From Newsgroup: sci.logic

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973 https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

The below PhD thesis is a nice gem,
including marvels such as:

2.3 Avoiding Unnecessary OCCUR Checks
It is possible to significantly reduce the number
of calls to OCCUR during a resolution unification
by the following observation. If two clauses are
being resolved, they are standardized apart.

Thus, a variable from the left-hand parent will not
occur in a term from the right-hand parent unless
during this unification, there has been a binding of a
variable from the right to a term from the left.

A similar statement holds for 1eft-to-right bindings.
Once again, in structure sharing, this condition
is easy to check.

The BAROQUE programming languages is also
a genious contraption, it can run functions
backwards, basically a Prolog based functional

language, using the reified IF-THEN-ELSE
from Ulrich Neumerkel. Only in 1973 there was
no EMACS yet. Now we have Prologers that know

EMACS but still know nothing otherwise.

Bye

Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973 https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350

BTW: Still ticking along with the primes.pl example:

test :-
len(L, 1000),
primes(L, _).

primes([], 1).
primes([J|L], J) :-
primes(L, I),
K is I+1,
search(L, K, J).

search(L, I, J) :-
mem(X, L),
I mod X =:= 0, !,
K is I+1,
search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
N > 0,
M is N-1,
len(L,


Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973 https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

2025 will be last year we hear of Python.
This is just a tears in your eyes Eulogy:

Python: The Documentary | An origin story https://www.youtube.com/watch?v=GfH4QL4VqJ0

The Zen of Python is very different
from the Zen of Copilot+ . The bloody
Copilot+ Laptop doesn't use Python

in its Artificial Intelligence:

AI Content Extraction
- Python Involced? rYi None at runtime,
Model runs in ONNX + DirectML on NPU

AI Image Search
- Python Involced? rYi None at runtime,
ON-device image feature, fully compiled

AI Phi Silica
- Python Involced? rYi None at runtime,
Lightweight Phi model packaged as ONNX

- AI Semantic Analysis? rYi None at runtime,
Text understanding done via compiled
ONNX operators

Bye

Mild Shock schrieb:

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350

BTW: Still ticking along with the primes.pl example:

test :-
-a-a len(L, 1000),
-a-a primes(L, _).

primes([], 1).
primes([J|L], J) :-
-a-a primes(L, I),
-a-a K is I+1,
-a-a search(L, K, J).

search(L, I, J) :-
-a-a mem(X, L),
-a-a I mod X =:= 0, !,
-a-a K is I+1,
-a-a search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
-a-a mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
-a-a N > 0,
-a-a M is N-1,
-a-a len(L,

Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973
https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Swiss AI Apertus
Model ID: apertus-70b-instruct
Parameters: 70 billion
License: Apache 2.0
Training: 15T tokens across 1,000+ languages
Availability: Free during Swiss AI Weeks (September 2025)

https://platform.publicai.co/docs

Bye

P.S.: A chat interface is here:

Try Apertus
https://publicai.co/

Mild Shock schrieb:

Hi,

2025 will be last year we hear of Python.
This is just a tears in your eyes Eulogy:

Python: The Documentary | An origin story https://www.youtube.com/watch?v=GfH4QL4VqJ0

The Zen of Python is very different
from the Zen of Copilot+ . The bloody
Copilot+ Laptop doesn't use Python

in its Artificial Intelligence:

AI Content Extraction
- Python Involced? rYi None at runtime,
-a Model runs in ONNX + DirectML on NPU

AI Image Search
- Python Involced? rYi None at runtime,
-a ON-device image feature, fully compiled

AI Phi Silica
- Python Involced? rYi None at runtime,
-a Lightweight Phi model packaged as ONNX

- AI Semantic Analysis? rYi None at runtime,
-a Text understanding done via compiled
-a ONNX operators

Bye

Mild Shock schrieb:

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350


BTW: Still ticking along with the primes.pl example:

test :-
-a-a-a len(L, 1000),
-a-a-a primes(L, _).

primes([], 1).
primes([J|L], J) :-
-a-a-a primes(L, I),
-a-a-a K is I+1,
-a-a-a search(L, K, J).

search(L, I, J) :-
-a-a-a mem(X, L),
-a-a-a I mod X =:= 0, !,
-a-a-a K is I+1,
-a-a-a search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
-a-a-a mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
-a-a-a N > 0,
-a-a-a M is N-1,
-a-a-a len(L,


Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973
https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf >>>

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Don't try this, don't ask Apertus how
many holes an emmentaler cheese has.

