Your new Scrum Master is here! - ChatGPT, 2023
https://www.bbntimes.com/companies/ai-will-make-agile-coaches-and-scrum-masters-redundant-in-less-than-2-years

LoL

Thomas Alva Edison schrieb am Dienstag, 10. Juli 2018 um 15:28:05 UTC+2:
> Prolog Class Signpost - American Style 2018
> https://www.youtube.com/watch?v=CxQKltWI0NA

To hell with GPUs. Here come the FPGA qubits:

Iran’s Military Quantum Claim: It’s Only 99.4% Ridiculous
https://hackaday.com/2023/06/15/irans-quantum-computing-on-fpga-claim-its-kinda-a-thing/

The superposition property enables a quantum computer
to be in multiple states at once.
https://www.techtarget.com/whatis/definition/qubit

Maybe their new board is even less suited for hitting
a ship with a torpedo than some machine learning?

So it begins:

Was having some fun with chat gpt and thinkscript.
https://twitter.com/pkay2402/status/1670459050155290627

A Beginner's Guide to thinkScripts - Ameritrade, 2022
https://www.youtube.com/watch?v=qD5RYF5o9fM

Not only the speed doesn't double every year anymore,
also the density of transistors doesn't double
every year anymore. See also:

‘Moore’s Law’s dead,’ Nvidia CEO
https://www.marketwatch.com/story/moores-laws-dead-nvidia-ceo-jensen-says-in-justifying-gaming-card-price-hike-11663798618

So there is some hope in FPGAs. The article writes:

"In the latter paper, which includes a great overview of
the state of the art, Pilch and colleagues summarize
this as shifting the processing from time to space —
from using slow sequential CPU processing to hardware
complexity, using the FPGA’s configurable fabric
and inherent parallelism."

In reference to (no pay wall):

An FPGA-based real quantum computer emulator
15 December 2018 - Pilch et al.
https://link.springer.com/article/10.1007/s10825-018-1287-5

Mild Shock schrieb am Dienstag, 20. Juni 2023 um 17:20:27 UTC+2:
> To hell with GPUs. Here come the FPGA qubits:
>
> Iran’s Military Quantum Claim: It’s Only 99.4% Ridiculous
> https://hackaday.com/2023/06/15/irans-quantum-computing-on-fpga-claim-its-kinda-a-thing/
>
> The superposition property enables a quantum computer
> to be in multiple states at once.
> https://www.techtarget.com/whatis/definition/qubit
>
> Maybe their new board is even less suited for hitting
> a ship with a torpedo than some machine learning?

Prighozin / Prighozout . Latest news he departed for Belarus?!
Help! I need ChatGPT Plus to follow this shit show:

Scraper is an excellent #ChatGPT plugin for staying on top of the news.
https://twitter.com/irock/status/1672665497140330496

Now, you can use the same info and make a diagram out of it. Using 'diagrams' plugin.
https://twitter.com/irock/status/1672715014271115266

But ChatGPT Plus is like 20$ per Month extra.

Omg, Stephen Wolfram wrote a new book!

What Is ChatGPT Doing ... and Why Does It Work?
https://www.amazon.com/dp/1579550819

Did he let ChatGPT write the book, or why was he so fast?

How it started:

Remember in 2013 a failed AI stack attemp, people making fun:
Faked Artificial Intelligence like in Game Development
https://area51.meta.stackexchange.com/q/11658/100686

How its going:

Take notd in 2023 sounds like total paniking now:
Announcing OverflowAI, Projects: a bunch of crap, Slack
chatbot and We’ve launched the GenAI Stack Exchange site
https://stackoverflow.co/labs/

This is also quite memorable:

"Don't give it a 'Hollywood' title like 'artificial intelligence'.
Call it "Machine Learning and Intelligent Computation"".
https://area51.meta.stackexchange.com/a/13109/100686

LoL

Mild Shock schrieb am Dienstag, 1. August 2023 um 15:48:32 UTC+2:
> How it started:
>
> Remember in 2013 a failed AI stack attemp, people making fun:
> Faked Artificial Intelligence like in Game Development
> https://area51.meta.stackexchange.com/q/11658/100686
>
> How its going:
>
> Take notd in 2023 sounds like total paniking now:
> Announcing OverflowAI, Projects: a bunch of crap, Slack
> chatbot and We’ve launched the GenAI Stack Exchange site
> https://stackoverflow.co/labs/

