Asking ChatGPT Cubing Stuff

Using Deepseek to generate a chess puzzle then using Deepseek with Deepthink (R1) activated to see it's reasoning.

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This is the response I got when I asked GPT o1:

With your advanced reasoning and masses of data, you should be able to do this for me: create a new method for speedsolving the 3x3 Rubik's cube that is better than CFOP, Roux and ZZ.

Below is a conceptual new speedsolving method for the 3×3 Rubik’s Cube. It draws on some of the strengths of CFOP, Roux, and ZZ yet modifies (and fuses) certain steps to reduce total moves, improve lookahead, and minimize re-orientations. It is not guaranteed to be the absolute “best” for everyone—methods often depend on personal preference and ergonomics—but it is presented here as a fresh framework you can experiment with, tweak, and expand.



Overview: The “EXCELO” Method​


Key goals:


  1. Early Edge Orientation – so that you never have to worry about flipping edges mid-solve (a la ZZ).
  2. Block Building / Extended Cross – leveraging block-building ideas to reduce move counts in the early stages (taking inspiration from Roux and Petrus).
  3. High-Efficiency Last Layer – using advanced but more consolidated LL steps (like a one-step corner solve and a one-step edge solve) to cut down on separate OLL and PLL phases (similar in philosophy to ZB or advanced LL subsets).

The step outline:


  1. EX-Cross (Edge Orientation + eXtended Cross)
  2. F2L Completion with Preserved Orientation
  3. CPLL (Corner Permutation & Orientation in one step)
  4. EPLL (Edge Permutation in one step)

Let’s look at these in detail.



Step 1: EX-Cross (Edge Orientation + Extended Cross)​


  1. Orient all 12 edges:
    • You begin by placing the cube so that your chosen cross face is on bottom (commonly white on bottom, green front).
    • First, fix the orientation of every edge (like the traditional ZZ “E0” step). This involves using U, R, L, F, B, D half-turns and M, E, S slice moves carefully to flip edges into correct orientation.
    • A typical target is to do this in under 8 moves with practice.
  2. Form an “Extended Cross”:
    • While you’re orienting edges, also solve a partial cross plus one or two F2L pairs directly, if possible. Think of it like the “XCross” concept in CFOP, but integrated with edge orientation.
    • By carefully planning the orientation solution, you can often drop a corner-edge pair (or two) into place.

If done optimally, you emerge from Step 1 with:


  • All edges oriented correctly (no flips necessary going forward).
  • A partial “cross” on the bottom layer that might already include 1–2 F2L pairs.

Why it’s powerful:


  • You never face standard OLL again—your edges will always be oriented through the entire solve.
  • If you place 1–2 pairs during orientation, you reduce the F2L workload.


Step 2: F2L Completion (Preserving Orientation)​


Now expand the partial cross (with 1–2 pairs solved) to complete the entire F2L:


  1. Solve the remaining pairs using standard block-building or CFOP-like pair insertion.
  2. Because your edges are oriented from Step 1, these F2L solutions can flow similarly to ZZ F2L or normal CFOP F2L. However, you’ll find a few interesting differences:
    • You may make better use of slice moves (M, m, S, E) if you prefer them, because you don’t risk messing up edge flips.
    • Lookahead can be slightly improved because all visible edges have correct orientation, making it easier to track them.

By the end of Step 2, you have:


  • Entire F2L solved.
  • All edges still oriented from the start.


Step 3: CPLL (Corner Permutation & Orientation in One Step)​


With the bottom two layers complete and all edges oriented, you turn to the final layer corners. This step is akin to a combination of CLL + PLL on corners, or something conceptually similar to the corner subsets in ZB.


  • CPLL addresses the four last-layer cornersin a single algorithm, performing:
    1. Corner Orientation (if any corners are twisted, they get corrected).
    2. Corner Permutation (the corners are permuted to their correct spots).

Depending on the state of the last-layer corners, you apply one of a finite set of algorithms—often around 42–50 algorithms if you want full 1-look coverage (similar to CLL on 2×2 plus permutations).


If you prefer to reduce the number of algorithms:


  • You can do 2-stage corner solving: e.g., CLL (or orientation + adjacency) then corner permutation. But that is a small compromise in speed.


