GiiKER — A bridge to the future

Cubing Diaries #3 — The present and the future of bluetooth in speedcubes

How you doing?

The GiiKER supercube is an apparently normal 3x3, with a very unpleasant noise and a shaky performance. That’s not why we’re here.

Through sensors installed in the centers of each face, this cube has bluetooth functionality, which allows you to connect your cube to any device and gain access to several different programs.

At first glance, this cube is an expensive 3x3 that will be used in some silly little games and soon after will be put aside. In this article, I want to show you why I am sure that this technology will revolutionize speedcubing as we know it.

Today

Initially, I’ll introduce you to ideas that have already been put together, even if lightly, only ~ 10 months after this technology became available commercially.

Supercube: The GiiKER cube is accompanied by an app that tries to do several things at once.

Its timer starts timing the solve as soon as you make the first move, and stops when the cube is solved. It records the solve so you can check it later, and provides you with a standard scramble. The statistics, reconstructions and analysis functionalities of this timer are very basic, almost nonexistent.

The app also features different games, all based on bluetooth gimmicks. Among them, the one that caught my attention the most was Cube Miner, in which you are a miner collecting gold and gems scattered around the cube ir order to exit the cave within a limited amount of moves.

In another avaliable game, you try to reproduce in your cube a pattern that the app presents within the shortest time possible. It has several levels with distinct patterns, and a grading system that rewards the fastest resolution.

Overall, the app is good, but leaves a lot to be desired. I feel like it’s going the right way, but is still in early stages. It still needs a lot of feedback from cubers and many hours of work so that the end product is something of recurring interest to experienced cubers.

The Cube Crash game, for example, where you must turn each face of the cube when bombs fall on their respective colors, could, with a little je ne se quoi, be easily transformed into Cubing Heroes, a revolution for the genre rythm gaming.

Coming soon to a store near you

This app appears to confirms your initial impression that the cube is a novelty that quickly wears out. The next item on the list begins to move away from that.

Smart timers: As I mentioned, the GiiKER app timer leaves a lot to be desired.

Therefore, the cuber Koki Takahashi (A.K.A. Hakatashi) has developed a timer that tries to take full advantage of the possibilities that the supercube offers.

When connecting a cube to the website, the timer gives you a scramble, and after scrambling the cube, it waits for your inspection. Again, you only need to turn one face for the timer to start running. At this point, it becomes clear why this timer is being mentioned here. As you solve the cube, the timer automatically records the solve and and reconstructs it, meaning that, if you use CFOP or Roux, it is able to interpret the moves you make and understand what part of the resolution they are.

When a solve is completed, the timer stops and shows you a reconstruction of that solve, detailing which color you used to cross, how much time you spent on each F2L pair, which PLL you, and more. On the statistics page, you can see how long it takes to recognize and execute each OLL and PLL, obtaining valuable information for your personal improvement.

Although it still falls a little to the side of simplicity, this website is a good look at the functionalities that can be expected from a bluetooth timer. No longer do you have to worry about rebuilding your exceptionally good resolutions in the middle of a training session, or timing each PLL to know which you need to drill. All this is work done for you.

Muscle memory: The normal process for learning algorithms occurs mainly in two different ways, often mixed. In the first method, you learn one or two algorithms at a time and apply them as they appear on regular solves, and after many solves, those algorithms will be embedded in your muscle memory and you can learn the next ones on the list.

The problem with this technique is it takes a long time. Each PLL, for example, does not appear so often for this to be an efficient method of learning them. Another alternative appears: specific scramblers (such as this one for PLL).

In them, you select the specific cases you are trying to learn and the site gives you corresponding scrambles. This evolution significantly reduces the time between algorithm repetitions, making the learning process much faster. Instead of waiting for cases to arise naturally in your resolutions, you force cases to appear quickly in succession.

The downside of this method is that, at the end of each algorithm, you need to scramble the cube to get to the next one, at the risk of getting the scramble wrong and having to solve a semi-solved cube. With the GiiKER cube, the process of memorizing and repeating algorithms got even better.

The website briefcubing.com, developed by Ashley Feniello, makes it so that the desired cases appear on the screen of your device. The cube then becomes a remote control that controls the virtual cube. After selecting the cases that interest you, the website displays which is the next one you need to solve. As you begin to execute the algorithm, your moves are recorded and then checked. If you get it wrong, with a U’ you can try again. If you get it right, you can quickly move to the next one with a U.

The ability to execute algorithm after algorithm, one after another, with no pause between them is fantastic. Your muscle memory will develop much faster because the time between executions is cut in half.

The detail that complets the usefulness of this website is that you can execute the algorithm from anywhere on the cube. In the video that shows how this website works, its creator uses a GiiKER cube without stickers so that it becomes simply a remote control without worrying that the stickers in the physical cube match what is on the screen.

