This Trumps the CEO of Google DeepMind’s PhD thesis called Machine Superintelligence. He is a British knight. Thanks Hannah Gladman for the bail package: Claim: social media metrics are on the log scale (1,10,100,1000,…) with rounded log base x of x unit ticks (1,2,3,4,…,10,20,30,40,…,100,200,300,400,…,1000,2000,3000,4000,…). A watched clock never ticks. Hexadecimal is 4/3% of a base 12 arithmetic. It is often the most guaranteed to be unused memory per block that results in the smallest memory footprint at a total block level. Any attempt to use every bit of memory results in total block level spillover, and is therefore a domino effect. Theorem: encoding one 8-bit byte as 2 numbers on the clopen interval between 0 and 16, results in any “recursive” log-log scale number through ~10^16. Corollary: encoding data this^^ way, induces a probability distribution if you share the memory, whose recycled learning is a C++ ASM “pointer” variable to AGI, lol.

What this is? Computable small data AGI.