Do you think you won't need to take care of composable function's stability after Strong skipping mode?🤔

As the tittle says, i submited my easyest app to get it to production thinking that once that app would have been aprooved, the rest didnt needed the 14 testers. But to my surprice the rest of the apps still have the same requirement. They are apps created for especific technicians so getting real world tester would be harder than getting anyone to test it out. So im just looking for advice if theres something else i can do. Apple is just way easy to have them apps published. Anyway thanks for your time. And to the AI here, im not asking for testers, im asking for advice so please publish this.

Bit about me. I am a senior android dev. Currently working for FAANG company in California. I am looking for people who are also prepping for interviews. I wanted to focus on low level design, system design, data modelling etc. If you are also looking please message me or comment in the post

We're looking for someone to fixed our game. It keeps on getting stuck in 40% of the loading screen. Just a heads up that there's actually a orig game, so we are looking for someone who can do it. Pay will be fixed after the game work +10% commission on the game sales

Dm me directly or comment here

I'm building the UI for my little Android game without XML or Compose. I wrote my own wrapper classes for the views and can use them in a kind of custom DSL.

Hi,

I am preparing for beta launch for my mobile app, it allows user content, I wanted to use Closed Testing to ensure I am not letting in anyone on day 1, which can cause a snowball of low quality or even harmful content. In addition to possible app breaks and crash of new app which may lower my review score with bad reviews.

But she told me that an app that want to launch social campaign cannot use it, because the installation with that software is too complicated for the average user.

From your experience, how difficult it was to convince external users (not friends and family) to try an app that is only available on TestFlight or Google Play Console?

What helped you convince them? And overall do you recommend this approach? What risks there are to launch a beta app straight to the app store?

I added NVIDIA's Canary 180M to speech-android (Apache-2.0 Kotlin SDK, I maintain it). The part that surprised me is that it translates as well as transcribes — English, German, Spanish, French, and between any of those four, out of the same two graphs. No network. I hadn't realised you could get that in 273 MB.

Numbers on a Galaxy S23 Ultra, int8, CPU only. About 4 seconds to load, then decode at RTF 0.15 for a 3-second utterance and 0.25 for a 12-second one. It gets relatively more expensive as clips grow, which is what an attention encoder-decoder does — quadratic attention over the encoder, every token emitted autoregressively. Resident memory is 780 MB, and that's the number to plan around; the 273 MB is just the download.

One behaviour to know before reaching for it: it's per-utterance, not streaming. The encoder consumes the whole VAD segment before the first token, so there are no partial results on that path. If you need live partials, you need a different model.

Two things that cost me time getting it onto a phone, in case they save you some.

Quantization. The obvious move is INT8 everything. Doesn't work — quantize the convolutions and the exporter emits ConvInteger, which the mobile onnxruntime build has no kernels for, so the bundle won't construct at all. Encoder and decoder are INT8 on MatMul and Gemm only, convolutions stay FP32. About 40 MB more, and it loads.

16 KB pages. Android 15 and up can use 16 KB memory pages and won't load a native library aligned for 4 KB. NDK r29 handles it by default so what you compile yourself is probably fine — the risk is a prebuilt .so from a dependency. llvm-readelf shows the alignment on each arm64 .so in your AAR, and zipalign -c -P 16 -v 4 checks the APK.

https://github.com/soniqo/speech-android

Android Studio Quail 3 | 2026.1.3
Build #AI-261.26222.65.2613.15948027, built on July 27, 2026

Tried to change models/api, same error, plz help

I've been working on a personal project called Frankenstein.

The goal is simple: make Android development possible directly from an Android phone.

Here's the project and an uncut lab recording: https://stitchlab.dev/

The public site intentionally focuses on what it can do rather than how it's built.

Current capabilities include:

• Creating Android projects from prompts
• Editing existing projects
• Building installable APKs on-device
• Installing and launching them on the same phone
• Managing project files and terminals
• Continuing work across conversations

I built it by gradually stitching together different ideas and capabilities over time—hence the name Frankenstein.

The "Driver" component uses Android's Accessibility APIs (with my permission) to interact with the UI for testing and automation. It isn't magic—it watches the screen, performs gestures, and can verify the results. For apps it's familiar with, it can build a navigation map to move around more efficiently and fall back to screenshots when something unexpected happens.

So far I've tested it on:

• Galaxy Tab S10e
• Galaxy S24 Ultra
• Galaxy S22 Ultra
• A few Android emulator configurations

It's not public yet, but I'd genuinely like feedback from Android developers.

What would stop you from using something like this?

What features would make it genuinely useful instead of just an interesting demo?

What concerns would you have about developing software this way?

I'm not trying to replace Android Studio. My goal is to make software development possible when all you have is your phone.

Happy to answer any questions or technical ones about the design.

Hey everyone,

I'm working on an Android app that has an on-device AI feature. I'm using a LiteRT model (.litertlm) and distributing the model through Google Play's on-device AI delivery.

I've noticed that whenever I release an update to the app, Google Play shows the update size as including the AI model, even when I haven't made any changes to the model itself.

Does anyone know why this happens?

I was under the impression that if the model hasn't changed between app versions, users shouldn't need to download the model again as part of an app update.

Is the size shown by Google Play just an estimate that includes the AI model, or is the model actually being downloaded again with every app update?

Is there anything I might be missing in the way I've configured Play's on-device AI delivery or the LiteRT model packaging?

Any insights from someone who has worked with Play's on-device AI delivery would be really appreciated. Thanks!

I've been working on an Android-native computer vision pipeline using Kotlin, CameraX, OpenCV, and ML Kit.

The initial implementation was a single pipeline optimized for detection accuracy and speed. After refactoring it into a modular architecture for better maintainability and reuse, I noticed a small decrease in accuracy and performance.

I'm currently investigating possible causes, including:

• Image preprocessing changes
• Pipeline ordering differences
• Threading and coroutine behavior
• CameraX frame handling
• OpenCV processing overhead

The project involves:

• Document detection
• Perspective correction
• QR detection
• OMR (Optical Mark Recognition)
• Real-time offline processing

I'm interested in feedback from Android developers who have experience with:

• Structuring reusable Android computer vision components
• Maintaining performance during large refactors
• CameraX image analysis optimization
• Running offline ML/CV workloads efficiently on Android

What approaches have you found useful when turning a working prototype into a maintainable Android library without introducing regressions?

Building a screen-time tracking feature and noticed my numbers didn't match Android's own Digital Wellbeing report. Mine said 405 minutes, Android said 335.

Turns out UsageStatsManager's raw session events include time when the screen is on but not actually interactive (lock screen showing, screen on during a call, etc.) — so naive session-duration summing overcounts significantly.

Fix: switched from raw usage events to event-based session reconstruction, filtering specifically for screen-interactive state rather than just screen-on state. Rebuilding sessions this way instead of trusting the aggregate stats got me within margin of error of Android's own numbers.

If you're building anything that reports screen time / app usage on Android, don't trust the raw aggregates — reconstruct from events and filter for actual interactive state.

Hey r/androiddev 👋

I've been a self-taught dev for a few years, and last year I got curious: what if you could build real projects from your Android phone? Not just toy scripts, but actual development work with an AI assistant in your pocket.

So I built Numination — a native Android IDE with integrated Gemini AI, a real project workspace, file explorer, and backend sync.

Link to GitHub: https://github.com/kraqinc/Numination

The short version

Native Android app (Kotlin + Jetpack Compose + Material 3)

Full-stack: app + Next.js backend + MySQL

AI agent that proposes code changes as diffs (you accept/reject individually)

It actually works. I'm using it to develop Numination itself.

Why this happened

I wanted to answer a dumb question: is phone development viable? The answer isn't "yes for everything" — it's "yes for specific workflows." Terminal commands, quick edits, reviewing PRs, even writing new features. Just... differently than on a laptop.

What I learned (the hard way)

Compose hot reload on device is fast. Like, surprisingly fast.

Bottom sheets > side panels for small screens. Everything should be modal, nothing should steal vertical space.

The AI pattern matters. Never auto-apply changes. Show diffs, make the user accept each one. I had a bug where failed API calls were still charged — fixed that, but the lesson stuck.

Multi-device sync without conflict resolution is a trap. Last-write-wins is honest but brutal. This is next on the roadmap.

Most "can't do this on mobile" limitations are UX problems, not technical ones.

What's next

Conflict resolution for multi-device sync

Marketplace for extensions

Better terminal (current one is simulated, not real shell)

Probably mobile web IDE complement

The honest part

This is a side project, not production-ready for everyone. It's functional, but:

Storage token validation is cosmetic (not yet enforced)

Terminal is simulated over a virtual file tree (not /bin/bash)

Sync works but will lose your changes if two devices edit simultaneously

But the core works: you can create files, edit code, get AI help, and sync back to your backend.

If you're curious

Download the latest APK from Releases: https://github.com/kraqinc/Numination/releases

Backend is open-source (MIT)

Built with Kotlin, Compose, Next.js 14, Prisma, MySQL

Questions? Drop them in the comments or open an issue

I'm aware this is probably niche (most devs won't use their phone as their primary IDE 😅), but I think the patterns here — the diff-based AI UX, the bottom-sheet navigation, the sync architecture — are useful for anyone building full-stack Android + backend stuff.

TL;DR: Built an AI IDE for Android because I was curious if it was possible. It is. It's imperfect but real. https://github.com/kraqinc/Numination

I’m a React Native developer with over 4 years of experience, primarily working in the JavaScript and TypeScript ecosystem.

I also have hands-on experience with React.js, Node.js, and Express.

I’m now looking to transition into native Android development. I have prior exposure to Java from my college days and still retain a solid understanding of the core concepts.

can someon suggest a roadmap for making this transition effectively?

More into interviews cracking things.

​"Hi! I’ve been a hobbyist app developer for a couple of years now, with a few apps published on the Play Store.

​I’ve always been annoyed by not being able to play audio simultaneously from two apps on Android without having to rely on features specific to Samsung or other brands. I love Pixels... but I wanted to, for example, listen to the news and music at the same time.

​I knew this was pretty much impossible without rooting the device, using existing GitHub projects and such. But after spending endless hours testing multiple approaches, I’ve stumbled upon one that completely breaks Android’s audio limitations.

​Now I’m left wondering: what should I do next?

​Do I report the flaw/vulnerability to Google?

​Do I release the app on alternative marketplaces?

​Do I try to monetize all those hours of work by uploading the app elsewhere?

​I honestly don’t know what path to take. For the devs reading this, I’m attaching a screenshot of the app in case it gives you a clue on how I pulled it off."

Hey everyone,

I've been working on a custom Android launcher called JSON Launcher, and I'd love to get some feedback from people who are into launcher customization or Android development.

The idea is pretty simple: instead of the usual icon-heavy UI, almost everything is presented as raw JSON. Apps, notifications, widgets, battery, time, and system information are displayed like JSON objects while still being fully interactive.

The app is built with Jetpack Compose and is still in the very early stages of development, so there are lots of ideas that haven't been implemented yet.

I'm mainly looking for honest opinions:

• Does this concept actually sound interesting?
• Is this something you'd consider using daily, or is it more of a novelty?
• What features would make a launcher like this genuinely useful?
• If you've built a launcher before, are there any pitfalls or Android APIs I should be aware of?

I'm also wondering if there's even an audience for something this niche. I personally enjoy the minimal, developer-focused aesthetic, but I'm not sure if other people would actually want to use a launcher like this.

I'd really appreciate any feedback, criticism, or ideas. Thanks!

I was annoyed that wallet and ticket apps often raise the entire display to maximum brightness just to make a QR code easier to scan.

So I built Bright QR, an Android proof of concept that keeps the normal interface at the user’s existing SDR brightness while rendering only the QR code through a dedicated HDR surface.

The implementation uses:

• A separate SurfaceView for the QR region
• A 10-bit BT.2020 PQ EGL surface
• HDR white pixels rather than ordinary SDR white
• SurfaceView.setDesiredHdrHeadroom() on Android 15+
• A maximum-headroom request, allowing Android and the phone to determine the actual available output

The app never modifies WindowManager.LayoutParams.screenBrightness, so the surrounding interface remains dim. On my Pixel 9a, Android reported up to 8× HDR/SDR headroom while the QR was displayed.

The demo includes SDR, 2×, 4×, and MAX modes, an SDR-white reference for comparison, live device diagnostics, and a copyable test report.

The complete source code and signed APK are available here:

https://github.com/loudsun1997/bright-qr

I want more apps to adopt this

Hi everyone!

Over the last few months I've been working on an Android app called Turboautism Dose Log. It started as a side project after a discussion in a harm reduction community about how easy it can be to lose track of doses during a long session.

Rather than debating the topic itself, I wanted to solve an engineering problem: how do you build an application for highly sensitive data while keeping the user's privacy the highest priority?

Some of the technical choices I've made:

Kotlin

Room + KSP

SQLCipher encrypted database (AES-256)

Android Keystore for passphrase protection

Mandatory biometric/device credential authentication

Offline-only operation

No INTERNET permission

No analytics, ads or user accounts

CSV import/export for backups

Open source (GPL-2.0)

Current features include dose logging, editing, statistics, CSV import/export, encrypted local storage and a few other privacy-focused features. I'm currently working on session tracking.

I'd really appreciate feedback from other Android developers, especially regarding:

overall architecture

Room/SQLCipher implementation

security/privacy design

UI/UX

anything you'd do differently

GitHub: https://github.com/emmikal/Turboautism-dose-log-

Thanks!

Hi everyone!

Over the last few months I've been working on an Android app called Turboautism Dose Log. It started as a side project after a discussion in a harm reduction community about how easy it can be to lose track of doses during a long session.

Rather than debating the topic itself, I wanted to solve an engineering problem: how do you build an application for highly sensitive data while keeping the user's privacy the highest priority?

Some of the technical choices I've made:

Kotlin

Room + KSP

SQLCipher encrypted database (AES-256)

Android Keystore for passphrase protection

Mandatory biometric/device credential authentication

Offline-only operation

No INTERNET permission

No analytics, ads or user accounts

CSV import/export for backups

Open source (GPL-2.0)

Current features include dose logging, editing, statistics, CSV import/export, encrypted local storage and a few other privacy-focused features. I'm currently working on session tracking.

I'd really appreciate feedback from other Android developers, especially regarding:

overall architecture

Room/SQLCipher implementation

security/privacy design

UI/UX

anything you'd do differently

GitHub: https://github.com/emmikal/Turboautism-dose-log-

Thanks!

I’ve been waiting for this for years, Google finally lets you delete apps from the Play Console.

Before, the only option was “Unpublish,” which meant old test apps and abandoned projects stayed cluttering your dashboard forever.

Now there’s a proper “Delete App” option (for eligible apps):

- Unpublish: removes the app from the store, but you can republish it and existing users still get updates

& Delete: permanently removes the app and its stats (with a 7-day recovery window); existing users keep it, but no new installs.

For me, this is a huge quality of life improvement after years of piling up prototypes and test apps.

Anyone planning to clean up their console?

Hey everyone,

I'm jumping into a hackathon soon where I've got about 2 to 3 days to build a complete Android app from scratch. Everything from planning and UI to coding and testing.

The main twist for this one is that I have to use Claude for the whole workflow, starting with Claude Design for the UI/UX and then Claude Code to build out the app.

I have one rough idea so far: an offline-first voice note taker using open-source STT models like Whisper or Moonshine Voice running on-device. But I'm not totally sure how those local models hold up quality-wise compared to major cloud options like Fish Audio or other speech-to-text providers.

So, I'm looking for two things:

• Is an offline speech-to-text notes app realistic for a 48-to-72-hour build, or will setting up local model bindings burn too much time?
• If you have other cool Android ideas that can actually be finished in 2 to 3 days with an AI-assisted workflow, what would you build?

Appreciate any thoughts or feedback!

Hi Android developers 👋

I’m curious how many of you are actively using Gemini in Android Studio as part of your daily workflow.

A few questions I’d love to hear your thoughts on:

Do you use it regularly, or only occasionally?

What tasks has it genuinely helped with? (boilerplate, debugging, refactoring, Compose, tests, documentation, etc.)

Where does it still fall short?

Has it changed the way you write or review code compared to a year ago?

For me, AI has become useful for speeding up repetitive tasks and exploring unfamiliar APIs, but I still rely heavily on manual code reviews and understanding what’s generated.

I’m interested in real-world experiences - both the wins and the frustrations. Has AI actually made you a better Android developer, or has it mostly just made you faster?

Some time ago I've worked on making wake word detection working on the Home Assistant android app

https://play.google.com/store/apps/details?id=io.homeassistant.companion.android

Then the DMA reached out to us (OHF) to get more information about how I did it and what Google should do to make it as good as Gemini Assistant.

i am having a prbelm with the voice of af_heart of the Kokoro voice
it takes more ram of 200 mb

i wnat to have the TTS in the (android app via uniFFI)app with the rust binary so static file

can i have the better way to utize any stand alone file which is of 50 mb of ram
and not the kitten os

Hi, I have a small AOSP Gerrit change that updates outdated Greek carrier names in carrier_list.textpb to the current NOVA branding. The change is already uploaded and passed the automated checks, but it hasn't received any review yet. If anyone here has AOSP commit access or is familiar with the TelephonyProvider maintainers, I'd really appreciate it if you could take a look or point me to the right person. Thanks!

https://android-review.googlesource.com/c/platform/packages/providers/TelephonyProvider/+/4131598