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Originally designed in 2006, Nautilus was developed with the primary goal of having a method that contains as few blind spots as possible. The result is a method that gives the user the ability to easily find pieces that are to be solved next. Nautilus has since been streamlined to be a top method with the ability to use algorithms for the majority of steps.

APB is a method that I developed in 2021. It has its roots in the Petrus method, but approaches each step in a new way. Instead of the highly intuitive steps of Petrus, APB shifts all of the work into memorized algorithms. This produces a method that removes much of the human error to produce consistent move efficiency and ergonomics.

The Roux method was created by Gilles Roux in 2003. I have been a Roux method user since early 2006. Since then I have developed several steps and techniques that improve the Roux method. These include the first development of steps called EOLR and EOLRb, a final step recognition system called DFDB, CMLL prediction techniques, non-matching corner recognition, and ACMLL.

The 42 method was created by Joseph Briggs in 2017. In the 42 method, there is a step that involves solving corners using a unique pseudo technique. In 2023 I developed another way of solving the corners. This is the direct, non-pseudo way, as well as a new way that reduces to a pseudo state instead of starting in a pseudo state as in the original style. This reduces the number of moves in the solve and makes the step easier to recognize. I also developed a new way of solving the final step of the method.

A standard system for notating turns and piece locations of the cube was developed in the late 1970s and early 1980s. Other systems have since been added on to the standard. In 2010 I discovered a way to notate not only individual pieces, but also groups of pieces.

When solving the Rubik's Cube, solvers typically solve pieces intuitively until they reach a point where they apply a memorized sequence of moves, called an algorithm. At this point, there can be several or many possible states and the state needs to be recognized and the associated algorithm recalled. With current methods of recognizing states, solvers first check the orientation of the pieces then look at additional stickers on those pieces. This can be a time consuming process. With Straughan recognition, a systematic approach is applied to allow for checking the minimum number of stickers. This potentially allows for determining the state before arriving at the step.