And absolutely don't try this, ask it
next to please answer in Schwitzerd|+tsch.

Bye

P.S.: Chatgpt can do it.

Mild Shock schrieb:

Hi,

Swiss AI Apertus
Model ID: apertus-70b-instruct
Parameters: 70 billion
License: Apache 2.0
Training: 15T tokens across 1,000+ languages
Availability: Free during Swiss AI Weeks (September 2025)

https://platform.publicai.co/docs

Bye

P.S.: A chat interface is here:

Try Apertus
https://publicai.co/

Mild Shock schrieb:

Hi,

2025 will be last year we hear of Python.
This is just a tears in your eyes Eulogy:

Python: The Documentary | An origin story
https://www.youtube.com/watch?v=GfH4QL4VqJ0

The Zen of Python is very different
from the Zen of Copilot+ . The bloody
Copilot+ Laptop doesn't use Python

in its Artificial Intelligence:

AI Content Extraction
- Python Involced? rYi None at runtime,
-a-a Model runs in ONNX + DirectML on NPU

AI Image Search
- Python Involced? rYi None at runtime,
-a-a ON-device image feature, fully compiled

AI Phi Silica
- Python Involced? rYi None at runtime,
-a-a Lightweight Phi model packaged as ONNX

- AI Semantic Analysis? rYi None at runtime,
-a-a Text understanding done via compiled
-a-a ONNX operators

Bye

Mild Shock schrieb:

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350


BTW: Still ticking along with the primes.pl example:

test :-
-a-a-a len(L, 1000),
-a-a-a primes(L, _).

primes([], 1).
primes([J|L], J) :-
-a-a-a primes(L, I),
-a-a-a K is I+1,
-a-a-a search(L, K, J).

search(L, I, J) :-
-a-a-a mem(X, L),
-a-a-a I mod X =:= 0, !,
-a-a-a K is I+1,
-a-a-a search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
-a-a-a mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
-a-a-a N > 0,
-a-a-a M is N-1,
-a-a-a len(L,


Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973
https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf >>>>

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye
--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Thank god it was only coffee and not orange juice:

Ozzy Pours The Perfect O.J.
https://m.youtube.com/watch?v=ojQUYq21G-o

Bye

Mild Shock schrieb:

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems
https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

I like the expert system description by

I used Claude code to help me create a Prolog
program of a little expert system to manage a
kitchen that needed to produce different dishes
with different appliances and to be able to
maximize revenue. -- bauhaus911

Instead of maximizing revenue you could also
maximize energy boost. So instead of having
a couple of morons on SWI-Prolog discourse,

like those that have parked their brain in the
nowhere and are going full throttle Donald
Trump / Kesh Patel Nazi, the system could

indeed recommend Orange Juice instead of
coffee. For the following brain benefits:

- Vitamin C powerhouse: ~50rCo60 mg per 100 ml,
giving a solid immune boost.

- Quick energy: natural sugars (glucose + fructose)
give your brain and body fast fuel.

- Hydration: mostly water, which helps maintain
energy and focus.

Have Fun! LoL

Bye

Mild Shock schrieb:

Hi,

Thank god it was only coffee and not orange juice:

Ozzy Pours The Perfect O.J.
https://m.youtube.com/watch?v=ojQUYq21G-o

Bye

Mild Shock schrieb:

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs
https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems
https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

You deleted like 10 posts of mine in the last
48 hours, which tried to explain why patching
is against "discourse".

Even Torbj||rn Lager agreed. I don't think
you can continue your forum in this style.
And then after you deleted a dozen of posts

of mine, I am not allowed to delete my posts?

You are simply completely crazy!!!

Bye

I got the following nonsense from you:

Jan, werCOve asked you to be less combative with

people here, but you continue to be extremely
aggressive towards other users of the site.

You have very helpful things to add, but when

you then go back and delete everything you post,
it obviates that helpfulness.

Mild Shock schrieb:

Hi,

I like the expert system description by

I used Claude code to help me create a Prolog
program of a little expert system to manage a
kitchen that needed to produce different dishes
with different appliances and to be able to
maximize revenue. -- bauhaus911

Instead of maximizing revenue you could also
maximize energy boost. So instead of having
a couple of morons on SWI-Prolog discourse,

like those that have parked their brain in the
nowhere and are going full throttle Donald
Trump / Kesh Patel Nazi, the system could

indeed recommend Orange Juice instead of
coffee. For the following brain benefits:

- Vitamin C powerhouse: ~50rCo60 mg per 100 ml,
-a giving a solid immune boost.