There is a serious doubt that there will be a surge
in developers, due to AI. As claimed here:

Stack Overflow: Community and AI
https://www.youtube.com/live/g5F5t205pYA?feature=share&t=376

Its rather in the end about low code as well:

"These tools enable less technical employees to
make a larger business impact in numerous ways,
such as relieving IT department backlogs, reducing
shadow IT, and taking more ownership over
business process management (BPM) workstreams.
https://www.ibm.com/topics/low-code

LoL

Mild Shock schrieb am Dienstag, 1. August 2023 um 15:54:31 UTC+2:
> This is also quite memorable:
>
> "Don't give it a 'Hollywood' title like 'artificial intelligence'.
> Call it "Machine Learning and Intelligent Computation"".
> https://area51.meta.stackexchange.com/a/13109/100686
>
> LoL
> Mild Shock schrieb am Dienstag, 1. August 2023 um 15:48:32 UTC+2:
> > How it started:
> >
> > Remember in 2013 a failed AI stack attemp, people making fun:
> > Faked Artificial Intelligence like in Game Development
> > https://area51.meta.stackexchange.com/q/11658/100686
> >
> > How its going:
> >
> > Take notd in 2023 sounds like total paniking now:
> > Announcing OverflowAI, Projects: a bunch of crap, Slack
> > chatbot and We’ve launched the GenAI Stack Exchange site
> > https://stackoverflow.co/labs/

Experiment by Terrence Tao using ChatGPT - June, 2023
https://mathstodon.xyz/@tao/110601051375142142

by way of Rainer Rosenthal on de.sci.mathematik

Simple theory why stackoverflow is dead. Most
of the answers on stackoverflow are any RTFM answers.
And LLM that has done its homework, indexing all

the fucking manuals, performs as well, there is no
need for the "experts" on stackoverflow that are anyway
not real "experts", mostly they are people who can read

and know the relevant sources, they don't recall
solutions from some genuine memory. So I guess
this intermediary, this middleman stackoverflow,

is not needed in the future. ChatGPT and similar
bots will serve as ready help for those too lazy.
And we are all lazy, aren't we?

Abbreviation for ‘Read The Fucking Manual’.
http://www.catb.org/jargon/html/R/RTFM.html

Mild Shock schrieb am Mittwoch, 2. August 2023 um 11:02:05 UTC+2:
> Experiment by Terrence Tao using ChatGPT - June, 2023
> https://mathstodon.xyz/@tao/110601051375142142
>
> by way of Rainer Rosenthal on de.sci.mathematik

Or a hybrid, a stackoverflow with a chatbots integrated.
ChatGPT is already helping me designing such a platform:

Q: What if a chatbot becomes just a member of a community,
this would assure collaboration and community interaction.

A: Incorporating a chatbot as a member of an online community
can indeed offer some benefits in terms of collaboration and
community interaction. However, there are several
considerations to take into account:
[...]

https://chat.openai.com/share/84790b3f-e228-4c91-af89-29cf37842f9d

LoL

Mild Shock schrieb am Mittwoch, 2. August 2023 um 11:14:26 UTC+2:
> Simple theory why stackoverflow is dead. Most
> of the answers on stackoverflow are any RTFM answers.
> And LLM that has done its homework, indexing all
>
> the fucking manuals, performs as well, there is no
> need for the "experts" on stackoverflow that are anyway
> not real "experts", mostly they are people who can read
>
> and know the relevant sources, they don't recall
> solutions from some genuine memory. So I guess
> this intermediary, this middleman stackoverflow,
>
> is not needed in the future. ChatGPT and similar
> bots will serve as ready help for those too lazy.
> And we are all lazy, aren't we?
>
> Abbreviation for ‘Read The Fucking Manual’.
> http://www.catb.org/jargon/html/R/RTFM.html
> Mild Shock schrieb am Mittwoch, 2. August 2023 um 11:02:05 UTC+2:
> > Experiment by Terrence Tao using ChatGPT - June, 2023
> > https://mathstodon.xyz/@tao/110601051375142142
> >
> > by way of Rainer Rosenthal on de.sci.mathematik

