Google has recently rolled out developer preview to its Wear OS line of wearables. This is the second developer review from the search giant following its update in March this year which was based on Android P. The new version is part of Google’s announcements at the I/O 2018.
While the first developer preview focused on features including dark mode, SDK restrictions among others, the latest preview introduces features like enhanced battery life, Action on Google among many others.
According to Google, the new preview has all the support for Google devices in the form of Actions for Google. As part of this, Google has redesigned its much famed home assistant – Google Assistant on the Wear OS which will allow the device to support many features including follow-on-suggestions, visual cards and text-to-speech. Google further informed that it had included support for Actions on Google for its Wear OS and that existing actions will also work on the latest and out-of-the-box Wear OS. It added that the feature is rolled out for Wear 0.2 users and does not depend on Android P.
The new developer preview also includes an improved battery-saver mode for its Wear OS devices. For example, if the Wear OS watch is in the battery saving mode, then it will depict a watch face that is power efficient and will also turn off some features like the tilt to wake, touch screen and radios. All the users have to do to come back from this mode is to simply press the side button once and to the fully operational mode by long pressing the same button. This will allow the users to easily manage all their tasks like replying to messages and paying with NFC.
Another feature to be included as part of the Google’s Wear OS developer preview is the Smart Reply for bridged notifications. While this feature has been enabled for bridged notifications for quite some time, the latest to be included is the Mandarin support. According to Google, this feature is especially useful for the Wear OS users in China. The feature is apt for on-device models and makes use of TensorFlow Lite which is specially optimized for devices will low memory and low power.