- Quick energy: natural sugars (glucose + fructose)
-a give your brain and body fast fuel.

- Hydration: mostly water, which helps maintain
-a energy and focus.

Have Fun! LoL

Bye

Mild Shock schrieb:

Hi,

Thank god it was only coffee and not orange juice:

Ozzy Pours The Perfect O.J.
https://m.youtube.com/watch?v=ojQUYq21G-o

Bye

Mild Shock schrieb:

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs
https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems
https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

I will consult a Lawyer of mine.
Maybe I can ask for a complete
tear down of all my content.

Bye

Mild Shock schrieb:

Hi,

You deleted like 10 posts of mine in the last
48 hours, which tried to explain why patching
is against "discourse".

Even Torbj||rn Lager agreed. I don't think
you can continue your forum in this style.
And then after you deleted a dozen of posts

of mine, I am not allowed to delete my posts?

You are simply completely crazy!!!

Bye

I got the following nonsense from you:

Jan, werCOve asked you to be less combative with

people here, but you continue to be extremely
aggressive towards other users of the site.

You have very helpful things to add, but when

you then go back and delete everything you post,
it obviates that helpfulness.

Mild Shock schrieb:

Hi,

I like the expert system description by

I used Claude code to help me create a Prolog
program of a little expert system to manage a
kitchen that needed to produce different dishes
with different appliances and to be able to
maximize revenue. -- bauhaus911

Instead of maximizing revenue you could also
maximize energy boost. So instead of having
a couple of morons on SWI-Prolog discourse,

like those that have parked their brain in the
nowhere and are going full throttle Donald
Trump / Kesh Patel Nazi, the system could

indeed recommend Orange Juice instead of
coffee. For the following brain benefits:

- Vitamin C powerhouse: ~50rCo60 mg per 100 ml,
-a-a giving a solid immune boost.

- Quick energy: natural sugars (glucose + fructose)
-a-a give your brain and body fast fuel.

- Hydration: mostly water, which helps maintain
-a-a energy and focus.

Have Fun! LoL

Bye

Mild Shock schrieb:

Hi,

Thank god it was only coffee and not orange juice:

Ozzy Pours The Perfect O.J.
https://m.youtube.com/watch?v=ojQUYq21G-o

Bye

Mild Shock schrieb:

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs
https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems
https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

I will consult a Lawyer of mine.
Maybe I can ask for a complete
tear down of all my content.

Bye

Mild Shock schrieb:

Hi,

You deleted like 10 posts of mine in the last
48 hours, which tried to explain why patching
is against "discourse".

Even Torbj||rn Lager agreed. I don't think
you can continue your forum in this style.
And then after you deleted a dozen of posts

of mine, I am not allowed to delete my posts?

You are simply completely crazy!!!

Bye

I got the following nonsense from you:

Jan, werCOve asked you to be less combative with

people here, but you continue to be extremely
aggressive towards other users of the site.

You have very helpful things to add, but when

you then go back and delete everything you post,
it obviates that helpfulness.

Mild Shock schrieb:

Hi,

I like the expert system description by

I used Claude code to help me create a Prolog
program of a little expert system to manage a
kitchen that needed to produce different dishes
with different appliances and to be able to
maximize revenue. -- bauhaus911

Instead of maximizing revenue you could also
maximize energy boost. So instead of having
a couple of morons on SWI-Prolog discourse,

like those that have parked their brain in the
nowhere and are going full throttle Donald
Trump / Kesh Patel Nazi, the system could

indeed recommend Orange Juice instead of
coffee. For the following brain benefits:

- Vitamin C powerhouse: ~50rCo60 mg per 100 ml,
-a-a giving a solid immune boost.

- Quick energy: natural sugars (glucose + fructose)
-a-a give your brain and body fast fuel.

- Hydration: mostly water, which helps maintain
-a-a energy and focus.

