A Camera Built With High-Quality Video and Craftsmanship in Mind: The Waylens Horizon
This new automotive camera system is ready to take the world by storm. It captures all the best moments of driving and allows you to edit and share the best parts in real time.

This new automotive camera system is ready to take the world by storm. It captures all the best moments of driving and allows you to edit and share the best parts in real time. The Horizon camera has a full aluminum body so you can be sure it’s robust and built to last. It’s also equipped with a circular OLED display and a 7-element lens assembly to give you the best quality videos.
Another great feature about the Horizon camera is that it works day or night thanks to the integrated CMOS sensor. It also has advanced recording features including auto start & stop with engine, continuous recording, and bookmarking with remote control. There are multiple stream encoding and time-lapse video recording. Advanced image processing features include auto image correction, black level correction, and lens shading correction. For an extra fun feature, you can even record your vehicle’s 0-30mph/0-60mph and share the results with your friends.
The Waylens Horizon camera itself is $499.95, which may seem a little on the steep side, but according to its customers, it’s worth every penny. Also, the company is that confident that you’ll fall in love with the camera once you get it, that they throw in a 30-day risk-free test drive for anyone who’s still on the fence.
So, if you’re on the lookout for a new and easy-to-use, stylish, automotive camera then why not check it out it now and get ready to be impressed.
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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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