What you need to know
- Project Rumble Pak aims at enhancing the content you view on the Netflix app on your smartphone by using haptic effects.
- Other ideas that turned out to be favorites at this year's event were The Voice of Netflix and TerraVision.
- Unlike Project Rumble Pak, the other two ideas aren't very practical.
Hack Days at Netflix give its employees a chance to come up with some amazing new ideas. At the company's most recent Hack Day, employees developed several crazy hacks targeted at Netflix's studio efforts. The most exciting new idea devised by the teams at this year's event was undoubtedly Project Rumble Pak, which aims at enhancing the viewing experience on smartphones with the help of haptic effects.
Similar to many mobile games and seats in a 4D movie theater, Project Rumble Pak delivers force feedback to users whenever there is a huge explosion or a blast in a scene. Since haptic feedback is already helping mobile gaming become more immersive, Project Rumble Pak definitely sounds like a practical idea. The haptic effects for the project were synchronized with Netflix content using technology from Immersion Corporation. However, it remains to be seen if the hack will actually roll out as an actual Netflix feature in the future.
Another project that was developed at this year's event was "Voice of Netflix", which uses a neural network to scan for words in Netflix content and then use them to assemble a new sentence in the voice of popular Netflix characters. If this hack sounds interesting to you, you can actually try it out yourself.
The third really cool idea that was experimented at the company's recent event is called TerraVision. Unlike the other two hacks, TerraVision is aimed at helping filmmakers and not the viewers. TerraVision apparently "revolutionizes the way filmmakers can search and discover filming locations." Filmmakers can simply drop in a photo of a location they are looking for and TerraVision will find the closest match from its library of location photos with the help of a computer vision model that has been trained to recognize places to enable reverse image search functionality.
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