Have Fun! LoL

Bye

Mild Shock schrieb:

Hi,

Thank god it was only coffee and not orange juice:

Ozzy Pours The Perfect O.J.
https://m.youtube.com/watch?v=ojQUYq21G-o

Bye

Mild Shock schrieb:

Hi,

It seems the LP (Linear programming)
library by SWI-Prolog has been also
ported to Scryer Prolog using the same DCG
design as demonstrated in SWI-Prolog again:

Module simplex
https://www.scryer.pl/simplex

What it requires from the Prolog system,
and is not covered by the ISO core standard,
are rational number, i.e. rdiv/2 etc.. and if
you feed it with floating point numbers,

judging from the source code, it might bark
that it has no CLP(R) available to solve it. CLP(R)
could be maybe a good candidate for Copilot+
machines, but I am currently not aware

of a Copliot+ Prolog system so to speak:

About Microsoft Copilot+ PCs
https://www.wired.com/story/what-is-copilot-plus-pc/

The DCG design could make it easy that a
solver somehow hands a problem to a NPU,
making it transparent for the end-user.

Bye

Mild Shock schrieb:

Hi,

For the LP (Linear programming) part, it
might be interesting to recall, that SWI-Prolog
has an according library:

A.55 library(simplex): Solve linear programming problems
https://eu.swi-prolog.org/pldoc/man?section=simplex

To model the constraint store, it doesnrCOt need
any native Prolog system support, since it uses
DCG for state threading. Linear programming was

long time the pinnacle of mathematical problem
solving. But some Articial Intelligence method do
typically go beyond the linear case, might also

tackle non-linear problems etc.. making heavy
use of a NPU (Neural Processing Unit). In May 2025
the first AI Laptops arrived with >40 TOPS NPUs.

Spearheaded by Microsoft branding it Copilot+.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Is there a silver lining of AI democratization?
With MedGamma I can analyse my own broken ribs.
If only I had a body scanner. I am currently exploring

options of LLM models that I could run on
my new AI soaked AMD Ryzen AI 7 350 laptop.
While Qualcomm spear headed LLM players with

their LM Studio. There is FastFlowLM
that can do Ryzen, and would utilize the NPU. For
example to run a distilled DeepSeek would amount to:

flm run deepseek-r1:8b

And yes there is MedGamma:

MedGemma:4B (Multimodal) Running Exclusively on AMD Ryzenrao AI NPU https://www.youtube.com/watch?v=KWzXZEOcgK4

Bye


Mild Shock schrieb:

Hi,

2025 will be last year we hear of Python.
This is just a tears in your eyes Eulogy:

Python: The Documentary | An origin story https://www.youtube.com/watch?v=GfH4QL4VqJ0

The Zen of Python is very different
from the Zen of Copilot+ . The bloody
Copilot+ Laptop doesn't use Python

in its Artificial Intelligence:

AI Content Extraction
- Python Involced? rYi None at runtime,
-a Model runs in ONNX + DirectML on NPU

AI Image Search
- Python Involced? rYi None at runtime,
-a ON-device image feature, fully compiled

AI Phi Silica
- Python Involced? rYi None at runtime,
-a Lightweight Phi model packaged as ONNX

- AI Semantic Analysis? rYi None at runtime,
-a Text understanding done via compiled
-a ONNX operators

Bye

Mild Shock schrieb:

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350


BTW: Still ticking along with the primes.pl example:

test :-
-a-a-a len(L, 1000),
-a-a-a primes(L, _).

primes([], 1).
primes([J|L], J) :-
-a-a-a primes(L, I),
-a-a-a K is I+1,
-a-a-a search(L, K, J).

search(L, I, J) :-
-a-a-a mem(X, L),
-a-a-a I mod X =:= 0, !,
-a-a-a K is I+1,
-a-a-a search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
-a-a-a mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
-a-a-a N > 0,
-a-a-a M is N-1,
-a-a-a len(L,


Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973
https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf >>>

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

I hope it doesn't turn my laptop into a
frying pan. The thingy had a few hickups
recently, like using 100% CPU and doing

nothing. Maybe IntelliJs fork join framework
was overdoing it. But NPUs are physically
designed for efficient AI math - more

computations per watt, less heat generated.
Lets see. But whats the technology behind
FastFlowLM? It might be a result of:

GAIA: An Open-Source Project from AMD for
Running Local LLMs on Ryzenrao AI. GAIA seems
to be an important piece of the Ryzen AI story.

Initially GAIA wanted to Provide a unified
software stack for Ryzen AI NPUs. But
AMD shifted focus to DirectML integration

with Windows. GAIA Absorbed into AMD's ROCm
Ecosystem, on the other hand XDNA (2024)
AMD's commercial NPU architecture goes

full circle back to Niklaus Wirth:

Hades: fast hardware synthesis tools and a reconfigurable coprocessor https://www.research-collection.ethz.ch/entities/publication/23b3a0e4-e5e7-44fe-9b5d-ab43e21859b2

It has FPGA-inspired reconfigurable fabric!

