Feeds

Ready to boost your workflows with pre-trained ML models? 

PyCharm‘s integration with Hugging Face is designed to bring a new level of productivity to your machine learning and development workflows. 

This integration helps you seamlessly find and use the best-fit model from the Hugging Face library, access model documentation, and manage models – all in your IDE.

The integration is available starting with PyCharm 2024.2.

Read the blog post to learn more about the integration and how to use it.

What is Hugging Face?

Hugging Face is a platform where machine learning and data science developers share pre-trained AI models. The platform provides tools to build, deploy, and train machine learning models.

Key features of the Hugging Face integration

PyCharm’s integration with Hugging Face is designed to streamline workflows for developers, enabling seamless access to pre-trained machine learning models from the IDE. This integration allows you to:

• Simplify model selection and usage.
• Import models as easily as importing any other library, allowing you to stay focused on your code. 
• Eliminate distractions caused by switching between tools and browsers. 
• Maintain control over your machine storage.

Lysandre Debut, the Chief Open-Source Officer at Hugging Face, shares:

“As a Hugging Face user, even more so than as a Hugging Face team member, the HF integration in PyCharm has been instrumental in speeding up the machine learning workflows I’ve worked on. As a result of this feature, I find myself keeping my focus in the IDE, with much less context switching during development.” Easily find the best model for your task

Looking to use a Hugging Face model but unsure which one fits your needs? Whether you’re building a text classifier, working on image recognition, or exploring other areas, PyCharm simplifies the process for you.

With the Hugging Face integration, you can quickly access a vast library of models tailored to various tasks. Right-click in the editor, select Insert HF Model, and explore models categorized by task type. To make your search even easier, you can filter models by likes, licensing, or specific tags.

For each model, PyCharm provides a detailed model card, including essential information and sample code that you can use. Found the perfect fit? Just click Use Model to insert the necessary code snippet directly into your project and start building immediately.

Seamlessly access model documentation

When working with machine learning models, quick access to documentation is handy – and PyCharm ensures you stay focused. With the Hugging Face integration, you can instantly view detailed model information without leaving your IDE or interrupting your workflow.

Simply hover over a model name in your code, and PyCharm will display its full model card, including the tasks the model is designed for, date of its last update, its origin, licensing details, and other details.

No more jumping between your browser and IDE – everything you need is right next to your code.

Manage models stored on your machine

Hugging Face models can take up significant storage space, but PyCharm makes it easy to stay in control. With this integration, you can view the models you’ve downloaded, identify those you no longer need, and declutter with ease. 

Just head to the Hugging Face tool window to see and manage your models.

PyCharm helps you to keep your system optimized while ensuring you retain access to the tools you actually need – all from the comfort of your IDE! 

Get started with PyCharm integrated with Hugging Face

PyCharm is a powerful IDE designed for machine learning, data science, and web development. It has everything you need for productive coding, including intelligent coding assistance (both local and AI-powered), smart navigation, and project-wide refactorings. 

It also provides a full suite of integrations tailored to streamline machine learning and data science workflows, including support for Jupyter Notebooks, Databricks, and popular scientific libraries like pandas, Polars, NumPy, scikit-learn, and more.

Use the PyCharm4HF code on the redeem page below to get started with PyCharm for free.

Get a 3-month PyCharm subscription for free

Need more guidance? Head to the documentation for step-by-step instructions on using Hugging Face with PyCharm.

We’d love to hear your thoughts! Share your experience with the integration in the comments below.

Unleashing Flexibility: How Headless Drupal is Shaping the Future of Content Delivery bhavinhjoshi Thu, 11/28/2024 - 10:57

Headless Drupal is changing the course for content management systems by offering a fresh approach to how digital content is delivered across platforms.

At its core, headless Drupal separates the backend (where content is stored and managed) from the frontend (where content is displayed). This decoupling allows for a greater range of flexibility and customization in how content is presented to users, leading to faster, more dynamic websites that can easily integrate with a multitude of modern tech stacks.

Why choose headless? 

The benefits are abundant. By going headless, organizations can seamlessly deploy content across diverse platforms—from mobile apps and kiosks to IoT devices. This means a blog post, product description, or interactive experience can be shared consistently, regardless of where or how it is consumed.

However, embracing headless architecture is not without its challenges. Managing separate systems can introduce complexity. Multiple moving parts can sometimes lead to increased difficulty in synchronizing the backend with various frontends. Fortunately, Drupal's API-first architecture mitigates these challenges by providing a strong foundation for developers to build upon, ensuring a cohesive integration between systems.

The real excitement comes with the capability to pair Drupal's robust backend with cutting-edge front-end technologies like React, Vue.js, or Angular. This combination results in richer, more engaging user experiences that cater to modern audiences' expectations for speed and interactivity. Developers have the freedom to innovate and tailor the user interface to meet the unique needs of their audience, unencumbered by traditional constraints.

Moreover, businesses are positioned to benefit tremendously from headless Drupal. They gain the ability to bring new features to market rapidly, allowing them to stay ahead in today’s fast-paced digital landscape. Scalability becomes less of a concern, as the architecture easily accommodates growth and a wide variety of digital touchpoints.

Headless Drupal doesn't just promise flexibility; it delivers adaptability essential for future-proofing digital strategies. In an era where customer engagement is king, and technology continues to evolve at unprecedented speeds, having a versatile and powerful content management system is indispensable.

The question isn't just whether headless Drupal is the future—it's about understanding how this evolution in content management can redefine your digital strategy to better connect with users across every platform.

Headless Drupal Drupal Planet Add new comment

In this tutorial, we will learn about Python lists (creating lists, changing list items, removing items, and other list operations) with the help of examples.

If you’ve worked with statistical data, you’ve likely encountered z-scores. A z-score measures how far a data point is from the mean, expressed in terms of standard deviations. It helps identify outliers and compare data distributions, making it a vital tool in data science.

In this guide, we’ll show you how to calculate z-scores in Python using a custom function and built-in libraries like SciPy. You’ll also learn to visualize z-scores for better insights.

