The Number of Apps are Over 2500 in AppStore for The New Apple TV
Apple has created a completely different ecosystem with new Apple TV and new tvOS operating system. As expected, developers very interested in the new ecosystem with opening the tvOS platform to them.
Apple has created a completely different ecosystem with new Apple TV and new tvOS operating system.
As expected, developers very interested in the new ecosystem with opening the tvOS platform to them.As you remember, the new Apple TV launched in November.And there are currently developed, and ready 2 thousand 624 apps can be downloaded to your Apple TV.
According to the AppFigures Data, the average of 447 new application loading to AppStore by developers every week.In short, if the curiosity of Apple TV goes like this, the number of applications will exceed 10 thousand in the first quarter of 2016.Just the table below demonstrates graphically the rise of the number of applications on Apple TV.
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New Apple TV attracted the attention of the players and game makers with a new generation remote control.New Apple TV returned an entertainment system that 38 percent of applications are games ready to play for users.61 percent of applications on Apple TV can be downloaded for free.85 percent of the paid applications are between 0.99 dollars and 2.99 dollars to meet the customer at affordable prices.The top categories are games, entertainment and education.The 28 apps of top 50 lists are from entertainment category.
It seems that Apple TV will create an entirely different ecosystem as iPhone and iPad for the users.
image credit: MacRumors
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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