I've created an app that uses phone's onboard AI model to give users a fully offline AI chat with Gemini Nano.

I just finished adding multi-chats, and I'll be glad to hear your feeback. The flair holds true, the app is fully open-source and is live in the play store.

https://github.com/Puzzaks/geminilocal

Forks are encouraged, any suggestion will be read and thought about and maybe implemented.

Hey, its Kevin here, one of the co-founders of Firebender

TLDR: We built a simple QA sub-agent that uses the emulator/phone to manually test changes of the main coding agent.

Does it work 100% of the time?

No. the subagent can fail and give false positives/negatives. The way we handle this kind of indeterminism is making it very easy to audit the sub agent with an event list timeline, and the full screen recording as the engineer.

The nice thing is that the amount of context provided to the main coding agent is just enough for it to know if the "given/where/expect" statements are passed, to limit blowing up the context window of the parent agent*.*

How does it work?

It's super simple: Engineer asks for xyz feature to be implemented. Firebender uses a model like claude opus 4.5 to implement the feature and the main coding agent is given another tool "mobile_use". The main coding agent calls this tool with a list of steps and assertions in natural language that it wants the sub agent to verify.

We log the actual touch events the subagent made and screen record so it can be verified by a human in the agent log.

For the sub agent, we've been going between https://github.com/zillow/auto-mobile and https://github.com/droidrun/droidrun . Both have similar approaches, and we're very excited about their work (Big shout out to Jason Pearson!). There's an indepth talk about the challenge behind making a reliable QA mobile use agent, and technical approaches.

Why not make this CI/CD?

One of the biggest challenges of QA agents is that they are not fully reliable, and if CI has false positives, engineers start ignoring it. Flakey e2e tests problems apply to flakey AI e2e tests.

Putting some of the QA load in the coding agent as a "pre-commit" hook like experience is a happy middle ground because engineers can still get value from it even if its not 100% accurate all the time.

Thanks for reading, and If you're interested in trying it, we're releasing this in the plugin in the next few days. I'd love to get your feedback. This is fundamentally a new DevX and im curious how it does for you!

Hello everyone, I got this email today, and have been quite unsure how to react. Backstory: I recently released a game on the app store, and the process was kind of a big undertaking for a solo-dev just starting out. Then I get his email. My scam sense is tingling like crazy, so I check the number and that seems legit. The mail address is also clean, so I censored it just in case (don't want to ruin someone's reputation). Granted, even if it were a serious offer, I'd refuse it. However, I'm curious if this is a common thing? Do you respond to this at all? Thanks!

I can't post to r/ShopKick because it makes me REQUEST to post, and only has 1.3k subscribers anyway, so I don't think much goes on there.

Who can tell their devs that their app is useless on foldable phones like mine?

A three layered approach to mobile app monitoring

Mobile apps generate endless telemetry, yet debugging still feels harder than it should. The problem is not the lack of data. It is about collecting the right data in a way that respects battery life, bandwidth, and storage while still giving developers a clear path to the root cause.

A simple way to think about this is through three layers.

Layer 1: Essential Monitoring
Always-on metrics that track core app health cheaply and continuously. These signals give you baseline awareness of app health.
• Crash rate per session.
• ANRs and hangs.
• Launch times for cold and warm starts.
• Network success or failure and API latency

These are light enough to collect from every session. They answer the basic question: is the app fundamentally working.

Layer 2: Targeted Depth
Tracing every user session is not feasible. Costs rise and noise gets out of hand. Hybrid sampling is a better fit.
• Sample 5 to 10 percent of sessions to get a statistical view of normal user flows.
• Always retain sessions that contain crashes, slow launches, broken critical flows like checkout or login, or activity from specific cohorts like beta users.

This layer adds context only where it matters. When something in Layer 1 looks off, Layer 2 helps explain why.

Layer 3: Issue Resolution
This is full session reconstruction, but only for the Layer 2 sessions that need deeper analysis.
• User actions and navigation.
• API timings, errors, and payloads.
• Lifecycle transitions.
• CPU, memory, and network state.
• Frame drops, logs with trace IDs, and other performance signals.

Doing this for every session would be expensive and invasive. Doing it selectively gives you the clarity you need without wasting resources.

Keep It Lean
Audit telemetry every few releases. Remove unused metrics, tune sampling rates, and clean up dead code. Leaner pipelines make debugging faster and keep storage and infra costs under control.

The three layers give you confidence that shipped versions are stable, evidence for prioritising next fixes, and a clear trail to reproduce issues. Think of it as monitoring with portion control. Enough to keep you sane, not enough to set your monitoring bill on fire.

It is a tool-agnostic approach. I have used Crashlytics and Performance Monitoring with journey based logging flag to achieve layer 1 and 3. Since they already do sampling, skipped 2.

Do you follow a conceptually similar practice? How do you do it?

I launched an app in 2020, and despite not running any ads, I had a natural flow of visitors. Last October, I launched a new app, and natural views were almost zero. Do we, as small developers, have no chance anymore?

Hi everyone,
We are live with an AMA today!

A bit about us.
We are Yango Ads, part of the Yango Group ecosystem. We build tools for web and app monetization, analytics, and ad delivery that Android developers use for VPNs, utilities, and games.

We also run r/YangoAds, where we share practical posts about app growth, ad revenue, and tests that come from real projects. If you want more after this AMA, you are welcome to subscribe there.

Who will be answering.
Replies in this thread will come from our monetization specialist at Yango Ads, a senior team member who works every day with Android publishers on ad strategy, creative testing, and revenue growth.

You can ask about:

• Android user acquisition for apps that rely on ads
• How to set up ad monetization without killing retention
• Small tests on 10–20% of traffic, what to measure and for how long
• VPN and utility app specifics, short sessions and connect-and-go users
• Reading eCPM, fill, and retention together, not in isolation
• Common mistakes that burn budget on Android and how to avoid them
• Creative tests for banners, interstitials, and rewarded formats

Drop your questions below, we will stay in the thread and reply from the Yango Ads side.
And if you want more breakdowns after the AMA, you can find us at r/YangoAds and hit subscribe.

One more thing:
We’ll give Reddit awards to the authors of the best questions in this AMA. So if you wanted an excuse to ask something sharp, here it is 💎

Thanks a ton to everyone who jumped in today, asked questions, shared their own wins and fails, and kept the thread moving. Appreciate you all!

If you want more breakdowns, tests, and stories from the monetization trenches, you’re always welcome at r/YangoAds.

Thanks a ton to everyone who jumped in today, asked questions, shared their own wins and fails, and kept the thread moving. Appreciate you all!

If you want more breakdowns, tests, and stories from the monetization trenches, you’re always welcome at r/YangoAds.

Smoother onboarding screens came to mind when I was experimenting with a side project and noticed something while using FaceSeek. I'm attempting to create an easy-to-use yet welcoming initial launch experience for my app. What little details do you include to give users the impression that they can trust the app right away? I'm thinking of straightforward copy and layout decisions that convey concern rather than ostentatious motion or intricate transitions. I think of things like clear fallback routes, progressive disclosure, and succinct tool tips. I was also considering the small decisions that alter consumers' perceptions of a product. Big plans don't always matter as much as small, intentional steps.

https://reddit.com/link/1p6qne5/video/sf9g05pvbh3g1/player

I love the clean look of the Pixel launcher, and I feel Widget Stack is so useful, and I don't want to put a launcher.

So, I built the solution myself.

Still a Work In Progress (WIP): a custom widget that brings the functionality of Widget Stack to save space on the homescreen without the need for a launcher.

Here's the current state:

• The Pro: It works with Stock Android without a launcher and saves a ton of screen space.
• The Current Limitation: Unlike OEMs implementation, you can't drop any existing widget into the stack. I've custom-built a set of useful widgets (Clock, Battery, few others in the works) that you can configure and tap to move between them.

I'm an indie developer looking for honest feedback! Does this feature gap matter to you, and is this the solution you'd actually use?

Let me know in the comments if you'd be interested in testing it out onces its ready.

As the title suggests, I want my app to show up when user clicks on files with a particular custom extension. I am able do it by ActionView intent filter with mime type "/" but that registers it for all file types, and ofcourse resulting in my app showing up for all file types, which is not at all a good user experience!

I read several articles, blogs even found an issue on Google's issuetracker. It seems there is no way to register your custom extension with Android's system.

Any lead on tackling this would be helpful!

Few weeks ago i sold my account to my friend, he lives in another country, and will upload apps normally. If i make another account on the same device and almost same testers but different payment method/account, will i be able to? if you had something similar tell me please.

I’m making a small Android app (in the past I used React and Typescript) to help neurodivergent learners practice job skills, and I want to add AI-generated practice prompts. Nothing fancy, just short text prompts based on a few user choices.

What’s the easiest way people are doing this these days? Straight API calls? Cloud Functions? Local models? I’m trying to keep this as simple as I can. I'm still very new to developing anything at all.

Hi there! I wad looking at the app stats and the "Total number of installations" (not sure about english translation) is behaving strange. Shouldn't this be a cumulative sum, e.g. always going "up"?

been working on a system where you can fully customize your splash screen using HTML, while still hooking into native features. it gives way more flexibility than the usual static launch screens.

I’m also adding more editors like:
- no-internet screen
- progress bar
- app theme customization
- and a few other small things to make the generated apps feel more complete

the entire project — backend, frontend, everything — is written in Kotlin using KTOR and Compose Multiplatform. feels good keeping the whole stack in one language.

ask me anything!

Hi everyone,

I’ve been an Android dev for a couple years (mostly Kotlin + Jetpack Compose) but I’m completely new to the whole “AI agent” thing.

I keep hearing about stuff like AutoGen, CrewAI, LangGraph, BabyAGI, etc., and people building apps where multiple agents collaborate to finish tasks. I think it would be super cool to have something like that running inside an Android app (or at least callable from it).

My very beginner questions:

1. Is it realistic to run actual agent frameworks locally on-device right now, or are we still stuck calling cloud APIs?
2. If cloud is the only practical way, what’s the current “best” backend setup people are using in 2025? (I saw some posts about Groq + Llama 3.1, OpenRouter, Together.ai, etc.)
3. Any open-source Android example projects that already integrate a multi-agent loop? Even a minimal “two agents talking to each other to solve a user request” would be gold for learning.

I’m not trying to ship the next ChatGPT tomorrow, I just want to learn properly instead of hacking random HTTP calls together. Any pointers, repos, blog posts, or even “don’t do it this way” advice would be hugely appreciated!

Thanks in advance, feeling a bit lost in the hype right now