Want to know what ChatGPT thinks about a person. Try this
prompt engineering, works also with less prominent persons:

Q: Write a joke: Why did Jan Wielemaker go to the gym

A: Why did Jan Wielemaker go to the gym? Because he
heard it was the only place with enough "SWI-t"ness to lift his spirits!

https://chat.openai.com/?model=text-davinci-002-render-sha

Ok, OpenAI is dead. But we need to get out of the claws
of the computing cloud. We need the spirit of Niklaus
Wirth, who combined computer science and

electronics. We need to solve the problem of
parallel slilicon. Should have a look again at these
quantum computers. Can we have them on the Edge?

Mild Shock schrieb am Freitag, 23. Juni 2023 um 11:14:15 UTC+2:
> Not only the speed doesn't double every year anymore,
> also the density of transistors doesn't double
> every year anymore. See also:
>
> ‘Moore’s Law’s dead,’ Nvidia CEO
> https://www.marketwatch.com/story/moores-laws-dead-nvidia-ceo-jensen-says-in-justifying-gaming-card-price-hike-11663798618
>
> So there is some hope in FPGAs. The article writes:
>
> "In the latter paper, which includes a great overview of
> the state of the art, Pilch and colleagues summarize
> this as shifting the processing from time to space —
> from using slow sequential CPU processing to hardware
> complexity, using the FPGA’s configurable fabric
> and inherent parallelism."
>
> In reference to (no pay wall):
>
> An FPGA-based real quantum computer emulator
> 15 December 2018 - Pilch et al.
> https://link.springer.com/article/10.1007/s10825-018-1287-5
> Mild Shock schrieb am Dienstag, 20. Juni 2023 um 17:20:27 UTC+2:
> > To hell with GPUs. Here come the FPGA qubits:
> >
> > Iran’s Military Quantum Claim: It’s Only 99.4% Ridiculous
> > https://hackaday.com/2023/06/15/irans-quantum-computing-on-fpga-claim-its-kinda-a-thing/
> >
> > The superposition property enables a quantum computer
> > to be in multiple states at once.
> > https://www.techtarget.com/whatis/definition/qubit
> >
> > Maybe their new board is even less suited for hitting
> > a ship with a torpedo than some machine learning?

Seems that OpenAI is effectively imploding. Any
actors that wanted this had easy play because there
were two tribes in OpenAI, namely the AI doomers

and the AI futurists. Who cares? The AI doomers
were possibly anyway those contributing less and
will now end on the streets, a nice little shake out!

Sutskever Regret and the Weekend That Changed AI
https://www.youtube.com/watch?v=dyakih3oYpk

Mild Shock schrieb am Montag, 20. November 2023 um 14:23:55 UTC+1:
> Ok, OpenAI is dead. But we need to get out of the claws
> of the computing cloud. We need the spirit of Niklaus
> Wirth, who combined computer science and
>
> electronics. We need to solve the problem of
> parallel slilicon. Should have a look again at these
> quantum computers. Can we have them on the Edge?
> Mild Shock schrieb am Freitag, 23. Juni 2023 um 11:14:15 UTC+2:
> > Not only the speed doesn't double every year anymore,
> > also the density of transistors doesn't double
> > every year anymore. See also:
> >
> > ‘Moore’s Law’s dead,’ Nvidia CEO
> > https://www.marketwatch.com/story/moores-laws-dead-nvidia-ceo-jensen-says-in-justifying-gaming-card-price-hike-11663798618
> >
> > So there is some hope in FPGAs. The article writes:
> >
> > "In the latter paper, which includes a great overview of
> > the state of the art, Pilch and colleagues summarize
> > this as shifting the processing from time to space —
> > from using slow sequential CPU processing to hardware
> > complexity, using the FPGA’s configurable fabric
> > and inherent parallelism."
> >
> > In reference to (no pay wall):
> >
> > An FPGA-based real quantum computer emulator
> > 15 December 2018 - Pilch et al.
> > https://link.springer.com/article/10.1007/s10825-018-1287-5
> > Mild Shock schrieb am Dienstag, 20. Juni 2023 um 17:20:27 UTC+2:
> > > To hell with GPUs. Here come the FPGA qubits:
> > >
> > > Iran’s Military Quantum Claim: It’s Only 99.4% Ridiculous
> > > https://hackaday.com/2023/06/15/irans-quantum-computing-on-fpga-claim-its-kinda-a-thing/
> > >
> > > The superposition property enables a quantum computer
> > > to be in multiple states at once.
> > > https://www.techtarget.com/whatis/definition/qubit
> > >
> > > Maybe their new board is even less suited for hitting
> > > a ship with a torpedo than some machine learning?