1. What is a z-score?

A z-score measures how many standard deviations a data point is from the mean. The formula for calculating the z-score of a data point X is:

\[Z_{X} = \frac{X - \overline{X}}{S}\]

Where:

• \(Z_{X}\) is the z score of the point \(X\);
• \(X\) is the value for which we want to calculate the Z score;
• \(\overline{X}\) is the mean of the sample;
• \(S\) is the standard deviation of the sample.

2. Python z score using a custom function

A custom function allows you to implement the z-score formula directly. Here’s how to define and use it in Python:

content_copy Copy

def calculate_z(X, X_mean, X_sd): return (X - X_mean) / X_sd

The function takes three arguments:

• a vector X of values for which you want to calculate the z-scores, like a pandas dataframe column, for example;
• the mean of the values in X;
• the standard deviation of the values in X.

Finally, in the return clause, we apply the z-score formula explained above.

To test our function, we will use data from Playfair (1821). He collected data regarding the price of wheat and the typical weekly wage for a “good mechanic” in England from 1565 to 1821. His objective was to show how well-off working men were in the 19th century. This dataset is available in the HistData R package and also on the webpage of Professor Vincent Arel-Bundock, a great source of datasets. It consists of 3 variables: year, price of wheat (in Shillings) and weekly wages (in Shillings).

We will be calculating the z-scores for the weekly wages. First we load the dataset directly from the website, as indicated in the code below.

content_copy Copy

import pandas as pd data = pd.read_csv("https://vincentarelbundock.github.io/Rdatasets/csv/HistData/Wheat.csv") print(data['Wages'].mean()) print(data['Wages'].std()) data["z-score_wages"] = calculate_z(data["Wages"], data["Wages"].mean(), data["Wages"].std())

The average weekly wage during the period was 11.58 Shillings, with a standard deviation of 7.34. With this information, we can calculate the Z score for each observation in the dataset. This is done and stored in a new column called “z-score_wages”.

If you check the first row of the data frame, you will find out that in 1565 the z score was around -0.9, that is, the wages were 0.9 standard deviations below the mean of the values for the whole period.

3. Python z score using SciPy

A second option to calculate z-scores in Python is to use the zscore method of the SciPy library as shown below. Ensure you set a policy for handling missing values if your dataset is incomplete.

In the code below, we calculate the z-scores for Wheat prices. If you look at the z-score summary statistics, you will see that the price of wheat varied between -1.13 and 3.65 standard deviations away from the mean in the observed period.

content_copy Copy

from scipy import stats data["z-score_wheat"] = stats.zscore(data["Wheat"], nan_policy="omit") data["z-score_wheat"].describe()

3. Visualising z scores

Below you can better visualize the basic idea of z scores: to measure how far away a data point is from the mean in terms of standard deviations. This visualization was created in D3, a JavaScript library for interactive data visualization. Click “See average wage” to see the averave wage for the whole period. Then check out how far from the mean each data point is and finally note that the z-score consists of this distance in terms of standard deviation.

1. See Average Wage 2. See Distance to the Mean 3. See Z-Scores Reset

4. Visualizing z scores with Matplotlib

The code below plots the wage z scores over time and shows them as the distance from the point to the mean, as demonstrated in the D3 visualization above. Please consult the lesson ‘Storytelling with Matplotlib - Visualizing historical data’ to learn more about Matplotlib visualizations.

content_copy Copy

# Calculate mean wage mean_wage = data["z-score_wages"].mean() # Create the plot fig, ax = plt.subplots(figsize=(10, 6)) # Scatter plot of wages over years ax.plot(data["Year"], data["z-score_wages"], 'o', color='#FF6885', label="Wage Z-scores", markeredgewidth=0.5) # Add a horizontal line for the mean wage ax.axhline(y=mean_wage, color='gray', linestyle='dashed', label=f"Mean Z-score = {mean_wage:.2f}") # Add gray lines connecting points to the mean for year, wage in zip(data["Year"], data["z-score_wages"]): ax.plot([year, year], [mean_wage, wage], color='gray', linestyle='dotted', linewidth=1) # Customize the plot ax.set_xlabel("Year") ax.set_ylabel("Z-scores") ax.set_title("Z-scores Over Time") ax.legend() # Show the plot plt.show()

Have questions or insights? Leave a comment below, and I’ll be happy to help.

Happy coding!

Conclusions

• A z score is a measure of how many standard deviations a data point is away from the mean. It can be easily calculated in Python;
• You can visualize z-scores using traditional python libraries like Matplotlib or Seaborn.

The end of Drupal 7 support is approaching (January 5, 2025), which means this system’s users need to migrate to a newer version. This article explains how to move smoothly from Drupal 7 to Drupal 11, the key steps, and what to pay attention to. I encourage you to read the article or watch the video in the “Nowoczesny Drupal” series.

Is your website the driving engine of your higher ed institution and a powerful catalyst for its goals? Is it effectively attracting prospective students, inspiring alumni, and building a vibrant community? There are always new strategies to boost your website's impact and ensure the answer is a resounding "yes"! As a team that specializes in higher education website design & development, we are passionate about sharing useful tips that can help those in this sector.

Join the FSF and friends on Friday, November 29 from 12:00 to 15:00 EST (17:00 to 20:00 UTC) to help improve the Free Software Directory.

Craft Your Perfect Proposal: MidCamp 2025 Session Proposal Workshop

🚀 Ready to take your session ideas to the next level? Whether you're a seasoned speaker or a first-time presenter, the MidCamp 2025 Session Proposal Workshop is here to help you craft standout submissions.

📅 Date: December 12, 2024
🕒 Time: 3:00 PM - 4:00 PM CST
🌐 Location: Virtual via MidCamp Slack (#speakers channel)

Facilitated by Aaron Feledy

This workshop will be led by Aaron Feledy, a seasoned Drupal contributor and experienced speaker. Aaron brings years of expertise in proposal crafting and conference speaking, offering practical advice to help you refine and elevate your session submissions.

Why Attend?