A downside to using this technique is that often, especially for advanced cubers, you use references in the cube itself, looking at what happens to some pair or block, to memorize the algorithm before the muscle memory completely manifests itself. Depending on what algset you are training, such as OLL or CMLL, these references may not be completely lost.

Another website, a little more robust, also with this purpose, is Alg Trainer, developed by Tao Yu. Despite a not so elegant interface, it has more options for algsets and customization of your experience.

This was an overview of what you can already do with a GiiKER cube.

Tomorrow

Now is the time to let my imagination run loose and explore future possibilities, not yet developed, that can be achieved with bluetooth cubes. Some of these are relatively close while others are far apart. I chose to put the most doable ideas first and the most uncontrolled ones last.

Solving on the internet: First of all, I’d like to ask for a minute of silence in honor of Twist The Web.

R.I.P

With bluetooth cubes, the interaction between cubers over the internet can be completely revolutionized.

Websites such as the late Twist The Web, of real-time competition, could bring a social aspect to solve reconstructions. Instead of just stating that you made an insane xcross, you would select it and show your friend exactly what you did.

The learning relationship can also improve. Instead of depending on a sad webcam, a cuber would teach something to another with a virtual cube, demonstrating exactly his point of view. Ideas that could not possibly be transmitted verbally or with a cube at a non-ideal angle could be viewed virtually with the GiiKER cube.

Finally, another aspect of the social interaction of cubers on the internet that could be improved with the GiiKER cube is that of streams. Beside the video of the cuber solving a cube, there could be a reconstruction of the last solve, with statistics and highlights of a solve. While the audience has the opportunity to personally interact with the streamer, watching while they solve a physical cube, it could also watch closely what was happening with the cube.

FM: During the FMC Brasil 2019 championship, in one of the looong six hours of using a wild FreeFOP, I found an interesting solution that ended in a PLL skip. Right away I couldn’t return to the solution I had found. And I thought, what if I had a way to review my move history?

And with bluetooth technology, there is a way! We can think of a program that completely tracks your solution and serves as an FM learning platform.

My initial idea, still a bit raw, would be similar to existing chess software. You would go to one point of resolution to another, investigating innumerable possibilities and, as soon as you want to, you would return to the starting point. This fluidity in an FM attempt would be facilitated with a feature of this program that would take you from any point of resolution to another in ~ 22 moves or less. You would just click where you want to go and the program would give you the “scramble” to get there, without without having to solve the cube or invert moves.

This software would allow you to explore your solution without worrying about writing down possible skeletons with the fear of not finding them again. Every move you made would be recorded. Its usefulness would not be restricted to FM, but would encompass analysis of solves in general. In a 3x3 speedsolve, you could freely evaluate different crosses or possible pair choices, eventually referring the program to a more efficient solution, always with an easy return to the original solution.

The biggest drawback that prevented such a program from existing (not to mention nobody wanting to work for free🙈) is the fact that controlling a virtual cube with a keyboard is not as intuitive as it appears to be. With the GiiKER cube, a virtual cube turns into the extension of a physical cube.

The application of such a program would be somewhat limited by the fact that much of the FM learning curve in competitions depends on learning how to manage the available time and how to use NISS naturally. If you used this program all your life to train FM, you might find that your ability in official attempts is a little lower than you expected.

The next step: Currently, the process of improvement in an event may be a little less linear than it should intuitively be. As you progress, the resources that broadly help you improve your technique become more scarce, and you need more and more panning among many tutorials to find one that will help you in your particular difficulties.

This barrier can be remedied with practice specifically aimed at the defects in your technique that hurt you the most. With this method, the basic process of improvement in advanced levels is: 1) detecting a structural problem in your technique of resolution; 2) investigating possible resources that teach you how to solve this problem and selecting the ones that seem most useful to you; 3) learning the new techniques that the selected resources propose to you and, finally, 4) repeatedly applying these new techniques in solves to the point where they become automatic.

The main source of inaccuracy in this process is precisely in steps 1 and 2, the most subjective ones when compared to steps 3 and 4. The difficulty of the first step is to be able to point out in your solves exactly what is wrong and why. Even if you study some of your reconstructions, you can not always clearly tell whether what you do is good or not. The process of detecting flaws may itself be flawed.

In the second step, once you have identified a problem (which has sometimes not been so well identified), you will be looking for possible solutions to this problem. Usually this is done with a wild internet search looking for anyone who is talking about the problem that you detected. You’ll find many videos, posts, tutorials, VHSs and more. Here comes the first issue in this step: you may not find just the resource that you needed to help you with your problem. After that, you will cut down all of these resources to just a few that are really useful. The second possible failure of this step is in cutting a feature that, perhaps from poor apparent quality, did not seem useful to you, but in fact was what you needed.