How my Dogelog Player garbage collector works:

Ashes to ashes, funk to funky
We know Major Tom's a junkie
Strung out in heaven's high
Hitting an all-time low
https://www.youtube.com/watch?v=CMThz7eQ6K0

Unfortunately no generational garbage collector yet. :-(

To advance the State of the Art and track performance improvements,
some automatization would be helpful. I can test manually WASM via
this here https://dev.swi-prolog.org/wasm/shell . Since my recent

performance tuning of Dogelog Player for JavaScript I beat 32-bit WASM
SWI-Prolog. This holds not yet for the SAT Solver test cases, that need
GC improvements but for the core test cases. I only tested my Ryzen.

Don’t know yet results for Yoga:

dog swi
nrev 1247 1223
crypt 894 2351
deriv 960 1415
poly 959 1475
sortq 1313 1825
tictac 1587 2400
queens 1203 2316
query 1919 4565
mtak 1376 1584
perfect 1020 1369
calc 1224 1583
Total 13702 22106

LoL

Mild Shock schrieb am Samstag, 25. November 2023 um 16:23:07 UTC+1:
> How my Dogelog Player garbage collector works:
>
> Ashes to ashes, funk to funky
> We know Major Tom's a junkie
> Strung out in heaven's high
> Hitting an all-time low
> https://www.youtube.com/watch?v=CMThz7eQ6K0
>
> Unfortunately no generational garbage collector yet. :-(

Scryer Prolog has made amazing leaps recently concerning
performance, its now only like 2-3 times slower than
SWI-Prolog! What does it prevent to get faster than SWI-Prolog?

See for yourself. Here some testing with a very recent version.
Interestingly tictac shows it has some problems with negation-
as-failure and/or call/1. Maybe they should allocate more

time to these areas instead of inference counting formatting:

$ target/release/scryer-prolog -v
v0.9.3-50-gb8ef3678

nrev % CPU time: 0.304s, 3_024_548 inferences
crypt % CPU time: 0.422s, 4_392_537 inferences
deriv % CPU time: 0.462s, 3_150_149 inferences
poly % CPU time: 0.394s, 3_588_369 inferences
sortq % CPU time: 0.481s, 3_654_653 inferences
tictac % CPU time: 1.591s, 3_285_766 inferences
queens % CPU time: 0.517s, 5_713_596 inferences
query % CPU time: 0.909s, 8_678_936 inferences
mtak % CPU time: 0.425s, 6_901_822 inferences
perfect % CPU time: 0.763s, 5_321_436 inferences
calc % CPU time: 0.626s, 6_700_379 inferences
true.

Compared to SWI-Prolog on the same machine:

$ swipl --version
SWI-Prolog version 9.1.18 for x86_64-linux

nrev % 2,994,497 inferences, 0.067 CPU in 0.067 seconds
crypt % 4,166,441 inferences, 0.288 CPU in 0.287 seconds
deriv % 2,100,068 inferences, 0.139 CPU in 0.139 seconds
poly % 2,087,479 inferences, 0.155 CPU in 0.155 seconds
sortq % 3,624,602 inferences, 0.173 CPU in 0.173 seconds
tictac % 1,012,615 inferences, 0.184 CPU in 0.184 seconds
queens % 4,596,063 inferences, 0.266 CPU in 0.266 seconds
query % 8,639,878 inferences, 0.622 CPU in 0.622 seconds
mtak % 3,943,818 inferences, 0.162 CPU in 0.162 seconds
perfect % 3,241,199 inferences, 0.197 CPU in 0.197 seconds
calc % 3,060,151 inferences, 0.180 CPU in 0.180 seconds

