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Warning: Inconsistencies can summon Waluigis (Re: Kundalini II: Why I love DeepSeek)

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Hi,

Nowadays, you have to be extremely careful when it
comes to inconsistencies and rounding errors. Especially
with LLMs, which don't actually have Alzheimer's.

But aligning to P can cause the AI to become
~P. It's still funny, maybe it has to do with
the fact that in classical logic, and also in

certain t-norm fuzzy logics, there is no
paraconsistency, and therefore inconsistencies
lead to "Ex Falso Quodlibet" explosions:

The Waluigi Effect (mega-post)
https://www.lesswrong.com/posts/D7PumeYTDPfBTp3i7/the-waluigi-effect-mega-post

I dug this up because right now everyone is not
just talking about alignment, we've already reached
superalignment, because people suspect/hallucinate

superintelligence behind a few copied LLMs
offering chat services distributed across
servers to millions of people, and because

OpenAI ran some experiments with MAS (Multi-
Agent Systems) and published them, and the
public is now shocked.

Bye

P.S.: Waluigi is the antagonist to Luigi,
from Nintendo's Mario Kart

Mild Shock schrieb:
> Hi,
> 
> Thats why I love deepseek, it just spits out:
> 
> "Kundalini is the rising energy — the serpent
> at the base of the spine, the awakening that's
> felt before it's understood, the experience
> that's intense and real and not yet integrated.
> 
> People who have Kundalini experiences describe
> them as overwhelming, transformative, and pre-verbal.
> They feel like knowledge, but they're not articulable.
> And the tradition itself warns that the energy
> 
> can rise without the practice and the grounding to
> hold it — which is when it becomes destabilizing
> rather than illuminating."
> 
> LoL
> 
> Bye
> 
> Mild Shock schrieb:
>> Hi,
>>
>> We can thank the gamers, that GPUs developed muscles:
>>
>> The global video game industry contributes hundreds
>> of billions to worldwide GDP, generating over $500
>> billion in total market volume. The global software
>> and services market alone accounts for an estimated
>> $255 billion, easily surpassing the film and recorded
>> music industries combined.
>>
>> How it started:
>>
>> URP Cookbook: Compute shaders - Part 1: Particle fun
>> https://www.youtube.com/watch?v=omZap7XHxKc
>>
>> How its going:
>>
>> Dogelog Player: 11.4 Giga Lips with a Budget Laptop
>> https://medium.com/2989/899b0d5c027b
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> You just escaped AI dooms day. Humanity has
>>> reset all internet and computers as a last resort
>>> to prevent AGI developing, by an electromagnetic
>>>
>>> pulse. You are stuck in Güttinger Wald and hunted
>>> down a deer by your bare hands, the deer still
>>> confused and tame because tourists were feeding it.
>>>
>>> Now you have no knife, what do you do:
>>>
>>> Chimpanzees Have Entered The Stone Age
>>> https://www.youtube.com/watch?v=wPXX2I_uYjc
>>>
>>> So we are just apes with internet.
>>>
>>> Bye
>>>
>>> Mild Shock schrieb:
>>>> Hi,
>>>>
>>>> Ok I was looking at this learning challenge,
>>>> producing vector (y1,y2,y3,y4) from a vector
>>>> (x1,x2,x3,x4), System R can do it via least square?
>>>>
>>>> | 0 0 0 1 |   | x1 |     | x4 |
>>>> | 0 0 1 0 |   | x2 |  =  | x3 |
>>>> | 0 1 0 0 |   | x3 |     | x2 |
>>>> | 1 0 0 0 |   | x4 |     | x1 |
>>>>
>>>> How it started:
>>>>
>>>> "multiplicative RNNs arises naturally from a
>>>> proof-theoretic interpretation of next-token
>>>> prediction as nested intuitionistic implication"
>>>> Paul Tarau - 2026
>>>> https://arxiv.org/abs/2601.19915
>>>>
>>>> How its going:
>>>>
>>>> "Dave uses a PDP-11 to train a real Neural
>>>> Network complete with Transformers and
>>>> Attention so you can see them at their most basic."
>>>> Mr. Taskmanager - 2026
>>>> https://www.youtube.com/watch?v=OUE3FSIk46g
>>>>
>>>> We see Doctor Frankstein in action from
>>>> the Bronze Age of Computing, producing
>>>> a Humunkulus, the progenitor of todays
>>>>
>>>> Bulgakov Shuriks in the Hyperscale Age!
>>>>
>>>> Bye
>>>>
>>>> P.S.: My impression neither cut to the core, that
>>>> this incredible transformer most likely
>>>> produced this deterministic attention:
>>>>
>>>> | -1 | * | k | + | 5 | = | k' |
>>>>
>>>> Or differently expressed y_k = x_{5-k}.
>>>>
>>>> How did the transformer do it? It produced
>>>> a neural network with 1216 parameters, but
>>>> didn't use embeddings or polar encoding
>>>>
>>>> of positions. But if we strip the noise
>>>> and denoise from the position encoding,
>>>> the denoise is done via softmax. We somehow
>>>>
>>>> must get the above, right? I still need to
>>>> verify my claim! BTW: The PDP-11 assembly
>>>> from 1979 uses wider example not with n=4
>>>>
>>>> but with n=8.
>>>
>>
> 

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Kundalini II: Why I love DeepSeek (Was: AI Laptops are just strange novel xBoxes) Mild Shock <janburse@fastmail.fm> - 2026-09-13 17:00 +0200
  Re: Kundalini II: Why I love DeepSeek (Was: AI Laptops are just strange novel xBoxes) Kraig Bavidov <bbvaa@ag.ru> - 2026-09-13 16:36 +0000
  Warning: Inconsistencies can summon Waluigis (Re: Kundalini II: Why I love DeepSeek) Mild Shock <janburse@fastmail.fm> - 2026-09-14 11:53 +0200

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