Submitting a session proposal can be daunting—but it doesn't have to be! This workshop is designed to guide you through the process, from brainstorming topics to refining your submission. Our expert facilitators will share insider tips on what makes a proposal stand out to reviewers and resonate with attendees.

What You’ll Learn:

• How to choose and frame a compelling topic
• Crafting clear, concise, and engaging abstracts
• Tips for tailoring your proposal to different audiences
• Insight into the MidCamp review process

Session Submissions Now Open

Ready to submit? Session submissions for MidCamp 2025 are now open! Visit the MidCamp 2025 session submission page for guidelines and start your journey to the stage.

How to Join:

Simply join the MidCamp Slack and head over to the #speakers channel on December 12th at 3:00 PM CST. No registration required—just jump in and start collaborating!

Learn how to upgrade Drupal with Composer, ensuring seamless updates, compatibility, and enhanced security.

An Austrian petitioner succeeded in realizing what the US government failed to see: that free software is vital for the infrastructure of a free society.

An Austrian petitioner succeeded in realizing what the US government failed to see: that free software is vital for the infrastructure of a free society.

Creating software is an achievement worth celebrating. But software is never static. Bugs need to be fixed, features need to be added, and security demands regular updates. In today’s landscape, with agile methodologies dominating, robust DevOps systems are crucial for managing an evolving codebase. That’s where GitHub Actions shine, empowering Python developers to automate workflows and ensure their projects adapt seamlessly to change.

GitHub Actions for Python empowers developers to automate workflows efficiently. This enables teams to maintain software quality while adapting to constant change.

Continuous Integration and Continuous Deployment (CI/CD) systems help produce well-tested, high-quality software and streamline deployment. GitHub Actions makes CI/CD accessible to all, allowing automation and customization of workflows directly in your repository. This free service enables developers to execute their software development processes efficiently, improving productivity and code reliability.

In this tutorial, you’ll learn how to:

• Use GitHub Actions and workflows
• Automate linting, testing, and deployment of a Python project
• Secure credentials used for automation
• Automate security and dependency updates

This tutorial will use an existing codebase, Real Python Reader, as a starting point for which you’ll create a CI/CD pipeline. You can fork the Real Python Reader code on GitHub to follow along. Be sure to deselect the Copy the master branch only option when forking. Alternatively, if you prefer, you can build your own Real Python Reader using a previous tutorial.

In order to get the most out of this tutorial, you should be comfortable with pip, building Python packages, Git, and have some familiarity with YAML syntax.

Before you dig into GitHub Actions, it may be helpful to take a step back and learn about the benefits of CI/CD. This will help you understand the kinds of problems that GitHub Actions can solve.

Get Your Code: Click here to download the free sample code you’ll use to learn about CI/CD for Python With GitHub Actions.

Take the Quiz: Test your knowledge with our interactive “GitHub Actions for Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

GitHub Actions for Python

In this quiz, you'll test your understanding of GitHub Actions for Python. By working through this quiz, you'll revisit how to use GitHub Actions and workflows to automate linting, testing, and deployment of a Python project.

Unlocking the Benefits of CI/CD

Continuous Integration (CI) and Continuous Deployment (CD), commonly known as CI/CD, are essential practices in modern software development. These practices automate the integration of code changes, the execution of tests, and the deployment of applications. This helps teams and open-source contributors to deliver code changes more frequently in a reliable and structured way.

Moreover, when publishing open-source Python packages, CI/CD will ensure that all pull requests (PRs) and contributions to your package will meet the needs of the project while standardizing the code quality.

Note: To learn more about what a pull request is and how to create one, you can read GitHub’s official documentation.

More frequent deployments with smaller code changes reduce the risk of unintended breaking changes that can occur with larger, more complex releases. For example, even though developers can format all code using the same linting tools and rules, policy can automatically block PRs from being merged if the code’s tests don’t pass.

In the next section, you’ll learn how GitHub Workflows can help you implement CI/CD on a repository hosted on GitHub.

Exploring GitHub Workflows

GitHub Workflows are a powerful feature of GitHub Actions. They allow you to define custom automation workflows for your repositories. Whether you want to build, test, or deploy your code, GitHub Workflows provide a flexible and customizable solution that any project on GitHub can use for free, whether the repository is public or private.

Even though there are many CI/CD providers, GitHub Actions has become the default among open-source projects on GitHub because of its expansive ecosystem, flexibility, and low or no cost.

Anatomy of a Workflow File

Workflow files are declaratively written YAML files with a predefined structure that must be adhered to for a workflow to run successfully. Your YAML workflow files are stored and defined in a .github/workflows/ folder in your project’s root directory.

Your workflow folder can have multiple workflow files, each of which will perform a certain task. You can name these workflow files anything you’d like. However, for the sake of simplicity and readability, it’s common practice to name them after the tasks they achieve, such as test.yml.

Each file has a few elements that are required, but many, many more that are optional. The GitHub Actions documentation is thorough and well-written, so be sure to check it out after you’ve finished reading this tutorial.

There are three main parts that make up the bulk of a workflow file: triggers, jobs, and steps. You’ll cover these in the next sections.

Workflow Triggers Read the full article at https://realpython.com/github-actions-python/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Developed by Anand Toshniwal, a Python script now automates the creation of .component.yml files for Single Directory Components in Drupal. Simplifying workflows and improving accuracy, this tool supports projects like the Starshot Demo Design System, enhancing efficiency for developers.

You may have seen this toot announcing OpenStreetMap's migration to Debian on their infrastructure.

🚀 After 18 years on Ubuntu, we've upgraded the @openstreetmap servers to Debian 12 (Bookworm). 🌍 openstreetmap.org is now faster using Ruby 3.1. Onward to new mapping adventures! Thank you to the team for the smooth transition. #OpenStreetMap #Debian 🤓

We spoke with Grant Slater, the Senior Site Reliability Engineer for the OpenStreetMap Foundation. Grant shares:

Why did you choose Debian?