If you could then get some way to refine the analysis of your technique so that defect detection is more accurate and refine the selection of resources that will be useful to you so that you learn what you really need, the process of improving in advanced levels of speedcubing would be extremely more efficient. I’m happy to tell you your troubles are over! (not yet because someone has to develop this program with much affection and dedication)

My idea is to use Machine Learning to create software that perfectly understands the steps needed to improve on the rubik’s cube. The first stage of development would be to get as many reconstructed solves as possible and feed them into that program. From these initial solutions, this algorithm would learn to tell the expected moves in a specific situation. He would learn to select the most efficient cross or the best next pair, for example.

With a trained program, you would be able to submit your resolutions for it to review them in order to perform steps 1 and 2 of the improvement process. In the first step, the program would be able to identify the structural problems of your technique by analyzing the characteristics of each part of your solves and comparing them with the technique of the average of people a little faster than you, as well as with the expected optimal solution . It could then signal what differs exceptionally from the average cuber, defining which areas you need to improve on.

The program’s ability to suggest the second step would initially be somewhat precarious, set manually by the developers. At that point, the program would have many of the same flaws that doing this step manually has. When a user inputted their reconstructions in this program, after the first step has been completed, the program would suggest some resources that were selected by the developer to guide you in solving the problems encountered.

However, the more people used this program to train and improve, the more the program would be able to tell which solutions that it presented were more or less effective. From this, it would be possible to relate a specific bad habit to a specific resource, specializing more and more the suggestions that the program can give. Each recorded solve would serve both to train the fault-detection mechanism and to improve the resource-suggestion service. The program would be progressively more accurate as more users took advantage of it.

An important detail is that the program would give a suggestion from a point of reference close to you, meaning it would use solves a little faster than yours to prescribe your practice. Otherwise, this program would just be a machine that shouts “MORE EFFICIENT F2L” because it would be comparing your F2L meh with that of sub-7 Cubists. The program is always showing you the next step you have to take to improve your skill, not the absolutely most efficient technique.

This is not the droid I’m looking for

The GiiKER cube is critical here because it can provide the program with the automatic reconstruction of the solve as well as the exact instant each move was made, information not normally available in a reconstruction. Each crumb of available information could be deeply analyzed in order to improve the two functionalities that this algorithm would offer.

Chris Tran, wizard of speedcubes, in a talk made during the 2018 US National Championship, presented the Argentum Project, which is a program with a premise that steps away from bluetooth technology, but with a very interesting final result to the goal that I’m aiming for.

Most of the talk was about the intended technology of automatic reconstruction of a solve from the recognition of only one face of the cube. At first, it does not seem possible to reconstruct an entire resolution from a single face. However, once you realize that any movement other than D affects the top face and that a D move would be detectable by its effect on the other faces in the next moves, you understand how this reconstruction would take place.

In a second phase of the project, Chris plans to use Deep Learning, a kind of machine learning, in conjunction with the GiiKER cube itself to train the Argentum program to be able to recognize the moves being made in a speed cube in any video, regardless of the recording angle, with some tolerance for poor video quality.

With that, you could scour Youtube looking for rubik’s cube videos, reconstruct each one of them and, in the talk, Chris Tran talks about building a database with the best algorithms possible, extinguishing secrets in speedsolving.

When you think about using this feature with the program I mentioned earlier, it’s clear that it could be used for more than just algorithms. You could pan YouTube looking for solutions that could train your algorithm. Any official or unofficial resolution would optimize the algorithm of analysis and training suggestions a bit more.

This program, equipped with existing reconstructions and possible reconstructions from the Argentum project, when coupled with the GiiKER cube, which will provide the reconstruction of your personal resolutions, has the power to transform the way you learn in the speedcubing. I hope that, when it is eventually developed, all cubers can use it to constantly outdo themselves.

WCA Competitions: After reading the title of this subtopic, it is likely that your pressure has risen a bit. I ask you to calm down, take a deep breath and keep your mind open.

Delegates around the world thinking about bluetooth cubes in official competitions

In 2012, a team of polish cubers called Opencubeware developed and implemented a system for official competitions that, using a device that connected to the stacktimer, automatically uploaded times after each resolution.

Each competitor, at the start of the competition, received a magnetic card to use as a badge. Before commencing a resolution, the competitor would pass that card over the device, which would recognize his identity. The judge then confirmed that the recognized card belonged to the competitor by pressing one of the available buttons.

The inspection process occurred as normal, with a manual timer controlled by the judge. When the competitor finished the solve and stopped the timer, it was up to the judge to apply a penalty or not, and then he would bring his card closer to the device. At that point, the resolution time was automatically posted to a cubecomps-like site, which could be checked in real time.