Bye

P.S.: Shit, I should have such a little toy
compiler as well somewhere in my notes I took
during a lecture. Array with For Loop to

model a hardware bus is really funny.

Mild Shock schrieb:

Hi,

Is there a silver lining of AI democratization?
With MedGamma I can analyse my own broken ribs.
If only I had a body scanner. I am currently exploring

options of LLM models that I could run on
my new AI soaked AMD Ryzen AI 7 350 laptop.
While Qualcomm spear headed LLM players with

their LM Studio. There is FastFlowLM
that can do Ryzen, and would utilize the NPU. For
example to run a distilled DeepSeek would amount to:

flm run deepseek-r1:8b

And yes there is MedGamma:

MedGemma:4B (Multimodal) Running Exclusively on AMD Ryzenrao AI NPU https://www.youtube.com/watch?v=KWzXZEOcgK4

Bye

Mild Shock schrieb:

Hi,

2025 will be last year we hear of Python.
This is just a tears in your eyes Eulogy:

Python: The Documentary | An origin story
https://www.youtube.com/watch?v=GfH4QL4VqJ0

The Zen of Python is very different
from the Zen of Copilot+ . The bloody
Copilot+ Laptop doesn't use Python

in its Artificial Intelligence:

AI Content Extraction
- Python Involced? rYi None at runtime,
-a-a Model runs in ONNX + DirectML on NPU

AI Image Search
- Python Involced? rYi None at runtime,
-a-a ON-device image feature, fully compiled

AI Phi Silica
- Python Involced? rYi None at runtime,
-a-a Lightweight Phi model packaged as ONNX

- AI Semantic Analysis? rYi None at runtime,
-a-a Text understanding done via compiled
-a-a ONNX operators

Bye

Mild Shock schrieb:

Hi,

Woa! I didn't know that lausy Microsoft
Copilot certified Laptops are that fast:

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dogelog Player 2.1.1 for Java

% AMD Ryzen 5 4500U
% ?- time(test).
% % Zeit 756 ms, GC 1 ms, Lips 9950390, Uhr 23.08.2025 02:45
% true.

% AMD Ryzen AI 7 350
% ?- time(test).
% % Zeit 378 ms, GC 1 ms, Lips 19900780, Uhr 28.08.2025 17:44
% true.

What happened to the Death of Moore's Law?
But somehow memory speed, CPU - RAM and GPU - RAM
trippled. Possibly due to some Artificial

Intelligence demand. And the bloody thing
has also a NPU (Neural Processing Unit),
nicely visible.

Bye

About the RAM speed. L1, L2 and L3
caches are bigger. So its harder to poison
the CPU. Also the CPU shows a revival of

Hyper-Threading Technology (HTT), which
AMD gives it a different name: They call it
Simultaneous multithreading (SMT).

https://www.cpubenchmark.net/compare/3702vs6397/AMD-Ryzen-5-4500U-vs-AMD-Ryzen-AI-7-350


BTW: Still ticking along with the primes.pl example:

test :-
-a-a-a len(L, 1000),
-a-a-a primes(L, _).

primes([], 1).
primes([J|L], J) :-
-a-a-a primes(L, I),
-a-a-a K is I+1,
-a-a-a search(L, K, J).

search(L, I, J) :-
-a-a-a mem(X, L),
-a-a-a I mod X =:= 0, !,
-a-a-a K is I+1,
-a-a-a search(L, K, J).
search(_, I, I).

mem(X, [X|_]).
mem(X, [_|Y]) :-
-a-a-a mem(X, Y).

len([], 0) :- !.
len([_|L], N) :-
-a-a-a N > 0,
-a-a-a M is N-1,
-a-a-a len(L,


Mild Shock schrieb:

Hi,

J Strother Moore II is the Original Gangster (OG)
of program sharng. Interestingly structure sharing
meant always program sharing in the theorem

proving community back then:

COMPUTATIONAL LOGIC: STRUCTURE SHARING AND
PROOF OF PROGRAM PROPERTIES
J Strother Moore II - 1973
https://era.ed.ac.uk/bitstream/handle/1842/2245/Moore-Thesis-1973-OCR.pdf >>>>

Only the WAM community managed to intsitutionalize
the term structure sharng, as a reduced form of
program sharing, namely goal argument sharing

not using pairs of two pointers with skeleton and binding
environment anymore, to indentify a Prolog term,
but simple single pointers for a Prolog term.

Bye

--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

Hi,

Was Linus Torvalds cautious or clueless?

"I think AI is really interesting and I think it
is going to change the world. At the same time,
I hate the hype cycle so much that I really don't
want to go there. So, my approach to AI right now
is I will basically ignore it because I think
the whole tech industry around AI is in a
very bad position, and its 90% marketing and
10% reality. And, in 5 years, things will change
and at that point, we will see what of the AI
is getting used for real workloads".

https://www.tweaktown.com/news/101381/linux-creator-linus-torvalds-ai-is-useless-its-90-marketing-while-he-ignores-for-now/index.html

I think his fallacy is to judge AI as hype.
So his timeline 2030 might have received a
suckerpunch by Copilot+ already now in late

2025. Before in 2024, when he made his statement,
AI was already not hype at all:

2009rCo2012 (Deep Learning Wave): GPUs began being
used for deep learning research, thanks to frameworks
like Caffe and Theano. This was when convolutional
networks for vision really took off.

2012rCo2015 (Big Data + Deep Learning): Data centers
started leveraging clusters of GPUs for large-scale
training, using distributed frameworks like
TensorFlow and PyTorch (from 2016). Text analysis
and recommendation systems were already benefiting from this.

2015rCo2020 (Specialized Accelerators): Companies
like Google (TPU), Nvidia (A100), and Qualcomm
(Hexagon DSP) developed purpose-built hardware
for AI inference and training. Large-scale NLP
models like BERT were trained in these environments.

2020rCo2024 (Commercial AI Explosion): On-device AI,
cloud AI services, Copilot+, Claude integrations rCo
all of these are the practical realization of what
had been quietly powering research and enterprise
workloads for over a decade.

Bye


--- Synchronet 3.21a-Linux NewsLink 1.2

From Newsgroup: sci.logic

On 9/30/2025 1:41 AM, Mild Shock wrote:

Hi,

Was Linus Torvalds cautious or clueless?

"I think AI is really interesting and I think it
is going to change the world. At the same time,
I hate the hype cycle so much that I really don't
want to go there. So, my approach to AI right now
is I will basically ignore it because I think
the whole tech industry around AI is in a
very bad position, and its 90% marketing and
10% reality. And, in 5 years, things will change
and at that point, we will see what of the AI
is getting used for real workloads".

The key thing to grasp is that when an LLM system is
self-correcting and can mostly learn entirely on its
own the typical human technological development cycle
is blown away. LLMs may get ten-fold more powerful
every year. Linus Torvalds does not seem to factor
that into his analysis.

The key element of this increase in power is simply
the maximum number of tokens that it can keep track of.
ChatGPT used to act like it has alzheimers when you
exceeded its 4000 token limit.

https://www.tweaktown.com/news/101381/linux-creator-linus-torvalds-ai- is-useless-its-90-marketing-while-he-ignores-for-now/index.html

I think his fallacy is to judge AI as hype.
So his timeline 2030 might have received a
suckerpunch by Copilot+ already now in late

2025. Before in 2024, when he made his statement,
AI was already not hype at all:

2009rCo2012 (Deep Learning Wave): GPUs began being
used for deep learning research, thanks to frameworks
like Caffe and Theano. This was when convolutional
networks for vision really took off.

2012rCo2015 (Big Data + Deep Learning): Data centers
started leveraging clusters of GPUs for large-scale
training, using distributed frameworks like
TensorFlow and PyTorch (from 2016). Text analysis
and recommendation systems were already benefiting from this.

2015rCo2020 (Specialized Accelerators): Companies
like Google (TPU), Nvidia (A100), and Qualcomm
(Hexagon DSP) developed purpose-built hardware
for AI inference and training. Large-scale NLP
models like BERT were trained in these environments.

2020rCo2024 (Commercial AI Explosion): On-device AI,
cloud AI services, Copilot+, Claude integrations rCo
all of these are the practical realization of what
had been quietly powering research and enterprise
workloads for over a decade.

Bye

--
Copyright 2025 Olcott "Talent hits a target no one else can hit; Genius
hits a target no one else can see." Arthur Schopenhauer
--- Synchronet 3.21a-Linux NewsLink 1.2