There is a large overlap between OpenStreetMap mappers and the Debian community. Debian also has excellent coverage of OpenStreetMap tools and utilities, which helped with the decision to switch to Debian.

The Debian package maintainers do an excellent job of maintaining their packages - e.g.: osm2pgsql, osmium-tool etc.

Part of our reason to move to Debian was to get closer to the maintainers of the packages that we depend on. Debian maintainers appear to be heavily invested in the software packages that they support and we see critical bugs get fixed.

What drove this decision to migrate?

OpenStreetMap.org is primarily run on actual physical hardware that our team manages. We attempt to squeeze as much performance from our systems as possible, with some services being particularly I/O bound. We ran into some severe I/O performance issues with kernels ~6.0 to < ~6.6 on systems with NVMe storage. This pushed us onto newer mainline kernels, which led us toward Debian. On Debian 12 we could simply install the backport kernel and the performance issues were solved.

How was the transition managed?

Thankfully we manage our server setup nearly completely with code. We also use Test Kitchen with inspec to test this infrastructure code. Tests run locally using Podman or Docker containers, but also run as part of our git code pipeline.

We added Debian as a test target platform and fixed up the infrastructure code until all the tests passed. The changes required were relatively small, simple package name or config filename changes mostly.

What was your timeline of transition?

In August 2024 we moved the www.openstreetmap.org Ruby on Rails servers across to Debian. We haven't yet finished moving everything across to Debian, but we will upgrade the rest when it makes sense. Some systems may wait until the next hardware upgrade cycle.

Our focus is to build a stable and reliable platform for OpenStreetMap mappers.

How has the transition from another Linux distribution to Debian gone?

We are still in the process of fully migrating between Linux distributions, but we can share that we recently moved our frontend servers to Debian 12 (from Ubuntu 22.04) which bumped the Ruby version from 3.0 to 3.1 which allowed us to also upgrade the version of Ruby on Rails that we use for www.openstreetmap.org.

We also changed our chef code for managing the network interfaces from using netplan (default in Ubuntu, made by Canonical) to directly using systemd-networkd to manage the network interfaces, to allow commonality between how we manage the interfaces in Ubuntu and our upcoming Debian systems. Over the years we've standardised our networking setup to use 802.3ad bonded interfaces for redundancy, with VLANs to segment traffic; this setup worked well with systemd-networkd.

We use netboot.xyz for PXE networking booting OS installers for our systems and use IPMI for the out-of-band management. We remotely re-installed a test server to Debian 12, and fixed a few minor issues missed by our chef tests. We were pleasantly surprised how smoothly the migration to Debian went.

In a few limited cases we've used Debian Backports for a few packages where we've absolutely had to have a newer feature. The Debian package maintainers are fantastic.

What definitely helped us is our code is libre/free/open-source, with most of the core OpenStreetMap software like osm2pgsql already in Debian and well packaged.

In some cases we do run pre-release or custom patches of OpenStreetMap software; with Ubuntu we used launchpad.net's Personal Package Archives (PPA) to build and host deb repositories for these custom packages. We were initially perplexed by the myriad of options in Debian (see this list - eeek!), but received some helpful guidance from a Debian contributor and we now manage our own deb repository using aptly. For the moment we're currently building deb packages locally and pushing to aptly; ideally we'd like to replace this with a git driven pipeline for building the custom packages in the future.

Thank you for taking the time to share your experience with us.

Thank you to all the awesome people who make Debian!

We are overjoyed to share this in-use case which demonstrates our commitment to stability, development, and long term support. Debian offers users, companies, and organisations the ability to plan, scope, develop, and maintain at their own pace using a rock solid stable Linux distribution with responsive developers.

Does your organisation use Debian in some capacity? We would love to hear about it and your use of 'The Universal Operating System'. Reach out to us at Press@debian.org - we would be happy to add your organisation to our 'Who's Using Debian?' page and to share your story!

About Debian

The Debian Project is an association of individuals who have made common cause to create a free operating system. This operating system that we have created is called Debian. Installers and images, such as live systems, offline installers for systems without a network connection, installers for other CPU architectures, or cloud instances, can be found at Getting Debian.

The next Kdenlive Café will be on the 3rd of December at 8 PM UTC.

Come chat with the team!

Join us at: https://meet.kde.org/b/far-twm-ebr

The post Kdenlive Café in December appeared first on Kdenlive.

With just 12 days remaining, DrupalCon Singapore 2024 is set to bring the global Drupal community together from 9 to 11 December at the PARKROYAL COLLECTION Marina Bay. This dynamic event will feature three days of inspiring sessions, hands-on training, and the inaugural Splash Awards in Asia, celebrating innovative Drupal projects. Hosted by the Drupal Asia Steering Committee and supported by the Drupal Association, the conference reflects the vibrant, collaborative nature of the Drupal ecosystem, offering attendees a unique opportunity to learn, network, and celebrate in one of Asia’s most dynamic cities.

Learn about Anna's journey. Since discovering Python and Django in 2015, Anna has been actively contributing to the Django community and taking up leadership roles within DSF and Django Girls.Learn about Anna's journey. Since discovering Python and Django in 2015, Anna has been actively contributing to the Django community and taking up leadership roles within DSF and Django Girls.

We interviewed Anna Makarudze from Zimbabwe to hear about her inspiring journey.

Since discovering Python and Django in 2015, Anna has been actively involved in the Django community. She helped organize PyCon Zimbabwe, and she has coached at Django Girls in Harare and Windhoek.

She served on the Board of Directors at Django Software Foundation for five years, and she is currently a Django Girls Foundation Trustee & Fundraising Coordinator.

During her journey in tech, Anna became aware of the lack of representation of women in tech industry, something that became more evident as she attended Django Under the Hood in 2016 where most of the attendees were white men, and only a few are women. That’s when she realized the importance of communities like Django Girls in supporting more women in the Django Community.

In this chat, Anna shared ways on how you can contribute and help support Django Girls+ Foundation.

She is currently studying her master’s degree in Blekinge Institute of Technology in Karlskrona, Sweden.

Be sure to listen to the episode to learn all about Anna’s inspiring story!

