# mobile-app-development Published articles for mobile-app-development. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Kotlin 2.4.20 Collection Checks and Experimental Context-Sensitive Resolution DevFeed: [Kotlin 2.4.20 Collection Checks and Experimental Context-Sensitive Resolution]() Original publisher: [Read original article]() Author: Andrii Dubovyk Published: 2026-09-14T06:32:51Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [Kotlin](), [Compiler](), [Code]() Tags: [android](), [android-development](), [collections](), [compiler](), [experimental](), [features](), [kotlin](), [list](), [mobile-app-development](), [syntax](), [technology]() ### AI overview The article explains two Kotlin features: allDistinctBy and allEqualBy in Kotlin 2.4.20 for checking collection values, and experimental context-sensitive resolution introduced in Kotlin 2.2 to reduce repetition when the expected type is known. ### Source excerpt Kotlin 2.4.20 gives your collections opinions, and 2.2's compiler finally learns to read the room Some Kotlin features get a lot of attention when they arrive. Others are much less noticeable, but still make everyday code a little easier to write. Two examples of the latter are allDistinctBy/ allEqualBy in Kotlin 2.4.20 and context-sensitive resolution, which was introduced experimentally in Kotlin 2.2. allDistinctBy/ allEqualBy: A Simpler Way to Check Collections For years, checking whether all values in a collection were unique often looked something like this: val users = listOf( User(name = "A", age = 20), User(name = "B", age = 25), User(name = "C", age = 30) ) users.map { it.age }.distinct().size == users.size // true It works, but it creates another list, removes duplicates, counts the result, and then compares the sizes just to get a Boolean answer. Kotlin 2.4.20 adds a more direct way to express the same check: users.allDistinctBy { it.age } // true users.allEqualBy { it.age } // false allDistinctBy returns false as soon as it finds a duplicate, so it doesn't need to process the entire collection or create a separate Set. allEqualBy does the opposite: it checks whether all elements produce the same value when passed through the selector. Both functions work with Iterable, Sequence, and arrays. They use structural equality. For floating-point values, they follow Double.equals semantics, so NaN is considered equal to NaN, while -0.0 is different from 0.0. They're still @ExperimentalStdlibApi, so you'll need to opt in before using them in production code. The underlying allDistinct() and allEqual() requests have also been around in YouTrack for quite a while, which makes their appearance in the standard library feel long overdue. Context-Sensitive Resolution: Less Repetition in when The second feature is more about syntax. When the compiler already knows the type you're working with, you can sometimes leave out the type qualifier. Before: enum class Problem { ## Setup On Device Gemma 4 Android Development on Emulator DevFeed: [Setup On Device Gemma 4 Android Development on Emulator]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-11T14:27:55Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [gemma4](), [Android](), [Emulator](), [android-development]() Tags: [android](), [android-app-development](), [android-development](), [androiddev](), [artificial-intelligence](), [emulator](), [gemma](), [gemma-4](), [google](), [mobile-app-development](), [on-device]() ### AI overview A tutorial explaining how to set up and run the Gemma 4 large language model locally on an Android Emulator, including downloading and testing the Google AI Edge Gallery sample app. It notes that emulator storage and memory may affect model use. ### Source excerpt Create Android Project with Downloadable Android On Device Gemma 4 Model Continue reading on Mobile App Development Publication " ## Android Studio Quail Using On Device Gemma 4 Experience DevFeed: [Android Studio Quail Using On Device Gemma 4 Experience]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-09T11:28:45Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android Studio](), [gemma4](), [Large Language Model](), [Development]() Tags: [agent](), [android](), [android-development](), [android-studio](), [development](), [gemma](), [gemma-4](), [llm](), [local](), [memory](), [mobile-app-development](), [on-device]() ### AI overview This article describes using the on-device Gemma 4 model in Android Studio Quail for Android development. It outlines selecting the local model option, choosing Gemma in Model Providers, and downloading a model, while noting the need for a fast machine with substantial memory. ### Source excerpt How's Android Studio Quail support for On-Device LLM Model Doing? Continue reading on Mobile App Development Publication " ## Android A2UI (AI to UI) Made Simple DevFeed: [Android A2UI (AI to UI) Made Simple]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-09T07:23:57Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Artificial Intelligence](), [Android](), [Jetpack Compose](), [Mobile](), [Protocol (disambiguation)](), [Google]() Tags: [a2ui](), [ai](), [android](), [android-app-development](), [api](), [artificial-intelligence](), [deprecated](), [google](), [jetpack-compose](), [mobile-app-development](), [software-development](), [user-interfaces]() ### AI overview This tutorial introduces A2UI, a protocol for rendering AI responses as user interfaces, and discusses trying it on Android with Jetpack Compose. It notes that an older Jetpack A2UI library is deprecated and that newer development is ongoing. ### Source excerpt Converting AI Prompt Output Into User Interface in Jetpack Compose Continue reading on Mobile App Development Publication " ## Android 17 AppFunction Walkthrough First Experience DevFeed: [Android 17 AppFunction Walkthrough First Experience]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-09-07T12:51:23Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android](), [Mobile](), [App](), [Development](), [Artificial Intelligence](), [Android Studio](), [Emulator](), [Android Gradle Plugin]() Tags: [ai](), [android](), [android-app-development](), [android-gradle-plugin](), [android-studio](), [app-development](), [artificial-intelligence](), [development](), [emulator](), [gemini](), [mobile](), [mobile-app-development](), [software-development]() ### AI overview A first-hand walkthrough of Android 17 AppFunction, which lets Gemini discover an app's functionality. The article covers the required Android Studio version, Android 17 emulator, Android SDK API 37.0 target, and Android Gradle Plugin requirements. ### Source excerpt Enabling Gemini to Find Your App Functionalities Continue reading on Mobile App Development Publication " ## Setting Up Koin in a Real Kotlin Multiplatform Project: A Step-by-Step Guide DevFeed: [Setting Up Koin in a Real Kotlin Multiplatform Project: A Step-by-Step Guide]() Original publisher: [Read original article]() Author: Gabriel Bronzatti Moro Published: 2026-09-07T12:01:03Z Content type: tutorial Language: en Sources: [Koin developers - Medium]() Topics: [Kotlin Multiplatform](), [compose-multiplatform](), [Kotlin](), [Android](), [iOS](), [ui]() Tags: [android](), [compose-multiplatform](), [dependencies](), [guide](), [implementation](), [ios](), [kmp](), [koin](), [kotlin](), [kotlin-multiplatform](), [mobile-app-development](), [module](), [viewmodel]() ### AI overview A step-by-step tutorial for setting up Koin in a Kotlin Multiplatform project targeting Android and iOS. It covers dependencies, data, domain, and UI modules, a platform bridge, platform entry points, and dependency injection in Composables. ### Source excerpt Hi everyone! 👋 In this article, we'll take a practical, step-by-step approach to setting up Koin in a real-world Kotlin Multiplatform (KMP) project. Instead of starting with an isolated example or a simple Hello World, we'll work with a project created by the CodandoTV community: the World Cup Best Team Simulator. The app allows users to browse football players by country and build their own dream team. It runs on both Android and iOS, using Kotlin Multiplatform for sharing business and data logic, and Compose Multiplatform to share the UI across platforms. Step 1: Add Koin Dependencies Add the Koin libraries to your version catalog: # gradle/libs.versions.toml [versions] koin = "4.2.2" [libraries] koin_core = { group = "io.insert-koin", name = "koin-core", version.ref = "koin" } koin_android = { group = "io.insert-koin", name = "koin-android", version.ref = "koin" } koin_compose = { module = "io.insert-koin:koin-compose", version.ref = "koin" } koin_compose_viewmodel = { module = "io.insert-koin:koin-compose-viewmodel", version.ref = "koin" } In the shared module shared/build.gradle.kts, split the dependencies by source set -- commonMain gets the shared Koin libraries, androidMain gets the Android-specific one: // shared/build.gradle.kts sourceSets { commonMain.dependencies { implementation(libs.koin.core) implementation(libs.koin.compose) implementation(libs.koin.compose.viewmodel) } androidMain.dependencies { implementation(libs.koin.android) } } In the Android app module androidApp/build.gradle.kts , add the Android Koin dependency: // androidApp/build.gradle.kts dependencies { implementation(libs.koin.android) implementation(libs.koin.core) }Step 2: Create the Data Module Create shared/src/commonMain/.../data/DataModule.kt . This module provides singleton instances -- repositories, and data sources that live for the entire app lifecycle. val dataModule = module { single { WorldCupRepositoryImpl(ioDispatcher = Dispatchers.IO) } }Step 3: Create ## Animating between numbers in Jetpack Compose DevFeed: [Animating between numbers in Jetpack Compose]() Original publisher: [Read original article]() Author: hitherejoe Published: 2026-09-02T12:37:43Z Content type: tutorial Language: en Sources: [Joe Birch]() Topics: [Jetpack Compose](), [ui](), [API](), [Tooling]() Tags: [android](), [android-app-development](), [android-apps](), [android-development](), [androiddev](), [animation](), [api](), [app-development](), [apps](), [jetpack-compose](), [jetpackcompose](), [mobile-app-development](), [ui]() ### AI overview This tutorial explains how to animate changing numerical portfolio values in Jetpack Compose. It uses animateFloatAsState with a target value and a tween animation configured with a 500ms duration and FastOutSlowInEasing. ### Source excerpt When it comes to displaying portfolio data inside of Sleevd, users can change the selected filters which will adjust the portfolio values based on that selection. Alongside the main graph, we show the total portfolio value along with some small statistics for collection counts. When the user moves between the filter options, the values of... Continue reading -> ## How To Get Apple To Respond In Days On AppStore Issue DevFeed: [How To Get Apple To Respond In Days On AppStore Issue]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-08-26T10:40:01Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [iOS](), [Mobile](), [Development]() Tags: [app-development](), [apple](), [developer](), [how-to](), [ios-app-development](), [ios-development](), [issue](), [mobile](), [mobile-app-development](), [software-development]() ### AI overview The article describes an App Store submission issue in which the Submit spinner stops and the process cannot continue. It presents this as a practical account of what to do when Apple does not respond for weeks. ### Source excerpt A True Story: What To Do When You're Stuck With AppStore Issue for Weeks Continue reading on Mobile App Development Publication " ## An insider argues that Google remains committed to Flutter and Dart DevFeed: [An insider argues that Google remains committed to Flutter and Dart]() Original publisher: [Read original article]() Author: Randal L. Schwartz Published: 2026-08-19T16:02:19Z Content type: opinion Language: en Sources: [Flutter Community - Medium]() Topics: [Flutter](), [Dart](), [Google](), [multiplatform](), [WebAssembly]() Tags: [cross-platform](), [dart](), [devrel](), [flutter](), [google](), [mobile-app-development](), [programming](), [software-development](), [wasm](), [webassembly]() ### AI overview This commentary argues, from the author's stated experience as a Flutter Google Developer Expert, that Google remains committed to Flutter and Dart despite restructuring-related concerns and reduced public communication. It cites renewed advocacy and investment in rendering, Dart ergonomics, WebAssembly compilation, and multiplatform performance. ### Source excerpt Every few months, like clockwork, the tech blogosphere gets flooded with the same recycled headline: "Is Flutter Dying?", "Why CTOs Are Quietly Leaving Flutter", or "Why Google is Killing Its Cross-Platform Bet." As someone who has been a Flutter Google Developer Expert for eight years now -- literally from day one of the GDE program -- and a five-decade software industry veteran, I usually just chuckle at the clickbait. "Flutter is dead." -- Said every six months since 2018. Meanwhile: Flutter is kicking tail on every single measurable scale. The alarmist articles point to standard corporate reorganizations, shifting tech job boards, and "state management fatigue" as evidence of Flutter's demise. But having watched this ecosystem evolve from an experimental alpha into an enterprise powerhouse, the reality on the ground is the exact opposite. Here is the real insider story on what's actually happening with Dart and Flutter. 1. The Inside Story: What Happened at Google? When tech companies restructured engineering teams recently, the internet spun a wild narrative that "Google put Flutter on life support." Having direct access to internal teams, I watched the commitment to Dart and Flutter remain steadfast within the organization. However, there was a temporary disconnect: internal engineering activity was roaring, but external communications and public advocacy had slowed down, leaving an information vacuum that clickbait writers eagerly filled. I personally called out to team leaders and senior VPs that this perception gap needed immediate correction. And the leadership responded strongly: Revitalized DevRel & Advocacy: A renewed surge in active community engagement, tutorials, and public roadmaps. Enterprise Adoption Transparency: Showcasing massive internal and external production milestones. Aggressive Core Investment: Deep work on the Impeller rendering engine, Dart 3.x ergonomics, WebAssembly (Wasm) compilation, and native multiplatform performance. Flutter is no ## A Local-to-Backend Workflow for Iterating on AI Features in Kotlin Multiplatform Apps DevFeed: [A Local-to-Backend Workflow for Iterating on AI Features in Kotlin Multiplatform Apps]() Original publisher: [Read original article]() Author: Paul Woitaschek Published: 2026-07-17T11:49:09Z Content type: tutorial Language: en Sources: [YAZIO Engineering - Medium]() Topics: [Artificial Intelligence](), [Kotlin Multiplatform](), [LLMs](), [Kotlin](), [Mobile]() Tags: [ai](), [kotlin](), [kotlin-multiplatform](), [llm](), [llms](), [mobile](), [mobile-app-development]() ### AI overview The article presents a workflow for building AI features in a Kotlin Multiplatform mobile app: define an app interface, implement and iterate on the prompt locally with Koog, then move the completed logic to a backend. It uses a voice-based nutrition-tracking example that converts natural-language input into typed proposed actions. ### Source excerpt TLDR: Define the interface in the app, iterate locally with Koog, move the logic to the backend when it's done. AI features have a weird reputation among mobile developers. The model sits behind an API, the prompt lives on a server someone else owns, and every change means a ticket, a review, a deploy. So the whole topic feels like backend territory. But an AI feature is just a feature. Text in, structured data out, some logic in between. The only unusual part: the core of that logic is a prompt. And prompts are not designed, they are iterated. You try an input, look at the output, adjust, try again. Dozens of times. So the real problem is not building the feature. It's making that loop fast. The pattern I would like to show: define an interface in the app, build the real UI against it, implement it locally with Koog, iterate until the output is solid, then move the implementation to the backend. In Kotlin that last step is mostly copy paste. The example: track everything in one sentence We build Yazio, a nutrition tracking app, as Kotlin Multiplatform: one shared codebase for iOS and Android. That matters here, because everything below is written once and runs on both platforms. Tracking today is many small interactions: search a food, pick a portion, add water, log a workout. Now imagine the user just says what happened: "I had two scrambled eggs and a coffee for breakfast, drank half a liter of water, went for a 30 minute run, and for lunch I ate the same as yesterday."Users can track using voice The app answers with a list of proposed actions. The user deletes the wrong ones, speaks again to add what's missing, and confirms everything at once. Speech to text is solved on both platforms, so from our side the input is just a string. Messy natural language in, exact typed data out. That's what LLMs are good at. It's also the kind of logic you won't get right on the first try. The constraint: the key can't live in the app You can't ship an OpenAI or Anthropic API ke ## Building AI Features in Kotlin Multiplatform Apps with Local Prompt Iteration DevFeed: [Building AI Features in Kotlin Multiplatform Apps with Local Prompt Iteration]() Original publisher: [Read original article]() Author: Paul Woitaschek Published: 2026-07-17T11:49:08Z Content type: tutorial Language: en Sources: [Stories by Paul Woitaschek on Medium]() Topics: [Kotlin Multiplatform](), [LLMs](), [Mobile](), [API](), [Back end](), [voice ai](), [anthropic](), [OpenAI]() Tags: [ai](), [anthropic](), [api](), [backend](), [kotlin](), [kotlin-multiplatform](), [llm](), [llms](), [mobile](), [mobile-app-development](), [openai](), [speech](), [voice]() ### AI overview This tutorial presents a workflow for building AI features in Kotlin Multiplatform mobile apps. It recommends defining the app interface first, implementing the feature locally with Koog to iterate quickly on prompts, and moving the implementation to a backend once the output is reliable. The example uses natural-language and voice-based nutrition tracking, with LLM output converted into proposed structured actions. ### Source excerpt TLDR: Define the interface in the app, iterate locally with Koog, move the logic to the backend when it's done. AI features have a weird reputation among mobile developers. The model sits behind an API, the prompt lives on a server someone else owns, and every change means a ticket, a review, a deploy. So the whole topic feels like backend territory. But an AI feature is just a feature. Text in, structured data out, some logic in between. The only unusual part: the core of that logic is a prompt. And prompts are not designed, they are iterated. You try an input, look at the output, adjust, try again. Dozens of times. So the real problem is not building the feature. It's making that loop fast. The pattern I would like to show: define an interface in the app, build the real UI against it, implement it locally with Koog, iterate until the output is solid, then move the implementation to the backend. In Kotlin that last step is mostly copy paste. The example: track everything in one sentence We build Yazio, a nutrition tracking app, as Kotlin Multiplatform: one shared codebase for iOS and Android. That matters here, because everything below is written once and runs on both platforms. Tracking today is many small interactions: search a food, pick a portion, add water, log a workout. Now imagine the user just says what happened: "I had two scrambled eggs and a coffee for breakfast, drank half a liter of water, went for a 30 minute run, and for lunch I ate the same as yesterday."Users can track using voice The app answers with a list of proposed actions. The user deletes the wrong ones, speaks again to add what's missing, and confirms everything at once. Speech to text is solved on both platforms, so from our side the input is just a string. Messy natural language in, exact typed data out. That's what LLMs are good at. It's also the kind of logic you won't get right on the first try. The constraint: the key can't live in the app You can't ship an OpenAI or Anthropic API ke ## Practical Kotlin Multiplatform: Setting up the Kotlin Multiplatform Networking Module with Ktor DevFeed: [Practical Kotlin Multiplatform: Setting up the Kotlin Multiplatform Networking Module with Ktor]() Original publisher: [Read original article]() Author: hitherejoe Published: 2026-07-10T16:16:31Z Content type: tutorial Language: en Sources: [Joe Birch]() Topics: [Kotlin Multiplatform](), [Kotlin](), [Ktor](), [networking](), [REST API](), [Authentication](), [Firebase](), [Gradle](), [implementation](), [Android](), [iOS]() Tags: [android](), [android-app-development](), [android-development](), [androiddev](), [app-development](), [authentication](), [firebase](), [gradle](), [implementation](), [ios](), [jetpack-compose](), [kotlin](), [kotlin-multiplatform](), [ktor](), [library](), [mobile-app-development](), [module](), [networking](), [rest-api]() ### AI overview A tutorial series installment that sets up a Kotlin Multiplatform networking module with Ktor for shared Android and iOS code. It describes building a portable Firebase Authentication and Firestore REST API layer without relying on platform-specific SDKs. ### Source excerpt This post is part one of a series adapted from Practical KMP, my book on building production Kotlin Multiplatform apps for Android and iOS. Part 1: Creating the Kotlin Multiplatform Module In this part of the book, we'll build the complete authentication remote store, defining a repository that handles every operation our app needs for... Continue reading -> ## Google IO 26: Gemini, Big Rocks, Opportunities for Android DevFeed: [Google IO 26: Gemini, Big Rocks, Opportunities for Android]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-07-05T09:06:38Z Content type: article Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android](), [Google](), [Artificial Intelligence](), [AI Models](), [android-development](), [AI tools for developers]() Tags: [ai](), [ai-models](), [ai-tools-for-developers](), [android](), [android-app-development](), [android-development](), [androiddev](), [gemini](), [google](), [google-io](), [mobile](), [mobile-app-development]() ### AI overview This article reviews Google I/O 26 with a focus on Gemini models, developer tools, the Android ecosystem, Android 17, and opportunities involving UI, performance, and AI. ### Source excerpt In the AI Age, Google continue to thrive in its mobile ecosystem Continue reading on Mobile App Development Publication " ## Android 17: 15 Important Changes to Keep Your App Working DevFeed: [Android 17: 15 Important Changes to Keep Your App Working]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-05-24T14:13:07Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Android](), [Development](), [android-development](), [Mobile]() Tags: [android](), [android-app-development](), [android-development](), [app-development](), [coding](), [development](), [mobile](), [mobile-app-development](), [performance](), [software-development]() ### AI overview This article outlines 15 changes developers should consider when preparing Android apps for Android 17, with emphasis on maintaining compatibility, performance, security, and Play Store compliance. ### Source excerpt Prepare Your App for a More Secure, High Performance, and Android XR Ready Future. Continue reading on Mobile App Development Publication " ## What's new in Flutter 3.44 DevFeed: [What's new in Flutter 3.44]() Original publisher: [Read original article]() Author: Khanh Nguyen Published: 2026-05-20T18:01:02Z Content type: release Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [SDK](), [Mobile](), [Agent Skills](), [Android](), [Developer experience](), [iOS](), [macOS]() Tags: [agent-skills](), [ai](), [android](), [dart](), [developer-experience](), [developer-tools](), [flutter](), [macos](), [mobile-app-development](), [open-source](), [release](), [sdk]() ### AI overview Flutter 3.44 introduces Hybrid Composition++ for Android, makes Swift Package Manager the default for iOS and macOS, and improves Vulkan support for Impeller. It also previews multi-window desktop support, separates Material and Cupertino from the core framework, and adds agent-oriented developer experience features. ### Source excerpt Flutter 3.44: Scaling to more users on more devices! Empowering developers at Google I/O 2026 Flutter 3.44 is here, and it's a big one! This release celebrates a milestone in the Flutter timeline: Hybrid Composition++ for Android, Swift Package Manager as the new iOS/macOS default, and improved Vulkan support for Impeller. We're previewing multi-window desktop support with Canonical as our new lead maintainer, and embarking on a major architectural evolution by decoupling Material and Cupertino from the core framework. We're redefining UX for agentic user experiences with GenUl and reimagining developer experience with Agentic Hot Reload and Dart & Flutter Agent Skills. Flutter is empowering the next generation of apps-everywhere from the 2026 Toyota RAV4 multimedia system to the upcoming LG webOS SDK. We're so excited to share all of the news and updates with you; welcome to Flutter 3.44! Flutter is everywhere, everyday, built by everyone, for everyone. This year's Flutter theme at Google I/O is: Flutter is everywhere, everyday, built by everyone, for everyone. "Everywhere, everyday" stems from a Tuesday realization I had using an app on my phone: Flutter apps were part of my life everywhere, from tracking my meals in the Bay Area to shopping while I was in Japan. The numbers back this up: The pub.dev ecosystem is more popular than ever, with over 1.3 billion package downloads, in the last 30 days alone. Flutter is now the second most popular development SDK for mobile on both major app stores with over 1.5 million monthly developers, a 50% increase in just one year. "Built by everyone, for everyone" reflects a chat with my Google I/O co-host, Kate Lovett, about the joy of our global community. Our 1,700+ committed and passionate contributors are the engine behind this progress, landing 5,800 changes in the core repository this past year. In this release cycle alone, we've landed 972 commits from 178 unique contributors, including 61 new contributors landing their ## Flutter's multiplatform value for agentic development DevFeed: [Flutter's multiplatform value for agentic development]() Original publisher: [Read original article]() Author: Michael Thomsen Published: 2026-05-18T18:42:10Z Content type: article Language: en Sources: [Flutter - Medium]() Topics: [Flutter](), [multiplatform](), [Artificial Intelligence](), [Dart](), [Development](), [Large Language Model]() Tags: [agentic](), [agentic-ai](), [agentic-development](), [ai](), [code](), [consistency](), [dart](), [flutter](), [flutter-app-development](), [llms](), [mobile-app-development](), [multiplatform](), [reuse]() ### AI overview The article explains how Flutter's single shared codebase can support multiplatform app development and agent-driven workflows. It argues that writing features once in Dart gives AI assistants unified context, reduces token usage and hallucination risk, and helps maintain consistency across platforms. It also describes reported benefits including code reuse, native compilation, and strongly typed Dart code. ### Source excerpt The fundamental value of multiplatform development with Flutter lies in building apps that support multiple platforms with just a single, shared source codebase, allowing developer teams to work in unison across all platforms. This is crucial in an AI-driven world where enhanced consistency, reduced token usage, and fast market reach become vital. By maintaining a single codebase, builders can focus their AI assistants on one unified context, drastically reduce token overhead, and minimize AI hallucinations. Instead of asking AI to translate features across fragmented, platform-specific languages, builders can leverage AI to write it once in Dart and instantly deploy it everywhere. Dash secretly hanging out in an alley doing agentive thingsThe existing value proposition Multiplatform development relies on enabling a single, shared source codebase. In our first-party Flutter apps, between 95% and 99% of the source code is shared. This massive code reuse unlocks several benefits: Faster time to market across multiple platforms because a team only needs to maintain one codebase. Guaranteed consistency across platforms, giving companies a single, consistent feature set to support across all their customers, regardless of their platform of choice. Native performance and stability because Flutter code is compiled to each platform's native machine code. Semantic guardrails increase security because the Dart language is strongly typed. The agentic value proposition While LLMs are good at translating requirements into code, using them to build separate native apps for each platform scales poorly. Replicating features across different languages using LLMs multiplies generation time and token usage, and can quickly lead to implementations drifting apart. Flutter's single-source solution eliminates these problems. But beyond just code sharing, Flutter's specific architecture makes it the ideal framework for agent-driven development. This emerging value proposition is driven by ## A Practical Guide to Paging 3 in Jetpack Compose DevFeed: [A Practical Guide to Paging 3 in Jetpack Compose]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2026-04-07T08:16:16Z Content type: tutorial Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Jetpack Compose](), [Android](), [Kotlin](), [GraphQL](), [API](), [Mobile](), [mobile-app-development](), [App]() Tags: [android](), [android-app-development](), [android-development](), [androiddev](), [api](), [graphql](), [guide](), [how-to](), [jetpack-compose](), [kotlin](), [mobile](), [mobile-app-development](), [programming]() ### AI overview A practical tutorial on using Paging 3 with Jetpack Compose to paginate server data from a GraphQL API in an Android application. It covers the limitations of loading very large lists at once and introduces built-in handling for loading states, retries, scroll-triggered fetching, and empty states. ### Source excerpt How To Elegantly Page Your Server Data on Your Android App Continue reading on Mobile App Development Publication " ## Revamping Myntra App Analytics persistence with KMP and SQLite DevFeed: [Revamping Myntra App Analytics persistence with KMP and SQLite]() Original publisher: [Read original article]() Author: Kartik Sharma Published: 2026-03-31T13:01:09Z Content type: article Language: en Sources: [Myntra]() Topics: [App](), [SQLite](), [Persistence](), [SDK](), [Databases](), [legacy](), [Error Handling]() Tags: [android](), [app](), [databases](), [error-handling](), [ios](), [kotlin-multiplatform](), [legacy](), [mobile-app-development](), [persistence](), [sdk](), [sqlite]() ### AI overview This engineering post describes Myntra's effort to re-architect the persistence layer of its app analytics SDK. It explains how a legacy Android pipeline using in-memory queues and SharedPreferences created performance and data-reliability bottlenecks as event volume grew, and introduces a SQLite-backed persistence engine intended to improve app stability and data integrity. ### Source excerpt Introduction In the dynamic landscape of e-commerce, data is the bedrock of decision-making, and app stability is the foundation of user experience. At Myntra, where millions of users engage with our platform daily, ensuring the reliability of both is paramount. This necessity drove us to re-architect a critical piece of our infrastructure: the persistence layer of the Myntra app's analytics SDK. This post details our journey of augmenting our legacy analytics SDK with a robust new persistence engine backed by SQLite[1], achieving significant gains in app stability and data integrity. Background: The Limits of the Legacy Pipeline For years, our legacy in-house analytics SDK was the workhorse for collecting user interaction data. However, as our app's event volume grew, the SDK's aging persistence and processing logic created significant bottlenecks that led to degraded app performance and compromised the reliability of analytics data our product teams depended on. On Android: The Legacy Pipeline and its Limits On Android, the legacy analytics pipeline followed a straightforward event processing model: Capture, Persist & Batch: Events generated by used actions were held in an in-memory queue and then serialized into a persistent queue backed by SharedPreferences[2]. Once a batch threshold was met (typically 10 events), the batch was dispatched to the server. Background Flushing: Periodic background jobs flushed unsent events and retried failed batches. Error Handling: Failed batches were pushed to a separate retry queue, with older events silently purged if the queue hit its cap. High level overview of legacy android pipeline At the time of the initial implementation, SharedPreferences was a pragmatic choice to prioritize velocity. The ecosystem for local databases on Android was still maturing, and raw SQLite often required significant boilerplate compared to the simplicity of a key-value store. However, SharedPreferences is architecturally limited for high-throughp ## How to Accept Payments in FlutterFlow Apps DevFeed: [How to Accept Payments in FlutterFlow Apps]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-24T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [Flutter](), [Mobile](), [mobile-app-development](), [web applications](), [Software as a service]() Tags: [checkout](), [firebase](), [flutter](), [how-to](), [merchant-of-record](), [mobile-app-development](), [no-code](), [no-code-tools](), [payment](), [payments](), [products](), [subscriptions](), [tax]() ### AI overview A step-by-step guide to integrating Dodo Payments into FlutterFlow mobile and web apps through a payment-link approach. It presents Dodo Payments as a Merchant of Record for checkout, subscriptions, and automatic tax compliance. ### Source excerpt Step-by-step guide to adding payment processing to FlutterFlow mobile and web apps. Use Dodo Payments for global checkout, subscriptions, and automatic tax compliance. ## Why Legacy Xcode Projects May Benefit from Replacing Groups with Folders DevFeed: [Why Legacy Xcode Projects May Benefit from Replacing Groups with Folders]() Original publisher: [Read original article]() Author: Elye - A Dev By Grace Published: 2025-12-07T13:07:57Z Content type: article Language: en Sources: [Mobile App Development Publication - Medium]() Topics: [Xcode](), [legacy](), [iOS]() Tags: [apple](), [coding](), [ios](), [ios-development](), [legacy](), [mobile-app-development](), [structure](), [xcode](), [xcode-16]() ### AI overview This article explains how Xcode project file organization differs between Groups and Folders. It notes that projects created before Xcode 16 use Groups by default, while new projects use Folders, and introduces reasons for considering Folders in legacy projects. ### Source excerpt Seven Reasons Folders Are Better -- Especially for Legacy Projects Continue reading on Mobile App Development Publication " ## 8 steps to success with Gemini in Android Studio DevFeed: [8 steps to success with Gemini in Android Studio]() Original publisher: [Read original article]() Author: hitherejoe Published: 2025-10-31T09:43:36Z Content type: tutorial Language: en Sources: [Joe Birch]() Topics: [Android Studio](), [configuration](), [Artificial Intelligence](), [context](), [context window](), [API keys]() Tags: [ai](), [ai-agents](), [android](), [android-app-development](), [android-apps](), [android-development](), [android-studio](), [androiddev](), [api](), [app-development](), [configuration](), [context-window](), [gemini](), [ide](), [mobile-app-development]() ### AI overview This tutorial presents configuration and workflow practices for using Gemini in Android Studio. It discusses API keys, larger context windows, agent rules, and Agents.MD files, with the goal of improving prompt results and trust in AI-assisted development. ### Source excerpt AI tooling is becoming a key part of our day-to-day and now with solid Gemini support in Android Studio, we're seeing engineers embed this deeper in their workflows. When they work well, AI Agents can help us to work more efficiently and give us more time to focus on other tasks. But when they don't... Continue reading -> ## Exploring Material 3 for Compose: Large Top App Bar DevFeed: [Exploring Material 3 for Compose: Large Top App Bar]() Original publisher: [Read original article]() Author: hitherejoe Published: 2025-08-04T14:59:31Z Content type: tutorial Language: en Sources: [Joe Birch]() Topics: [Compose](), [navigation]() Tags: [android](), [android-app-development](), [android-apps](), [android-development](), [androiddev](), [app-development](), [compose](), [how-to](), [implement](), [jetpack-compose](), [jetpackcompose](), [material-design](), [mobile-app-development](), [navigation](), [screen](), [state](), [visibility]() ### AI overview This tutorial explains how to use and customize Material 3's LargeTopAppBar composable in Compose. It covers the app bar's expanded and collapsed states and the scroll behaviors that control its transitions. ### Source excerpt In the last post, we took a look into the new Loading Indicator in the Material 3 expressive package. As we continue to dive into different parts of Material 3 we'll discover more components that we can plug into our apps. Up next we have the LargeTopAppBar - this composable brings dynamic, scroll-responsive navigation to... Continue reading -> ## Why Huawei's HarmonyOS Next could matter for small computing devices DevFeed: [Why Huawei's HarmonyOS Next could matter for small computing devices]() Original publisher: [Read original article]() Author: Jasper Morgan Published: 2025-04-03T08:46:50Z Content type: opinion Language: en Sources: [Snapp Mobile - Medium]() Topics: [Operating system](), [huawei](), [Open Source](), [Microcontrollers](), [Robotics]() Tags: [harmony-os](), [huawei](), [microcontrollers](), [mobile-app-development](), [open-source](), [operating-systems](), [os](), [robotics]() ### AI overview This opinion article examines Huawei's HarmonyOS Next, a version of HarmonyOS without Android support, and argues that an operating system designed for small computing devices could have a valuable role as those devices become more widespread. ### Source excerpt Last year Huawei ditched Android and in 2025 will be releasing more and more devices running its HarmonyOS Next software platform. HarmonyOS is based on an open source operating system (OpenHarmony) that started as an R&D project at Huawei in 2012. The operating system has been architected to support a wide array of small computing devices from microcontrollers to tablets. It is already used in several of Huawei's TVs, wearables and wireless router products. In 2024 a 'pure' version of the OS, without Android support, was released. This has been branded HarmonyOS Next. Several questions leap to mind: 1/ Does the world actually need a new OS? 2/ Why should we care about what Huawei is doing -- especially those of us outside China? 3/ Finally, what are the chances of success for a new OS? Let's look a little deeper into these topics and try to understand what's going on in this space. 1/ Does the world need a new OS? YES Let me explain why I have come to this conclusion. Computing needs have changed We have seen a steady growth of small form factor computing. We carry computing devices in our pockets, they are found in devices throughout our homes, they are part of our vehicles, they are in medical devices, and in countless industrial settings. Within this context, one could argue that phones are at the larger end of the spectrum when it comes to small computing devices. Single board computers and microcontrollers can be found in mechanical keyboards, security cameras, hand-held medical equipment, at the checkout in our shops etc. Going further, it's not a huge leap to see that the field of robotics is due to take off with the advent of AI for real time context-aware decision making. My first argument, therefore, is that an operating system specifically designed for small computing devices will have a valuable role to play. (Neither iOS nor Android were designed or have evolved to address this need.) Innovation and competition are good Several operating system innovati ## Compose Multiplatform and the Native App Future - Kevin Galligan DevFeed: [Compose Multiplatform and the Native App Future - Kevin Galligan]() Original publisher: [Read original article]() Published: 2024-08-30T00:00:00Z Content type: opinion Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [compose-multiplatform](), [Kotlin Multiplatform](), [cross-platform](), [Mobile](), [ui](), [Development]() Tags: [community](), [compose](), [compose-multiplatform](), [cross-platform](), [kotlin](), [kotlin-multiplatform](), [libraries](), [mobile](), [mobile-app-development](), [ui]() ### AI overview The article argues that Kotlin Multiplatform and Compose Multiplatform can combine cross-platform efficiency with native mobile user experiences. It describes sharing UI and underlying architecture while adding native UI where needed, reducing the risk of a full rewrite. ### Source excerpt Fully native mobile apps was for years the default mobile app development option. As cross-platform solutions and device capability have improved, "good enough" has become the default choice for anything that isn't a big, public app. Kotlin Multiplatform and Compose, which can seamlessly blend shared and native UI, can make "native" the default choice again. [Next page]()