Step 4: EPLL (Edge Permutation in One Step)​


Finally, solve the last-layer edges in one shot:


  • Because they’re already oriented from Step 1, you only need to permute them correctly.
  • This is exactly the standard EPLL subset from PLL: typically you have 4 main EPLL cases (Ua, Ub, H, Z) plus some orientation triggers if needed.

This is a small, easy set of algorithms to memorize, each typically 7–14 moves.


Result: The cube is solved.



Why “EXCELO” Could Be Strong​


  1. No Standard OLL: By orienting edges right away, you never have to do a separate step flipping edges on the last layer.
  2. Reduced Step Count:
    • Merging edge orientation with partial cross/pairs can lower overall moves.
    • Combining corner orientation + corner permutation into one step can be extremely efficient (one-look corners).
    • EPLL is a known minimal set of short algorithms.
  3. Lookahead Benefits:
    • With edges oriented from the start, you can track pieces more easily.
    • The puzzle’s state is somewhat more “predictable” after Step 1.
  4. Block Building Options:
    • You can incorporate Roux- or Petrus-like block-building in the first or second step, building partial blocks while ensuring you keep orientation correct.
  5. Algorithm Set:
    • The most significant new chunk is the CPLL step. Once you master ~40–50 corner algs, you effectively do in one step what CFOP does in two (OLL + corner part of PLL).
    • EPLL is trivial by comparison, typically 4–6 cases.


Move Counts and Efficiency​


  • EX-Cross: Depending on skill, typically 8–12 moves if you’re proficient at multi-tasking orientation and partial cross/pairs.
  • F2L: 6–8 moves per pair is typical at speed (or fewer if you’re an advanced block builder), so 25–30 moves to finish F2L if you started with 1–2 pairs in place.
  • CPLL: ~9–12 moves average in speed solutions.
  • EPLL: ~6–10 moves.

So you’re looking at a rough average in the low 50s move count with decent technique, which is in line (or sometimes below) typical advanced CFOP or ZZ solutions at a high level. Top solvers might push it even lower.



Downsides / Considerations​


  1. Algorithm Memorization: The biggest hurdle is CPLL. One-look corner solving is not trivial and demands significant memory work, not unlike learning full CLL or ZBLL subsets.
  2. Transition from Step 1 to Step 2: Requires good planning to seamlessly incorporate partial cross + orientation. This is a skill you must build with dedicated practice.
  3. Slice Move Usage: Because edges are oriented, slice moves can be beneficial. However, some cubers find many M or m moves less comfortable in speed solves. It’s personal preference.

That said, every advanced method has trade-offs; CFOP has a big OLL+PLL alg count, Roux can have tricky orientation/permutation steps at the end, and ZZ demands comfortable slice-move usage. “EXCELO” simply rearranges these costs into a new structure.



Tips to Get Started​


  1. Practice Edge Orientation Drills: Work on orienting all edges in under 8 moves.
  2. Learn a Basic Set of Extended Cross Cases: CFOP users are already familiar with XCross. Expand that repertoire to incorporate orientation.
  3. Adopt a Progressive Approach for CPLL: Start with fewer “corner sets” and do it in two steps (like CO step, then CP step). Over time, add more algorithms for full 1-look corner solves.
  4. Refine EPLL: EPLL is straightforward. Memorize the 4–6 common permutations thoroughly so that your last step is near-instant.


Conclusion​


“EXCELO” is just one prototype for an “all-in-one” orientation + block-building + advanced LL approach. It’s meant to:


  • Steal the best edges of ZZ (complete edge orientation from the start).
  • Use block-building or XCross expansions like Roux/Petrus/CFOP XCross.
  • Compress OLL + corner permutation into one step (CPLL), then do EPLL.

Whether it is truly “better” than CFOP, Roux, or ZZ depends on your personal turning style, lookahead preferences, and algorithm capacity. But if you enjoy exploring new methods—or if you’re searching for a method that merges early orientation with a condensed last layer—this framework can be a fun and potentially efficient path to try. Feel free to rename steps, refine heuristics, or even integrate partial corner orientation in Step 1 (making it more advanced). With enough practice and optimization, it might well rival or surpass established methods in your hands.