Topic discussed

• Introductions
• Her role in Django Girls+ Foundation
• Moving from Zimbabwe to Sweden, and adjusting to her new life
• Django Girls tutorials and website
• How to contribute to Django Girls website
• Discovering Python, Flask, and Django in 2015
• Attending Django Under the Hood in 2016
• Representation of women in tech
• The role of Django Girls+ in tech
• How to help and support Django Girls+
• What to expect from Django Girls+ in 2025 onwards

Links from the show

• Django Girls+: https://djangogirls.org/en/
• Blekinge Institute of Technology: https://bth.se/
• Muzinda Hub: https://x.com/muzindahub
• Treehouse: https://teamtreehouse.com/
• Django Software Foundation: https://www.djangoproject.com/foundation/
• Django Under the Hood: https://djangounderthehood.com/

Links from Django Girls+

• Coach at an event: https://coach.djangogirls.org/

• Organize an event: https://organize.djangogirls.org/

• Work on the website: https://github.com/DjangoGirls/djangogirls

• Contribute to our resources:

  • https://github.com/DjangoGirls/tutorial
  • https://github.com/DjangoGirls/coach-manual
  • https://github.com/DjangoGirls/organizer-manual

• Translate our resources:

  • https://github.com/DjangoGirls/translation-guide

• Donate to us:

  • https://www.patreon.com/djangogirls
  • https://donate.stripe.com/14kdTJ8PT6Fa3CwaEF
  • https://djangogirls.org/en/donate/donate/

• For corporate companies: https://djangogirls.org/en/donate/

Reaching the five-year mark at the Open Source Initiative (OSI) has been a huge privilege. It’s been a whirlwind of progress, personal growth, and community engagement—filled with highs, great challenges, and plenty of Open Source celebrations. As I reflect on this milestone, it’s impossible not to feel both gratitude and excitement for what lies ahead. This isn’t just a story about my career—it’s about the evolution of Open Source, the incredible people I’ve worked with, and the values we’ve championed.

Joining OSI: first steps into the journey

Back in 2017, under the leadership of OSI’s General Manager Patrick Masson, I stepped into the role of Director of Community and Development at the OSI. That year, we began planning a celebration of the 20th anniversary of Open Source for 2018, a massive undertaking. This wasn’t just about throwing a party. It was a celebration of the immense impact Open Source has had on the global tech community.

And yet, the timing was crucial. Open Source, which had steadily grown over the past two decades, faced challenges on multiple fronts: from sustainability and diversity to startups attempting to redefine the term “Open Source” and obscure its principles. The emergence of faux Open Source licenses, like the Commons Clause and the Server Side Public License, only added to the urgency of defending the very core of our mission.

The 20th Anniversary world tour

We embarked on the OSI’s 20th Anniversary World Tour in 2018, organizing over 100 activities across 40 events globally, rallying the support of affiliates, sponsors, and the Open Source community at-large. The goal wasn’t just to celebrate but to affirm the values we held dear—transparency, collaboration, and freedom in software development. This culminated in the signing of the Affirmation of the Open Source Definition by over 40 foundations and corporations, a powerful statement that we would not back down in the face of challenges.

For the 20th Anniversary, the OSI contributed to 40 events worldwide

Stepping back amidst the pandemic

When the pandemic hit in 2020, everything came to a sudden halt. With schools closing and my young children—a one-year-old and a four-year-old—at home, the demands of balancing work and family life became overwhelming. I made the difficult decision to step down from my role at the OSI. It wasn’t easy to step away from something I loved, but at the time, it felt necessary.

A year later, I joined a security startup as Head of Community, where I led a cutting-edge Open Source project and held a leadership role at the Confidential Computing Consortium at the Linux Foundation. This new role allowed me to expand my expertise in community management and Open Source security—critical topics that would soon come to the forefront.

Return to the OSI: a new chapter

In early 2023, the startup I was working for had to close its doors unfortunately. But just as one chapter ended, another began. Stefano Maffulli, OSI’s new Executive Director, reached out to me with an opportunity to return to the OSI. This time, the focus was on addressing a growing concern: the clarity around licenses and security vulnerabilities in the Open Source supply chain.

I jumped at the chance to come back. In the beginning of this new chapter, I had the opportunity to work on two challenges: to manage the ClearlyDefined community, a project aimed at improving transparency in Open Source licensing and security, and organizing activities around the 25th Anniversary of Open Source.

The 25th Anniversary world tour

The 25th anniversary of Open Source marked an incredible milestone, not just for the OSI, but for the whole tech community. We organized activities across 36 conferences from around the world with a combined attendance of over 125,000 people. We contributed with 12 keynotes, 24 talks, 6 workshops, and 18 webinars.

For the 25th Anniversary, the OSI contributed to 36 events worldwide

It was a wonderful experience connecting with organizers of these conferences and engaging with speakers, volunteers and attendees. I personally contributed to 4 keynotes, 6 talks, 15 events, and co-organized the OSI track at All Things Open and the Deep Dive: AI webinars.

Throughout the year, our focus shifted from reviewing the past of Free and Open Source software to exploring the future of Open Source in this new era of AI.

Open Source AI Definition: a new challenge

With the popularization of Generative AI, many players in industry started using “Open Source AI” to describe their projects. Legislators around the world also started drafting laws that would have a huge impact on Open Source and AI developments. Stefano was already exploring this new challenge with the Deep Dive: AI series, but we realized that establishing an Open Source AI Definition was going to be critical. Along with the Board of Directors, we started planning a new direction for the OSI.

We organized several activities around Open Source and AI in partnership with major Open Source conferences, from talks and panels to workshops. We also launched forums for online discussions, and hosted webinars and town halls to make our activities more inclusive. The work felt more important than ever.

One of the biggest undertakings was organizing a multistakeholder co-design process, led by Mer Joyce, where we brought together global experts to establish a shared set of principles that can recreate permissionless, pragmatic and simplified collaboration for AI builders.

