The glasses will also enable live streamers to directly interact with their audience.
Meta’s upcoming second-generation Ray-Ban Stories will let users stream videos to Facebook and Instagram.
Meta, the parent company of popular social media platforms like Facebook, Instagram, WhatsApp, and more, is reportedly planning to develop the next generation of Ray-Ban Stories smart glasses. These glasses will allow users to livestream video to viewers who can provide real-time feedback.
According to documents from tech journalist Janko Roettgers, the upcoming second-generation Ray-Ban Stories will let users stream videos to Facebook and Instagram. Viewers will also be able to whisper in the user’s ear, as reported by The Verge.
Users of the second-generation Ray-Ban Stories can livestream to Facebook and Instagram. Although support for other services is unknown at this time, according to Roettgers. “The glasses will also enable live streamers to directly interact with their audience. The built-in headphones will relay comments through audio,” Roettgers said.
Furthermore, the smart glasses might also incorporate adaptive volume and extra audio features. With this feature, the glasses will automatically monitor the ambient noise level, and increase playback volume in noisy surroundings.
Current-generation Ray-Ban Stories feature integrated stereo speakers and can be used as a Bluetooth headset, plus the device offers a more direct integration with Spotify, with users being able to skip tracks and more simply by tapping the frame. Meta is looking to bring similar features to other music services, but it is not confirmed which service will be next, the report said.
Meanwhile, Meta on Thursday launched its own AI tool called Code Llama to generate new code and debug human-written work. The large language model (LLM) can use text prompts to generate and discuss code. Code Llama has the potential to be used as a productivity and educational tool to help programmers write more robust, well-documented software.
Code Llama is a code-specialised version of Llama 2 that was created by further training Llama 2 on its code-specific datasets, sampling more data from that same dataset for longer.
