholderVerticalAlign = PlaceholderVerticalAlign.TextCenter,
                   ),
               ) {
                   DancingBot(
                       modifier = Modifier
                           .padding(top = 32.dp)
                           .fillMaxSize(),
                   )
               },
           ),
       )
       BasicText(
           text,
           modifier = Modifier
               .align(Alignment.Center)
               .padding(bottom = 64.dp, start = 16.dp, end = 16.dp),
           style = MaterialTheme.typography.titleLarge,
           autoSize = TextAutoSize.StepBased(maxFontSize = 220.sp),
           maxLines = 6,
           onTextLayout = { result ->
               placeHolderSize = result.layoutInput.style.fontSize * 3.5f
           },
           inlineContent = inlineContent,
       )
   }
}

Composable visibility with onLayoutRectChanged

With Compose 1.8, a new modifier, Modifier.onLayoutRectChanged, was added. This modifier is a more performant version of onGloballyPositioned, and includes features such as debouncing and throttling to make it performant inside lazy layouts.

In Androidify, we’ve used this modifier for the color splash animation. It determines the position where the transition should start from, as we attach it to the “Let’s Go” button:

var buttonBounds by remember {
   mutableStateOf<RelativeLayoutBounds?>(null)
}
var showColorSplash by remember {
   mutableStateOf(false)
}
Box(modifier = Modifier.fillMaxSize()) {
   PrimaryButton(
       buttonText = "Let's Go",
       modifier = Modifier
           .align(Alignment.BottomCenter)
           .onLayoutRectChanged(
               callback = { bounds ->
                   buttonBounds = bounds
               },
           ),
       onClick = {
           showColorSplash = true
       },
   )
}

We use these bounds as an indication of where to start the color splash animation from.

Learn more delightful details

From fun marquee animations on the results screen, to animated gradient buttons for the AI-powered actions, to the path drawing animation for the loading screen, this app has many delightful touches for you to experience and learn from.

Check out the full codebase at github.com/android/androidify and learn more about the latest in Compose from using Material 3 Expressive, the new modifiers, auto-sizing text and of course a couple of delightful interactions!

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

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:

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.

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),
    )
}

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.

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 Get Started With Kotlin Multiplatform for Shared Business Logic, offered by Matt Dyor, Dustin Lam, and Tomáš Mlynarič, provides hands-on guidance for extracting business logic from native Android and iOS apps into a shared KMP module.
• 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