One of the projects I’m particularly proud of is the “Voices of the Open Source AI Definition” series. Through this initiative, we’ve been able to share the stories of the volunteers from the co-design process involved in shaping the Open Source AI Definition (OSAID). These stories highlight the diversity and passion of the community, bringing a human element to the often technical discussions around Open Source and AI.

Voices of the Open Source AI Definition

The work around the Open Source AI Definition developed by the OSI was cited over 100 times in the press worldwide, educating and countering misinformation. Our work was featured at The New York Times, The Verge, TechCrunch, ZDNET, InfoWorld, Ars Technica, IEEE Spectrum, MIT Technology Review, among other top media outlets.

ClearlyDefined: bringing clarity to licensing

As part of my community management role on the ClearlyDefined project, I had the opportunity to contribute under the exceptional leadership of E. Lynette Rayle (GitHub) and Qing Tomlinson (SAP), whose guidance was instrumental in driving the project forward. One of my major accomplishments was the creation of a brand-new website and comprehensive documentation. 

We engaged with the ORT (Open Source Review Toolkit) and Scancode communities, building stronger connections. We made important technical updates, including upgrading to the latest version of Scancode and expanding our license support beyond just SPDX, making it easier for developers and organizations to navigate the increasingly complex landscape of licensing. This important work led to the release of version 2.0 of ClearlyDefined.

Another highlight was a new harvester implementation for conda, a popular package manager with a large collection of pre-built packages for various domains, including data science, machine learning, scientific computing and more.

Additionally, the adoption of ClearlyDefined by the GUAC community from OpenSSF was a testament to the growing importance of our work in bringing clarity to the Open Source supply chain not just in terms of licensing but also security.

ClearlyDefined’s new features: LicenseRef, conda support, and GUAC integration

We presented our work across three continents: in Europe at ORT Community Days, in North America at SOSS Fusion, and in Asia at Open Compliance Summit. 

Finally, we made progress toward a more open governance model by electing leaders for the ClearlyDefined Steering and Outreach Committees.

Open Policy: from cybersecurity to AI

On the policy side, I had the privilege to work along with Deb Bryant and Simon Phipps, who  kept track of policies affecting Open Source software, in particular the Securing Open Source Software Act in the US and the Cyber Resilience Act in Europe. As for policies involving Open Source and AI, I followed the European AI Act and US AI Bill of Rights and contributed with a compilation of a list of compelling responses from nonprofit organizations and companies to NTIA’s AI Open Model Weights RFC.

OpenSource.net: fostering knowledge sharing

I also helped to launch OpenSource.net, a platform designed to foster knowledge sharing. Led by Editor-in-Chief Nicole Martinelli, this platform has become a space for diverse perspectives and contributions, furthering the reach and impact of our work.

As part of the Practical Open Source (POSI) program, which facilitates discussions on doing business with and for Open Source, I was able to bring contributions from outstanding entrepreneurs and intrapreneurs.

Looking ahead: excitement for the future

In the last 2 years at the OSI, I’ve had the privilege of publishing over 50 blog posts, as well as organizing, speaking at, and attending multiple events worldwide. The work we’ve done has been both challenging and rewarding, but it’s the community—the people who believe in the power of Open Source—that makes it all worthwhile.

As I celebrate five years at the OSI, I’m more energized than ever to continue this journey. The world of Open Source is evolving, and I’m excited to be part of shaping its future. There’s so much more to come, and I can’t wait to see where the next five years will take us.

This is more than just a personal milestone—it’s a celebration of the impact Open Source has had on the world and the endless possibilities it holds for the future.

You too can join the OSI: support our work

The work we do is only possible because of the passionate, engaged community that supports us. From advocating for Open Source principles to driving initiatives like the Open Source AI Definition and ClearlyDefined, every step forward has been powered by collaboration and shared commitment.

But there’s so much more to be done. The challenges facing Open Source today—from licensing to policy—are growing in scale and complexity. To meet them, we need your help. By joining or sponsoring the Open Source Initiative, you enable us to continue this vital work: educating, advocating, and building a stronger, more inclusive Open Source ecosystem.

Your support isn’t just a contribution; it’s an investment in the future of Open Source. Together, we can ensure that Open Source remains open for all. Join us in shaping the next chapter of this incredible journey.

That’s a lot of sites!

Yes, it is! 10+ years ago we migrated 200 sites to a new server - and in 2024 we set up Cloudflare protection for well over 1000 sites.

Aegir is a hosting system built in Drupal, for Drupal. It lets you create and manage Drupal sites and all their databases, filesystems and virtual hosts. With Aegir, it’s easy to manage hundreds or thousands of sites via a simple UI. Each site has a node to represent it, and this project stored a whole bunch of additional Cloudflare metadata against the Site Nodes.

Keeping a PaaS product online at all times comes with a high level of responsibility. After code quality assurance and testing, DDOS attacks of all sizes and types are a high risk threat. The cost of protecting our availability, unsurprisingly, was non-trivial and became a point requiring fresh research and investment. Reducing the general load and the potential attack load on our servers would serve to support our quality of service.

In the Spring of 2024 we set up a proof of concept using Cloudflare, which would allow us to make a significant ongoing cost saving whilst also playing with some really cool APIs.

The plan

In order to put all our sites behind Cloudflare, we needed to:

* Get Aegir talking to Cloudflare via their API, and build the automatic processes to support the setup process
* Create a clear interface for starting and tracking setup per site
* Create a clear dashboard for tracking progress overall
* Go! Change the nameserver records for every domain, to point to Cloudflare

Here are some of the key interesting parts of our story (which had negligible downtime, btw!)

Interacting with Cloudflare’s API

It was greatly pleasing to find that Cloudflare let you do almost everything over an API (given the right authentication!). Given the scale of their company, it shouldn’t be a huge surprise I suppose!

