Android - Reddit

Who Actually Wins? (Camera Comparison test) Sony Xperia 1 VIII vs 17 Pro Max vs Samsung Galaxy S26 Ultra vs Vivo X300 Ultra vs Oppo Find X9 Ultra vs Xiaomi 17 Ultra vs Honor Magic 8 Pro - FlashingDroid
https://www.youtube.com/watch?v=1OiubdgYL8U

https://redd.it/1u9t6vv
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Nothing wants you to review its next products before they launch
https://www.androidauthority.com/nothing-launches-community-review-program-3679144/

https://redd.it/1u9sbe0
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Android developer verification: Building a safer ecosystem together
https://android-developers.googleblog.com/2026/06/android-developer-verification.html?m=1

https://redd.it/1u9hgdj
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Oppo Find X9 Ultra Review - $1,600 Enthusiast Import Trap - Drew Lipps
https://www.youtube.com/watch?v=Lvfxg3a_gZg

https://redd.it/1u9f3c8
@reddit_android

Android XR’s second device already looks interesting

Saw Project Aura and, weirdly, Android XR got a lot more interesting to me.
Not because I trust Google. I’ve been using Android since the Nexus days. I know the drill.
Glass died. Daydream died. Half of Google’s “future” platforms die. And now Android itself is getting more annoying with sideloading restrictions and AI bloat.
So yeah, I’m not exactly all-in on Google here.
But I do want Android XR to work.
Mostly because the alternative is Meta owning XR, and 🙄 that.
I never believed the whole “Horizon OS is opening up” thing. Meta’s version of “open” always feels like: open, as long as they still control the store, accounts, rules, and money.
That’s not Android. That’s just a cheaper walled garden.
Aura caught my eye because it’s not another Quest clone. It’s glasses + puck. Different shape, different use case, different assumptions.
That alone makes Android XR feel more interesting than Horizon OS to me.
Google can still screw this up. They probably will, in some very Google way.
But I’d still rather gamble on messy Android XR than let Meta own the whole category.
I don’t need Google to be perfect.
I just need them to be less Meta.

https://redd.it/1u8ylyv
@reddit_android

Any games that use the actual physical boundaries of an Android tablet?

I'm looking for some games meant to be played on a tablet that use the actual physical boundaries of the tablet....like for example, say an air hockey game where if the puck hits the edge of the tablet that it reacts to having hit the edge of the tablet....Anything like this? Thanks guys!

https://redd.it/1u90cxt
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Phones are about to get worse. - Android Police
https://www.youtube.com/watch?app=desktop&v=IKOnWNZV29A

https://redd.it/1u96wbd
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Daily Superthread (Jun 18 2026) - Your daily thread for questions, device recommendations and general discussions!

Note 1. You can search for previous daily threads.

Note 2. Join our IRC and Telegram chat-rooms! Please see our wiki for instructions.

Please post your questions here. Feel free to use this thread for general questions/discussion as well.

https://redd.it/1u94brj
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Sony unveils the Lytia 910, its first LOFIC sensor, and it boasts 100dB dynamic range
https://www.gsmarena.com/sony_unveils_the_lytia_910_its_first_lofic_sensor_and_it_boasts_100db_dynamic_range-news-73318.php

https://redd.it/1u91tz0
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Base Galaxy S27 may disappoint with no major display or camera upgrades
https://www.sammobile.com/news/base-galaxy-s27-may-disappoint-no-major-display-camera-upgrades/

https://redd.it/1u927k7
@reddit_android

I trained a neural network on my Android phone using Pydroid 3 — S-tier, 92% win rate, no servers

I wanted to see if my Android phone could train a real neural network. No cloud, no GPU, no TensorFlow — just Pydroid 3 and pure Python.

**The result:** A Q-learning neural network that reached S-tier in a fighting game and beat scientific algorithms.

**What it does:**

\- ⚔️ Fighting game: 81.2% win rate against 9 different bots (including Minimax)

\- ✊ Rock-Paper-Scissors: 92% against Exp3 and UCB1 (algorithms from research papers)

\- 🎭 Mafia (social game): 40% win rate (2x better than random)

**How it works:**

\- Pure Python lists and loops — no NumPy

\- Manual backpropagation (\~200 lines)

\- Replay Buffer (500 examples)

\- \~75 parameters, model size < 5 KB (JSON)

\- 10,000+ training battles on a phone

**Performance on Android:**

\- 500 fights: \~10 seconds

\- 5,000 RPS rounds: \~30 seconds

\- All trained locally in Pydroid 3

**Why this matters:**

You don't need a gaming PC or cloud GPU to experiment with neural networks. An Android phone is enough to train a working AI that beats algorithms from scientific papers.

Happy to answer questions about training on mobile!