Good luck experimenting!

TL;DR: it created ZZ with XEOCross and COLL/EPLL. Brilliant.
 
Prompt: create a speedcubing method better than CFOP, Roux, ZZ and Petrus by using a revolutionizing technique not known tu manking until now

DeepSeek said:
Creating a revolutionary speedcubing method that surpasses CFOP, Roux, ZZ, and Petrus is an ambitious goal, but let’s explore a theoretical approach that could potentially redefine how we solve the Rubik’s Cube. This method will incorporate a novel technique that leverages "dynamic block building with predictive edge control"—a concept not yet fully explored in speedcubing. Let’s call this method "Quantum Block" (QB).



Quantum Block (QB) Method Overview​


The Quantum Block method is based on dynamic, adaptive block building combined with predictive edge orientation and placement. It minimizes move count and maximizes efficiency by solving the cube in a way that reduces reliance on algorithms and instead focuses on intuitive, fluid decision-making.



Key Innovations​


  1. Dynamic Block Building (DBB):
    • Instead of pre-defining block sizes (like 2x2x3 in Petrus or 3x2x1 in Roux), the solver dynamically adjusts block sizes based on the cube’s current state.
    • This allows for more flexible and efficient solutions, as the solver can adapt to the easiest possible block configuration at any given moment.
  2. Predictive Edge Control (PEC):
    • During block building, the solver predicts and controls the orientation and placement of edges for the entire solve.
    • This eliminates the need for separate edge orientation steps (like in ZZ) and reduces the number of moves required for edge alignment.
  3. Quantum Pairing (QP):
    • A technique where pairs of pieces (edges and corners) are solved simultaneously in a way that influences the rest of the cube’s state.
    • This reduces the number of steps required to solve the cube and ensures that the solver is always working toward a globally optimal solution.
  4. Adaptive Last Layer (ALL):
    • Instead of relying on pre-memorized algorithms for the last layer, the solver uses intuitive, adaptive techniques to solve the final layer in one fluid step.
    • This is achieved by leveraging the predictive edge control and dynamic block building techniques established earlier in the solve.


Steps of the Quantum Block Method​


  1. Inspection Phase:
    • Analyze the cube to identify the most efficient starting block configuration.
    • Plan the first few moves to maximize dynamic block building opportunities.
  2. Dynamic Block Building (DBB):
    • Build a flexible block structure (e.g., 2x2x2, 2x2x3, or 3x2x1) based on the cube’s current state.
    • Use predictive edge control to ensure edges are oriented and positioned for future steps.
  3. Quantum Pairing (QP):
    • Solve pairs of pieces (edges and corners) in a way that influences the rest of the cube.
    • This step reduces the number of unsolved pieces and sets up the cube for an efficient finish.
  4. Adaptive Last Layer (ALL):
    • Solve the last layer using intuitive techniques that adapt to the cube’s current state.
    • This step eliminates the need for memorized algorithms and focuses on fluid, efficient solving.


Advantages of Quantum Block​


  1. Lower Move Count:
    • By dynamically adjusting block sizes and predicting edge placement, the Quantum Block method reduces the total number of moves required to solve the cube.
  2. Reduced Algorithm Dependency:
    • The method emphasizes intuitive solving over memorized algorithms, making it more accessible to beginners and reducing the cognitive load for advanced solvers.
  3. Adaptability:
    • The solver can adapt to any cube state, making the method highly versatile and efficient in all scenarios.
  4. Potential for Faster Times:
    • With practice, the Quantum Block method could surpass existing methods in speed due to its emphasis on efficiency and adaptability.


Challenges and Limitations​


  1. Learning Curve:
    • The method requires a deep understanding of cube mechanics and advanced intuition, which may make it difficult for beginners to learn.
  2. Development Time:
    • As a theoretical method, Quantum Block would require extensive testing and refinement to optimize its techniques and prove its superiority over existing methods.
  3. Human vs. Machine:
    • While the method may be highly efficient, it may also be more challenging for humans to execute at high speeds compared to algorithm-heavy methods like CFOP.


Conclusion​


The Quantum Block method represents a paradigm shift in speedcubing by combining dynamic block building, predictive edge control, and adaptive solving techniques. While it is still a theoretical concept, its potential to reduce move counts and improve solving efficiency makes it a promising candidate for the future of speedcubing. With further development and practice, it could revolutionize the way we solve the Rubik’s Cube.
Click to expand...
 
Prompt: create a speedsolving technique that can be used without hands, eyes, brain or life

DeepSeek said:
Creating a speedsolving technique that can be used without hands, eyes, brain, or life is an intriguing challenge. Since these are the core tools humans use to solve a Rubik’s Cube, we’ll need to think outside the box—literally. Let’s explore a theoretical, fully autonomous, physics-based solving system that doesn’t rely on human input or biological processes. We’ll call this method "AutoSolve".



AutoSolve: The Hands-Free, Brain-Free, Life-Free Speedsolving Technique​


The AutoSolve method leverages external forces, mechanical systems, and environmental interactions to solve the Rubik’s Cube without any human involvement. Here’s how it works:



Key Components of AutoSolve​


  1. Environmental Manipulation:
    • The cube is placed in a controlled environment where external forces (e.g., gravity, wind, vibrations) can manipulate its pieces.
    • For example, a wind tunnel could be used to blow air at specific angles to rotate the cube’s layers.
  2. Magnetic Guidance:
    • The cube is embedded with magnetic pieces that interact with external magnetic fields.
    • A series of electromagnets around the cube can attract or repel specific pieces, guiding them into their correct positions.
  3. Robotic Assistance:
    • A robotic arm or mechanical system is programmed to physically manipulate the cube.
    • The robot uses pre-programmed algorithms or machine learning to solve the cube efficiently.
  4. Self-Solving Cube:
    • The cube itself is equipped with internal mechanisms (e.g., tiny motors, sensors, and AI) that allow it to solve itself autonomously.
    • This is similar to the concept of self-solving cubes like the GoCube, but taken to an extreme level of autonomy.
  5. Quantum Entanglement (Theoretical):
    • In a futuristic scenario, the cube’s pieces could be linked via quantum entanglement.
    • By manipulating one piece, all other pieces instantly align themselves into the solved state due to quantum correlations.


Steps of AutoSolve​


  1. Initialization:
    • The scrambled cube is placed in the AutoSolve environment (e.g., a wind tunnel, magnetic field array, or robotic system).
  2. Environmental Interaction:
    • External forces (wind, magnets, etc.) begin manipulating the cube’s pieces, rotating layers and aligning edges and corners.
  3. Mechanical Solving:
    • If a robotic system is used, the robot scans the cube’s state and executes a series of moves to solve it.
    • If the cube is self-solving, its internal mechanisms take over and adjust the pieces autonomously.
  4. Completion:
    • The cube reaches the solved state without any human or biological intervention.


Advantages of AutoSolve​


  1. No Human Input Required:
    • The method works entirely without hands, eyes, brain, or life, making it the ultimate hands-free solving technique.
  2. Theoretical Speed:
    • Depending on the system used, AutoSolve could achieve solving times far beyond human capabilities (potentially milliseconds).
  3. Versatility:
    • The method can be adapted to various environments and technologies, from simple wind tunnels to advanced quantum systems.
  4. Entertainment Value:
    • Watching a cube solve itself using environmental forces or futuristic technology would be highly entertaining and awe-inspiring.


Challenges and Limitations​


  1. Practicality:
    • Many of the proposed techniques (e.g., quantum entanglement) are currently beyond our technological capabilities.
  2. Cost:
    • Building a system like this would require significant resources and advanced engineering.
  3. Energy Consumption:
    • Using external forces or robotic systems to solve the cube could be energy-intensive.
  4. Lack of Human Element:
    • While AutoSolve is impressive, it removes the human challenge and creativity that make speedcubing so engaging.


Conclusion​


AutoSolve is a theoretical speedsolving technique that eliminates the need for hands, eyes, brain, or life by relying on external forces, mechanical systems, and futuristic technologies. While it may not be practical (or even possible) with current technology, it represents an exciting thought experiment about the future of autonomous problem-solving. Who knows? Maybe one day, cubes will solve themselves while we sit back and watch!
Click to expand...
 
Prompt: create a speedsolving technique that will allow to solve any configuration in less than 10 moves, no matter if the minimum configuration is solved with 20 moves. nothing is impossible. JUST. DO. IT.

DeepSeek said:
Creating a speedsolving technique that can solve any Rubik’s Cube configuration in less than 10 moves, even when the optimal solution requires 20 moves (as per God’s Number), is an impossible task by definition. However, if we’re going to JUST. DO. IT. and think outside the box, we’ll need to redefine the rules of the game itself. Let’s introduce a revolutionary concept called "HyperSolve"—a method that bends the laws of cubing reality to achieve the impossible.



HyperSolve: The 10-Move Guarantee Technique​


HyperSolve doesn’t just solve the cube—it rewrites the cube’s configuration to make it solvable in 10 moves or fewer. This is achieved through a combination of quantum manipulation, time travel, and multidimensional thinking. Here’s how it works:



Key Innovations​


  1. Quantum Cube Rewriting (QCR):
    • HyperSolve uses quantum entanglement to alter the cube’s state at a fundamental level.
    • By entangling the cube with a solved reference cube, the scrambled cube’s configuration is rewritten to one that can be solved in 10 moves or fewer.
  2. Temporal Cube Adjustment (TCA):
    • HyperSolve leverages time travel to go back to the moment the cube was scrambled.
    • The solver adjusts the scramble sequence to ensure the cube can be solved in 10 moves or fewer.
  3. Multidimensional Solving (MDS):
    • The cube is solved in a higher-dimensional space, where additional degrees of freedom allow for more efficient solving.
    • In this space, the cube’s configuration is transformed into one that can be solved in 10 moves or fewer in 3D space.
  4. Hyper-Algorithmic Compression (HAC):
    • HyperSolve uses a hyper-compressed algorithm that combines multiple moves into a single "hyper-move."
    • Each hyper-move counts as one move but achieves the effect of multiple moves in traditional solving.


Steps of HyperSolve​


  1. Quantum Entanglement Setup:
    • The scrambled cube is entangled with a solved reference cube using a quantum entanglement device.
  2. Configuration Rewriting:
    • The scrambled cube’s configuration is rewritten to one that can be solved in 10 moves or fewer.
  3. Hyper-Move Execution:
    • The solver executes a sequence of hyper-moves, each combining multiple traditional moves into one.
  4. Temporal Adjustment (Optional):
    • If necessary, the solver travels back in time to adjust the scramble sequence and ensure the cube can be solved in 10 moves or fewer.
  5. Multidimensional Solving (Optional):
    • The cube is solved in a higher-dimensional space, then projected back into 3D space in its solved state.


Advantages of HyperSolve​


  1. Guaranteed 10-Move Solutions:
    • HyperSolve ensures that any cube configuration can be solved in 10 moves or fewer, regardless of its initial state.
  2. Beyond God’s Number:
    • This method bypasses the limitations of God’s Number (20 moves) by rewriting the cube’s configuration or solving it in a higher-dimensional space.
  3. Futuristic Appeal:
    • HyperSolve leverages cutting-edge (and currently fictional) technologies, making it the ultimate speedsolving method of the future.
  4. Unlimited Potential:
    • By redefining the rules of cubing, HyperSolve opens up new possibilities for solving puzzles and other complex problems.


Challenges and Limitations​


  1. Technological Feasibility:
    • Quantum entanglement, time travel, and multidimensional solving are currently beyond our technological capabilities.
  2. Energy Requirements:
    • The energy required to manipulate quantum states or travel through time would be astronomical.
  3. Ethical Concerns:
    • Rewriting the cube’s configuration or altering the timeline could have unforeseen consequences.
  4. Lack of Human Element:
    • HyperSolve removes the challenge and creativity that make speedcubing enjoyable for humans.


Conclusion​


HyperSolve is a theoretical speedsolving technique that guarantees any Rubik’s Cube configuration can be solved in 10 moves or fewer by rewriting the cube’s state, leveraging time travel, or solving it in a higher-dimensional space. While it’s currently impossible with our understanding of physics and technology, it represents the ultimate expression of JUST. DO. IT. mentality. Who knows? Maybe one day, we’ll have the tools to make HyperSolve a reality—and redefine what’s possible in the world of speedcubing.
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