With 1000 sites approx. to deal with, there was a strong drive to automate as much of this process as possible. So there was much celebration when it became clear that we would be able to do almost every step automatically, including:

* create everything we needed for the site domain, with an appropriate plan under our chosen account
* upload DNS records, query them and update them
* perform real-time ownership verification and SSL validation

Simple admin setup - Obtain DNS records for hundreds of domains, store them against Site Nodes, press GO

A large portion of the domains (about 500) are under our control, so we were able to bulk export the DNS records, process and save them against their Aegir Site Nodes. Some simple processing removed the SOA and NS records so that we would be able to send the records straight to Cloudflare when setup started.
These ‘easy’ sites, for which we had the DNS records, would be processed in bulk with a lot of Go! button clicks, and then making the relevant nameserver changes with the domain provider.
(The domain provider did offer to do bulk updates for us, but there seemed to be a 24h delay before action was taken - so it was quicker to do these changes ourselves.)
 

Cropped screen grab of our progress pie chart

 

 

Creating a self-service setup mechanism via Drupal config + settings.php

Domains that we only had nameserver control for would have to be updated by the customer. But how would they know what nameservers to set up, and how would they trigger the setup process? Stats to the rescue!
As part of the Drupal 10 rebuild of the platform we added a Statistics module, which collects a selection of data points from each site and passes them to the corresponding node on Aegir for storage. They’re then aggregated and sent back to the sites so that customers can compare their performance to the cohort averages.
We created a form interface for the user to trigger setup when ready, and then smuggled the outputs over to Aegir in amongst the performance stats 🤭 

This self service route did still require a lot of chasing, but generally performed well as it allowed the users to perform the nameserver change at their choice of timing, rather than requiring scheduled calls and appointments on top of an already high administrative load.

Surfacing useful data, stats and buttons for the team

When developing a technical tool that ideally needs a single fire-and-forget button to kick things off, you not only need that one button but also a lot of clear visual cues to help others understand what’s going on.

We tied the setup steps to interface outputs, with clear dependency messaging and reporting.
Reporting eventually included a message in our Slack channel ⭐️
 

Our custom Aegir Site dashboard, with various setup steps all nice and green
The results

After just a few weeks, we had 990 sites set up on Cloudflare - 90% of the most important setups. It turned out to be very difficult to get hundreds of different people, groups and stakeholders to make DNS nameserver changes quickly (even when you tell them it’s urgent!), so the process would continue a little longer.

Already we can measure the success - thanks to Cloudflare’s caching, we’ve seen decent reductions in bandwidth use and the number of requests hitting the server.
 

Our Cloudflare Dashboard on the Production Aegir server. Inactive sites didn't need our fuller protection setup steps, so the blue and pink bars don't match exactly.

In a recent email, KDABian Leon Matthes highlighted some of his go-to command line tools for everyday use on Unix. His recommendations sparked a lively exchange among our colleagues, each sharing their own favorite utilities.

Many of these tools offer efficient alternatives to standard Unix programs, speeding up the workflow or otherwise enriching the development experience.

This article aims to serve as a resource for the wider community, encouraging others to explore these tools and upgrade their command line setup for improved productivity.

The following common Unix tools are listed in this document, with their alternatives specified in their respective sections:

• cd
• ls
• man
• find
• grep
• cat
• diff / git diff

Additionally, there are some tools here that do not replace common programs, but are extremely useful when working in a shell environment. These are broken into two broad categories:

• prompts & shells
• misc.

Many of these tools can be considered examples of the “rewrite it in Rust” approach (RIIR), which in this document will be denoted with Ferris the crab: 🦀

Finally, there is a bonus Rust crate listed at the end that is helpful for writing CLI programs of your own.

Note: for scripting, stick with standard Unix tools, as they’re more widely distributed and will work in most other Unix environments. The tools in this article are meant to improve quality of life working within your own shell environment on a daily basis.

cd autojump

autojump is an alternative to cd, invoked with the command j. It jumps to what it determines to be the best fit for whatever directory name you give it. For example, j cxx-qt will change directory to the cxx-qt directory, even if it’s nested several levels deep from the current working directory.

If autojump doesn’t prioritize a directory correctly, its priority weight can be increased or decreased. j -i XXX will increase the priority weight of the current working directory by XXX, while j -d XXX will do the opposite and decrease the weight accordingly.

autojump also supports opening a file browser at a matched location rather than cd‘ing into it, by invoking jo rather than j.

Unfortunately, autojump has not been in active development for the past two years, and can break in some environments. The next tool, zoxide, will be more reliable if autojump breaks for you.

zoxide 🦀

zoxide is a similar program to autojump, actually drawing inspiration from it. Invoked with the command z, it remembers paths that have been visited in the past and matches the best fitting directory. The tool also supports interactive selection of the correct match via the zi command, which uses the tool fzf for fuzzy finding. fzf is listed later in this article.

zoxide can also be used the same way as plain cd, and can target absolute and relative paths. It can be configured to be invoked from the j and ji commands, or even cd to fully replace the cd command.

Finally, it is implemented in Rust, resulting in better performance than autojump.

ls lsd 🦀

lsd is a more feature-rich version of ls, including colors, icons, tree-view, additional formatting, and more. It is also highly configurable through a yaml config file.

eza 🦀

eza is a small, fast ls rewrite that knows about symlinks, extended attributes, and git, and has support for hyperlinks. Similar to lsd, it supports colors and icons, which are configurable with a yaml config file.

man tldr

This project has several clients written in different languages. The most mature client is an npm package.

tldr is an alternative to using man for most common use cases. It is a collection of pages that provides brief descriptions and usage examples for loads of programs, standard Unix and otherwise. It’s much faster if you don’t need to read about every option in detail in order to find the correct way to invoke a command or select the proper option quickly.

For example, if the man page for tar is too long to read when looking for the right option to do something, simply running tldr tar will show a list of examples that will usually have the options you need.

cheat.sh / cht.sh

cheat.sh and its associated shellscript cht.sh, provide access to a superpowered aggregation of cheatsheets for programming languages and CLI tools, including tldr pages and other sources.

It has a great system for querying cheatsheets, and you don’t even need to install a tool (though you can), as the query response can be retrieved by curl. It’s also usable inside editors, for easy referencing while programming.

