Based on this YouTube video's total view count of 5.92M views and industry-standard rates, the estimated total earning is $4.15K - $11.8K through ad revenue. Historical data is not yet available to calculate daily, weekly, or monthly averages.

Explore Large Scale Rubik's Cube Simulation - Solving 32768 Layers with 5,927,206 views, 37,221 likes, and 2,468 comments. Experience the impact of this video content that has captured audience attention.

This record is now obsolete! A new record has been set https://youtu.be/y7J3sNR8aC4 6,442,450,944 tiles 2706.16 hours Each face is exactly 2^30 tiles, equivalent to a 1 gigapixel image. Here is a link to a full size (2x zoomed) image of the front face. (https://easyzoom.com/image/146053/album/0/4?mode=manage) The grid size in the scales animation is 1m for the small squares and 10m for the large. The cube is 622m in each dimension (based a 3x3 cube being 57mm) The solver is a single threaded 64 bit application written in c++. The source code has been posted to GitHub (https://github.com/ShellPuppy/RCube). If anyone is interested in a challenge, the application is capable of solving up to a 65,536 layered cube but I predict it will take around 2 years to complete and require just over 24gb of ram. The app is capable of saving its state every few hours in case a reboot is necessary. I'm fairly certain it's not possible to make this app multi-threaded since there is no reasonable way to break the problem into sub problems that can be distributed; imagine 2 people trying to solve a cube at the same time. The algorithm I used is explained in more detail the GitHub readme. In short, it solves centers then corners then edges and uses a very efficient commutator that can move many tiles at once. The edges and corners are brute force solved (there is a lot of room for improvement there). I ran this simulation on an 8700k @ 5ghz and it took a total of 2706.16 hours to complete and generated over 500gb of image data. I set it to export a set of images for each face about every 150,000 moves. Unfortunately there aren't enough pixels in a 4k video to show every tile on the cube so I had the app export 4-megapixel images for each face and frame of the animation (each face actually contains exactly 1 gigapixel). Those 6 image sequences were used to texture a cube for the final render. In addition the app exports some statistics to a log file which was used to generate the stats in the final video. The zoom-out sequence nearly broke my machine. I generated 3 full size images (front, top, and right) and used it to texture a 3d cube then moved the camera extremely close. I used an s-curve function (https://en.wikipedia.org/wiki/Sigmoid_function) for the motion of the camera. Note: rotations of 2 were counted as single move in the stats.

With SocialCounts.org’s view counter, track your YouTube video’s live view count and YouTube likes count in real time with fast, reliable updates.

Watch every YouTube video live view count rise with our real-time YouTube views tracker—built for accuracy and minimal delay.

Follow YouTube real time views as they happen, using our dedicated view counter for YouTube videos.

Get up-to-date live view count on YouTube and see real-time growth with SocialCounts.org’s smart tracking tools.