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Kundalini II: Why I love DeepSeek (Was: AI Laptops are just strange novel xBoxes)

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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

#647501 — Kundalini II: Why I love DeepSeek (Was: AI Laptops are just strange novel xBoxes)

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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#647503

Mild Shock wrote:

> 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.

100% irrelevant to the principles of relativity. You dont even know what a 
KiB and a Mib is.

[toc] | [prev] | [next] | [standalone]

#647504 — Warning: Inconsistencies can summon Waluigis (Re: Kundalini II: Why I love DeepSeek)

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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