The cheatsheet sources also include StackOverflow answers, which allow for flexible usage like so:

$ cht.sh js parse json

A nice way to use cheat.sh without installing anything is to put the following function in your .bashrc or .zshrc:

function cheat() { if ! curl -s "cheat.sh/${*//\ /+}"; then echo "Failed to get the cheatsheet" >&2 return 1 fi }

Then you can use it like

$ cheat tar

or

$ cheat cpp number to string find fd 🦀

fd is an alternative for many use cases of find.

While it is not as powerful as find, it is designed to be faster and more user-friendly for the majority of find‘s use cases.

No more find -name .rs — just use fd .rs and you’re good to go.

fd is super fast and will search through your entire filesystem in a matter of seconds (Leon recorded 1.4s on his machine with 177GB of files).

By default, fd will not include any hidden files that are ignored by .gitignore, which is very useful as it doesn’t give you any random junk in your build directory and speeds up the search even further.

Use --hidden and --no-ignore (or -H and -I, respectively) to search for everything.

fzf

fzf is a fuzzy-finder that can match files, command history, processes, hostnames, bookmarks, git commits, and more, with interactive selection. It’s also usable as a vim or neovim plugin.

grep ripgrep 🦀

ripgrep (rg as a command) provides an incredibly fast grep alternative with a simpler interface.

Did you ever have to wait for grep to finish searching a directory or had to look up the right options for specific usage? Well, no more! Ripgrep will search the contents of large build directories within milliseconds.

Just running rg Q_PROPERTY will search through the entire current directory if you don’t pipe something into it, which is very convenient and the way it should have been in the first place. The output is also a lot friendlier and easier to read.

Like fd, use --hidden and --no-ignore to search in hidden and ignored directories/files.

cat bat 🦀

bat, written by the creator of fd, is a superpowered Rust rewrite of cat. It provides syntax highlighting, line numbers, support for displaying non-printable characters, support for showing multiple files at once, and pipes to less by default for large output. It can also be integrated with fzf, find, fd, ripgrep (rg), git show, git diff, man, and more. A tool you’ll read about later in this document, delta, uses bat as a dependency to display diffs.

diff / git diff diff-so-fancy

diff-so-fancy is an alternative to vanilla git diff that improves readability, with better formatting, colors, and highlighting. Configure git to use diffsofancy as its pager, and git diff will provide the more readable diffsofancy output.

delta 🦀

delta is an alternative to diffsofancy that’s written in Rust. It includes additional features, like line numbers, syntax highlighting, side-by-side, improved navigation between files in large diffs, hyperlinked commit hashes, improvements for display of git blame and merge conflicts, and the ability to syntax-highlight grep output. delta uses bat under the hood and, as such, they can share color themes.

prompts & shells starship 🦀

starship is a prompt written in Rust, which can be used with a number of popular shells including bash, zsh, fish, and even Windows shells like cmd and PowerShell.

The prompt is both stylish and informative, providing information like git branch, versioning, and more in the prompt itself. It also indicates the time length of command execution, whenever the run lasts for more than a few seconds — very helpful for gauging the duration of a build. This prompt should be utilized with a NerdFont for icon support.

Starship is also extremely configurable, so any details can be omitted or added; the look and feel of the prompt can be completely customized, etc.

zsh prompts & plugins

Some shell users argue that switching from bash is definitely worthwhile, with zsh being the favorite choice for many. It’s highly configurable and has a rich plugin ecosystem.

oh-my-zsh is a zsh framework that makes completions, searching in history, the prompt, etc., much nicer than the defaults. It can, of course, be tweaked much further from there. If you find oh-my-zsh to be a bit bloated, there are more lightweight alternatives such as ZimFW or Zinit.

Some zsh plugins can also serve as alternatives to previously mentioned tools. There are quite a few useful plugins listed here (they should work with any plugin manager): https://github.com/unixorn/awesome-zsh-plugins.

Some that stand out include:

• zsh-vi-mode
  • a proper Vim mode for the terminal, much better than the default one
• fast-syntax-highlighting
  • nice while-you-type syntax highlighting in the shell
• fzf-marks
  • alternative workflow to zoxide and similar cd improvements
  • this can also be used with bash
• zsh-autoenv
  • customize environment variables by directory, similar to the tool direnv mentioned later

Note: If you are interested in switching away from bash but don’t like zsh, consider trying fish.

Update Systems topgrade 🦀

topgrade is a tool which conveniently updates several package managers with one command.

Just run topgrade and every package manager under the sun will be updated, one after the other. This even includes flatpaks and updates to your systems package manager, very convenient.

Misc. direnv

direnv is an extension for several common Unix shells, focused on environment management. More specifically, it is intended to unclutter .profile and export different environment variables depending on the current directory.

On each prompt, it checks for the existence of a .envrc file in the current directory, otherwise looking for .env, and loads or unloads environment variables accordingly. It’s a single executable and very fast.

direnv is also extensible with bash files in .config.

blobdrop

blobdrop enables drag-n-drop of files from a terminal window, which can be very convenient when using the command line as a file browser. It was written by fellow kdabian Magnus Gross.

bonus! clap

bonus is not quite a tool, but a Rust crate used frequently for writing CLI tools, called clap.

clap is used in the codebases of a majority of the Rust tools in this document, as it’s incredibly useful. It’s the reason most Rust CLI tools have great UX.

Using the derive feature, you can simply mark up a struct of data you need from your command line arguments and clap will generate a beautiful and convenient CLI interface for you. Just take a look at the derive documentation to get an idea of what this looks like.

Other Tools

We hope these tools can improve your workflow, as they certainly make our lives on the command line far more enjoyable.

If you have any additions to this list, feel free to discuss them in the comments.

About KDAB

If you like this article and want to read similar material, consider subscribing via our RSS feed.

Subscribe to KDAB TV for similar informative short video content.

KDAB provides market leading software consulting and development services and training in Qt, C++ and 3D/OpenGL. Contact us.

The post CLI++: Upgrade Your Command Line appeared first on KDAB.