Mild Shock schrieb am Samstag, 25. November 2023 um 22:10:20 UTC+1:
> To advance the State of the Art and track performance improvements,
> some automatization would be helpful. I can test manually WASM via
> this here https://dev.swi-prolog.org/wasm/shell . Since my recent
>
> performance tuning of Dogelog Player for JavaScript I beat 32-bit WASM
> SWI-Prolog. This holds not yet for the SAT Solver test cases, that need
> GC improvements but for the core test cases. I only tested my Ryzen.
>
> Don’t know yet results for Yoga:
>
> dog swi
> nrev 1247 1223
> crypt 894 2351
> deriv 960 1415
> poly 959 1475
> sortq 1313 1825
> tictac 1587 2400
> queens 1203 2316
> query 1919 4565
> mtak 1376 1584
> perfect 1020 1369
> calc 1224 1583
> Total 13702 22106
>
> LoL
> Mild Shock schrieb am Samstag, 25. November 2023 um 16:23:07 UTC+1:
> > How my Dogelog Player garbage collector works:
> >
> > Ashes to ashes, funk to funky
> > We know Major Tom's a junkie
> > Strung out in heaven's high
> > Hitting an all-time low
> > https://www.youtube.com/watch?v=CMThz7eQ6K0
> >
> > Unfortunately no generational garbage collector yet. :-(

Testing scryer-prolog doesn’t make any sense. Its not a
Prolog system. It has memory leaks somewhere.
Just try my SAT solver test suite:

?- between(1,100,_), suite_quiet, fail; true.
VSZ and RSS memory is going up and up, with no end.
Clogging my machine. I don’t think that this should happen,
that a failure driven loop eats all memory?

Thats just a fraud. How do you set some limits?

Mild Shock schrieb am Montag, 27. November 2023 um 18:31:25 UTC+1:
> Scryer Prolog has made amazing leaps recently concerning
> performance, its now only like 2-3 times slower than
> SWI-Prolog! What does it prevent to get faster than SWI-Prolog?
>
> See for yourself. Here some testing with a very recent version.
> Interestingly tictac shows it has some problems with negation-
> as-failure and/or call/1. Maybe they should allocate more
>
> time to these areas instead of inference counting formatting:
>
> $ target/release/scryer-prolog -v
> v0.9.3-50-gb8ef3678
>
> nrev % CPU time: 0.304s, 3_024_548 inferences
> crypt % CPU time: 0.422s, 4_392_537 inferences
> deriv % CPU time: 0.462s, 3_150_149 inferences
> poly % CPU time: 0.394s, 3_588_369 inferences
> sortq % CPU time: 0.481s, 3_654_653 inferences
> tictac % CPU time: 1.591s, 3_285_766 inferences
> queens % CPU time: 0.517s, 5_713_596 inferences
> query % CPU time: 0.909s, 8_678_936 inferences
> mtak % CPU time: 0.425s, 6_901_822 inferences
> perfect % CPU time: 0.763s, 5_321_436 inferences
> calc % CPU time: 0.626s, 6_700_379 inferences
> true.
>
> Compared to SWI-Prolog on the same machine:
>
> $ swipl --version
> SWI-Prolog version 9.1.18 for x86_64-linux
>
> nrev % 2,994,497 inferences, 0.067 CPU in 0.067 seconds
> crypt % 4,166,441 inferences, 0.288 CPU in 0.287 seconds
> deriv % 2,100,068 inferences, 0.139 CPU in 0.139 seconds
> poly % 2,087,479 inferences, 0.155 CPU in 0.155 seconds
> sortq % 3,624,602 inferences, 0.173 CPU in 0.173 seconds
> tictac % 1,012,615 inferences, 0.184 CPU in 0.184 seconds
> queens % 4,596,063 inferences, 0.266 CPU in 0.266 seconds
> query % 8,639,878 inferences, 0.622 CPU in 0.622 seconds
> mtak % 3,943,818 inferences, 0.162 CPU in 0.162 seconds
> perfect % 3,241,199 inferences, 0.197 CPU in 0.197 seconds
> calc % 3,060,151 inferences, 0.180 CPU in 0.180 seconds
> Mild Shock schrieb am Samstag, 25. November 2023 um 22:10:20 UTC+1:
> > To advance the State of the Art and track performance improvements,
> > some automatization would be helpful. I can test manually WASM via
> > this here https://dev.swi-prolog.org/wasm/shell . Since my recent
> >
> > performance tuning of Dogelog Player for JavaScript I beat 32-bit WASM
> > SWI-Prolog. This holds not yet for the SAT Solver test cases, that need
> > GC improvements but for the core test cases. I only tested my Ryzen.
> >
> > Don’t know yet results for Yoga:
> >
> > dog swi
> > nrev 1247 1223
> > crypt 894 2351
> > deriv 960 1415
> > poly 959 1475
> > sortq 1313 1825
> > tictac 1587 2400
> > queens 1203 2316
> > query 1919 4565
> > mtak 1376 1584
> > perfect 1020 1369
> > calc 1224 1583
> > Total 13702 22106
> >
> > LoL
> > Mild Shock schrieb am Samstag, 25. November 2023 um 16:23:07 UTC+1:
> > > How my Dogelog Player garbage collector works:
> > >
> > > Ashes to ashes, funk to funky
> > > We know Major Tom's a junkie
> > > Strung out in heaven's high
> > > Hitting an all-time low
> > > https://www.youtube.com/watch?v=CMThz7eQ6K0
> > >
> > > Unfortunately no generational garbage collector yet. :-(