GitHub in comments.

https://redd.it/1u8stc9
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Pixel Screenshots no longer exclusively uses on-device AI
https://9to5google.com/2026/06/17/pixel-screenshots-cloud-ai/

https://redd.it/1u8vff8
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Qualcomm Takes Spatial Computing into the AI Era with Snapdragon Reality Elite
https://www.qualcomm.com/news/releases/2026/06/qualcomm-takes-spatial-computing-into-the-ai-era-with-snapdragon

https://redd.it/1u843wc
@reddit_android

[DEV] I built an Android app that runs Whisper Base Q8 fully offline, handling long audio chunking on devices down to 2GB RAM. No data leaves your phone.

Hey everyone,

https://preview.redd.it/8614i1pfht7h1.jpg?width=1080&format=pjpg&auto=webp&s=cd314472f90beecc17b4cbe432af47ae6f4469e0

As an Android developer, I’ve always been frustrated by how speech-to-text apps rely heavily on cloud APIs, compromising privacy and requiring active internet connections. I wanted to build a solution that runs **100% locally on the device**.

However, running heavy models like OpenAI's Whisper and Silero VAD locally on budget Android hardware comes with massive memory bottlenecks and unexpected crashes.

To fix this, I built **Transcribe Offline**. Instead of defaulting to Whisper Tiny (which has terrible accuracy), I managed to optimize **Whisper Base Q8** to run smoothly even on **2GB RAM devices** using a few engineering workarounds:

* **Semantic Chunking via Silero VAD:** Instead of blindly cutting audio into fixed time slots (which cuts through words and ruins the context), the app uses local Silero VAD to detect natural human speech boundaries. I added a **negative 200ms offset** to ensure the start of sentences is never chopped off.
* **Flat Memory Footprint:** Audio chunks are processed sequentially and instantly cleared from memory, meaning the app handles a 2-hour recording with the same flat memory usage as a 2-minute clip. No Out-Of-Memory (OOM) crashes.
* **Native C++ Performance:** Core engines are compiled via Android NDK/JNI to leverage hardware acceleration and keep the main UI thread completely fluid.

The app is completely private, requires zero permissions other than reading your local files, and outputs clean text or standard `.srt` subtitles with precise timestamps.

If you are interested in the engineering details, I wrote a quick deep dive on Medium about how I overcame the memory and text-cutting limitations: 🔗[Read the Engineering Deep Dive on Medium](https://medium.com/@creazyheart92/how-i-optimized-on-device-ai-audio-transcription-for-low-end-android-devices-down-to-2gb-ram-49f1a3847337)

The app is live on the Play Store, and I would absolutely love your honest feedback, feature requests, or any questions about the on-device pipeline!

👉[**Get Transcribe Offline on Google Play**](https://play.google.com/store/apps/details?id=egyption.developer.transcribe)

https://redd.it/1u861fk
@reddit_android

Building Computer Vision apps for Android in 2026: Has the bottleneck shifted from AI models to mobile engineering?

I've been reading case studies and following discussions around Android apps that use Computer Vision for OCR, object detection, image segmentation, quality inspection, and video analytics.

A few years ago, most conversations focused on model accuracy and training. But now it seems like the bigger challenges are:

Running models efficiently on-device with TensorFlow Lite or MediaPipe.
Balancing latency, battery usage, and model size.
Handling fragmented Android hardware and varying NPUs.
Building reliable pipelines for image/video processing in production.
Deciding when to use on-device inference vs cloud inference.

Interestingly, many successful products seem to rely as much on strong mobile engineering and UX as on the AI model itself.

I'm curious if other Android developers feel the same way.

For people who have built or worked on Computer Vision apps:

What has been the hardest problem for you?
Has model development become easier while deployment remains the real challenge?
Which frameworks or approaches have worked best in production?

Would love to hear real-world experiences and lessons learned.

https://redd.it/1u89w3l
@reddit_android

Paul Dunlop (Android Onboarding/Settings Product Lead) details Android Switch improvements in Android 17, including direct migration of signed-in Google accounts between iOS and Android, cross-platform app data migration APIs, seamless eSIM transfers, & more
https://www.threads.com/@pauldunlop/post/DZr4isfjanp

https://redd.it/1u8c7g4
@reddit_android

Samsung Galaxy XR Arrives in the UK
https://news.samsung.com/uk/samsung-galaxy-xr-arrives-in-the-uk

https://redd.it/1u8cmca
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Moto G Stylus 2026 Review: Motorola's most complicated phone! - StevealiciousTech
https://www.youtube.com/watch?v=BvTMkYzoEPk

https://redd.it/1u88qli
@reddit_android

Daily Superthread (Jun 17 2026) - Your daily thread for questions, device recommendations and general discussions!

Note 1. You can search for previous daily threads.

Note 2. Join our IRC and Telegram chat-rooms! Please see our wiki for instructions.

Please post your questions here. Feel free to use this thread for general questions/discussion as well.

https://redd.it/1u8879s
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Honor X70 Pro Max silently launched with an 8,560mAh battery
https://www.gsmarena.com/honor_x70_pro_max_silently_launched_with_an_8560mah_battery-news-73268.php

https://redd.it/1u7v054
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