ConwayLife.com

Posted: **September 23rd, 2018, 10:53 am**

https://ibb.co/etNnP9 What am i doing wrong/ What should i do?

Posted: **September 24th, 2018, 10:27 am**

I was doing it very wrong. Now after a bit of trying i get this

Sarp@Lenovo ~/apgmera
$ ./recompile.sh
Skipping updates; use --update to update apgluxe automatically.
Downloading lifelib...
Submodule 'lifelib' (https://gitlab.com/apgoucher/lifelib.git) registered for path 'lifelib'
Cloning into '/home/Sarp/apgmera/lifelib'...
Submodule path 'lifelib': checked out 'a3aeef64c51cf301d46c7e19ca5d94e2cc2b30d3'
Rule unspecified; assuming b3s23.
Symmetry unspecified; assuming C1.
Configuring rule b3s23; symmetry C1
Valid symmetry: C1
Success!
g++ -c -Wall -Wextra -pedantic -O3 -march=native --std=c++11 main.cpp -o main.o
make: g++: Command not found
make: *** [makefile:32: main.o] Error 127

Edit: I made it work

Posted: **September 27th, 2018, 10:20 am**

Has anybody looked into the speed differences for outer-totalistic rules between v3.x and v4.x? I'm experiencing a major speedup if I search B3/S2/C1 on v3.28, with a 100-150% speedup against v4.x. I will post further results here if I get the time to do more tests.

Posted: **September 27th, 2018, 7:14 pm**

Rule speed differences between v3.x and v4.x, on an AMD A6-5200 running on both v3.28 and v4.52-ll1.52:

B368/S245: 2100-2200 soups/sec for v3.x vs ~115 soups/sec for v4.x
B3678/S34678: 18000-19000 soups/sec for v3.x vs 7700-7800 soups/sec for v4.x
B36/S23: 2-3 soups/sec for v3.x vs 3-6 soups/sec for v4.x

Posted: **September 28th, 2018, 4:33 am**

cordership3 wrote:Rule speed differences between v3.x and v4.x, on an AMD A6-5200 running on both v3.28 and v4.52-ll1.52:

B368/S245: 2100-2200 soups/sec for v3.x vs ~115 soups/sec for v4.x
B3678/S34678: 18000-19000 soups/sec for v3.x vs 7700-7800 soups/sec for v4.x
B36/S23: 2-3 soups/sec for v3.x vs 3-6 soups/sec for v4.x

That's v4.52-ll1.62, right? But yes, I can confirm that Day & Night at least is much slower in 4.x:

Code: Select all

$  ./apgluxe.exe -s AppleBottomSpeedTest -n 1000000 -v 0

Greetings, this is apgluxe v4.52-ll1.62, configured for b3678s34678/C1.

Lifelib version: ll1.62
Compiler version: 6.4.0
Python version: '2.7.14 (default, Oct 31 2017, 21:12:13)  [GCC 6.4.0]'

Using seed AppleBottomSpeedTest
Instruction set AVX1 detected
Running 1000000 soups per haul:
Linear-growth pattern detected: yl60_1_181_e51e1689ba5b06d2da9b5b06d15287a2
b3678s34678/C1: 228315 soups completed (22797.304 soups/second current, 22797.304 overall).
b3678s34678/C1: 481868 soups completed (25317.324 soups/second current, 24057.314 overall).
b3678s34678/C1: 741351 soups completed (25909.436 soups/second current, 24674.688 overall).
b3678s34678/C1: 1000000 soups completed (25906.350 soups/second current, 24981.888 overall).
----------------------------------------------------------------------
1000000 soups completed.
Attempting to contact payosha256.
^C
$ ./apgmera-b3678s34678-C1.exe -s AppleBottomSpeedTest -n 1000000 -v 0

Howdy, this is apgmera v3.28-ab9, configured for B3678/S34678/C1.

Using seed AppleBottomSpeedTest
Universe created.
Running 1000000 soups per haul:
Instruction set AVX1 supported.
b3678s34678/C1: 10000 soups done (soups/s: 53475 current, 53475 overall)
b3678s34678/C1: 20000 soups done (soups/s: 53475 current, 53475 overall)
b3678s34678/C1: 30000 soups done (soups/s: 58139 current, 54945 overall)
b3678s34678/C1: 40000 soups done (soups/s: 58479 current, 55788 overall)
b3678s34678/C1: 50000 soups done (soups/s: 53475 current, 55309 overall)
b3678s34678/C1: 60000 soups done (soups/s: 53191 current, 54945 overall)
b3678s34678/C1: 70000 soups done (soups/s: 64102 current, 56089 overall)
Linear-growth pattern detected: yl60_1_181_e51e1689ba5b06d2da9b5b06d15287a2
b3678s34678/C1: 80000 soups done (soups/s: 49504 current, 55172 overall)
b3678s34678/C1: 90000 soups done (soups/s: 58139 current, 55487 overall)
b3678s34678/C1: 100000 soups done (soups/s: 58139 current, 55741 overall)
b3678s34678/C1: 110000 soups done (soups/s: 58479 current, 55979 overall)
b3678s34678/C1: 120000 soups done (soups/s: 58139 current, 56153 overall)
b3678s34678/C1: 130000 soups done (soups/s: 58479 current, 56325 overall)
b3678s34678/C1: 140000 soups done (soups/s: 53191 current, 56089 overall)
b3678s34678/C1: 150000 soups done (soups/s: 58479 current, 56242 overall)
b3678s34678/C1: 160000 soups done (soups/s: 58139 current, 56357 overall)
b3678s34678/C1: 170000 soups done (soups/s: 64102 current, 56761 overall)
b3678s34678/C1: 180000 soups done (soups/s: 58479 current, 56854 overall)
b3678s34678/C1: 190000 soups done (soups/s: 58139 current, 56920 overall)
b3678s34678/C1: 200000 soups done (soups/s: 64102 current, 57240 overall)
b3678s34678/C1: 210000 soups done (soups/s: 64102 current, 57534 overall)
b3678s34678/C1: 220000 soups done (soups/s: 58139 current, 57561 overall)
b3678s34678/C1: 230000 soups done (soups/s: 53475 current, 57370 overall)
b3678s34678/C1: 240000 soups done (soups/s: 58479 current, 57416 overall)
b3678s34678/C1: 250000 soups done (soups/s: 58139 current, 57444 overall)
b3678s34678/C1: 260000 soups done (soups/s: 64102 current, 57675 overall)
b3678s34678/C1: 270000 soups done (soups/s: 58139 current, 57692 overall)
b3678s34678/C1: 280000 soups done (soups/s: 58479 current, 57720 overall)
b3678s34678/C1: 290000 soups done (soups/s: 64102 current, 57918 overall)
b3678s34678/C1: 300000 soups done (soups/s: 58139 current, 57926 overall)
b3678s34678/C1: 310000 soups done (soups/s: 58479 current, 57943 overall)
b3678s34678/C1: 320000 soups done (soups/s: 64102 current, 58118 overall)
b3678s34678/C1: 330000 soups done (soups/s: 58139 current, 58119 overall)
b3678s34678/C1: 340000 soups done (soups/s: 64102 current, 58279 overall)
b3678s34678/C1: 350000 soups done (soups/s: 58139 current, 58275 overall)
b3678s34678/C1: 360000 soups done (soups/s: 58479 current, 58280 overall)
b3678s34678/C1: 370000 soups done (soups/s: 58139 current, 58276 overall)
b3678s34678/C1: 380000 soups done (soups/s: 53475 current, 58139 overall)
b3678s34678/C1: 390000 soups done (soups/s: 58139 current, 58139 overall)
b3678s34678/C1: 400000 soups done (soups/s: 64102 current, 58275 overall)
b3678s34678/C1: 410000 soups done (soups/s: 64102 current, 58404 overall)
b3678s34678/C1: 420000 soups done (soups/s: 58479 current, 58406 overall)
b3678s34678/C1: 430000 soups done (soups/s: 64102 current, 58527 overall)
b3678s34678/C1: 440000 soups done (soups/s: 58139 current, 58518 overall)
b3678s34678/C1: 450000 soups done (soups/s: 58479 current, 58517 overall)
b3678s34678/C1: 460000 soups done (soups/s: 58139 current, 58509 overall)
b3678s34678/C1: 470000 soups done (soups/s: 64102 current, 58618 overall)
b3678s34678/C1: 480000 soups done (soups/s: 58139 current, 58608 overall)
b3678s34678/C1: 490000 soups done (soups/s: 64102 current, 58710 overall)
b3678s34678/C1: 500000 soups done (soups/s: 58479 current, 58706 overall)
b3678s34678/C1: 510000 soups done (soups/s: 58139 current, 58694 overall)
b3678s34678/C1: 520000 soups done (soups/s: 58479 current, 58690 overall)
b3678s34678/C1: 530000 soups done (soups/s: 58139 current, 58680 overall)
b3678s34678/C1: 540000 soups done (soups/s: 64102 current, 58772 overall)
b3678s34678/C1: 550000 soups done (soups/s: 58139 current, 58760 overall)
b3678s34678/C1: 560000 soups done (soups/s: 64102 current, 58848 overall)
b3678s34678/C1: 570000 soups done (soups/s: 58479 current, 58841 overall)
b3678s34678/C1: 580000 soups done (soups/s: 64102 current, 58925 overall)
b3678s34678/C1: 590000 soups done (soups/s: 58139 current, 58911 overall)
b3678s34678/C1: 600000 soups done (soups/s: 64102 current, 58991 overall)
b3678s34678/C1: 610000 soups done (soups/s: 58479 current, 58982 overall)
b3678s34678/C1: 620000 soups done (soups/s: 64102 current, 59058 overall)
b3678s34678/C1: 630000 soups done (soups/s: 64102 current, 59132 overall)
b3678s34678/C1: 640000 soups done (soups/s: 58139 current, 59116 overall)
b3678s34678/C1: 650000 soups done (soups/s: 58139 current, 59101 overall)
b3678s34678/C1: 660000 soups done (soups/s: 64102 current, 59171 overall)
b3678s34678/C1: 670000 soups done (soups/s: 64102 current, 59239 overall)
b3678s34678/C1: 680000 soups done (soups/s: 64102 current, 59305 overall)
b3678s34678/C1: 690000 soups done (soups/s: 58479 current, 59293 overall)
b3678s34678/C1: 700000 soups done (soups/s: 64102 current, 59357 overall)
b3678s34678/C1: 710000 soups done (soups/s: 58139 current, 59339 overall)
b3678s34678/C1: 720000 soups done (soups/s: 64102 current, 59401 overall)
b3678s34678/C1: 730000 soups done (soups/s: 53475 current, 59311 overall)
b3678s34678/C1: 740000 soups done (soups/s: 64102 current, 59370 overall)
b3678s34678/C1: 750000 soups done (soups/s: 64102 current, 59429 overall)
b3678s34678/C1: 760000 soups done (soups/s: 58139 current, 59412 overall)
b3678s34678/C1: 770000 soups done (soups/s: 64102 current, 59468 overall)
b3678s34678/C1: 780000 soups done (soups/s: 58479 current, 59455 overall)
b3678s34678/C1: 790000 soups done (soups/s: 64102 current, 59510 overall)
b3678s34678/C1: 800000 soups done (soups/s: 58139 current, 59492 overall)
b3678s34678/C1: 810000 soups done (soups/s: 58479 current, 59480 overall)
b3678s34678/C1: 820000 soups done (soups/s: 64102 current, 59532 overall)
b3678s34678/C1: 830000 soups done (soups/s: 53191 current, 59447 overall)
b3678s34678/C1: 840000 soups done (soups/s: 64102 current, 59498 overall)
b3678s34678/C1: 850000 soups done (soups/s: 58479 current, 59486 overall)
b3678s34678/C1: 860000 soups done (soups/s: 64102 current, 59536 overall)
b3678s34678/C1: 870000 soups done (soups/s: 58139 current, 59519 overall)
b3678s34678/C1: 880000 soups done (soups/s: 64102 current, 59568 overall)
b3678s34678/C1: 890000 soups done (soups/s: 53475 current, 59491 overall)
b3678s34678/C1: 900000 soups done (soups/s: 64102 current, 59539 overall)
b3678s34678/C1: 910000 soups done (soups/s: 64102 current, 59586 overall)
b3678s34678/C1: 920000 soups done (soups/s: 58139 current, 59570 overall)
b3678s34678/C1: 930000 soups done (soups/s: 58479 current, 59558 overall)
b3678s34678/C1: 940000 soups done (soups/s: 64102 current, 59603 overall)
b3678s34678/C1: 950000 soups done (soups/s: 64102 current, 59647 overall)
b3678s34678/C1: 960000 soups done (soups/s: 58139 current, 59631 overall)
b3678s34678/C1: 970000 soups done (soups/s: 58479 current, 59618 overall)
b3678s34678/C1: 980000 soups done (soups/s: 64102 current, 59661 overall)
b3678s34678/C1: 990000 soups done (soups/s: 58139 current, 59645 overall)
b3678s34678/C1: 1000000 soups done (soups/s: 64102 current, 59687 overall)
----------------------------------------------------------------------
1000000 soups completed.
Attempting to contact payosha256.
^C
$

~25k soups/sec in 4.x, vs. ~60k soups/sec in 3.x --- ouch! This is on a Core i7-3770K running at a nominal 3.50 GHz, with no load other than the usual background processes.

Posted: **September 28th, 2018, 3:04 pm**

Apple Bottom wrote:[...]
That's v4.52-ll1.62, right? But yes, I can confirm that Day & Night at least is much slower in 4.x: [...]

Yes, I meant ll1.62, and thanks for also testing speed differences (I had forgotten how to manually specify a seed).

Posted: **October 6th, 2018, 7:54 am**

Tried it on Android

Code: Select all

$ ./recompile.sh
Skipping updates; use --update to update apgluxe automatically.
Ensuring lifelib is up-to-date...
Rule unspecified; assuming b3s23.
Symmetry unspecified; assuming C1.
Configuring rule b3s23; symmetry C1
Valid symmetry: C1
Success!
g++ -c -Wall -Wextra -pedantic -O3 -march=native --std=c++11 main.cpp -o main.o
clang-7: error: the clang compiler does not support '-march=native'
make: *** [makefile:32: main.o] Error 1
$

Posted: **October 6th, 2018, 11:15 am**

Sarp wrote:Tried it on Android

Code: Select all

$ ./recompile.sh
Skipping updates; use --update to update apgluxe automatically.
Ensuring lifelib is up-to-date...
Rule unspecified; assuming b3s23.
Symmetry unspecified; assuming C1.
Configuring rule b3s23; symmetry C1
Valid symmetry: C1
Success!
g++ -c -Wall -Wextra -pedantic -O3 -march=native --std=c++11 main.cpp -o main.o
clang-7: error: the clang compiler does not support '-march=native'
make: *** [makefile:32: main.o] Error 1
$

You're not going to get apgluxe to run on ARM, or Power9, or anything else that isn't x86_64.

Version 1.x (the Golly Python script) is the cross-platform alternative.

Posted: **October 14th, 2018, 2:11 am**

1) How can I set a limit on RAM usage?
2) Are there any RAM usage optimizations that could be possible?

Posted: **October 14th, 2018, 2:51 am**

danny wrote:1) How can I set a limit on RAM usage?
2) Are there any RAM usage optimizations that could be possible?

Re 1) --- ulimit should help. This is a shell built-in, BTW (depending on your shell anyway), so check the bash/... man page for more.

Re 2) --- yes.

Posted: **October 17th, 2018, 4:29 pm**

It's been over a year since the mention of searching range-extended outer-totalistic rules; has any progress been made in this area?

Posted: **October 17th, 2018, 5:03 pm**

danny wrote:1) How can I set a limit on RAM usage?
2) Are there any RAM usage optimizations that could be possible?

Which version do you have? Since v4.44, it tends to use < 200 megabytes per thread.

muzik wrote:It's been over a year since the mention of searching range-extended outer-totalistic rules; has any progress been made in this area?

Not at the moment. My current CA-related software project is adding Python bindings to lifelib (which is about 50% complete, I'd say).

Posted: **October 23rd, 2018, 7:23 pm**

I tried to update to v4.53 but got this error:

Code: Select all

python: can't open file 'lifelib/rule3asm.py': [Errno 2] No such file or directory

How do I fix this?

EDIT: Nevermind, it fixed itself somehow.

Posted: **October 28th, 2018, 9:26 pm**

In recent news, apgsearch v4.54-ll2.0.4 has been released.

The salient changes are:

Fixed a bug in stabilise3(), which now gives an overall 1% boost in soup searching speed.
For B3/S23, apgsearch now reports estimated lifespans of soups lasting > 25000 generations.

Internally, apgsearch has now migrated to lifelib 2, which is better maintained and more thoroughly tested. As the test coverage expands, previously identified bugs are more likely to get caught and fixed in the process.

More details about lifelib 2 will be discussed shortly -- the exciting aspect is that it has comprehensive Python bindings, together with integration with Jupyter notebooks and Chris Rowett's LifeViewer.

