37 articles on AI for Android performance
Showing 17 of 37 articles (Page 2 of 2)
Caren Chang, Joanna Huang, and Chengji Yan from Google reveal how they're making Gemini Nano v3 940 tokens/second fast while keeping quality consistent across devices. The secret? LoRA adapters and rigorous eva...
Meta just shared how they moved billions of daily users to their new on-device ML framework. The performance gains are substantial.
Sandhya Mohan and Jose Alcérreca from Google just dropped Agent Mode for Gemini in Android Studio. This isn't another code completion tool—it's an AI that creates execution plans, makes multi-file changes, and ...
Caren Chang, Chengji Yan, and Taj Darra from Google just made on-device AI dramatically easier for Android developers. Four new ML Kit APIs let you integrate Gemini Nano without any prompt engineering or fine-t...
Duolingo's QA team cut manual regression testing by 70% using AI. Here's how they made it work at scale.
Sandhya Mohan from Google just dropped the biggest Gemini in Android Studio update since launch. If you're still manually writing commit messages and test scenarios, this changes everything.
DoorDash handles 2 billion orders annually, and every ETA prediction matters. Their old tree-based models couldn't keep up with the complexity.
Taj Darra just announced that Google is opening Gemini Nano to all Android developers. On-device AI just became accessible for production-ready mobile apps.
Uber's mobile testing was broken. Engineers spent 30-40% of their time maintaining test scripts that broke with every UI change. So they built an AI that tests apps like a human would.
Very Good Ventures took the stage at Google Cloud Next 2024 to show how Flutter and Gemini are changing the game development playbook. Literally.
Dave Burke, VP of Engineering at Google, just dropped the playbook for integrating Gemini AI into Android apps. If you've been waiting for the right moment to add generative AI to your mobile stack, this is it.
Dave Burke from Google just dropped the blueprint for running foundation models directly on your Android device. No cloud required, no network dependency, just pure on-device AI.
Snap Research just proved that Vision Transformers can run as fast as MobileNet on actual mobile devices. Yes, really.
Ishan Khanna from Tinder shares a hard-earned lesson: that shiny third-party SDK could be a ticking time bomb for your app. Here's the framework his team uses to avoid disaster.
Pinterest just shipped a feature that 60% of their top beauty searches were literally asking for. Yanis Markin walks through how they combined ML with inclusive design to solve it.
Cesario Putera from Blibli.com shows how Google's ML Kit brings powerful face detection to Android apps with surprisingly little code. No PhD required.
Meta has been quietly building the foundation for interfaces that understand speech, vision, touch, and text simultaneously. The future of mobile isn't single-mode anymore.