by Marie Achour.  

Hello Moodlers,

 

Welcome to this edition of ‘Showcase Shorts’ – it’s a big one, so get a cup of tea and get ready to enjoy a ton of demos from the Moodle HQ Product team.

 

The Moodle LMS teams have been hard at work on our next batch of features, following the plans we shared with you in our last note.

Work on our new AI subsystem prototype is progressing quickly. In this video, you can watch how we designed our first feature, AI-enabled summarisation, and get a sneak peek of what it will look like when it’s done.

We are continuing to improve the Notifications available in Moodle, making sure the right information gets to the right people at the right time. You can hear about our latest enhancements in this video:

Our main course pages are going to look better than ever with the introduction of sub-sections in our course layouts. Check out what to expect by watching this video:

The upgrades we need to make to badges & icons are also progressing, with some of the updates you can look forward to shown in this video:

Finally, the improvements to our Assignment activity continue. This video shows a few of the useful updates you can expect soon. 

 

Moodle Community Developers should watch the following video, which provides updates on our mission to ‘Break down the monolith’ and some new considerations for Plugin Type Deprecation.

 

 

Our journey with Learning Catalogues continues in Moodle Workplace. Watch this video to learn what to expect next and about some of the research that is informing our designs. 

We are also working on many other improvements for Moodle Workplace, some of which you can see come to fruition in this video.

 

In other big news, we have now released the Moodle LMS & Moodle Workplace 4.4 versions of our Mobile Apps, so you can now benefit from all our new features on your phones! Well done to the App team for all their hard work in getting us there.

 

And finally, in case you missed the memo, the Early Bird date that lets you get your tickets for MoodleMoot Global 2024 at a discounted rate has been extended to the 13 of August – so if you haven’t already, now is absolutely the time to register for the event! Visit www.moodlemoot.org to find out how.

That’s it for this edition of our ‘Showcase Shorts’; we hope you’ve enjoyed the updates.

Until Next Sprint!

The Moodle Product Team

Here’s a short video outlining the features of the LocalGov Drupal KeyNav module.

If you’ve been looking for a way to search your codebase with regex patterns, here it is: Grepfruit. Ideal for CI/CD pipelines and beyond, Grepfruit lets you search for, e.g., TODO comments, excluding files or directories, truncating the output, and providing colorized results for easy readability.

There is a DoS vulnerability in REXML gem. This vulnerability has been assigned the CVE identifier CVE-2024-41946. We strongly recommend upgrading the REXML gem.

Details

When parsing an XML that has many entity expansions with SAX2 or pull parser API, REXML gem may take long time.

Please update REXML gem to version 3.3.3 or later.

Affected versions

• REXML gem 3.3.2 or prior

Credits

Thanks to NAITOH Jun for discovering and fixing this issue.

History

• Originally published at 2024-08-01 03:00:00 (UTC)

Posted by kou on 1 Aug 2024

 I’m pleased to announce the release of Python 3.13 release candidate 1.

https://www.python.org/downloads/release/python-3130rc1/

 

This is the first release candidate of Python 3.13.0

This release, 3.13.0rc1, is the penultimate release
preview. Entering the release candidate phase, only reviewed code
changes which are clear bug fixes are allowed between this release
candidate and the final release. The second candidate (and the last
planned release preview) is scheduled for Tuesday, 2024-09-03, while the
official release of 3.13.0 is scheduled for Tuesday, 2024-10-01.

There will be no ABI changes from this point forward in the 3.13 series, and the goal is that there will be as few code changes as possible.

Call to action

We strongly encourage maintainers of third-party Python projects to
prepare their projects for 3.13 compatibilities during this phase, and
where necessary publish Python 3.13 wheels on PyPI to be ready for the
final release of 3.13.0. Any binary wheels built against Python
3.13.0rc1 will work with future versions of Python 3.13. As always, report any issues to the Python bug tracker.

Please keep in mind that this is a preview release and while it’s as close to the final release as we can get it, its use is not recommended for production environments.

Core developers: time to work on documentation now

• Are all your changes properly documented?
• Are they mentioned in What’s New?
• Did you notice other changes you know of to have insufficient documentation?

