GPL(GENERAL PUBLIC LICENSE) FREEDOM 0:
The freedom to run the program as you wish, for any purpose.
FREEDOM 1:
The freedom to study how the program works, and change it so it does your computing as you wish.
FREEDOM 2:
The freedom to redistribute copies so you can help others.
FREEDOM 3:
The freedom to distribute copies of your modified versions to others.

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Read the full article on GamingOnLinux.

Performance Co-Pilot (PCP) is a robust framework for collecting, monitoring, and analyzing system performance metrics. Available in the repos for Fedora and RHEL, it allows administrators to gather a wide array of data with minimal configuration. This guide walks you through tuning PCP’s pmlogger service to better fit your needs—whether you’re debugging performance issues or running on constrained hardware.

Is the default setup of PCP right for your use case? Often, it’s not. While PCP’s defaults strike a balance between data granularity and overhead, production workloads vary widely. Later in this article, two scenarios will be used to demonstrate some useful configurations.

Prerequisites: Getting PCP up and running

First, install the PCP packages:

$ sudo dnf install pcp pcp-system-tools

Then enable and start the core services:

$ sudo systemctl enable --now pmcd.service
$ sudo systemctl enable --now pmlogger.service

Verify both services are running:

$ systemctl status pmcd pmlogger

Understanding pmlogger and its configuration

PCP consists of two main components:

• pmcd: collects live performance metrics from various agents.
• pmlogger: archives these metrics over time for analysis.

The behavior of pmlogger is controlled by files in /etc/pcp/pmlogger/control.d/. The most relevant is local, which contains command-line options for how logging should behave.

Sample configuration:

$ cat /etc/pcp/pmlogger/control.d/local

You’ll see a line like:

localhost y y /usr/bin/pmlogger -h localhost ... -t 10s -m note

The -t 10s flag defines the logging interval—every 10 seconds in this case.

Scenario 1: High-frequency monitoring for deep analysis

Use case: Debugging a transient issue on a production server.
Goal: Change the logging interval from 10 seconds to 1 second.

Edit the file (nano editor used in the examples, please use your editor of choice):

$ sudo nano /etc/pcp/pmlogger/control.d/local

Change -t 10s to -t 1s.

Restart the logger:

$ sudo systemctl restart pmlogger.service

Verify:

$ ps aux | grep '[p]mlogger -h localhost'
$ pminfo -f

Expected output snippet:

records: 10, interval: 0:00:01.000

Scenario 2: Lightweight monitoring for constrained systems

Use case: Monitoring on a small VM or IoT device.
Goal: Change the logging interval to once every 60 seconds.

Edit the same file:

$ sudo nano /etc/pcp/pmlogger/control.d/local

Change -t 10s to -t 60s.

Restart the logger:

$ sudo systemctl restart pmlogger.service

Confirm:

$ ps aux | grep '[p]mlogger -h localhost'
$ pminfo -f

Expected output:

records: 3, interval: 0:01:00.000

Managing data retention: logs, size, and cleanup

PCP archives are rotated daily by a cron-like service. Configuration lives in:

$ cat /etc/sysconfig/pmlogger

Default values:

PCP_MAX_LOG_SIZE=100
PCP_MAX_LOG_VERSIONS=14

• PCP_MAX_LOG_SIZE: total archive size (in MB).
• PCP_MAX_LOG_VERSIONS: number of daily logs to keep.

Goal: Keep logs for 30 days.

Edit the file:

$ sudo nano /etc/sysconfig/pmlogger

Change:

PCP_MAX_LOG_VERSIONS=30

No service restart is required. Changes apply during the next cleanup cycle.

Final thoughts

PCP is a flexible powerhouse. With just a few changes, you can transform it from a general-purpose monitor into a specialized tool tailored to your workload. Whether you need precision diagnostics or long-term resource tracking, tuning pmlogger gives you control and confidence.

So go ahead—open that config file and start customizing your system’s performance story.

Note: This article is dedicated to my wife, Rupali Suraj Patil, who inspires me every day.

Fruit is a cross-platform UCI chess engine.

The post Fruit – chess engine appeared first on LinuxLinks.

In today’s cloud-dominated IT landscape, many organizations are rethinking their dependencies on proprietary software platforms. Rising licensing fees, abrupt policy shifts, and vendor lock-in have caused concern across industries — particularly in the public sector, where digital sovereignty and long-term control over infrastructure are critical. The increasing frequency of “licensing rugpulls” from major vendors such as VMware has only accelerated this shift.

NUBO, a French interministerial cloud project, built and operated by the Ministry of Economics and Finance, has emerged as a leading example of how open-source technologies — especially OpenStack — can power sovereign, secure, and scalable digital infrastructure.

This article explores why NUBO chose OpenStack, the challenges they encountered, and how their experience reflects the broader movement toward open-source cloud platforms as a foundation for digital independence.

