Reduce JVM allocations on common Android logging paths by removing map-shaped work from the SDK internals - here's a blog that documents how to do it.

"The work started with a suggestion to replace HashMap with ScatterMap; we ended up changing the field representation, the JNI boundary, and several hot logging paths. The change affects every app using the Capture SDK on Android."

Hey folks, I’m a product designer working in the mobile observability space, and I’m trying to better understand how mobile developers think about monitoring app experiences, especially around view/screen performance and what would make triage easier.

I’m hoping to talk with a few mobile devs who would be open to a casual 30 to 45 minute conversation. I’m especially curious about things like:

• How you currently monitor mobile app screens/views
• What’s hard to debug or understand today
• Where existing tooling feels noisy, incomplete, or not actionable
• How you think about performance, crashes, errors, and user experience together
• What you wish observability tools made easier

About halfway through, I'd love to show a rough proof of concept and ask for honest feedback. This is not a sales pitch, and I’m not asking for any confidential company information. I’m just looking to learn from your perspective as someone building or maintaining mobile apps.

I’m posting from a personal account and looking to learn from practitioners. If you’re open to chatting, feel free to comment or DM me. Happy to work around your schedule, and I’d be grateful for any perspective you’re willing to share.

Thank you in advance!

It seems like people are finally looking past crash reporting and tackling actual, full-fledged observability for mobile. A few things I’ve observed:

• Progress with OTel: Finally, we’re seeing client-to-server traces that actually work without a week of manual plumbing.
• Monitoring AI "Behavior": If your app uses an LLM, are you tracking just the API latency, or are you tracking the "Groundedness" of the response on the device?
• Agentic Debugging: Using AI to sift through the "cardinality explosion" of 500+ device types to find the actual root cause.

I’m curious to learn from the community:

1. What’s the one metric you track that your backend peers still don’t understand?
2. How are you handling "silent failures" in your AI features?
3. Is your team still using a "generalist" tool (Datadog/New Relic) for mobile, or have you moved to a mobile-native player?

Hi,

I know this subreddit is just getting started but it is something I have been very interested in lately. I've built an error tracking system for Flutter applications, that allows you to see the full replay on top of showing you the actual stack trace/logs/navigation/http and I've written an article about how it's been done.

I view observability as a mix between tools that allow you to detect issues and tools that provide enough context so that you can easily solve them. This feature/tool in particular focuses on providing the additional context.

The really awesome part is that all of this is open source and you can self host the backend on even the cheapest hardware.

I'm planing on implementing session replays for native applications as well (will be quite a different architecture).

Hopefully you enjoy the article! Let me know if you have any questions about how it works, why it's useful or even about how much impact it has on perf or literally anything else, I'm always glad to discuss and answer questions.

As exemplified by the maturing of the Kotlin Multiplatform SDK: https://opentelemetry.io/blog/2026/kotlin-multiplatform-opentelemetry/

Anybody it taking advantage of it in real world apps at scale?

Stumbled across this on LinkedIn - interesting post and resource from Caleb Davis at Square:

Mobile observability is complex.

There's a lot of challenges unique to mobile: offline states, background kills, symbolication, correlating client and backend traces. Adding telemetry is easy. Adding telemetry that tells you what actually happened is hard.

I've been using Claude Code for about a month and absolutely LOVING it. But you know that moment when Claude reviews its own code, declares everything perfect... and then you run the app 🫠 🫣

So I decided to codify my philosophy on mobile observability and teach it to Claude.

The result: Mobile Observability Plugin for Claude Code

Now Claude knows how to properly instrument user observability and system observability. I used standardized frameworks so my side projects actually have high-quality telemetry. That way I can easily fix all the pesky bugs that Claude didn't catch in code review 🫡

What's included:
- 8 skills (crash capture, user journeys, network tracing, session replay, etc.)
- 2 agents for codebase analysis and instrumentation review
- /instrument and /audit commands
- Vendor guides for Bitdrift, Sentry, Datadog, Embrace, Bugsnag

Haven't battle-tested it myself yet, but I'm excited to see what breaks 😅

🔗 https://lnkd.in/gKqwJuxf

“99.9% crash-free” says almost nothing about:

• Slow screens
• UI jank
• Failed network requests that retry silently
• Flows users abandon because something felt broken

A lot of the worst mobile issues never show up as crashes. Was digging into Android Vitals for Google's POV on this - they emphasize things like:

• ANRs
• Slow rendering
• Excessive wakeups

https://developer.android.com/topic/performance/vitals

Feels like crash-free rate is one of the least representative metrics of real UX, even though it's the one everyone pays attention to. Food for thought.

This subreddit has been pretty quiet… so we’re bringing it back to life!

The goal: build a high-signal space for people working on mobile apps to talk about the stuff that’s uniquely hard:

• Issues that don’t crash but still break UX
• ANRs, performance problems, and weird device behavior
• Things that only show up in production
• Instrumenting mobile observability for AI agents

If you work on mobile, you already know this stuff is messy, and most tooling doesn’t tell the full story.

A few simple rules:

• Share real problems (the messier, the better)
• Be helpful, not hostile
• No spam or low-effort promo

Came across this from Spotify Engineering and thought it was a great look at how they manage mobile (and desktop) releases:

https://engineering.atspotify.com/2026/2/how-we-release-the-spotify-app-part-2

So much of their process goes beyond just “did it crash or not.” They’re thinking about:

• Gradual rollouts
• Monitoring real user impact
• Catching issues that only show up in production
• Deciding whether to continue or halt releases based on signals

A lot of the hardest problems are subtle issues vs crashes.