Every developer's AI workflow and needs are unique, and it's important to be able to choose how AI helps your development. In January, we introduced the ability to choose any local or remote AI model to power AI functionality in Android Studio, and today, we're announcing the availability of Gemma 4 for AI coding assistance in Android Studio. This new local model trained on Android development provides the best of both worlds: the privacy and cost-efficiency of on-device processing alongside state-of-the-art reasoning and tool-calling capabilities.

AI assistance, locally delivered

By running locally on your machine, Gemma 4 gives you AI code assistance that doesn't require an internet connection or an API key for its core operations. Key benefits include:

• Privacy and security: Your code stays on your machine. Gemma 4 processes all Agent Mode requests locally, making it an ideal choice for developers working with data privacy requirements or in secure corporate environments.
• Cost efficiency: Run complex agentic workflows without worrying about hitting quotas. Gemma 4 is optimized to run efficiently on modern development hardware, utilizing local GPU and RAM to provide snappy, responsive assistance.
• Offline availability: Use the agent to write code even when you don’t have an internet connection.
• State-of-the-art reasoning: Gemma 4 delivers best-in-class reasoning, capable of complex multi-step coding tasks in Agent Mode.

Powerful agentic coding

Gemma 4 was trained for Android development with agentic tool calling capabilities. When you select Gemma 4 as your local model, you can leverage Agent Mode for a variety of development use cases, such as:

• Designing new features: Developers can ask the agent to build a new feature or an entire app with commands like “build a calculator app” and the agent will not only generate the UI code but will use Android best practices like writing in Kotlin and using Jetpack Compose.
• Refactoring: You can give high-level commands such as "Extract all hardcoded strings and migrate them to strings.xml." The agent will scan your codebase, identify instances requiring changes, and apply the edits across multiple files simultaneously.
• Bug fixing and build resolution: If a project fails to build or has persistent lint errors, you can prompt the agent to "Build my project and fix any errors." The agent will navigate to the offending code and iteratively apply fixes until the build is successful.

Recommended hardware requirements

The 26B MoE is recommended for Android app developers using a machine with the minimum hardware requirements. Total RAM needed includes both Android Studio and Gemma.

Get started

1. Install an LLM provider, such as LM Studio or Ollama, on your local computer.
2. In Settings > Tools > AI > Model Providers add your LM Studio or Ollama instance.

1. Download the Gemma 4 model from Ollama or LM Studio. Refer to hardware requirements for model size selection.
2. In Agent Mode, select Gemma 4 as your active model.

For a detailed walkthrough on configuration, check out the official documentation on how to use a local model.

We are excited to see how Gemma 4 enables more private, secure, and powerful development workflows. As always, your feedback is essential as we continue to refine the AI experience in Android Studio. If you find a bug or issue, please file an issue. Also you can be part of our vibrant Android developer community on LinkedIn, YouTube, or X. Happy coding!

At Google, we’re committed to bringing the most capable AI models directly to the Android devices in your pocket. Today, we’re thrilled to announce the release of our latest state-of-the-art open model: Gemma 4.

These models are the foundation for the next generation of Gemini Nano, so code you write today for Gemma 4 will automatically work on Gemini Nano 4-enabled devices that will be available later this year. With Gemini Nano 4, you’ll benefit from our additional performance optimizations so you can ship to production across the Android ecosystem with the most efficient on-device inference.

You can get early access to this model today through the AICore Developer Preview.

Because Gemma 4 natively supports over 140 languages, you can expect improved localized, multilingual experiences for your global audience. Furthermore, Gemma 4 offers industry-leading performance with multimodal understanding, allowing your apps to understand and process text, images, and audio. To give you the best balance of performance and efficiency, Gemma 4 on Android comes in two sizes:

• E4B: Designed for higher reasoning power and complex tasks.
• E2B: Optimized for maximum speed (3x faster than the E4B model!) and lower latency.

The new model is up to 4x faster than previous versions and uses up to 60% less battery. Starting today, you can experiment with improved capabilities including:

• Reasoning: Chain-of-thought commands and conditional statements can now be expected to return higher quality results. For example: “Determine if the following comment for a discussion thread passes the community guidelines. The comment does not pass the community guideline if it contains one or more of these reason_for_flag: profanity, derogatory language, hate speech”. If the review passes the community guidelines, return {true}. Otherwise, return {false, reason_for_flag}.”
• Math: With better math skills, the model can now more accurately answer questions. For example: “If I get 26 paychecks per year, how much should I contribute each paycheck to reach my savings goal of $10,000 over the course of a year?”
• Time understanding: The model is now more capable when reasoning about time, making it more accurate for use cases that involve calendars, reminders, and alarms. For example: “The event is at 6PM on August 18th, and a reminder should be sent out 10 hours before the event. Return the time and date the reminder should be sent.”
• Image understanding: Use cases that involve OCR (Optical Character Recognition) - such as chart understanding, visual data extraction, and handwriting recognition - will now return more accurate results.