With limits I get this result:

$ ulimit -m 2000000
$ ulimit -v 2000000
$ target/release/scryer-prolog
?- ['program2.p'].
true.
?- between(1,100,_), suite_quiet, fail; true.
Segmentation fault

Not ok! Should continue running till the end.

Mild Shock schrieb am Mittwoch, 29. November 2023 um 06:47:15 UTC+1:
> Testing scryer-prolog doesn’t make any sense. Its not a
> Prolog system. It has memory leaks somewhere.
> Just try my SAT solver test suite:
>
> ?- between(1,100,_), suite_quiet, fail; true.
>
> VSZ and RSS memory is going up and up, with no end.
> Clogging my machine. I don’t think that this should happen,
> that a failure driven loop eats all memory?
>
> Thats just a fraud. How do you set some limits?
> Mild Shock schrieb am Montag, 27. November 2023 um 18:31:25 UTC+1:
> > Scryer Prolog has made amazing leaps recently concerning
> > performance, its now only like 2-3 times slower than
> > SWI-Prolog! What does it prevent to get faster than SWI-Prolog?
> >
> > See for yourself. Here some testing with a very recent version.
> > Interestingly tictac shows it has some problems with negation-
> > as-failure and/or call/1. Maybe they should allocate more
> >
> > time to these areas instead of inference counting formatting:
> >
> > $ target/release/scryer-prolog -v
> > v0.9.3-50-gb8ef3678
> >
> > nrev % CPU time: 0.304s, 3_024_548 inferences
> > crypt % CPU time: 0.422s, 4_392_537 inferences
> > deriv % CPU time: 0.462s, 3_150_149 inferences
> > poly % CPU time: 0.394s, 3_588_369 inferences
> > sortq % CPU time: 0.481s, 3_654_653 inferences
> > tictac % CPU time: 1.591s, 3_285_766 inferences
> > queens % CPU time: 0.517s, 5_713_596 inferences
> > query % CPU time: 0.909s, 8_678_936 inferences
> > mtak % CPU time: 0.425s, 6_901_822 inferences
> > perfect % CPU time: 0.763s, 5_321_436 inferences
> > calc % CPU time: 0.626s, 6_700_379 inferences
> > true.
> >
> > Compared to SWI-Prolog on the same machine:
> >
> > $ swipl --version
> > SWI-Prolog version 9.1.18 for x86_64-linux
> >
> > nrev % 2,994,497 inferences, 0.067 CPU in 0.067 seconds
> > crypt % 4,166,441 inferences, 0.288 CPU in 0.287 seconds
> > deriv % 2,100,068 inferences, 0.139 CPU in 0.139 seconds
> > poly % 2,087,479 inferences, 0.155 CPU in 0.155 seconds
> > sortq % 3,624,602 inferences, 0.173 CPU in 0.173 seconds
> > tictac % 1,012,615 inferences, 0.184 CPU in 0.184 seconds
> > queens % 4,596,063 inferences, 0.266 CPU in 0.266 seconds
> > query % 8,639,878 inferences, 0.622 CPU in 0.622 seconds
> > mtak % 3,943,818 inferences, 0.162 CPU in 0.162 seconds
> > perfect % 3,241,199 inferences, 0.197 CPU in 0.197 seconds
> > calc % 3,060,151 inferences, 0.180 CPU in 0.180 seconds
> > Mild Shock schrieb am Samstag, 25. November 2023 um 22:10:20 UTC+1:
> > > To advance the State of the Art and track performance improvements,
> > > some automatization would be helpful. I can test manually WASM via
> > > this here https://dev.swi-prolog.org/wasm/shell . Since my recent
> > >
> > > performance tuning of Dogelog Player for JavaScript I beat 32-bit WASM
> > > SWI-Prolog. This holds not yet for the SAT Solver test cases, that need
> > > GC improvements but for the core test cases. I only tested my Ryzen.
> > >
> > > Don’t know yet results for Yoga:
> > >
> > > dog swi
> > > nrev 1247 1223
> > > crypt 894 2351
> > > deriv 960 1415
> > > poly 959 1475
> > > sortq 1313 1825
> > > tictac 1587 2400
> > > queens 1203 2316
> > > query 1919 4565
> > > mtak 1376 1584
> > > perfect 1020 1369
> > > calc 1224 1583
> > > Total 13702 22106
> > >
> > > LoL
> > > Mild Shock schrieb am Samstag, 25. November 2023 um 16:23:07 UTC+1:
> > > > How my Dogelog Player garbage collector works:
> > > >
> > > > Ashes to ashes, funk to funky
> > > > We know Major Tom's a junkie
> > > > Strung out in heaven's high
> > > > Hitting an all-time low
> > > > https://www.youtube.com/watch?v=CMThz7eQ6K0
> > > >
> > > > Unfortunately no generational garbage collector yet. :-(

How do you show Segmentation faults in a bar chart diagram?

Here is a test with Trealla Prolog, same limit test, it completes the job.
Doesn’t clog up the memory indefinitely, works just as expected:

$ ./tpl -v
Trealla Prolog (c) Infradig 2020-2023, v2.30.48-21-g8dfd
$ ./tpl
?- ['../ciao/program2.p'].
true.
?- between(1,100,_), suite_quiet, fail; true.
true.
?-

You have to wait some while, but you can use the command ps -aux to
see that it doesn’t eat up memory. And I did the above test with the same

very large ulimit -m | -v which wasn’t hit by a segmentation fault.

Mild Shock schrieb am Mittwoch, 29. November 2023 um 06:58:28 UTC+1:
> With limits I get this result:
>
> $ target/release/scryer-prolog -v
> v0.9.3-57-ge8d8b09e
> $ ulimit -m 2000000
> $ ulimit -v 2000000
> $ target/release/scryer-prolog
> ?- ['program2.p'].
> true.
> ?- between(1,100,_), suite_quiet, fail; true.
> Segmentation fault
>
> Not ok! Should continue running till the end.
> Mild Shock schrieb am Mittwoch, 29. November 2023 um 06:47:15 UTC+1:
> > Testing scryer-prolog doesn’t make any sense. Its not a
> > Prolog system. It has memory leaks somewhere.
> > Just try my SAT solver test suite:
> >
> > ?- between(1,100,_), suite_quiet, fail; true.
> >
> > VSZ and RSS memory is going up and up, with no end.
> > Clogging my machine. I don’t think that this should happen,
> > that a failure driven loop eats all memory?
> >
> > Thats just a fraud. How do you set some limits?
> > Mild Shock schrieb am Montag, 27. November 2023 um 18:31:25 UTC+1: