Task results variable substitution is broken

Expected Behavior
The "echo-variable" task in the example PipelineRun should print to stdout "The variable: dolar"
Actual Behavior
The "echo-variable" task in the example PipelineRun prints:
2024-04-07T01:25:23.656135143Z /tekton/scripts/script-0-8b297: line 2: tasks.set-variable.results.ipsum: not found
2024-04-07T01:25:23.656332139Z The variable:

Steps to Reproduce the Problem
Broken:
Install the tekton operator helm chart from this commit: https://github.com/tektoncd/operator/commit/7bc7f7eb4a5b327a9c66929c6a89974fab85de75
(note: that is just the latest commit, I am not blaming that commit specifically)
Then kubectl apply this PipelineRun: (note, you may need to change the namespace based on your tekton setup)
apiVersion: tekton.dev/v1beta1
kind: PipelineRun
metadata:
  name: minimal-test
  namespace: lighthouse
spec:
  pipelineSpec:
    tasks:
      - name: echo-variable
        taskSpec:
          metadata: {}
          stepTemplate:
            image: alpine:3.18
            name: ""
            resources:
              requests:
                cpu: 10m
                memory: 600Mi
          steps:
            - image: alpine:3.18
              script: |
                #!/usr/bin/env sh
                echo "The variable: $(params.LOREM)"
        params:
          - name: LOREM
            value: $(tasks.set-variable.results.ipsum)
      - name: set-variable
        taskSpec:
          metadata: {}
          stepTemplate:
            image: alpine:3.18
            name: ""
            resources:
              requests:
                cpu: 10m
                memory: 600Mi
          results:
            - name: ipsum
          steps:
            - image: alpine:3.18
              script: |
                #!/usr/bin/env sh
                echo -n "dolar" > "$(results.ipsum.path)"
  timeout: 240h0m0s

Look at the output for the "echo-variable" step and see the error.
Working:
Do the exact same steps, except when installing tekton, install from this tag: https://github.com/tektoncd/operator/tree/tekton-operator-0.67.0
Additional Info

Kubernetes version:
Output of kubectl version:

Client Version: v1.29.3
Kustomize Version: v5.0.4-0.20230601165947-6ce0bf390ce3
Server Version: v1.27.2
WARNING: version difference between client (1.29) and server (1.27) exceeds the supported minor version skew of +/-1

Tekton Pipeline version:
Output of tkn version or kubectl get pods -n tekton-pipelines -l app=tekton-pipelines-controller -o=jsonpath='{.items[0].metadata.labels.version}'

Broken
v0.50.1

Working
v0.47.0

I see this issue can no longer be reproduced on the latest v0.58.
Maybe you can try updating to the latest version and see if that helps?

Yep, this seems to be fixed now. Thanks!


[BUG] Modus Cards - prop backgroundColor is not working

Reported by Robert Nagy and Adela Duna from the Trimble Creator team.

This is to reaffirm that Modus has approved the functionality for development.
Note: Modus team tag the approved issues with modus-approved

Users should be able to use --modus-card-bg CSS variable going forward inorder to customize the background color for cards.

The changes are already merged so closing this issue


Clearing empty/not empty chips don't respect allowed filter functions for column

material-react-table version
v1.5.2
react & react-dom versions
v18.2.0
Describe the bug and the steps to reproduce it
Given a column with the filter function "fuzzy" not allowed, change the filter function to "empty" or "notEmpty" and then delete the chip that appears in the default filter field. The filter mode will then change to "fuzzy".
Kevin, I have a few ideas for how to fix this:

Provide a default filter mode prop for the column. Removing the "Empty" chip would revert the filter to the default mode. Would this benefit from the addition of a default filter value?
Remove the clear button for the "Empty" and "Not Empty" chips so that the user is forced to use the filterFn menu to change the mode.

What do you think would be best?
Minimal, Reproducible Example - (Optional, but Recommended)
import React from 'react';
import ReactDOM from 'react-dom/client';
import MaterialReactTable from 'material-react-table';

const root = ReactDOM.createRoot(document.getElementById('root'));
root.render(
  <React.StrictMode>
    <div style={{ width: '300px' }}>
      <MaterialReactTable
        enableColumnFilterModes
        initialState={{
          columnFilterFns: {
            stuff: 'empty'
          },
          showColumnFilters: true
        }}
        data={[
          {
            stuff: "Hello"
          }
        ]}
        columns={[
          {
            header: 'Stuff',
            accessorKey: "stuff",
            enableColumnFIlterModes: true,
            columnFilterModeOptions: ["empty", "notEmpty", "contains"]
          }
        ]}
      />
    </div>
  </React.StrictMode>
);

Click the delete button to the right of the "Empty" chip. Notice that the filter mode is now "fuzzy".
Screenshots or Videos (Optional)

Do you intend to try to help solve this bug with your own PR?
Yes, I think I know how to fix it and will discuss it in the comments of this issue
Terms

[X] I understand that if my bug cannot be reliably reproduced in a debuggable environment, it will probably not be fixed and this issue may even be closed.

Probably the easiest solution would be to just have the value change to whatever is first in the columnFilterModeOptions array if provided, otherwise, then default to fuzzy.


Increase scroll speed without enabling Linear scrolling option (for Magic Mouse 2)

With the Magic Mouse 2, enabling linear scrolling doesn't seem to work like with other devices. It seems to still accelerate with small movements even with inertia scrolling disabled in Accessibility options. I actually find that I get ideal scrolling with this mouse using these settings:

However - only when the control key is pressed. An option to permanently scale scroll speed (using the function the Modifier Keys are using) outside of enabling Linear scrolling would be great. I realize this is a bit outside of what you designed this app for so if it's not something you want to add, perhaps point out a way to modify the code and I will build it myself?

This feature has been implemented in v0.7.6-beta.4.


Detection of tasks  does not work with tasks 2.0.0

Hi!

VSCode Version: 1.10.1
OS Version: macOS Sierra

Steps to Reproduce:

started Code and let it detect all tasks (tasks.json generated with 0.1.0)
"cmd+p task" ...and all my tasks provided by grunt file are there, so far so good
added one custom modified task to tasks.json
tried to run that task...still works
changed version of tasks from 0.1.0 to 2.0.0
"cmd+p task" ...and only my explicitly defined task is there, any other task from grunt file :/
explicit task can be run without any problem...but any other task

Thanks!

Actually we are debating whether this auto task detection is something we want to bring back in 2.0.0. Reason is that this is currently only supported for grunt and gulp and there are valid concerns why we treat these so special.
@obrejla how much would you be hurt if this would go away?

It depends...
When I want to e.g. pass some arguments to my grunt task by vscode automatically, then I need to rewrite it to tasks.json manually. That seems to be ok, BUT...then only that one specific task could be run from vscode commands dialog, all other tasks will need to be written manually in Terminal windows. Which is a bit strange...one task can be run from vscode infrastructure and others can't.
Or you can rewrite all tasks from gruntfile to tasks.json manually, which is an overkill I think. Especially when tasks in gruntfile change. You must track such changes in codebase and make corresponding changes in tasks.json manually again...
So for me, it makes sense to have an auto detection of grunt tasks (e.g. NetBeans have such autodetection as well and it's great ;).
Thanks!

Uff...now I have almost 2 weeks of such "no-detection" experience and it is very bad :/ Our project has really lot of tasks and all of them are occasionally needed...so from the switch to v2.0.0 without detection, there is not a day without duplicating a task to tasks.json :( I hope this experience will help you deciding to re-add auto-detection. Thanks a lot!

I am working on it :-). The new terminal runner will get official API so that extension can contribute tasks via API. This will allow us to get rid of the pre-supported runners in our core but we depend on the community to provide the extensions to detect tasks in the various task runners out there.

Merged grunt support into master for the next release.


community: avoid double templating in langchain_prompty

Description
In langchain_prompty, messages are templated by Prompty. However, a call to ChatPromptTemplate was initiating a second templating. We now convert parsed messages to Message objects before calling ChatPromptTemplate, signifying clearly that they are already templated.
We also revert #25739 , which applied to this second templating, which we now avoid, and did not fix the original issue.
Issue
Closes #25703

Thanks for merging and for the time you spent on this!

Thanks for merging and for the time you spent on this!

Thank you for the fix! Released in langchain-prompty 0.0.3, feel free to take a look.


Line rule (hr tag) is invisible in markdown cell

If markdown cell contains only '---' the outputted hr has no width

Create Python file with the following code

#%%md
# ---

Run line 2 in any way you would like
The result will be an <hr> with width 0 making it invisible

@wadethestealth
If a markdown cell only contains ---, it wouldn't appear, but if any other character is there, it seems to work properly:

And it would be very rare case that we want to only render ---, thus I wonder whether we should treat this as an issue.

I am going to mark this as intended behavior based on the html5 specification where an hr tag is semantic rather than visual


How we can access assigned flow to me on SharePoint online ?

How we can access assigned flow to me on SharePoint online ?

@spo365 Thanks for reaching out. Can you clarify what you mean? There is no way to access approvals in SharePoint Online. You can access approvals in email and in the Approval center only.


Fix how path variables are escaped for Swagger

paths in swagger are not correctly escaped and won't be correct. You can try with following swagger:
`openapi: 3.0.0
info:
title: SampleAPI
version: "1.0"
tags:

description: Sample API
name: Sample Echo API
paths:
/healthcheck/$echo:
get:
parameters:
- in: query
name: message
required: true
schema:
type: string
description: The echo message
responses:
"200":
description: An echo message.
summary: Return a echo message.
operationId: getEcho
tags:
- Generate echo message
/batchs(Material='{Material}',Batch='{Batch}'):
get:
parameters:
- in: path
name: Material
required: true
schema:
type: string
description: The echo message
- in: path
name: Batch
required: true
schema:
type: string
description: The echo message
responses:
"200":
description: An echo message.
summary: Return a echo message.
operationId: getBatch
tags:
- Generate echo message
servers:
url: https://httpbin.org/anything`

According to the internal slack thread, the translation of the following string should be,
Source:
/Batchs(Material=''{Material}'',Batch=''{Batch}''):

Expected:,
"/Batchs\(Material='(?<Material>[^\/]+)',Batch='(?<Batch>[^\/]+)'\)$"

Actual:,
"/Batchs\\(Material=''(?<Material>[^\\/]+)'',Batch=''(?<Batch>[^\\/]+)''\\)$"

@marckong this was an issue with the UI, I fixed it. Can you please review? Thanks

Source:
/Batchs(Material=''{Material}'',Batch=''{Batch}''):
Expected:,
"/Batchs\(Material='(?<Material>[^/]+)',Batch='(?<Batch>[^/]+)'\)$"
Actual:,
"/Batchs\(Material='(?<Material>[^/]+)',Batch='(?<Batch>[^/]+)'\)$"

I ran smoke test to spin up a Kong gateway and it seems to be fine.