 

Major new features of the 3.13 series, compared to 3.12

Some of the new major new features and changes in Python 3.13 are:

New features

• A new and improved interactive interpreter, based on PyPy’s, featuring multi-line editing and color support, as well as colorized exception tracebacks.
• An experimental free-threaded build mode,
  which disables the Global Interpreter Lock, allowing threads to run
  more concurrently. The build mode is available as an experimental
  feature in the Windows and macOS installers as well.
• A preliminary, experimental JIT, providing the ground work for significant performance improvements.
• The locals() builtin function (and its C equivalent) now has well-defined semantics when mutating the returned mapping, which allows debuggers to operate more consistently.
• The (cyclic) garbage collector is now incremental, which should mean shorter pauses for collection in programs with a lot of objects.
• A modified version of mimalloc is now included, optional but enabled by default if supported by the platform, and required for the free-threaded build mode.
• Docstrings now have their leading indentation stripped, reducing memory use and the size of .pyc files. (Most tools handling docstrings already strip leading indentation.)
• The dbm module has a new dbm.sqlite3 backend that is used by default when creating new files.
• The minimum supported macOS version was changed from 10.9 to 10.13 (High Sierra). Older macOS versions will not be supported going forward.
• WASI is now a Tier 2 supported platform. Emscripten is no longer an officially supported platform (but Pyodide continues to support Emscripten).
• iOS is now a Tier 3 supported platform, with Android on the way as well.

Typing

• Support for type defaults in type parameters.
• A new type narrowing annotation, typing.TypeIs.
• A new annotation for read-only items in TypeDicts.
• A new annotation for marking deprecations in the type system.

Removals and new deprecations

• PEP 594 (Removing dead batteries from the standard library) scheduled removals of many deprecated modules: aifc, audioop, chunk, cgi, cgitb, crypt, imghdr, mailcap, msilib, nis, nntplib, ossaudiodev, pipes, sndhdr, spwd, sunau, telnetlib, uu, xdrlib, lib2to3.
• Many other removals of deprecated classes, functions and methods in various standard library modules.
• C API removals and deprecations. (Some removals present in alpha 1 were reverted in alpha 2, as the removals were deemed too disruptive at this time.)
• New deprecations, most of which are scheduled for removal from Python 3.15 or 3.16.

(Hey, fellow core developer, if a feature you find important is missing from this list, let Thomas know.)

For more details on the changes to Python 3.13, see What’s new in Python 3.13. The next pre-release of Python 3.13 will be 3.13.0rc2, the final release candidate, currently scheduled for 2024-09-03.

 

More resources

• Online Documentation
• PEP 719, 3.13 Release Schedule
• Report bugs at Issues · python/cpython · GitHub.
• Help fund Python directly (or via GitHub Sponsors), and support the Python community.

 

Enjoy the new releases

Thanks to all of the many volunteers who help make Python Development
and these releases possible! Please consider supporting our efforts by
volunteering yourself or through organization contributions to the
Python Software Foundation.

Whatevs,

Your release team,
Thomas Wouters
Łukasz Langa
Ned Deily
Steve Dower

Join us for a Complimentary Live Webinar Sponsored by Linux Foundation Training & Certification Learn More and Register

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Is machine learning, also known as “artificial intelligence”, really aiding workers and increasing productivity? A study by Upwork – which, as Baldur Bjarnason so helpfully points out, sells AI solutions and hence did not promote this study on its blog as it does with its other studies – reveals that this might not actually be the case. Nearly half (47%) of workers using AI say they have no idea how to achieve the productivity gains their employers expect. Over three in four (77%) say AI tools have decreased their productivity and added to their workload in at least one way. For example, survey respondents reported that they’re spending more time reviewing or moderating AI-generated content (39%), invest more time learning to use these tools (23%), and are now being asked to do more work (21%). Forty percent of employees feel their company is asking too much of them when it comes to AI. ↫ Upwork research This shouldn’t come as a surprise. We’re in a massive hype cycle when it comes to machine learning, and we’re being told it’s going to revolutionise work and lead to massive productivity gains. In practice, however, it seems these tools just can’t measure up to the hyped promises, and in fact is making people do less and work slower. There’s countless stories of managers being told by upper management to shove machine learning into everything, from products to employee workflows, whether it makes any sense to do so or not. I know from experience as a translator that machine learning can greatly improve my productivity, but the fact that there are certain types of tasks that benefit from ML, doesn’t mean every job suddenly thrives with it. I’m definitely starting to see some cracks in the hype cycle, and this study highlights a major one. I hope we can all come down to earth again, and really take a careful look at where ML makes sense and where it does not, instead of giving every worker a ChatGPT account and blanket demanding massive productivity gains that in no way match the reality on the office floor. And of course, despite demanding massive productivity increases, it’s not like workers are getting an equivalent increase in salary. We’ve seen massive productivity increases for decades now, while paychecks have not followed suit at all, and many people can actually buy less with their salary today than their parents could decades ago. Demands imposed by managers by introducing AI is only going to make this discrepancy even worse.

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