Flutter Feed #2 comin in hot!

Roundup of everything in the Flutterverse

🐸 Very Good Ventures releases Dart Frog 1.0

Flutter developers can benefit from Dart Frog as it makes learning Dart much easier. With Dart Frog, constructing back-end routes resembles building widget trees in Flutter. This allows developers to share code between front-end and back-end, implementing functionality once for both application sides.

Dart Frog also enables a backend for front-end pattern that normalizes data so developers can utilize it effortlessly. This grants developers more control over optimizing and caching backend information. Dart Frog is highly configurable with any cloud service. Developers can choose whatever infrastructure works best for their needs.

In my opinion, Dart Frog simplifies Dart learning for Flutter developers through familiar syntax, code sharing, normalized data, and flexible cloud hosting making it much easier for Flutter devs to build full-stack applications efficiently.

And yes, most importantly it’s open source!

 Vision Pro struggling to attract developer interest

Apple's Vision Pro developer labs are failing to entice developers. Apple established these exclusive labs in Cupertino, London, Munich, Shanghai, Singapore and Tokyo. Their goal was to educate developers on building apps for Apple's spatial computing platform. However, developer attendance has been lackluster so far.

Mark Gurman of Bloomberg reports that developer sign-ups for these labs are low. The labs have sparse attendance, despite Apple's expectations that developers would be eager to learn about spatial computing opportunities.

The two big contradictory thought groups are:

• Is this a massive opportunity? Much like when the App Store launched and the very first developers were rewarded handsomely for being early adopters?

• Traction and monetization potential is yet to be seen - would this be worth the additional efforts?

What do you think?

💻On-Device ML using Flutter & Tensorflow

Love this tutorial from Roman which demonstrates building an on-device machine learning model with Flutter. It trains a model to convert Celsius to Fahrenheit in Google Colab, exports it as a TensorFlow Lite model, and uses the tflite_flutter package to integrate it into a Flutter app.

On-device ML provides low latency, keeps data private, and works offline. Limitations are increased app size and processing power constraints. Overall, the tutorial showcases how Flutter developers can leverage on-device ML for personalized, responsive experiences without relying on the cloud. Integrating ML models directly into Flutter apps unlocks unique capabilities while maintaining user privacy and connectivity independence.