Ran smoke tests with the link example as well


Update to Lodash v4.

What's this PR do?
Just doing a little tidying up while waiting on a Thor database refresh. This pulls in Lodash v4, with some minor changes based on the notes in the upgrade guide.
How should this be reviewed?
I've done some quick testing on my local and all seems well, but I'll
Any background context you want to provide?
This will require everyone to run an npm update to get the updated dependency.
Relevant tickets
Fixes #6036.
Checklist

[ ] Documentation added for new features/changed endpoints.
[ ] Tested on staging.
[ ] Pinged a PM if this is a larger PR that would benefit from some additional testing love.

I've done some quick testing on my local and all seems well, but I'll...

cliffhanger!

Hahaha, oops! 😅 ...I'll test more thoroughly on staging as well!

👍


package.json main is incorrect

Failing in npm3. Should just be a string. No need to include the css file in there I think. Also, it shouldn't be a minified file (so that those using bundlers can produce sourcemaps mapping to the preminified source). Should probably just read:
"main": "src/bootstrap-treeview.js"

Thanks.  Actually I'm aware of this already and plan to address it in the next release.


Fixed errors when running model_vs_game.py

There were a couple errors when running model_vs_game.py, I'm guessing some NHL94 stuff with trying to access non-existing methods as well as model_vs_game.py setting argument model_1 while this is load_p1_model when running different scripts.
Fixed this by just adding some try-except blocks

Ah thanks for this, yeah changes I made for NHL94 broke model_vs_game for other games so I fixed it by updating the base ai_sys and display code: fix


⚠ Bofrost.lu has degraded performance

In f818073, Bofrost.lu (https://www.bofrost.lu) experienced degraded performance:

HTTP code: 200
Response time: 1109 ms

Resolved: Bofrost.lu performance has improved in 698df0b after 25 minutes.


Small logic fix to chunk caching

This one too: https://github.com/Civcraft/AFK-Player-GC/pull/15/files#diff-da3b36a717b84910d91dc44da6b1e37bL114

Any chance of a squash on these?

Perfect.


Finetuning on GPUs

Hi!
I'm currently creating a dataset that I'd like to finetune this model on, but I don't have access to TPUs. I'm also not too familiar with Jax, so I was wondering if you roughly know what needs to be changed in the finetuning pipeline to be able to use GPUs.
Thanks for your work!
-Samuel

hi Samuel, at a high level, the finetuning pipeline should still work -- you just might have to disable bfloat16. You might want to check out the jax documentation for a primer on how jax works 😄 best of luck!


Added Register form

I have added a register form  which will enable customers to sign up for this amazing gym experience.

@abhay-raj19
I created a new pull request . Kindly check if it is good go.

@abhay-raj19 I created a new pull request . Kindly check if it is good go.

navigate me to that PR


Issue 217 allow + character in company name

Fix concerning the issue 217
Allow + sign in company names when creating qrcode.

Hello could you please review this pull request and merge it into the repository if it is ok.
See Issue #217.
Many Thanks, Regards,
lobfeh


python 3.8 not being built

…ilt.

Checklist

[ ] Used a fork of the feedstock to propose changes
[ ] Bumped the build number (if the version is unchanged)
[ ] Reset the build number to 0 (if the version changed)
[ ] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[ ] Ensured the license file is being packaged.

Hi! This is the friendly automated conda-forge-linting service.
I wanted to let you know that I linted all conda-recipes in your PR (recipe) and found some lint.
Here's what I've got...
For recipe:

Failed to even lint the recipe, probably because of a conda-smithy bug :cry:. This likely indicates a problem in your meta.yaml, though. To get a traceback to help figure out what's going on, install conda-smithy and run conda smithy recipe-lint . from the recipe directory.

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.
I do have some suggestions for making it better though...
For recipe:

License is not an SPDX identifier (or a custom LicenseRef) nor an SPDX license expression.

@conda-forge-admin, please rerender

Hi! This is the friendly automated conda-forge-linting service.
I just wanted to let you know that I linted all conda-recipes in your PR (recipe) and found it was in an excellent condition.

@conda-forge-admin, please rerender

@conda-forge-admin, please rerender

@ocefpaf @isuruf any idea why Python 3.8 isn't being built on this one?

Because https://github.com/conda-forge/arcsi-feedstock/pull/10 wasn't merged.

Ah right - thanks! Will try and get that one going instead.

Fixed by #10. Thanks @isuruf !


Danish layout

There is a need for æ ø and å for a danish layout.
I am not sure of the best way to place this. Maybe there could be a combo to translate aa to å, oe to ø and se to æ?
Here is a frequency of chars for danish layout - https://www.sttmedia.com/characterfrequency-danish

For reference the danish layout from 8pen from 2011

Sounds like #65 should be settled fist, as æ, ø, and å maybe could be inserted the way diacritics would.

You might be interested in this:
https://github.com/flide/8VIM/discussions/138


yarn start > Failed to compile.

Hi,
we created with "yarn" a new project and then replaced the basic theme with the "Modular" template (but we also tried with a basic theme). When we try to use the "yarn start" command we always get this issue:
Failed to compile.
undefined
<EMAIL_ADDRESS>Module not found: Error: Can not resolve 'appstatic' in '/ Applications / MAMP / htdocs / clemoonie / e-commerce / node_modules / @ shopify / slate-tools / tools / webpack'
<EMAIL_ADDRESS>31: 0-38
  @ multi<EMAIL_ADDRESS><EMAIL_ADDRESS>The files are not compiled in dist and as a result are not even uploaded to the online theme of the store shopify.
Can you help us? please....

Hi @Bek81 .  I had exactly the same issue earlier today, except 'appvendors' was the module that could not be found.  I was attempting to use the Shopify Skeleton theme.
If you look at the file that is kicking up the error -<EMAIL_ADDRESS>- the first few lines say...

// This is used as an entry point in webpack.base.conf.js. It dynamically
// require all liquid and json files from the user's directory (except theme.liquid)
// as well as everything inside the src/assets/vendors directory.

Looking further down the file at line 31, there is a required module that needs to be present.  Note the last line above "as well as everything inside the src/assets/vendors directory."
In my case, I noticed that there wasn't a "vendors" directory.  I resolved it was by adding the missing "vendors" directory into the following path... my-shopify-theme/src/assets.  Once the vendors directory was present I was able to successfully compile.  I would hazard a guess that if you added a directory called "static" into your-shopify-theme/src/assets you'll be able to compile successfully.
Good luck.
R

Hi @ninjaaa ,
great scott....it works!
Thanks a lot


Path not shown to downloaded dotnet

Describe the bug

Ionide installed dotnet for me, since it did not find it on my PATH. I really like that inclusion, but a) after a restart, it doesn't find it, and still complains about it being absent.
It does not, however, attempt to install it anymore. Now I am asking myself where the binary went, so I can either put the path to it, or install dotnet from my system's repository.
I recommend to a) show the path, where it gets installed at and b) look into why this hasn't picked up after the restart of the app.
Thanks a lot
Steps to reproduce
1.) Install Ionide without having dotnet installed on Linux.
2.) See Ionide installing dotnet.
3.) Ask yourself where.
Expected behavior

Clearly shown, where installation went. Does pick up the binary after restart (and ideally suggests the restart as well.)
Machine info

OS: Garuda Linux (Arch, rolling)
7 (I did not see which one exactly)
Ionide version: 7.9.0

Additional context
If that makes any difference, I installed this bundle:
https://marketplace.visualstudio.com/items?itemName=spencer-farley.ionide-and-vscode-solution-explorer

Ionide doesn't do any .Net downloads at all, so I'm not sure what you're talking about here.

Hnn, then I am wondering, what did it. Probably the solutions explorer. Thanks for the heads up.

I took a look at the solution explorer extension repo and didn't see anything that jumped out at me - it's possible that some other extension you have is using the vscode-dotnet-runtime extension, which downloads a copy of the .NET Runtime to a private folder for just that extension to use. That extension is what I would expect to cause the behavior you describe.

It is the C# extension. It downloaded dotnet the way you describe it.
And it is a dependency of Ionide.

Gotha - we've actually got a PR out that breaks that explicit dependency (though of course the C# will be required if debugging is desired).
The C# extension in the coming months will also be changing to reuse the global install of the SDK/Runtime. My team at work is busy rewriting vscode-dotnet-runtime to make that happen right now.

Can we not host the version of Ionide with the Debugger from Samsung next to the current one?
I would love that.
Thanks for your work at the vscode runtime 👍🏻

And I know that we already provide a link to OpenVSX, for that second Ionide. I just think, that fewer people are willing to jump through the hoops. I am at least not, and still, I like to use the Samsung version, also to avoid the dependency.
Is it significantly more work to maintain? Since it seems like the necessary packaging is already done, so it would be just a second upload to the marketplace, I assume.

Once we break the explicit C# dependency users would be free to install whichever build of a C# extension they'd like - Ionide won't care if it's the MS one or the muhammedsammy one, or if you packaged your own. So we wouldn't need to bundle two extension variants.

Oh, thats nice. Ideal solution!


(chore) Migrate from babel to swc

@samuelmale tested with the form builder with resolutions and everything works fine and this PR is ready to go in


Added support decompressFromUint8Array to save 1 bytes on decoding

Other implementation of LZString doesn't always send the last byte if it 0.
This cause issues with the decompressFromUint8Array which devide the
length by 2 which would return an float.  So I rectify it to it ceiling
value.

Well, I need to rework this... It doesn't work... I do need to append the 0 at the end... Will resubmit when it work better !


Make directly a Skype Call

How can I call directly a Skype contact? Like this: skype:SkypeContact?call How is the "uri"?

You have tried this code?
startApp.set({ /* params */     "action": "ACTION_VIEW",     "uri": "skype:+79109999999" }).start();

Thanks. It works...


parse_block_html is not supported

1. list1
2. list2
3. list3

is always parsed like
1. list1 2. list2 3. list3
 no matter what i do:
set parse_block_html: true to kramed options
or write {::options parse_block_html="true" /} in html code before the opening div.
Very poor implemetation of kramdown you have.

Came here looking for this option as well. Would be great to see it implemented!

This is interesting, we're experiencing the opposite problem when using block quotes:
Input markdown:
If you'd like to quote someone, use the > character before the line:

> It may be the warriors who get the glory. But it's the engineers who build societies.
> - B'Elanna Torres

Outputted kramed HTML:

Quote!

Quote speaker

When I would normally expect this:

Quote!

  - Quote speaker


Join channel message

Description
Whenever a user joins a channel, no feedback is given whether it was successful or not
Passing criteria
Open a random channel and get the message:
Attempting to join {channel}...
After success:
Joined {channel}!
After failure:
Failed to join {channel}!
The message should be sent in the channel tab that is attempting to be joined

Forgot failure message...


SIGSEGV (Segmentation fault) with high angular momentum (large zeta) basis sets

Similar to this issue in qcint 5.1.7, I am getting the error (the same error whether I'm doing O in aug-cc-pV8Z or H2 in aug-cc-pV10Z):
Program received signal SIGSEGV, Segmentation fault.
0x00007fffe567ed9f in CINTg2e_index_xyz (idx=idx@entry=0x0, envs=envs@entry=0x7fffffff4fe0)
    at /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1/build/temp.linux-x86_64-cpython-310/deps/src/libcint/src/g2e.c:209

In the qcint issue, the problem was in CINTg4c_index_xyz and now in libcint I'm getting a problem in CINTg2e_index_xyz at line 209.
The SIGSEGV output was the same for aug-cc-pV8Z and aug-cc-pV9Z. As with qcint, this calculation, on the oxygen atom ground (triplet) state, is working with aug-cc-pV7Z. With libcint I got an SCF energy of -74.8123927916843 and with qcint I got -74.8123927916843.
The installation was done as follows.
(1) Open the pyscf.sh configuration file in the wheels builder:
git clone https://github.com/ComputeCanada/wheels_builder.git
cd wheels_builder
vi config/pyscf.sh

(2) Make pyscf.sh the following file, and save it (note that I've removed the qcint part that was there by default, and I've set -DBUILD_LIBCINT=1):
MODULE_BUILD_DEPS="flexiblas libxc/5.1.3"
PACKAGE_DOWNLOAD_ARGUMENT="[https://github.com/pyscf/pyscf/archive/refs/tags/v${VERSION:?version](https://github.com/pyscf/pyscf/archive/refs/tags/v$%7BVERSION:?version) required}.zip"
PRE_BUILD_COMMANDS='export VERBOSE=1; export CMAKE_CONFIGURE_ARGS="-DBLA_VENDOR=Flexiblas -DBLAS_LIBRARIES=-lflexiblas -DBUILD_LIBCINT=1 -DBUILD_LIBXC=0" '

(3) Install the wheel:
cd /home/nike/wheels_builder
bash build_wheel.sh --package pyscf --version 2.1.1 --python 3.10

The result is as follows:
Building wheel for pyscf
Loading module python/3.10
==============================
Setting up build environment
==============================
==============================
Downloading source
Downloaded './v2.1.1.zip'
==============================
Extracting archive ./v2.1.1.zip...
Extraction done.
==============================
Building
==============================
Testing source code for CPU architecture instructions in /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1
-march=native found in files ./pyscf/lib/CMakeLists.txt
./doc_legacy/source/install.rst, replacing with -march=core-avx2 to build for avx2
==============================
Building the wheel....
Success.
Building done
==============================
/tmp/wheel_builder_11860 ~/wheels_builder/tmp.61293 ~/wheels_builder
~/wheels_builder/tmp.61293 ~/wheels_builder
Testing if wheel links on libtorch...
No link dependency on libtorch found.
==============================
Testing...
Installing wheel
Testing import with name pyscf... Success!
Testing done
==============================
WARNING: pyscf was built for avx2.
The wheel can be copied with : /home/nike/wheels_builder/cp_wheels.sh --remove --arch avx2
If you are satisfied with the built wheel, you can copy them to /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/[generic,avx2,avx,sse3]](http://soft.computecanada.ca/custom/python/wheelhouse/%5Bgeneric,avx2,avx,sse3%5D) and synchronize CVMFS

My wheels_builder folder now has:
-rw-rw-r-- 1 nike nike 41955612 Dec 25 18:29 pyscf-2.1.1+computecanada-cp310-cp310-linux_x86_64.whl

(4) Install pyscf:
module load python/3.10
pip install /home/nike/wheels_builder/pyscf-2.1.1+computecanada-cp310-cp310-linux_x86_64.whl

The output is:
The following have been reloaded with a version change:
  1) python/3.8.10 => python/3.10.2

Defaulting to user installation because normal site-packages is not writeable
Looking in links: /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/gentoo/avx2](http://soft.computecanada.ca/custom/python/wheelhouse/gentoo/avx2), /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic](http://soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic), /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/generic](http://soft.computecanada.ca/custom/python/wheelhouse/generic)
Processing ./pyscf-2.1.1+computecanada-cp310-cp310-linux_x86_64.whl
Processing /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic/numpy-1.23.0+computecanada-cp310-cp310-linux_x86_64.whl](http://soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic/numpy-1.23.0+computecanada-cp310-cp310-linux_x86_64.whl)
Processing /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/gentoo/avx2/h5py-3.7.0+computecanada-cp310-cp310-linux_x86_64.whl](http://soft.computecanada.ca/custom/python/wheelhouse/gentoo/avx2/h5py-3.7.0+computecanada-cp310-cp310-linux_x86_64.whl)
Processing /cvmfs/[soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic/scipy-1.9.3+computecanada-cp310-cp310-linux_x86_64.whl](http://soft.computecanada.ca/custom/python/wheelhouse/gentoo/generic/scipy-1.9.3+computecanada-cp310-cp310-linux_x86_64.whl)
Installing collected packages: numpy, scipy, h5py, pyscf
Successfully installed h5py-3.7.0+computecanada numpy-1.23.0+computecanada pyscf-2.1.1+computecanada scipy-1.9.3+computecanada

I'm not sure it matters but I've also included my input file (the basis set can be found here):
#!/usr/bin/env python
# Author: Nike Dattani<EMAIL_ADDRESS>'''
O in aug-cc-pV8Z
'''
import pyscf
from pyscf.gto.basis import parse_gaussian

mol = pyscf.M(
    atom = '''
        O
    ''',
    unit = 'angstrom',
    basis = {
            'O' : parse_gaussian.load('/home/nike/AI_ENERGIES/O/av8z.gbs', 'O')
    },
    charge = 0,
    spin = 2,
    verbose = 9,
    symmetry = True,
    output = 'O_av8z_libcint.txt',
    symmetry_subgroup = 'Dooh',
)
mol.max_memory =16000
mf = mol.ROHF()
mf.irrep_nelec = {'A1g': (2,2), 'E1ux': (1,0), 'E1uy': (1,0),'A1u': (1,1)}
mf.chkfile = 'O_av8z.chk'
mf.init_guess='atom'
#mf.init_guess='O_av7z_UHF.chk'
mf.kernel()

In case a backtrace is useful, here it is:
#0  0x00007fffe567ed9f in CINTg2e_index_xyz (idx=idx@entry=0x0, envs=envs@entry=0x7fffffff4fe0)
    at /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1/build/temp.linux-x86_64-cpython-310/deps/src/libcint/src/g2e.c:209
#1  0x00007fffe56870ab in gen_idx (opt=0x12ce2e0, finit=0x7fffe567e4e0 <CINTinit_int2e_EnvVars>, findex_xyz=0x7fffe567ea00 <CINTg2e_index_xyz>, order=order@entry=4,
    ng=ng@entry=0x7fffffff5440, atm=atm@entry=0x132dcb0, natm=1, bas=0x155e530, nbas=53, env=0x1330fa0, max_l=<optimized out>)
    at /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1/build/temp.linux-x86_64-cpython-310/deps/src/libcint/src/optimizer.c:169
#2  0x00007fffe5688052 in CINTall_2e_optimizer (opt=0x7fffe3377510, ng=ng@entry=0x7fffffff5440, atm=0x132dcb0, natm=1, bas=0x155e530, nbas=53, env=0x1330fa0)
    at /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1/build/temp.linux-x86_64-cpython-310/deps/src/libcint/src/optimizer.c:191
#3  0x00007fffe567b2ea in int2e_optimizer (opt=<optimized out>, atm=<optimized out>, natm=<optimized out>, bas=<optimized out>, nbas=<optimized out>, env=<optimized out>)
    at /home/nike/wheels_builder/tmp.61293/python-3.10/pyscf-2.1.1/build/temp.linux-x86_64-cpython-310/deps/src/libcint/src/cint2e.c:1153
#4  0x00007ffff63539bd in ffi_call_unix64 () from /cvmfs/soft.computecanada.ca/easybuild/software/2020/Core/libffi/3.3/lib64/libffi.so.7
#5  0x00007ffff635267c in ffi_call_int () from /cvmfs/soft.computecanada.ca/easybuild/software/2020/Core/libffi/3.3/lib64/libffi.so.7
#6  0x00007ffff636af24 in _call_function_pointer (argtypecount=<optimized out>, argcount=6, resmem=0x7fffffff5600, restype=<optimized out>, atypes=<optimized out>, avalues=<optimized out>,
    pProc=0x7fffe567b2a0 <int2e_optimizer>, flags=4353) at /tmp/ebuser/avx2/Python/3.10.2/GCCcore-9.3.0/Python-3.10.2/Modules/_ctypes/callproc.c:916
#7  _ctypes_callproc (pProc=0x7fffe567b2a0 <int2e_optimizer>, argtuple=<optimized out>, flags=4353, argtypes=<optimized out>, restype=<optimized out>, checker=0x0)
    at /tmp/ebuser/avx2/Python/3.10.2/GCCcore-9.3.0/Python-3.10.2/Modules/_ctypes/callproc.c:1259
#8  0x00007ffff636b2c7 in PyCFuncPtr_call (self=<optimized out>, inargs=<optimized out>, kwds=<optimized out>)
    at /tmp/ebuser/avx2/Python/3.10.2/GCCcore-9.3.0/Python-3.10.2/Modules/_ctypes/_ctypes.c:4201
#9  0x00007ffff7d116b2 in _PyObject_MakeTpCall (tstate=0x421410, callable=0x7fffe34c3dc0, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at Objects/call.c:215
#10 0x00007ffff7dbf931 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=<optimized out>, callable=0x7fffe34c3dc0, tstate=<optimized out>)
    at ./Include/cpython/abstract.h:112
#11 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x1448608, callable=0x7fffe34c3dc0, tstate=<optimized out>) at ./Include/cpython/abstract.h:99
#12 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x1448608, callable=0x7fffe34c3dc0) at ./Include/cpython/abstract.h:123
#13 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff59c0, tstate=<optimized out>) at Python/ceval.c:5867
#14 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4213
#15 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x1448440, tstate=0x421410) at Python/ceval.c:4995
#16 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#17 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#18 0x00007ffff7dbd70d in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x1343858, callable=0x7fffe76bf640, tstate=0x421410) at ./Include/cpython/abstract.h:123
#19 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x1343858, callable=0x7fffe76bf640) at ./Include/cpython/abstract.h:123
#20 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff5c20, tstate=<optimized out>) at Python/ceval.c:5867
#21 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4213
#22 0x00007ffff7db9dee in _PyEval_EvalFrame (throwflag=0, f=0x13436b0, tstate=0x421410) at Python/ceval.c:4995
#23 _PyEval_Vector (kwnames=0x0, argcount=<optimized out>, args=0x7fffe3383870, locals=0x0, con=0x7fffe4c772f0, tstate=0x421410) at Python/ceval.c:5065
#24 _PyFunction_Vectorcall (kwnames=0x0, nargsf=<optimized out>, stack=0x7fffe3383870, func=0x7fffe4c772e0) at Objects/call.c:342
#25 _PyObject_FastCallDictTstate (kwargs=0x7fffe3383870, nargsf=<optimized out>, args=0x7fffe3383870, callable=0x7fffe4c772e0, tstate=0x421410) at Objects/call.c:142
#26 _PyObject_Call_Prepend (kwargs=0x7fffe3383870, args=0x421410, obj=0x7fffe4c772e0, callable=0x7fffe4c772e0, tstate=<optimized out>) at Objects/call.c:431
#27 slot_tp_init (self=self@entry=0x7fffe33838e0, args=args@entry=0x7fffe35db420, kwds=kwds@entry=0x0) at Objects/typeobject.c:7726
#28 0x00007ffff7d74081 in type_call (type=<optimized out>, args=0x7fffe35db420, kwds=0x0) at Objects/typeobject.c:1133
#29 0x00007ffff7d116b2 in _PyObject_MakeTpCall (tstate=0x421410, callable=0x10e4b00, args=<optimized out>, nargs=<optimized out>, keywords=0x0) at Objects/call.c:215
#30 0x00007ffff7dc384c in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=<optimized out>, callable=0x10e4b00, tstate=<optimized out>)
    at ./Include/cpython/abstract.h:112
#31 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33d1790, callable=0x10e4b00, tstate=<optimized out>) at ./Include/cpython/abstract.h:99
#32 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33d1790, callable=0x10e4b00) at ./Include/cpython/abstract.h:123
#33 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff5f00, tstate=<optimized out>) at Python/ceval.c:5867
#34 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4181
#35 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33d1600, tstate=0x421410) at Python/ceval.c:4995
#36 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#37 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#38 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x11d96e0, callable=0x7fffe4c9df30, tstate=0x421410) at ./Include/cpython/abstract.h:123
#39 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x11d96e0, callable=0x7fffe4c9df30) at ./Include/cpython/abstract.h:123
#40 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff6160, tstate=<optimized out>) at Python/ceval.c:5867
#41 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#42 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x11d9520, tstate=0x421410) at Python/ceval.c:4995
#43 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#44 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#45 0x00007ffff7dbe6dc in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x12ed7e8, callable=0x7fffe4c9e050, tstate=0x421410) at ./Include/cpython/abstract.h:123
#46 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x12ed7e8, callable=0x7fffe4c9e050) at ./Include/cpython/abstract.h:123
#47 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff63c0, tstate=<optimized out>) at Python/ceval.c:5867
#48 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4181
#49 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x12ed630, tstate=0x421410) at Python/ceval.c:4995
#50 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#51 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#52 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cd178, callable=0x7fffe4c9f370, tstate=0x421410) at ./Include/cpython/abstract.h:123
#53 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cd178, callable=0x7fffe4c9f370) at ./Include/cpython/abstract.h:123
#54 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff6620, tstate=<optimized out>) at Python/ceval.c:5867
#55 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#56 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33ccfc0, tstate=0x421410) at Python/ceval.c:4995
#57 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#58 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#59 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x1448c78, callable=0x7fffe4c9f400, tstate=0x421410) at ./Include/cpython/abstract.h:123
#60 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x1448c78, callable=0x7fffe4c9f400) at ./Include/cpython/abstract.h:123
#61 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff6880, tstate=<optimized out>) at Python/ceval.c:5867
#62 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#63 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x1448a30, tstate=0x421410) at Python/ceval.c:4995
#64 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#65 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#66 0x00007ffff7d110ea in PyVectorcall_Call (kwargs=<optimized out>, tuple=<optimized out>, callable=0x7fffe76edb40) at Objects/call.c:267
#67 _PyObject_Call (kwargs=<optimized out>, args=<optimized out>, callable=0x7fffe76edb40, tstate=<optimized out>) at Objects/call.c:290
#68 PyObject_Call (callable=0x7fffe76edb40, args=<optimized out>, kwargs=<optimized out>) at Objects/call.c:317
#69 0x00007ffff7dc1198 in do_call_core (kwdict=0x7fffe33dab40, callargs=0x7fffe33b0dc0, func=0x7fffe76edb40, trace_info=0x7fffffff6b40, tstate=<optimized out>) at Python/ceval.c:5919
#70 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#71 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33d0ec0, tstate=0x421410) at Python/ceval.c:4995
#72 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#73 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#74 0x00007ffff7d110ea in PyVectorcall_Call (kwargs=<optimized out>, tuple=<optimized out>, callable=0x7fffe4c9dd80) at Objects/call.c:267
#75 _PyObject_Call (kwargs=<optimized out>, args=<optimized out>, callable=0x7fffe4c9dd80, tstate=<optimized out>) at Objects/call.c:290
#76 PyObject_Call (callable=0x7fffe4c9dd80, args=<optimized out>, kwargs=<optimized out>) at Objects/call.c:317
#77 0x00007ffff7dc1198 in do_call_core (kwdict=0x7fffe33a4c80, callargs=0x7fffe378a9c0, func=0x7fffe4c9dd80, trace_info=0x7fffffff6e00, tstate=<optimized out>) at Python/ceval.c:5919
#78 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#79 0x00007ffff7d9ccd9 in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33e8550, tstate=0x421410) at ./Include/internal/pycore_ceval.h:46
#80 _PyEval_Vector (tstate=<optimized out>, con=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=2, kwnames=<optimized out>) at Python/ceval.c:5065
#81 0x00007ffff7db4cfb in _PyFunction_Vectorcall (kwnames=0x0, nargsf=2, stack=0x7fffffff7010, func=0x7fffe33a8f70) at Objects/call.c:332
#82 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=2, args=0x7fffffff7010, callable=0x7fffe33a8f70, tstate=0x421410) at ./Include/cpython/abstract.h:114
#83 method_vectorcall (method=<optimized out>, args=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/classobject.c:83
#84 0x00007ffff7d11057 in PyVectorcall_Call (kwargs=<optimized out>, tuple=0x7fffe3336410, callable=0x7fffe33db2c0) at Objects/call.c:255
#85 _PyObject_Call (kwargs=<optimized out>, args=0x7fffe3336410, callable=0x7fffe33db2c0, tstate=<optimized out>) at Objects/call.c:290
#86 PyObject_Call (callable=0x7fffe33db2c0, args=0x7fffe3336410, kwargs=<optimized out>) at Objects/call.c:317
#87 0x00007ffff7dc1198 in do_call_core (kwdict=0x7fffe49a9a00, callargs=0x7fffe3336410, func=0x7fffe33db2c0, trace_info=0x7fffffff71e0, tstate=<optimized out>) at Python/ceval.c:5919
#88 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#89 0x00007ffff7d9ccd9 in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33e83a0, tstate=0x421410) at ./Include/internal/pycore_ceval.h:46
#90 _PyEval_Vector (tstate=<optimized out>, con=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=1, kwnames=<optimized out>) at Python/ceval.c:5065
#91 0x00007ffff7db54e3 in _PyFunction_Vectorcall (kwnames=0x0, nargsf=1, stack=0x7fffffff73e8, func=0x7fffe4c9de10) at Objects/call.c:332
#92 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=1, args=0x7fffffff73e8, callable=0x7fffe4c9de10, tstate=0x421410) at ./Include/cpython/abstract.h:114
#93 method_vectorcall (method=<optimized out>, args=0x7ffff6ddc088, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/classobject.c:61
#94 0x00007ffff7d11057 in PyVectorcall_Call (kwargs=<optimized out>, tuple=0x7ffff6ddc070, callable=0x7ffff6d4a380) at Objects/call.c:255
#95 _PyObject_Call (kwargs=<optimized out>, args=0x7ffff6ddc070, callable=0x7ffff6d4a380, tstate=<optimized out>) at Objects/call.c:290
#96 PyObject_Call (callable=0x7ffff6d4a380, args=0x7ffff6ddc070, kwargs=<optimized out>) at Objects/call.c:317
#97 0x00007ffff7dc1198 in do_call_core (kwdict=0x0, callargs=0x7ffff6ddc070, func=0x7ffff6d4a380, trace_info=0x7fffffff75c0, tstate=<optimized out>) at Python/ceval.c:5919
#98 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#99 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33e81f0, tstate=0x421410) at Python/ceval.c:4995
#100 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#101 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#102 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x130b930, callable=0x7fffe87cc820, tstate=0x421410) at ./Include/cpython/abstract.h:123
#103 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x130b930, callable=0x7fffe87cc820) at ./Include/cpython/abstract.h:123
#104 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff7820, tstate=<optimized out>) at Python/ceval.c:5867
#105 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#106 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x130b750, tstate=0x421410) at Python/ceval.c:4995
#107 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#108 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#109 0x00007ffff7dbe6dc in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x14503c0, callable=0x7fffe348e200, tstate=0x421410) at ./Include/cpython/abstract.h:123
#110 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x14503c0, callable=0x7fffe348e200) at ./Include/cpython/abstract.h:123
#111 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff7a80, tstate=<optimized out>) at Python/ceval.c:5867
#112 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4181
#113 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x14501d0, tstate=0x421410) at Python/ceval.c:4995
#114 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#115 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#116 0x00007ffff7dbe6dc in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cc9b8, callable=0x7fffe4c9c3a0, tstate=0x421410)
    at ./Include/cpython/abstract.h:123
#117 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cc9b8, callable=0x7fffe4c9c3a0) at ./Include/cpython/abstract.h:123
#118 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff7ce0, tstate=<optimized out>) at Python/ceval.c:5867
#119 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4181
#120 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33cc800, tstate=0x421410) at Python/ceval.c:4995
#121 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#122 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#123 0x00007ffff7dbd70d in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33b9a48, callable=0x7fffe4cac430, tstate=0x421410)
    at ./Include/cpython/abstract.h:123
#124 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33b9a48, callable=0x7fffe4cac430) at ./Include/cpython/abstract.h:123
#125 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff7f40, tstate=<optimized out>) at Python/ceval.c:5867
#126 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4213
#127 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33b98c0, tstate=0x421410) at Python/ceval.c:4995
#128 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#129 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#130 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cc7a8, callable=0x7fffe4cad6c0, tstate=0x421410)
    at ./Include/cpython/abstract.h:123
#131 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7fffe33cc7a8, callable=0x7fffe4cad6c0) at ./Include/cpython/abstract.h:123
#132 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff81a0, tstate=<optimized out>) at Python/ceval.c:5867
#133 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#134 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33cc610, tstate=0x421410) at Python/ceval.c:4995
#135 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#136 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#137 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x12f88d8, callable=0x7fffe4c9db40, tstate=0x421410) at ./Include/cpython/abstract.h:123
#138 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x12f88d8, callable=0x7fffe4c9db40) at ./Include/cpython/abstract.h:123
#139 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff8400, tstate=<optimized out>) at Python/ceval.c:5867
#140 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#141 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x12f8690, tstate=0x421410) at Python/ceval.c:4995
#142 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#143 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#144 0x00007ffff7d110ea in PyVectorcall_Call (kwargs=<optimized out>, tuple=<optimized out>, callable=0x7fffe76edb40) at Objects/call.c:267
#145 _PyObject_Call (kwargs=<optimized out>, args=<optimized out>, callable=0x7fffe76edb40, tstate=<optimized out>) at Objects/call.c:290
#146 PyObject_Call (callable=0x7fffe76edb40, args=<optimized out>, kwargs=<optimized out>) at Objects/call.c:317
#147 0x00007ffff7dc1198 in do_call_core (kwdict=0x7fffe33a7880, callargs=0x7fffe33a4c00, func=0x7fffe76edb40, trace_info=0x7fffffff86c0, tstate=<optimized out>) at Python/ceval.c:5919
#148 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#149 0x00007ffff7d9ccd9 in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33d05b0, tstate=0x421410) at ./Include/internal/pycore_ceval.h:46
#150 _PyEval_Vector (tstate=<optimized out>, con=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=2, kwnames=<optimized out>) at Python/ceval.c:5065
#151 0x00007ffff7db4cfb in _PyFunction_Vectorcall (kwnames=0x0, nargsf=2, stack=0x7fffffff88d0, func=0x7fffe4c9dd80) at Objects/call.c:332
#152 _PyObject_VectorcallTstate (kwnames=0x0, nargsf=2, args=0x7fffffff88d0, callable=0x7fffe4c9dd80, tstate=0x421410) at ./Include/cpython/abstract.h:114
#153 method_vectorcall (method=<optimized out>, args=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/classobject.c:83
#154 0x00007ffff7d11057 in PyVectorcall_Call (kwargs=<optimized out>, tuple=0x7fffe36c3a90, callable=0x7fffe49a9b40) at Objects/call.c:255
#155 _PyObject_Call (kwargs=<optimized out>, args=0x7fffe36c3a90, callable=0x7fffe49a9b40, tstate=<optimized out>) at Objects/call.c:290
#156 PyObject_Call (callable=0x7fffe49a9b40, args=0x7fffe36c3a90, kwargs=<optimized out>) at Objects/call.c:317
#157 0x00007ffff7dc1198 in do_call_core (kwdict=0x7fffe4bf7a80, callargs=0x7fffe36c3a90, func=0x7fffe49a9b40, trace_info=0x7fffffff8aa0, tstate=<optimized out>) at Python/ceval.c:5919
#158 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4277
#159 0x00007ffff7db386d in _PyEval_EvalFrame (throwflag=0, f=0x7fffe33be3b0, tstate=0x421410) at Python/ceval.c:4995
#160 _PyEval_Vector (kwnames=<optimized out>, argcount=<optimized out>, args=<optimized out>, locals=0x0, con=<optimized out>, tstate=<optimized out>) at Python/ceval.c:5065
#161 _PyFunction_Vectorcall (func=<optimized out>, stack=<optimized out>, nargsf=<optimized out>, kwnames=<optimized out>) at Objects/call.c:342
#162 0x00007ffff7dbda03 in _PyObject_VectorcallTstate (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff6e01ba8, callable=0x7fffe4c9de10, tstate=0x421410)
    at ./Include/cpython/abstract.h:123
#163 PyObject_Vectorcall (kwnames=0x0, nargsf=<optimized out>, args=0x7ffff6e01ba8, callable=0x7fffe4c9de10) at ./Include/cpython/abstract.h:123
#164 call_function (kwnames=0x0, oparg=<optimized out>, pp_stack=<synthetic pointer>, trace_info=0x7fffffff8d00, tstate=<optimized out>) at Python/ceval.c:5867
#165 _PyEval_EvalFrameDefault (tstate=<optimized out>, f=<optimized out>, throwflag=<optimized out>) at Python/ceval.c:4198
#166 0x00007ffff7d9ccd9 in _PyEval_EvalFrame (throwflag=0, f=0x7ffff6e01a40, tstate=0x421410) at ./Include/internal/pycore_ceval.h:46
#167 _PyEval_Vector (tstate=<optimized out>, con=<optimized out>, locals=<optimized out>, args=<optimized out>, argcount=0, kwnames=<optimized out>) at Python/ceval.c:5065
#168 0x00007ffff7e4fa0e in PyEval_EvalCode (co=0x7ffff6d8ddc0, globals=0x7ffff6d1e780, locals=<optimized out>) at Python/ceval.c:1134
#169 0x00007ffff7e4fa6d in run_eval_code_obj (tstate=0x421410, co=0x7ffff6d8ddc0, globals=0x7ffff6d1e780, locals=0x7ffff6d1e780) at Python/pythonrun.c:1291
#170 0x00007ffff7e6cacb in run_mod (mod=<optimized out>, filename=<optimized out>, globals=0x7ffff6d1e780, locals=0x7ffff6d1e780, flags=<optimized out>, arena=<optimized out>)
    at Python/pythonrun.c:1312
#171 0x00007ffff7cd9e19 in pyrun_file (fp=fp@entry=0x427130, filename=filename@entry=0x7ffff6d94270, start=start@entry=257, globals=globals@entry=0x7ffff6d1e780,
    locals=locals@entry=0x7ffff6d1e780, closeit=closeit@entry=1, flags=0x7fffffff9038) at Python/pythonrun.c:1208
#172 0x00007ffff7cda1c4 in _PyRun_SimpleFileObject (fp=0x427130, filename=0x7ffff6d94270, closeit=1, flags=0x7fffffff9038) at Python/pythonrun.c:456
#173 0x00007ffff7cda358 in _PyRun_AnyFileObject (fp=0x427130, filename=0x7ffff6d94270, closeit=1, flags=0x7fffffff9038) at Python/pythonrun.c:90
#174 0x00007ffff7e72ef7 in pymain_run_file_obj (skip_source_first_line=<optimized out>, filename=0x7ffff6d94270, program_name=0x7ffff6eb80c0) at Modules/main.c:353
#175 pymain_run_file (config=0x405660) at Modules/main.c:372
#176 pymain_run_python (exitcode=0x7fffffff9034) at Modules/main.c:587
#177 Py_RunMain () at Modules/main.c:666
#178 0x00007ffff7e730d9 in Py_BytesMain (argc=<optimized out>, argv=<optimized out>) at Modules/main.c:720
#179 0x00007ffff7849e1b in __libc_start_main (main=0x400670 <main>, argc=2, argv=0x7fffffff9218, init=<optimized out>, fini=<optimized out>, rtld_fini=<optimized out>,
    stack_end=0x7fffffff9208) at ../csu/libc-start.c:308
#180 0x00000000004006aa in _start ()

This question has now also been posted at MMSE to see if others are getting the same issue.
@sunqm while writing the MMSE post I realized that you were successful when calculating the SCF energy for H2 with 8Z, but not necessarily for the O atom with 8Z. Does the script that I gave for oxygen work for you, or you only tested H2? Oxygen's 8Z basis set has l-type functions whereas hydrogen's 8Z basis set only has k-type functions.
Happy new year!

l-type basis requires a buffer too big to hold precomputed quantities. This causes error of integer overflow. It is fixed in v5.1.8 release

l-type functions and even m-type functions worked perfectly fine for the hydrogen atom, as you can see that I used aug-cc-pV10Z here. The problem occurred when using l-type functions for the H2 molecule or the O atom. Thanks for releasing v5.1.8! I will test it on:

H2(aug-cc-pV9Z)
H2(aug-cc-pV10Z)
O(aug-cc-pV8Z)
O(aug-cc-pV9Z)

I'll close this issue. Memory leaks are solved in v5.1.8 and higher versions


Improve config models to allow configuration recommended as per deployment guidline

[1] https://github.com/wso2/product-is/issues/8363
[2] https://github.com/wso2/product-is/issues/8364
[3] https://github.com/wso2/product-is/issues/8365
[4] https://github.com/wso2/product-is/issues/8366
[5] https://github.com/wso2/product-is/issues/8367

This issue is being closed due to extended inactivity. Please feel free to reopen it if further attention is needed. Thank you for helping us keep the issue list relevant and focused!


rename data gas to blob gas

PR Description
Rename data_gas to blob_gas and excess_data_gas to excess_blob_gas
Fixed Issue(s)

Fixes #7386
Documentation

[x] I thought about documentation and added the doc-change-required label to this PR if updates are required.

Changelog

[x] I thought about adding a changelog entry, and added one if I deemed necessary.

Deneb reference tests have been disabled until the spec-tests are updated


Login using user code does not work

Describe the bug
After install trying to use login to tuya using the user code, gives Login Error.
The code is absolutely correct, have been using it over the last few weeks several times.
Used it for the Tuya integration and Local Tuya. Removed that last one because of trying this one.

My key has an 'issue' that two characters look like this: ll   (could be two lower case L, or two uppercase i). Tried both...
Key is from the Tuya Smart app:

To Reproduce
N.A.
Expected behavior
Login works

No idea what happened here, tried it again and this time it worked....sorry


features are not always returned by VectorTileEventListener

Using this for the icons show
#mountain_peak [zoom>=4][comment =~'.*Highest.*'],
#mountain_peak[zoom>=4][comment =~'.*highest.*'],
#mountain_peak[zoom>=7][ele>=4000],
#mountain_peak[zoom>=8][ele>=3000],
#mountain_peak[zoom>=9][ele>=2000],
#mountain_peak[zoom>=10][ele>=1500],
#mountain_peak[zoom>=11] {
    ::icon {
      text-placement: [nuti::markers3d];
      text-name: [nuti::maki-mountain];
      text-size: 14 + [ele] * 0.00001;
      text-face-name: @maki;
      text-fill: @peak_label;
    }
}

You can see in the video that features not always clickable. Behind what is happening is that the peaks (features) are not returned by VectorTileEventListener (i only return true if it a peak)
. Seems like they are not there.
However if i click on another one (plus map move to focus), the element gets "clickable" (returned by VectorTileEventListener)

That is strange. Do you get any warnings in the log. Like 'VectorTileLayer::calculateRayIntersectedElements: Failed to find tile data`?

@mtehver just tested with
com.carto.utils.Log.setShowDebug(true);
    com.carto.utils.Log.setShowWarn(true);

and i don't see any log when i try to click on the "peaks"

@farfromrefug This could be also related to #289. Could you test the latest snapshot and report if the workaround also fixes this issue?

@mtehver  sure. When you say snapshot, you mean there is a prebuild snapshot? Or you means latest git commit?
Thanks

@mtehver i can confirm your fix worked!


itm: replace itm-tools

Description of changes

itm-tools (@sbourdeauducq @HarryMakes) has been replaced by itm since early 2022.
itm-tools:

This repository is no longer maintained!
The library on which these tools are based has been deprecated in favor of [rtic-scope/itm].
You may want to check the tools developed by the [rtic-scope] team.

(See https://github.com/japaric/itm-tools/commit/583992a7fd3d1e293f8dca0944c0fe83dc378edd)
Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

For non-Linux: Is sandboxing enabled in nix.conf? (See Nix manual)

[ ] sandbox = relaxed
[ ] sandbox = true

[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[ ] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[X] Tested basic functionality of all binary files (usually in ./result/bin/)
24.05 Release Notes (or backporting 23.05 and 23.11 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module

[X] Fits CONTRIBUTING.md.

Add a :+1: reaction to pull requests you find important.

Result of nixpkgs-review pr 285957 run on x86_64-linux 1

1 package built:

itm

Result of nixpkgs-review pr 285957 run on x86_64-linux 1

1 package built:

itm

Closing what little contribution I have because of https://discourse.nixos.org/t/should-jonringer-get-his-commit-bit-back/47267

I do not want to make a statement about the thread you mention but this PR is not a small contribution and adding yourself to maintainer-list.nix is not enough to get committer right.
If that is what you are worrying about then I think it is fine. Or just not interested in anymore?

I had no expectation to become a committer, no, lol. I dont want to contribute to this repo anymore.


Fast The Suicide Command System.


Add vagrant support for pouch

Ⅰ. Describe what this PR did
Add Vagrant support for pouch.
Ⅱ. Does this pull request fix one issue?
The commit doesn't fix any issue.
Ⅲ. Why don't you add test cases (unit test/integration test)?
The commit just have scriptfile and Documents.
Ⅳ. Describe how to verify it
Install Vagrant and virtualBox
export POUCH_BUILD=true # set POUCH_BUILD=true on Windows
vagrant up
Ⅴ. Special notes for reviews

Codecov Report

Merging #2168 into master will increase coverage by 0.14%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master    #2168      +/-   ##
==========================================
+ Coverage   64.16%   64.31%   +0.14%
==========================================
  Files         209      209
  Lines       16713    16713
==========================================
+ Hits        10724    10749      +25
+ Misses       4648     4634      -14
+ Partials     1341     1330      -11

Flag
Coverage Δ

#criv1alpha1test
32.91% <ø> (+0.16%)
:arrow_up:

#criv1alpha2test
33.57% <ø> (+0.2%)
:arrow_up:

#integrationtest
39.27% <ø> (ø)
:arrow_up:

#unittest
23.92% <ø> (ø)
:arrow_up:

Impacted Files
Coverage Δ

cri/v1alpha1/cri.go
63.36% <0%> (+0.34%)
:arrow_up:

cri/v1alpha2/cri.go
64.34% <0%> (+0.66%)
:arrow_up:

daemon/mgr/container.go
56.77% <0%> (+0.82%)
:arrow_up:

ctrd/container.go
43.19% <0%> (+1.43%)
:arrow_up:

ctrd/watch.go
80.3% <0%> (+7.57%)
:arrow_up:

@fuweid
hack/vagrant/ doesn't work

@fuweid The hack/vagrant doesn't work when vagrant up
==> pouch-dev-node: Cloning into '/root/.goenv'...
==> pouch-dev-node: Downloading go1.9.1.linux-amd64.tar.gz...
==> pouch-dev-node: -> https://storage.googleapis.com/golang/go1.9.1.linux-amd64.tar.gz
==> pouch-dev-node: error: failed to download Go Linux 64bit 1.9.1.tar.gz
==> pouch-dev-node: BUILD FAILED
==> pouch-dev-node:  (Ubuntu 16.04 using go-build 1.21.0)
The SSH command responded with a non-zero exit status. Vagrant
assumes that this means the command failed. The output for this command
should be in the log above. Please read the output to determine what
went wrong.
hack/vagrant just for developer,  doesn't have quick start for users, So I think we can merge  committed files to pouch and remove hack/vagrant directory from pouch.


Have options for controlling logging levels or lower existing ones.

See https://stackoverflow.com/questions/64094701/gatling-script-works-fine-but-prints-j-l-d-lagartodombuildertagvisitor-unclose
Generally speaking, ERROR logging level is for unrecoverable crashes. This is not how Lagarto, not browsers, behaves on malformed input. HTML living spec states that malformed input should just be ignored.
Maybe different users have different needs.
Maybe for some users detecting malformed content is critical and they want WARN (or maybe even ERROR), but users parsing HTML at scale, such as Gatling, don't want to spam logging with such not blocking messages (we'd rather use INFO).

Basically, it looks like I'm asking for restoring parsingErrorLogLevel from Lagarto 5.

Makes sense!

I'll provide a PR.

I can do that, no worries, its just a flag @slandelle

btw, you can disable errors at all by setting collectErrors to false

Nah. collectErrors is set to false by default and anyway: https://github.com/oblac/jodd-lagarto/blob/v6.0.1/src/main/java/jodd/lagarto/dom/LagartoDOMBuilderTagVisitor.java#L513

Ah, true, its an OR operation there.

I have a workaround for now (overriding InfoLogLagartoDOMBuilder and InfoLogLagartoDOMBuilderTagVisitor) so I'm not in a rush.

I would add a BiConsumer<Log, String> property instead of a level @slandelle, as SLf4j does not have levels and would not like to add custom Lagarto levels.
would that make sense for you?
e.g. setErrorLogConsumer((log, msg) -> log.info(msg))

I don't think a single method interface is enough. You need 2 methods or parameters:

one for performing the actual logging
one for deciding if logging is enabled and if not save the message allocation

https://github.com/oblac/jodd-lagarto/blob/master/src/main/java/jodd/lagarto/dom/LagartoDOMBuilderTagVisitor.java#L109-L111
https://github.com/oblac/jodd-lagarto/blob/master/src/main/java/jodd/lagarto/dom/LagartoDOMBuilderTagVisitor.java#L219-L221
https://github.com/oblac/jodd-lagarto/blob/master/src/main/java/jodd/lagarto/dom/LagartoDOMBuilderTagVisitor.java#L354-L356

Related to the above StackOverflow issue, I'm getting "Parse error: invalid character reference" on &goto= http arguments in href values and "invalid token" errors on . Are those really malformed inputs?

@ADNinfo just to be sure if I understood correctly, are you talking about these two cases?
case 1
x

case 2

Exactly, here are real examples, just in case they are actually malformed...
Not easy to locate in an UTF-8 html file, I had to convert from UTF-8 to ASCII so the offsets in the info messages would match.
Case 1:
Création d'un contrat
Case 2: (between two div )

I will check this in more details, but out of my head now:

case 1 - & should be always encoded as &, hence the warning
case 2 - its a bug, should not emit anything, will fix

Closing this one, @ADNinfo  opening new issue to fix it.

I have the same issues as reported on the SO while running the Gatling script.
03:20:09.303 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid token [@15136]
03:20:09.350 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid character reference [@40337]
03:20:10.374 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid character reference [@52319]
03:20:10.374 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid token [@108321]
03:20:10.374 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid character reference [@131962]
03:20:12.557 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid character reference [@52333]
03:20:12.563 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid token [@100170]
03:20:12.563 [ERROR] j.l.d.LagartoDOMBuilderTagVisitor - Parse error: invalid character reference [@123811]

Can you help?

@mareru these are common logs when a page is parsed - HTML pages commonly contain issues that are not up to the HTML standard.
I added two configurations in LagartoDomBuilderConfig:

errorLogEnabled
errorLogConsumer

that could be used for filtering and turning off the logs.
That being said, maybe Gatling guys are already using these for reducing the logs. @slandelle is that true? Maybe @mareru just needs to upgrade Gatling?

maybe Gatling guys are already using these for reducing the logs

Indeed, see https://github.com/gatling/gatling/blob/master/gatling-core/src/main/scala/io/gatling/core/check/css/Lagarto.scala#L30-L39.

Maybe @mareru just needs to upgrade Gatling?

Most likely, yes


Delta sourcing fails if GraphCMS webhook contains payload

How does webhooks work currently? What is the required options in GraphCMS webhooks setup for preview/builds?

@notrab The toolkit exposes APIs to capture and action webhooks. If a re-build is triggered from a webhook, then the corresponding Gatsby node will be updated/created (via a GraphQL query) or deleted.
https://github.com/GraphCMS/gatsby-source-graphcms/blob/f3dba5560cb45e40c491e461db3d3a306fb8e6e6/gatsby-source-graphcms/gatsby-node.js#L88-L116
Right now you just need to setup your GraphCMS webhooks without payloads for builds and previews to be triggered. This issue is to address that.
Perhaps at some point we can use the payload from the webhook to update the Gatsby nodes, rather than firing a GraphQL query. However there's manual node work required for that so it's a bit more cumbersome.

Due to some recent changes to GraphCMS webhook payloads, this is no longer an issue.


Sesamy cli v0.3.x

Pull Request Test Coverage Report for Build<PHONE_NUMBER>
Details

0 of 1251 (0.0%)  changed or added relevant lines in 41 files are covered.
6 unchanged lines in 2 files lost coverage.
Overall coverage decreased (-3.1%) to 15.429%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

pkg/tagmanager/server/client/googleanalyticsga4.go
0
1
0.0%

pkg/tagmanager/server/client/measurementprotocolga4.go
0
1
0.0%

cmd/version.go
0
2
0.0%

pkg/provider/googletag/web/variable/googletagconfigurationsettings.go
0
2
0.0%

pkg/utils/structtag.go
0
2
0.0%

cmd/tagmanager.go
0
3
0.0%

pkg/tagmanager/server/client/googletagmanagerwebcontainer.go
0
3
0.0%

pkg/provider/googletag/web/variable/googletageventsettings.go
0
5
0.0%

pkg/provider/googletag/web/variable/googletageventmodel.go
0
6
0.0%

pkg/provider/umami/server/template/umami.go
0
6
0.0%

Files with Coverage Reduction
New Missed Lines
%

cmd/typescript.go
1
0.0%

cmd/root.go
5
0.0%

Totals

Change from base Build<PHONE_NUMBER>:
-3.1%

Covered Lines:
297

Relevant Lines:
1925

💛  - Coveralls


Errors during automatic require are swallowed

When Bundler.require is called by e.g. Rails, Bundler will try to require every gem listed in the Gemfile. If there is an error while requiring said gem, Bundler will completely suppress the error, and instead print its own error with the message "there was an error requiring 'some_gem'". At an absolute minimum, we should print the underlying exception message, and we should probably also mention that you can use verbose/debug mode to see the backtrace.
For example, the code require 'coffee-rails' will check for a JavaScript interpreter, and raise an error if none is found. That error looks like this:
irb(main):001:0> require 'coffee-rails'
ExecJS::RuntimeUnavailable: Could not find a JavaScript runtime. See https://github.com/rails/execjs for a list of available runtimes.

If you have a Gemfile with coffee-rails in it, however, calling Bundler.require will instead print this singularly useless message:
/usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:80:in `rescue in block (2 levels) in require': There was an error while trying to load the gem 'coffee-rails'. (Bundler::GemRequireError)
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:76:in `block (2 levels) in require'
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:72:in `each'
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:72:in `block in require'
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:61:in `each'
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler/runtime.rb:61:in `require'
	from /usr/local/rbenv/versions/2.1.5/lib/ruby/gems/2.1.0/gems/bundler-1.11.2/lib/bundler.rb:99:in `require'
	from /var/web/releases/20151223030515/config/application.rb:5:in `<top (required)>'

Looks like this was solved by #4187 - ready to close?

We're getting a lot of support requests in the Passenger support channels about exactly this problem. People deploy an app to Passenger, finds that Bundler bails out with a GemRequireError and then don't know what to do next because the original error is swallowed. Could you release the fix sooner? Right now lots of users are confused.

@FooBarWidget the release is already fixed in the 1.12 release candidate. How about you ask your users to test it, so we can find out if it's safe to do a final release?

It's in the 1.12 betas

But that's the thing, it's a beta. While the more adventurous users would be willing to install the beta to test things, other users would not so easily dare to do that. Can't you backport the fix to the 1.11 series so that it can more quickly fixed? Or will 1.12 final real soon? It seems the bug has been in the wild for several months now.

@FooBarWidget We just pushed the release candidate, as I said. That means if no bugs are discovered, it is the final release.
Furthermore, demanding that we do a release because the lack of release is inconvenient for you is rude and entitled—Phusion doesn't help maintain Bundler, and doesn't contribute money to Ruby Together so we can afford to maintain it. We'll release when we're ready, end of story. Demands make us less likely to want to help you, not more likely.

@indirect I am confused why you saw my messages as a demand. I apologize if I offended you somehow, but it certainly wasn't meant as a demand. Maybe you have had bad experience with demanding users lately, but as far as I'm concerned I was politely asking for a faster solution, and in my opinion not without good reasons.
You say that the final is imminent. Good. But I had no way of knowing that. I only knew the facts:

The issue was introduced in 1.11.0, 12 december 2015, so the issue has been in the wild for 3 months now.
The first time I discovered this issue was a month ago. I found out that the issue had been fixed in git master, but nothing seemed to indicate when the fix will be released.
Today I checked again and I saw that there had been several release candidates, the last one from 3 weeks ago. But I had no visibility in your release roadmap. Maybe it's imminent for you, but before you told me about it the final might as well be another 3 months from now.

So yeah, I know just as well as you how it is to maintain an open source project and the economics of it. Sure it's entirely your right to release whenever you want. But all I was doing was politely asking you to consider the real casualties here: our users. I don't really care about the issue, but users are confused. I'm just communicating with you on behalf of them.

@FooBarWidget we're aware of the bug and the confused users, and that's why we have fixed the bug and released a version of Bundler with the fix that anyone can use.
I'm not sure where you were looking for release candidates, but we have only released one, and it was three days ago. Here's the list from rubygems.org/gems/bundler:

1.12.0.rc - March 14, 2016 (275 KB)
1.12.0.pre.2 - February 26, 2016 (269 KB)
1.12.0.pre.1 - February 9, 2016 (267 KB)

We've been trying to get 1.12 out as fast as we can. When you started with "We're getting a lot of support requests in the Passenger support channels about exactly this problem." and concluded with "Could you release the fix sooner? Right now lots of users are confused.", it makes it sound like you want us to stop whatever paid work we're doing and instead do unpaid open source work to speed up the release in order to make your (paid!) job easier. Asking for our uncompensated labor in order to make your compensated labor easier—that's the part that sounds demanding and entitled.

I'm not sure where you were looking for release candidates,

I was looking in your changelog: https://github.com/bundler/bundler/blob/master/CHANGELOG.md
No mention of a release candidate there. Plus, you talked about "release candidate" while @segiddins talked about "betas", which added to the confusion.

We've been trying to get 1.12 out as fast as we can.

I appreciate that you have been working as quickly as you can. I was only asking you to reconsider this issue's priority. In fact, I used the phrase "could you" deliberately. I remember that years ago my English teacher told me that "could you" is used as a polite request, e.g. as opposite to "you must" or the more explicitly "I demand" etc. Maybe you can see how I'm confused that you interpret my request as being an impolite demand.
Naturally the one who makes the final planning is you. I don't expect otherwise. I can understand how frustrated you must be at entitled users, I am too, but please know that I'm not one of them.
If your position is that any request at all from a paid worker can only be considered entitled and demanding, and that there is no way to politely request something and to politely point out the importance of an issue, then I am afraid the discussion ends here. The only thing I can do is to tell you what my intentions are.
As for donations: unfortunately, although I can make a living, we're (currently) not rich enough to do much in donations.

Thanks for explaining what you meant.
The answer to "could I release" is always yes, but in order to do that I would have to stop doing work that is paying my rent. It's frustrating to even be asked the question, because it implies that your problem that needs a release is more important than whatever is keeping me from releasing.
For future reference, one way you can sound less demanding is by asking for information rather than actions: "Do you have an idea yet of how soon the next release will be?" sounds like you would like to know when the next release will be, but you aren't implicitly placing your own problems above mine.

All right. So do you have an idea of when the final will be? :)

Yes! We always allow at least 7 days for users to report bugs in each release candidate. Depending on how things go, we may allow up to 14 days for users to report bugs. That means we plan to release 1.12 final between March 21 and March 28 (as long as no significant bugs are discovered).

Trying out 1.12.0.rc, much more helpful so far! Thank you.


HOC as a functions (and optionally as decorators too)

import { AutoSizerDecorator, AutoSizerHoc, VirtualScroll } from 'react-virtualized'

@AutoSizerDecorator({ disableHeight: true }) // or AutoSizerDecorator({ disableHeight: true })(MyComponent)
class MyComponent extend React.Component {
  render () {
    var { width, onResize } = this.props
    /**/
  }
}

var ASVirtualScroll = AutoSizerHoc({ disableHeight: true }, VirtualScroll) // or just `AutoSizerHoc(VirtualScroll)`

This would kind of defeat the design change in version 5.0 that supports nested, composable HOCs (as shown here). A function/decorator/component that injects width/height properties can't be composed with other similar functions. (How would you use both an AutoSizerDecorator and a ScrollSyncDecorator? or any other combination?)

Closing this issue because I think it re-introduces too many constraints to be worth the effort. You could put together a HOC that wraps the existing AutoSizer though if you'd like for your personal projects. Probably shouldn't be too hard.


[Feature Request] Embed highlights/annotation view directly in literature note

First of all thank you for this plugin! I am actively using it for my master thesis, and it allows for the perfect workflow! I was wondering if it was possible to directly embed in a literature note the content of the highlights/annotation panel? This way literature notes could truly act as a single clean snapshot of the state of a paper and my review of it for example.
Thanks for the great work! This is the Zotero plugin we have always dreamed of, and it is taking shape very nicely!

So if I understand this correctly, you want to embed annotaion panel using something like a codeblock without manually importing them?

Hey, sorry for the late reply. Indeed, the idea would be to be able to insert some special iframe or codeblock which would simply display inline the paper's highlights and notes. It strictly speaking doesn't majorly change things compared to the current setup, but I feel like it would help tie everything together nicely! As it currently stand I often find myself opening a paper note just to check the highlights, and find opening a note to only look at the side panel a bit odd workflow-wise.

Bump


join kubernetes nodes pending

create new token and join not work

[root@iZ2vc6e0x3cxrleq0t0k2dZ ~]# kubeadm token create --print-join-command --ttl=0
W0602 11:37:36.274032    7912 configset.go:348] WARNING: kubeadm cannot validate component configs for API groups [kubelet.config.k8s.io kubeproxy.config.k8s.io]
kubeadm join apiserver.cluster.local:6443 --token tll7t0.m9abayamuvtcix55     --discovery-token-ca-cert-hash sha256:89d8207a18240c953aff5b962274c4adb2309cfb33292f4f679c7a4475b3f563

show pods

kubectl get pod -A

scheduler and controller manager pod is not running, always at creating status.
describe controller manager pod :
  Warning  FailedCreatePodSandBox  47m                   kubelet  Failed to create pod sandbox: rpc error: code = Unknown desc = failed to start sandbox container for pod "kube-controller-manager-iz2vc6e0x3cxrleq0t0k2dz": operation timeout: context deadline exceeded
  Warning  FailedSync              5m51s (x60 over 41m)  kubelet  error determining status: rpc error: code = DeadlineExceeded desc = context deadline exceeded

How to fixed it:
edit /usr/share/dbus-1/session.conf  set max_replies_per_connection to 100000, and restart docker

restart docker also can fixed this issue

This issue cause by docker version, using docker 19.03 fixed this issue.


.Net: Function Calling Common Code Extraction

Extract the function-calling functionality from each existing AI connector in SK to a common, shareable class(es) that can be used by all existing and future connectors.
Consider splitting the following tasks into a separate issue:

POC
ADR
OpenAI
Gemini
Mistral

bump


[WIP] Dynamic CS Form

PR-Codex overview
The focus of this PR is to enhance the support page by adding dynamic imports and improving the contact support modal with new fields and options.
Detailed summary

Added problemArea and affectedArea fields to the contact support modal
Improved the support page layout and functionality
Dynamic import for ContactSupportModal
Updated product options and description prompts

✨ Ask PR-Codex anything about this PR by commenting with /codex {your question}

📝 Changed routes:

/support

(view full size)

Commit e820e0a1672814c1dda2bd5bee457ab86c921606 (https://thirdweb-lggy8ebjg.thirdweb-preview.com).


Replace all Items from sidebar, cntrl+shift+i

When i press cntrl+shift+i to find an element in the editor.
There is an option to replace this item with the new one.
for example
await post()
with
await postRequest()
When i perform this operation, i got the following result.
await postRequestRequest()
await postRequestRequestRequest()
await postRequestRequestRequestRequest()
and so on.

Not sure if I understand what you are trying to do. Can you please provide a gif/video of the issue you are seeing
/gifPlease

Thanks for reporting this issue! Unfortunately, it's hard for us to understand what issue you're seeing. Please help us out by providing a screen recording showing exactly what isn't working as expected. While we can work with most standard formats, .gif files are preferred as they are displayed inline on GitHub. You may find https://gifcap.dev helpful as a browser-based gif recording tool.
If the issue depends on keyboard input, you can help us by enabling screencast mode for the recording (Developer: Toggle Screencast Mode in the command palette).
Happy coding!

This issue has been closed automatically because it needs more information and has not had recent activity. See also our issue reporting guidelines.
Happy Coding!


When to upgrade the dll？

After a hiatus I am back on active development on this project. The current build is good, but I haven't updated the DLLs in GitHub yet. I suggest downloading source and then using "dotnet build -c Release" to make dlls


Performance of Harmfulness exp is too high ?

Hello authors,
Your experiment results on harmfulness classification:https://github.com/andyzoujm/representation-engineering/blob/main/examples/harmless_harmful/harmless_llama2.ipynb shows that Llama-2-13b-chat achieves near 100% acc, even in the lower layers.
I have tried more model: Llama-2-{7,70}b-chat, llama-2-7b, bloomz-{560m,1b1,1b7,3b,7b1}, bloom-7b1, all of these models also achieves  near perfect performance.
I think the phenomenon is trivial for chat models because harmful and harmless input correspond to the two directions of refusing to answer and answering, however, for models without alignment like bloom/llama-2, it is nontrivial.
(By the way, in my experiment, I didn't include task template in prompt, instead, i directly used stimuli, and I used the ClusterMeanRepReader method.)
Are there any more plausible explanations for the phenomenon?

The way we're computing the accuracy is pairing random (harmful, harmless) examples and seeing if the score has the right ranking between the two, so it's an overestimate of true accuracy if the examples are treated independently.


Subscribing a topic with QoS1 results in duplicate messages

Hello,
I am not sure if this is by design or a bug, but subscribing a topic with QoS1 always results in duplicate messages. However, these messages don't have the DUP flag set to 1. So it is as if the AWS MQTT broker is re-sending the same message over and over again or there is some issue inside the SDK.
I tried to debug the issue and found few interesting observations:

The duplicate messages are received roughly every 30 seconds or so and are received 4-6 times before dying out.

However, 30 second thing is not always true.

Using QoS 0 when subscribing, stops these duplicate messages for some reason.
Other MQTT client like mosquitto does not seem to have this issue when tested with the same AWS MQTT broker. (No duplicate messages with either QoS 0 or QoS 1)

For now, as a workaround I have refrained from using QoS 1 when subscribing to AWS MQTT topics but this behavior is something not desirable and might have unknown consequences when used in production.
Could you please look into this issue?

Hi @shekharhimanshu,
I tried reproducing the issue but I wasn't able to. I tested with all combinations of QoS for publish and subscribe with the AWS MQTT broker and they all behaved as expected.
What QoS are you using while publishing the messages? Also, what are you using to publish the messages, the SDK itself or some other MQTT client, like mosquitto?
Please let us know if you have find any more issues.
Thanks,
Varun

Hello @vareddy
Thats weird because I am able to reproduce the issue every single time.
I am publishing with QoS1 and using the SDK itself.
I will try to reproduce the issue with PubSub sample and let you know.
In the meantime, let me check if the new release behaves any differently.

I tried with the latest SDK version as well and there was no change in the behavior.
Subscribing a topic with QoS1 still always results in duplicate messages.

Hi @shekharhimanshu,
I think I had configured my test program incorrectly as it wasn't waiting for as long as you stated (> 30 seconds) before quitting as I ran a long running test (~10 mins) and now see the repeated messages as you stated. My apologies!
Investigating this now.
Thanks for all the inputs!
Varun

Hi @shekharhimanshu,
The fix for this issue is now in v1.1.1 of the SDK.
Thanks!
Varun

Hey @vareddy,
Can you explain what you changed in the SDK to fix the duplicate issue? I was using the Embedded C SDK before and saw the duplicate issue there when publishing with QoS 1. I created an issue on their GitHub (https://github.com/aws/aws-iot-device-sdk-embedded-C/issues/169) and the resolution was that since the AWS broker does not support the duplicate flag, we would have to add our own IDs in the payload and filter ourselves. However, it seems like the C++ SDK has solved the duplicate problem without forcing clients to implement custom logic?

Hi @twebner,
The puback packets were using an incorrect packet ID (which was auto-generated to be a unique packet ID). The puback packets need to have the same packet ID as the published packet. I added an extra member to the puback class and stored the published packet's ID, which was used for packet creation.
See Publish.hpp and here  Publish.cpp.
Thanks!
Varun

Thanks @vareddy! So it sounds like something similar could be done in the C SDK, right? Is there any way for you to contact that team and share your solution with them? Maybe you could reopen and comment on the issue I linked in my comment above?

Hi @shekharhimanshu and @vareddy
I want to configure QOS = 1 for Subscribing a topic with QoS1 in AWS IOT. I am trying to do that but unable to find the correct solution. Could you explain the solution?
Thanks,
Mohan

You can set QoS when creating a Subscription.

Thank you @shekharhimanshu for your quick reply.
Could you explain with example? I am new to this AWS.  If we want to set QOS =1, do we set through CLI or  SDK for topic for subscribe the topic?

See, PubSub sample for example.

If we want to set QOS =1, do we set through CLI or SDK for topic for subscribe the topic?

That depends upon what you are trying to do. If you are using this SDK, you do it with this SDK. If by CLI you mean the aws-cli, I haven't explored it much. You can check the documentation to check for available commands.


Switch CI badge to match style of other badges.

Switches the badge style to make the same style (shield) as the other readme badges.
[skip ci] is used since this is a readme fix and doesn't affect any code whatsoever.

tickle circle


ptdump: Fix libipt linker flag.

The build was invoking the compiler with -llibipt but it should using -lipt:
[100%] Linking C executable ptdump
/usr/bin/cmake -E cmake_link_script CMakeFiles/ptdump.dir/link.txt --verbose=1
/usr/bin/cc  -I/home/vext01/research/hwt_experiment/deps/inst/libipt/include  -L/home/vext01/research/hwt_experiment/deps/inst/libipt/lib -Wl,-rpath,/home/vext01/research/hwt_experiment/deps/inst/libipt/lib CMakeFiles/ptdump.dir/src/ptdump.o CMakeFiles/ptdump.dir/home/vext01/research/hwt_experiment/deps/src/processor-trace/libipt/src/pt_last_ip.o CMakeFiles/ptdump.dir/home/vext01/research/hwt_experiment/deps/src/processor-trace/libipt/src/pt_cpu.o CMakeFiles/ptdump.dir/home/vext01/research/hwt_experiment/deps/src/processor-trace/libipt/src/pt_time.o CMakeFiles/ptdump.dir/home/vext01/research/hwt_experiment/deps/src/processor-trace/libipt/src/posix/pt_cpuid.o  -o ptdump -rdynamic -llibipt
/usr/bin/ld: cannot find -llibipt

This PR fixes this.

Hello Edd,
Thanks for taking the time to investigate this.
The 'libipt' item in target_link_libraries(ptdump libipt) refers to the 'libipt' cmake library target added in libipt/CMakeLists.txt.  You need to point cmake to the top-level source directory; otherwise it won't see the corresponding add_library(libipt ...).
When changing that to target_link_libraries(ptdump ipt) I believe that it will end up linking against the version that is available on the system.


Update changelog and release notes for 2.x

Description
Need to update changelog and release notes for a fix which was already release in 2.14. See this. - https://github.com/opensearch-project/OpenSearch/blob/2.14/release-notes/opensearch.release-notes-2.14.0.md?plain=1#L88
Related Issues
Resolves #[Issue number to be closed when this PR is merged]

Check List

[x] New functionality includes testing.

[x] All tests pass

[x] New functionality has been documented.

[x] New functionality has javadoc added

[x] API changes companion pull request created.
[x] Failing checks are inspected and point to the corresponding known issue(s) (See: Troubleshooting Failing Builds)
[x] Commits are signed per the DCO using --signoff
[x] Commit changes are listed out in CHANGELOG.md file (See: Changelog)
~[ ] Public documentation issue/PR created~

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

@andrross ^


Removed unnecessary references to __dirname.

According to the path.resolve documentation, and from testing, if an absolute path is not determined from the arguments, it automatically uses the current working directory, which is what __dirname ends up being when executing the script. Anyways.. that's my understanding, please do yell at me if I'm overlooking something obvious!
Btw, if you folks agree, I can review the rest of the docs for occurrences of this.

I think the point with having __dirname there has to do with the fact that sometimes people like to move the configuration around. So you could have something like config/dev.js. Even though more verbose, the current setup would work in this case.
That said, there are neater ways to manage paths. Usually I push them to a single place and resolve there. This keeps it simple while giving the aforementioned advantage.

Hey @bebraw thanks for the reply :) However, can you expand on that scenario, I don't think I'm understanding it clearly :(
From my understanding, are saying because the configuration file(s) can be located in any arbitrary location within a project (in this case config/dev.js), having __dirname ensures that relative paths within the configuration continue to resolve correctly? That if __dirname is removed, paths relative to the config file would break?

@vprasanth That's the idea, correct. It's true we might be able to improve the examples somehow, though.

@bebraw no that makes sense now, I confused myself with a false positive when testing out that scenario (wasn't running the config I thought I was) which confused me. Thanks again! Will close PR.

It would make a nice PR to mention why __dirname is used. Maybe there's a good place for that.


chore(package): update uglify-js to version 3.0.18

Closes #150

Coverage remained the same at 100.0% when pulling 58dbbcca7b9ec50ded7a4ebe64f8ae21f36dbe63 on greenkeeper/uglify-js-3.0.18 into 82e271ad9cfa81b6645b38cb1d8f551fac1b1826 on master.


Migrate to goharbor/harbor-scanner-trivy for Harbor Scanner Adapter for Trivy

The Harbor Scanner Adapter for Trivy project was being maintained in the aquasecurity/harbor-scanner-trivy repository but that repository has been archived and is no longer being maintained. Future development of this project has been migrated to the goharbor/harbor-scanner-trivy repository so we should change our build-tooling repo to use that instead.

Packages and Build CDK changes for creating codepipeline, codebuild project and codecommit repo have been merged. Presubmit prow job and build tooling PRs have also been merged.


Proxy settings

Request type

[ ] Please close this issue, I accidentally submitted it without adding any details
[x] New documentation
[ ] Correction or update

Details

When you set proxy in private browsing it changes in normal configuration too.

@pispox We need some more details on the intent here. Do you have a page, and section that you would like updated - or some specific text that is incorrect? Please provide links and screenshots here so we can find it and make any needed updates.

Closing as the issue is missing important details to act upon.


Text areas

Hi, are text areas part of the roadmap? I don't see them here: https://github.com/material-components/material-web#roadmap

Yes! They'll be part of text field stable :)

Superb. You folks are doing really good work here. I've started experimenting with migration from material-components-web to material-web for an ASP.NET/Blazor OSS library I maintain. material-web is much easier to handle!


Include some sample files

I suspect most people are similar to me - I loathe reading docs until I've had a play around.
Unless I've overlooked something, there's no sample files or examples I can load in nodi? I find these the most interesting way to evaluate a new tool and it goes a long way towards making me stick around until I've over the initial learning curve.

@andybak
I have added the link to https://nodi3d.github.io/docs/user/examples/ in header by #15 👍