From Newsgroup: comp.lang.prolog

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.
--- Synchronet 3.21f-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

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.

--- Synchronet 3.21f-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

Hi,

His last paper , that I am refering to, his
last opus , could have some merrits nevertheless.
Like if you are interested in intuitionistic logic,

and ever wondered what makes (a1 -> (a2 -> .. (an -> b)
..)) different from ((.. (b -> a1) .. -> an-1) -> an).
Kind of loophole to encode lists? But transformer

doesn't work with lists, its not that a transformer
would directly learn:

reverse(X, Y) :- reverse(X, [], Y).

reverse([], X, X).
reverse([X|Y], Z, T) :- reverse(Y, [X|Z], T).

Because transformers don't use LISP consing
representation. Neither for the input token stream
nor for the output token stream,

that ChatGPT shows tokens gradually appearing in
his UX is only a Gimmick.

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.

--- Synchronet 3.21f-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

Hi,

How it started:

Epstein Island LEGO Set
Build the legendary island, assemble the pool, and get
the party started with all your kids. This set captures
the intensity and atmosphere of the island. https://www.youtube.com/watch?v=ttwO_eeqkWc

How its going:

The EU approach to age verification
The European Commission's approach to age verification
focuses on developing user-friendly, privacy-preserving
solutions alongside Member States. https://digital-strategy.ec.europa.eu/en/policies/eu-age-verification

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.

--- Synchronet 3.21f-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

Hi,

Lets get emotional! While Varoufakis painted
the picture of cloud capital. That might have
mobilized "The Internationale", or another

more defensive less motolotov throwing song:

Pink Floyd - Run Like Hell (Live)
https://www.youtube.com/watch?v=lKgOe1Rl8YY

Now since Athropic is teaming with xAI, we
might ask do we see the next OneDrive of Prolog
on the horizon. Even a tame Erlang dream:

populate the Web with clever Prolog agents!
https://trinity.elfenbenstornet.se/

Might have a nasty Prolog as SaaS aspect!
As long as we talk about services and not
assets, we might miss something. Who owns

the present and future LLMs/LRMs?

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.

--- Synchronet 3.22a-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

Hi,

Even the Buddos are cluless, while Tarau might
indeed appear in the anals of the Borg, as a
notable human being, seeing connections.

While the Buddos are the man mountains of
Janathan Swists Gulliver's Travel, creating
huge egg montains, replaying some rewriting

school inventions. They might nevertheless be
strapped down by Liliputians:

Gulliver captzured by the Liliputians https://www.lookandlearn.com/history-images/M301092/Scene-from-Gullivers-Travels

But who are these Liliputians? Well just
toying around with a deep seek v4 derivate in
LM Studio, a model that came out 9 days ago.

Etc.. etc.. it shows more text, all generated
on a laptop that was even only $1000 since
end of year 2025, there were some discounts.

The laptop has the Windows Copilot+ specs.
The secrete sauce? Some general matrix
multiplications (GEMM) tucked in your iGPU:

What is Xe Matrix eXtensions (XMX)? https://www.intel.com/content/www/us/en/support/articles/000091112/graphics.html

Bye


Mild Shock schrieb:

Hi,

Lets get emotional! While Varoufakis painted
the picture of cloud capital. That might have
mobilized "The Internationale", or another

more defensive less motolotov throwing song:

Pink Floyd - Run Like Hell (Live)
https://www.youtube.com/watch?v=lKgOe1Rl8YY

Now since Athropic is teaming with xAI, we
might ask do we see the next OneDrive of Prolog
on the horizon. Even a tame Erlang dream:

populate the Web with clever Prolog agents! https://trinity.elfenbenstornet.se/

Might have a nasty Prolog as SaaS aspect!
As long as we talk about services and not
assets, we might miss something. Who owns

the present and future LLMs/LRMs?

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.

--- Synchronet 3.22a-Linux NewsLink 1.2

From Newsgroup: comp.lang.prolog

Hi,

Interesting, the Copilot+ minimal
requirement is 40 TOPS. Now Wiki is
mumbling something of 35 TOPS + 15%,

for the new A18 chip, that is found
in iPhones and the new Mac Neo. The
new Mac Neo is only $500 , half of

my discount AI laptop, and is selling
like hotcakes. I should try it, see
what AI workloads it can do locally.

Bye

P.S.: The Mac Neo is jokingly called
Ne-Meow in this hands on video by Bijan
Bowen, showing some vibe web coding:

MacBook Neo Local AI Test
https://www.youtube.com/watch?v=75PFpW9SOL0

Mild Shock schrieb:

Hi,

Even the Buddos are cluless, while Tarau might
indeed appear in the anals of the Borg, as a
notable human being, seeing connections.

While the Buddos are the man mountains of
Janathan Swists Gulliver's Travel, creating
huge egg montains, replaying some rewriting

school inventions. They might nevertheless be
strapped down by Liliputians:

Gulliver captzured by the Liliputians https://www.lookandlearn.com/history-images/M301092/Scene-from-Gullivers-Travels

But who are these Liliputians? Well just
toying around with a deep seek v4 derivate in
LM Studio, a model that came out 9 days ago.

Etc.. etc.. it shows more text, all generated
on a laptop that was even only $1000 since
end of year 2025, there were some discounts.

The laptop has the Windows Copilot+ specs.
The secrete sauce? Some general matrix
multiplications (GEMM) tucked in your iGPU:

What is Xe Matrix eXtensions (XMX)? https://www.intel.com/content/www/us/en/support/articles/000091112/graphics.html

Bye

Mild Shock schrieb:

Hi,

Lets get emotional! While Varoufakis painted
the picture of cloud capital. That might have
mobilized "The Internationale", or another

more defensive less motolotov throwing song:

Pink Floyd - Run Like Hell (Live)
https://www.youtube.com/watch?v=lKgOe1Rl8YY

Now since Athropic is teaming with xAI, we
might ask do we see the next OneDrive of Prolog
on the horizon. Even a tame Erlang dream:

populate the Web with clever Prolog agents!
https://trinity.elfenbenstornet.se/

Might have a nasty Prolog as SaaS aspect!
As long as we talk about services and not
assets, we might miss something. Who owns

the present and future LLMs/LRMs?

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.

--- Synchronet 3.22a-Linux NewsLink 1.2