Posted: **October 29th, 2018, 9:29 am**

Please update to v4.55, which computes exact (rather than approximate) lifespans of methuselahs.

Also, the prefix 'meth' has been extended to 'methuselah'.

Posted: **November 3rd, 2018, 4:07 pm**

For those who didn't realize -- like me --, apgluxe 4.56 and lifelib 2.0.5 now support searching BSFKL rules.

Side note: would it be possible to set up some sort of automated announcement system for new releases, e.g. a Twitter bot that tweets commits made to the apgluxe repo? Much as I love the tool, I don't check the repo for updates every day.

Posted: **November 3rd, 2018, 4:20 pm**

I could certainly post to this thread whenever there's been a major update. I was waiting to announce the latest lifelib (which has Python bindings!) after the testing phase completes.

Posted: **November 3rd, 2018, 5:57 pm**

calcyman wrote:I could certainly post to this thread whenever there's been a major update. I was waiting to announce the latest lifelib (which has Python bindings!) after the testing phase completes.

That would be excellent.

Posted: **November 5th, 2018, 7:20 am**

Any plans on supporting snoitareneG, B0 non-totalistic, or extended range one-dimensional rules in the near future?

EDIT: or alternating rules?

Posted: **November 5th, 2018, 5:23 pm**

cordership3 wrote:Rule speed differences between v3.x and v4.x, on an AMD A6-5200 running on both v3.28 and v4.52-ll1.52:

B368/S245: 2100-2200 soups/sec for v3.x vs ~115 soups/sec for v4.x
B3678/S34678: 18000-19000 soups/sec for v3.x vs 7700-7800 soups/sec for v4.x
B36/S23: 2-3 soups/sec for v3.x vs 3-6 soups/sec for v4.x

Thanks for noticing this. I've enabled the 'incubator' method of object separation for all non-B0 lifelike rules, and replaced the 'apg::pattern' with (a pair of!) 'apg::upattern's in linearlyse(). In Golly language, this corresponds to using QuickLife instead of HashLife. Now we get huge speed improvements, especially for rules with common linear growths:

B368/S245: 8300 soups/sec in v4.58-ll2.0.15
B3678/S34678: 53000 soups/sec in v4.58-ll2.0.15
B36/S23: 28 soups/sec in v4.58-ll2.0.15

Even for non-linear-growth rules, the 'incubator' object separation improves (for example) the speed of B38/S23 from 2300 soups/second to 3300 soups/second.

B3/S23 performance remains identical (since it already used the 'incubator' method), except for switch engines which should be processed much more quickly. Here are the results from the AppleBottomSpeedTest seed:

Code: Select all

ubuntu@ip-172-31-19-250:~/apgmera (master) 
$ ./apgluxe -t 1 -v 0 -s AppleBottomSpeedTest -n 500000

Greetings, this is apgluxe v4.58-ll2.0.15, configured for b3s23/C1.

Lifelib version: ll2.0.15
Compiler version: 5.4.0 20160609
Python version: '2.7.12 (default, Dec  4 2017, 14:50:18)  [GCC 5.4.0 20160609]'

Using seed AppleBottomSpeedTest
Running 500000 soups per haul:
Instruction set AVX-512 detected
b3s23/C1: 58979 soups completed (5896.606 soups/second current, 5896.606 overall).
Linear-growth pattern detected: yl144_1_16_afb5f3db909e60548f086e22ee3353ac
b3s23/C1: 117287 soups completed (5830.776 soups/second current, 5863.688 overall).
b3s23/C1: 176208 soups completed (5892.028 soups/second current, 5873.131 overall).
b3s23/C1: 233594 soups completed (5737.820 soups/second current, 5839.300 overall).
b3s23/C1: 292451 soups completed (5885.136 soups/second current, 5848.466 overall).
Linear-growth pattern detected: yl144_1_16_afb5f3db909e60548f086e22ee3353ac
Linear-growth pattern detected: yl144_1_16_afb5f3db909e60548f086e22ee3353ac
Linear-growth pattern detected: yl144_1_16_afb5f3db909e60548f086e22ee3353ac
b3s23/C1: 350879 soups completed (5842.518 soups/second current, 5847.473 overall).
Rare oscillator detected: xp8_gk2gb3z11
b3s23/C1: 410199 soups completed (5931.051 soups/second current, 5859.412 overall).
Linear-growth pattern detected: yl144_1_16_afb5f3db909e60548f086e22ee3353ac
b3s23/C1: 470380 soups completed (6016.521 soups/second current, 5879.053 overall).
b3s23/C1: 500000 soups completed (5960.002 soups/second current, 5883.786 overall).
----------------------------------------------------------------------

I think this is the first time I've ever seen it momentarily exceed 6000 soups/second for b3s23/C1.

Also, because people complained about the garbage collection status updates, I've disabled them in lifelib whenever the lifetree memory is less than 1 GB. This means they'll be absolutely silent on apgluxe, which uses 100 MB lifetree memory. For other lifelib-based software, such as slmake, you'll still get the GC status updates.

muzik wrote:Any plans on supporting snoitareneG, B0 non-totalistic, or extended range one-dimensional rules in the near future?

EDIT: or alternating rules?

If you write your own rule genera, then you can add support for them yourself (except for one-dimensional rules, because they're not really compatible with apgcodes). Here's an example of how I implemented Brian Prentice's BSFKL rulespace:

https://gitlab.com/apgoucher/lifelib/co ... 3db996436f

The idea is that you need to both provide a Python module to generate the code which implements the rule, and a regex in genuslist.py to 'register' those rulestrings as belonging to that genus.

If there's enough popular demand for a particular rulespace (such as AbhpzTa's "please support BSFKL natively!"), and someone explains clearly and unambiguously how the rule works (I struggled to understand the description of Extended Generations, for instance), then I'll consider adding that rulespace as another genus myself.

Posted: **November 5th, 2018, 6:05 pm**

calcyman wrote: Thanks for noticing this. I've enabled the 'incubator' method of object separation for all non-B0 lifelike rules, and replaced the 'apg::pattern' with (a pair of!) 'apg::upattern's in linearlyse(). In Golly language, this corresponds to using QuickLife instead of HashLife. Now we get huge speed improvements, especially for rules with common linear growths:

B368/S245: 8300 soups/sec in v4.58-ll2.0.15
B3678/S34678: 53000 soups/sec in v4.58-ll2.0.15
B36/S23: 28 soups/sec in v4.58-ll2.0.15

I just did my own (rather brief) tests on those three rules before and after updating to v4.58:
B368/S245: Massive increase from ~300 soups/sec to ~4400 soups/sec
B3678/S34678: Decent increase from ~15k soups/sec to ~23k soups/sec
B36/S23: Appears to me to have decreased from ~25 soups/sec to ~10 soups/sec

Not sure what caused the decrease for HighLife.

EDIT: Okay then, looks like Move/Morley glitched out again:
https://catagolue.appspot.com/haul/b368 ... 9aa79b0eca
https://catagolue.appspot.com/haul/b368 ... a28bcd5ee6

EDIT 2: And now the same thing just happened to D&N:
https://catagolue.appspot.com/haul/b367 ... 7bbe4b60b5

Posted: **November 5th, 2018, 6:29 pm**

Ian07 wrote: EDIT: Okay then, looks like Move/Morley glitched out again:
https://catagolue.appspot.com/haul/b368 ... 9aa79b0eca
https://catagolue.appspot.com/haul/b368 ... a28bcd5ee6

EDIT 2: And now the same thing just happened to D&N:
https://catagolue.appspot.com/haul/b367 ... 7bbe4b60b5

It's because you changed includes/params.h without also changing lifelib. I think this happened because I changed some of the API between apgmera and lifelib, and you might have compiled incompatible versions.

Version 4.6 now aborts if you try to execute it in an inconsistent state.

EDIT: Also, methuselah checking is about 8x faster now.

Posted: **November 6th, 2018, 8:26 am**

Ian07 wrote:I tried to update to v4.53 but got this error:
Code: Select all
python: can't open file 'lifelib/rule3asm.py': [Errno 2] No such file or directory
How do I fix this?

EDIT: Nevermind, it fixed itself somehow.

I got this same error when upgrading to the latest version. Just running the same command with --update again, however, returns "your copy of apgluxe is already up-to-date" and searches as normal.

Posted: **November 6th, 2018, 9:19 am**

Please immediately update to v4.62 -- it fixes a problem causing GoL objects to appear in other lifelike rules.

Posted: **November 6th, 2018, 9:43 am**

calcyman wrote:Please immediately update to v4.62 -- it fixes a problem causing GoL objects to appear in other lifelike rules.

i left mine running at home...

ConwayLife.com

apgsearch v4.0

How can I make it work?

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