I don’t think Morgue adequately explored the distinction between the truman-atrix (one player simulated reality) and the truman-atrix-plus (multi-player simulated reality). He only briefly mentions that our dreaming state while asleep at night is like our individual reality (ie truman-atrix) and our dreaming state while awake during the day is like creating our shared reality (sort of like the truman-atrix-plus but not quite).

Nonetheless, Morgue provides a good introductory video to the various concepts regarding the simulation hypothesis, and the associated quantum physics ideas. It might be a bit heavy for some.

Morgue mentioned metaphysical reality is a mathematical mental reality based on immaterial frequencies, so our reality is:

dreamlike because it’s mind (immaterial mental frequencies)

code-like because it’s structured through eternal mathematical law, which is code-like

holographic because these frequencies form an interference pattern that project the dimensional dream reality that we inhabit together

So, the various analogies to * simulation theory/simulation reality* contain ALL the analogies, as they are all valid: dream, hologram, code directing the hologram projections.

I thought the Rubik’s cube analogy about how zero is a structure of positive and negative values was interesting. So, the cube can recombine into many different realities/combinations, while maintaining zero, which is nothingness, which cannot be created or destroyed, like energy itself, so it is eternal, as are we. We are the Absolute! I AM! I AM the absolute, and YOU ARE the absolute, together making the WE ARE the absolute, networked nodal minds.

As Morgue says, the bedrock of reality then is based on ZERO, an immaterial existence that has eternal existence that is a structure of nothingness that is exploring all the different patterns that it can take on while maintaining being nothing.

As a former programmer, I know it would be extremely difficult to create a complex program AND ALSO make sure the sum of every underlying bit adds up to zero. All computer operations boil down to binary digits of 0 or 1, which represent the on or off state of allowing electricity to flow through a **transistor**, a tiny physical device that acts like a digital switch in computers. The transistor turns on when enough electricity flows through and stays off otherwise. The microchips in our computers contain billions of these tiny on-off transistor switches.

In general, most computer programs don't necessarily "average" to zero in the sum of their bits. However, there are some interesting patterns and exceptions to consider:

**Random bit patterns**: In many cases, the bits in a computer program are randomly distributed, and the sum of these bits can be expected to converge to zero as the number of bits increases. This is because each bit has a 50% chance of being either 0 or 1, and the law of large numbers suggests that the average will approach zero as the number of trials (in this case, bits) increases.**Binary representations**: When representing numbers using binary (base-2), many numbers have a natural tendency to be close to zero due to the way binary arithmetic works. For example, the average value of a set of random binary numbers is often close to zero because many numbers have an even number of 1s, which cancel out when added.**Program-specific behavior**: Some programs may exhibit patterns that lead to an average value close to zero. For instance, if a program is designed to perform calculations that involve many small values, the sum of these values might be close to zero. However, this is not a general property of all computer programs.**Exceptions**: There are some notable exceptions where the sum of bits in a program might not average to zero:

a. **Cryptographic algorithms**: In some cryptographic applications, such as hash functions or encryption algorithms, the goal is to produce a non-zero output that is difficult to predict or manipulate. In these cases, the sum of bits may not average to zero.

b. **Program-specific properties**: Certain programs may have specific properties or constraints that lead to an average value that is not zero. For example, some algorithms rely on the properties of prime numbers or other mathematical structures that don't necessarily average to zero.

c. **Bugs or anomalies**: In rare cases, a program may contain bugs or anomalies that cause the sum of bits to deviate significantly from zero.

In summary, while many computer programs may exhibit an average value close to zero due to random bit patterns or binary representations, this is not a universal property of all programs. The behavior of a program's bits depends on its specific design, implementation, and purpose.

I know the extremely complicated code that must be running our reality would be complex enough to require a super-intelligence to make sure the sum of every bit balances out to exactly zero.

## Shocking QUANTUM Discovery: Are You TRAPPED in a SIMULATION?