Join the Developer Preview today to download these models in preview models and start building next-generation features right away.

// Define the configuration with a specific track and preference
val previewFullConfig = generationConfig {
    modelConfig = ModelConfig {
        releaseTrack = ModelReleaseTrack.PREVIEW
        preference = ModelPreference.FULL
    }
}

// Initialize the GenerativeModel with the configuration
val previewModel = GenerativeModel.getClient(previewFullConfig)

// Verify that the specific preview model is available
val previewModelStatus = previewModel.checkStatus()
if (previewModelStatus == FeatureStatus.AVAILABLE) {
    // Proceed with inference
    val response = previewModel.generateContent("If I get 26 paychecks per year, how much I should contribute each paycheck to reach my savings goal of $10k over the course of a year? Return only the amount.")

} else {
    // Handle the case where the preview model is not available
    // (e.g., print out log statements)
}

Claude Code skills are packaged folders of instructions, scripts and resources that teach the AI coding agent how to perform specialised tasks.

Each skill has a SKILL.md file containing YAML metadata and step by step instructions. Claude scans the names and descriptions of installed skills and loads the full content only when your request matches.

Because skills are part of an open standard adopted by multiple agent frameworks, a single skill can work across Claude Code, OpenAI Codex, Cursor and other tools.

Installing Claude Code skills

Skills can be installed globally or per project. Personal skills go into ~/.claude/skills/ and become available across all your projects, while project specific skills live in .claude/skills/ inside your repository so everyone who clones the repo can use them.

Most skills are plain Markdown folders: clone the repository and copy the skill directory into the appropriate folder. For example, the Swift Development skill’s README shows that you can install it by copying the Swift Development skill directory into ~/.claude/skills/ and then invoking it via /swift-development.

Recommended skills for mobile developers

Below is a curated list of Claude Code skills that are particularly valuable for mobile developers working with native iOS, Android or Kotlin Multiplatform (KMP). The list covers a range of design and development tasks. Each skill is linked to its repository or marketplace page along with a brief description.

Swift Development
https://github.com/hmohamed01/swift-development
This comprehensive skill turns Claude into a competent Swift engineer. It packages patterns for SwiftUI navigation, concurrency (async/await, actors) and testing frameworks, along with helper scripts for building, running tests, formatting code and managing simulators. Installation is simple: copy the Swift Development directory into your personal skills folder and invoke it with /swift-development.

iOS Mobile Design
https://mcpmarket.com/tools/skills/ios-mobile-design
For designers and developers building polished iOS apps, this skill provides expert guidance on Apple’s Human Interface Guidelines. It teaches Claude how to apply SwiftUI layout systems, dynamic type, SF Symbols and modern navigation patterns so interfaces feel native to iPhone, iPad and visionOS. Key features include accessible design with dynamic type, responsive layouts, Material You style theming and Dark Mode optimisation.

iOS App Builder
https://mcpmarket.com/tools/skills/ios-app-builder
This skill transforms Claude into a senior iOS developer who builds and ships native apps using a command line only workflow. It uses Xcode’s xcodebuild and simctl tools to scaffold projects, run tests and launch apps without opening Xcode. The skill emphasises a “prove, don’t promise” methodology: it verifies logic through tests and simulator screenshots, manages the full app life cycle from scaffolding to App Store readiness and includes features like performance profiling and memory leak detection

SwiftUI agent skill
https://github.com/twostraws/swiftui-agent-skill
A SwiftUI skill create by Paul Hudson (hacking with swift).
This skill helps identify and fix common mistakes AI agents make when writing SwiftUI – things like modern API usage, performance, accessibility, and more.

Android Development
https://github.com/dpconde/claude-android-skill
Designed for modern Android projects, this skill teaches Claude clean architecture with separate UI, domain and data layers. It covers Jetpack Compose patterns and best practices, multi module project structures, offline first architecture using Room and reactive streams, dependency injection with Hilt and comprehensive testing strategies. After cloning the repository into your skills folder, you can ask the agent to scaffold features, create Compose screens or set up repositories following Android best practices.

Android Kotlin Development
https://fastmcp.me/skills/details/241/android-kotlin-development
This skill focuses on building native Android apps with Kotlin. The description explains that it covers Model-View-ViewModel architecture with Jetpack, Compose for declarative user interfaces, Retrofit for API calls, Room for local storage and navigation patterns. It includes detailed instructions and code samples for models, API services and MVVM view models, making it a practical guide for Kotlin developers.

Mobile Android Design
https://fastmcp.me/skills/details/644/mobile-android-design
When you need to design Android interfaces that follow Google’s Material Design 3 guidelines, this skill is indispensable. It teaches Claude how to build adaptive layouts with Jetpack Compose, implement navigation patterns like bottom navigation and drawers, use dynamic colour and Material You theming, and create accessible UI for phones, tablets and foldables. It is especially helpful for modernising existing apps or ensuring new designs comply with Material Design principles.

Jetpack Compose Expert
https://github.com/aldefy/compose-skill
This skill provides Claude with real source code and extensive reference guides for Jetpack Compose. Without it, AI tools often misuse remember, generate inefficient recompositions or misorder modifiers; with the skill, Claude picks the right state primitive, applies stability annotations and orders modifiers correctly. The reference guides cover state management, view composition, performance, navigation, animation, lists, side effects, modifiers, theming, accessibility and deprecated patterns. Installation requires cloning the repository and copying the Jetpack Compose expert skill directory into your skills folder; Claude will automatically activate it when you mention Compose or related API names.

Kotlin Multiplatform (KMP) Abstraction Guide
https://fastmcp.me/skills/details/2190/kotlin-multiplatform
KMP projects often involve tough decisions about what to share between platforms and what to keep platform specific. This skill guides those decisions by providing a platform abstraction decision tree. It explains when to use commonMain, jvmAndroid or expect/actual patterns, covers targets like Android, JVM/Desktop and iOS, and offers triggers for questions such as "should I share this ViewModel?". It helps developers place code correctly and prepare for future web or WebAssembly targets.

A developer uses Copilot to write 30 lines of code in 10 minutes, but then spends 45 minutes reviewing it – checking for bugs, edge cases, and code that doesn’t match team standards.

The time saved during writing gets completely eaten up during validation. And this is exactly what happens repeatedly across teams trying to adopt AI at scale.

At the Pragmatic Summit, Laura Tacho (CTO at DX) shared some interesting research on AI in coding:

Almost 93% of developers use AI assistants every month, and about 27% of production code now comes from AI. Yet, despite all this, overall productivity has barely budged – staying around a 10% boost since AI tools arrived.

AI adoption is everywhere…

The numbers are clear:

• 92.6% of developers use AI coding assistants monthly
• 75% use them weekly
• 26.9% of production code contains AI-authored segments

84% of developers use AI tools, according to Stack Overflow’s 2025 survey. Adoption is now standard – the numbers are probably even bigger now.

…Yet work isn’t moving any quicker

The gap between adoption and productivity appears first as a trust problem.

46% of developers don’t fully trust the output, and that skepticism has a reason: reviewing AI-generated code frequently requires more effort than reviewing human-written one.

The DX AI Measurement Framework (published by vendor DX but structured as an industry standard) identifies this directly:

Code generated by AI may be less intuitive for human developers to understand, potentially creating bottlenecks when issues arise or modifications are needed.

This is why productivity hasn’t jumped. Developers might write code faster with AI, but they end up spending the same time checking, fixing, and making sense of what AI produces. In the end, the overall development cycle doesn’t get any shorter.

Sonar’s research confirms the pattern at scale: 42% of committed code now includes AI assistance, yet 96% of developers say they don’t fully trust AI-generated code. And this is exactly what we see: output is everywhere, but the confidence in it is not.

Why productivity has stalled?

That 10% productivity bump comes down to a workflow mismatch.

Teams started using AI to write code faster, but didn’t adjust how they review, test, or integrate it. In other words, writing got quicker, but everything that comes after stayed just as slow.

The DX research notes a broader context relevant here: most organizations see their biggest bottlenecks not in code generation, but:

In the outer loop, or in human factors like collaboration, alignment, and the ability to do deep, focused work.

AI addresses one specific problem, and that’s code-writing speed. But, as we can see, the overall development cycle has other constraints.

Teams that actually see productivity gains from AI usually do two things: they figure out exactly where AI adds value, and they tweak their workflows to make the most of it. Teams that just deploy AI without changing how they work? They get adoption, but no real boost in productivity.

The 10% productivity ceiling sticks because the time spent validating AI-written code cancels out the speed gains. Most teams focus on writing faster, but few have optimized for faster validation.

It’s an obvious obstacle, but maybe also an opportunity.

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