A Foundation Built on Open Source

For over 25 years, NUBO’s technical teams have relied on a wide range of open-source technologies, including Linux, KVM, and Open vSwitch. Their commitment to open source has never been about cost savings alone. Instead, it stems from two key priorities:

1. Technical excellence: These tools meet the demanding requirements of government-level operations.
2. Strategic and financial independence: Avoiding proprietary licenses and subscription-based models helps ensure freedom from vendor lock-in and long-term cost predictability.

Rather than investing in commercial licenses or external support contracts, NUBO has consistently chosen to invest in people — building deep internal expertise across the stack. This approach empowers them to adapt, innovate, and retain control over their digital infrastructure.

Why OpenStack?

When NUBO set out to build its own interministerial private cloud, open source infrastructure was the natural choice. Among available cloud platforms, OpenStack quickly stood out as the only open source solution mature enough to meet the project’s unique demands.

OpenStack’s advantages aligned perfectly with NUBO’s goals:

• Modular and API-Driven Architecture: OpenStack’s component-based design lets NUBO select only the services needed and integrate them cleanly with the rest of its stack. Each service is fronted by an API, offering maximum flexibility.
• Infrastructure Abstraction: OpenStack is more than just a virtual machine orchestrator. It provides a unified abstraction layer for compute, storage, and networking — making it a full-fledged cloud infrastructure platform.
• Scalability and Reliability: Built for both small and large deployments, OpenStack supports NUBO’s mission to deliver robust services to government organizations, now and into the future.

Maturing with the Ecosystem

NUBO began testing OpenStack between 2013 and 2015. At the time, the project was powerful but unwieldy. Deployments were difficult, configuration was inconsistent, and reliability was not where it needed to be.

A major turning point came in 2017, when the Kolla and Kolla-Ansible projects gained traction. By containerizing OpenStack services and standardizing deployment practices, Kolla addressed the complexity and made it feasible for NUBO to maintain and upgrade OpenStack with confidence. The team developed a continuous integration and testing pipeline — including a full “virtual Nubo” deployment environment — to validate each release before it hit production.

Today, NUBO manages regular updates to its OpenStack platform and contributes back to the ecosystem, as both a user and innovator.

Technical Challenges — and Solutions

While OpenStack has evolved, NUBO’s experience shows that successful deployments still require deep technical knowledge. The team encountered several performance and reliability challenges, which they addressed through targeted troubleshooting and adaptation:

• Network Performance Issues: As the number of security groups on hypervisors grew, network performance degraded. The solution was to switch from the default driver to the OpenFlow driver, which handled the filtering rules more efficiently.
• MariaDB Query Bottlenecks: NUBO’s database cluster was generating performance issues due to excessive joins. A single configuration change, once diagnosed, dramatically improved response times.
• Neutron Clustering Failures: An overload on one Neutron server caused a domino effect across clustered nodes. The team had to disable clustering temporarily to stabilize the environment.
• NFS Storage Freezes: Using the NetApp Cinder driver, NUBO experienced unpredictable VM freezes. Migrating to NFS 4.1 resolved the issue.

These experiences underscore the value of OpenStack’s flexibility: most problems could be solved internally, without waiting for vendor fixes — a major advantage in a sovereignty-focused model.

Observability and Security: Key Areas of Focus

Despite many improvements, observability remains a challenge. Solutions like Ceilometer and Monasca either underperformed or lacked maintenance when attempting to implement them at the time. In response, NUBO built a custom observability stack using Prometheus, Grafana, and VictoriaMetrics, along with EFK (Elasticsearch, Fluentd, Kibana) for log management.

Security has also been a major priority, in line with France’s SecNumCloud cybersecurity standards. The team worked in particular on refining internal network segmentation, internal-external API separation, multi-factor authentication and HTTPS across all internal communications.

Preparing for the Future: Kubernetes and Ceph

Looking ahead, NUBO is in the process of building a Kubernetes-based offering, Kubo, based on Cluster-API for managing the lifecycle of multiple clusters. The plan is to offer this service by 2026, maintaining the level of security that complies with the ANSSI’s SecNumCloud reference framework, just like their OpenStack-based infrastructure. As part of this Kubo project, they plan to make or contribute to a sovereign Kubernetes distribution.

Simultaneously, the team is evaluating Ceph as a possible future storage backend. Although powerful, Ceph requires specialized expertise — particularly in sensitive areas like data integrity and replication — and the team is considering eventual adoption.

Lessons from NUBO: Open Source Enables Sovereignty

NUBO’s experience offers a compelling lesson for governments, enterprises, and institutions worldwide: digital sovereignty is achievable — but it requires a commitment to open source principles, long-term investment in people, and readiness to manage complexity.

OpenStack has matured into a powerful platform capable of supporting mission-critical workloads at national scale. In contrast to proprietary platforms that may change their licensing or roadmap at any time, OpenStack empowers users with transparency, control, and community-led innovation.

While challenges remain — especially around observability and long-term operations — the benefits of control, adaptability, and independence far outweigh the costs. NUBO’s experience provides a roadmap for others to follow — proving that open source is not just a cost-saving measure, but a strategic asset in the defense of national autonomy and digital freedom.

The post Why NUBO Chose OpenStack to Build a Sovereign, Open Source Cloud Infrastructure appeared first on Superuser.

From cupcake recipes replacing bomb instructions to AC/DC blasting through nuclear facilities, these amusing cyber incidents prove that sometimes the best security breaches make us laugh - funny hacking stories.

The post Funny Hacking Stories That Will Make You Question Everything You Know About Hackers appeared first on blackMORE Ops.

Linux/Unix programming has entered a new era where ai machine learning technologies revolutionize development workflows, dramatically reducing time spent on repetitive tasks while enhancing overall productivity. Programmers utilizing these powerful tools can automate complex processes, analyze patterns in code, and optimize resource utilization within the Linux ecosystem.

Understanding AI Machine Learning in Linux/Unix Environments

The Linux operating system has become the preferred platform for AI development due to its open-source nature, stability, and exceptional support for machine learning frameworks. Linux distributions offer superior resource management capabilities, making them ideal for handling the computational demands of modern AI workloads. Many organizations leverage Linux-based systems to build, train, and deploy sophisticated AI models that drive business innovation.

Key Machine Learning Libraries for Linux/Unix Programmers

Linux programmers have access to a rich ecosystem of machine learning libraries that facilitate advanced AI development. TensorFlow stands out for neural network implementation with robust GPU acceleration support. PyTorch has gained popularity among researchers for its dynamic computation graph that simplifies model adjustments during training. For traditional machine learning algorithms, Scikit-Learn provides an accessible entry point, while Keras offers a higher-level API that streamlines deep learning development. Computer vision applications benefit from OpenCV’s extensive functionality, enabling ai machine learning systems to process and analyze visual data efficiently. Additional frameworks like Apache MXNet, Caffe, and Hugging Face Transformers expand the toolbox for specialized applications across data science, natural language processing, and computer vision domains.

Setting up Your Linux System for AI Development

Creating an optimal environment for AI development begins with selecting the right Linux distribution. Ubuntu and Debian remain popular choices due to their comprehensive package repositories and extensive community support. Next, install essential components including Python, Jupyter Notebook, and Anaconda for data science workflows. GPU acceleration requires proper configuration of NVIDIA CUDA or AMD ROCm drivers to maximize performance. Containerization through Docker offers consistent development environments across machines while facilitating deployment. OVHcloud Public Cloud services provide scalable computing resources specifically designed for AI workloads, with options for GPU-accelerated instances that dramatically reduce training times. Using virtualization technologies allows developers to isolate dependencies and create reproducible setups across teams, making collaborative ai machine learning projects more manageable.

Practical Applications for Automating Programming Tasks

Linux systems have become the backbone of AI and machine learning development, offering programmers powerful tools to streamline their workflows. By leveraging AI-driven automation within Linux/Unix environments, developers can significantly reduce repetitive tasks and focus on more creative aspects of programming. The open-source nature of Linux makes it particularly well-suited for integrating machine learning capabilities into the development process.

Code Completion and Bug Prediction Tools

AI-powered code completion tools are revolutionizing Linux/Unix programming by analyzing vast code repositories to predict and suggest the next lines of code as developers type. These systems learn from millions of open-source projects, understanding patterns and best practices in languages commonly used in Linux environments like Python, C++, and Bash scripting. GPU acceleration through NVIDIA CUDA support makes these predictions nearly instantaneous, with TensorFlow and PyTorch frameworks powering the underlying deep learning models.

Bug prediction capabilities take this a step further by scanning code for potential issues before they manifest. Using machine learning algorithms from frameworks like Scikit-Learn, these tools analyze code structure and identify patterns associated with known bugs. For instance, tools like source{d} Lookout can be installed on repositories to analyze pull requests and comment directly on GitHub, helping developers ship secure code faster. This proactive approach to quality assurance is especially valuable in Linux environments where system stability is paramount for mission-critical applications.

Automating Testing and Deployment Processes

Machine learning excels at automating complex testing scenarios in Linux/Unix systems. AI models can generate test cases by learning from existing codebases, creating more comprehensive test coverage than manual approaches. This is particularly valuable for identifying edge cases that human testers might miss. OVHcloud’s Public Cloud offers robust infrastructure for running these resource-intensive testing frameworks, providing the necessary computing power without significant hardware investments.

Deployment automation benefits tremendously from AI integration through containerization technologies like Docker. Machine learning models can analyze deployment patterns and system performance metrics to optimize resource allocation dynamically. These systems learn from past deployments to predict resource needs, minimizing downtime and maximizing efficiency. The combination of Linux’s robust resource management capabilities with AI-driven automation creates a powerful environment for continuous integration/continuous deployment (CI/CD) pipelines. Organizations using virtualization and containers can scale their machine learning workflows seamlessly while maintaining security and stability that Linux environments are known for.

The post Linux/Unix Programming: How AI Machine Learning Can Save You Time and Increase Efficiency appeared first on Unixmen.

In March, ALESCo approved a proposal to build EPEL packages from Fedora’s source RPMs (SRPMs) to maintain long-term feature parity for our x86_64_v2 support initiative. Last month, these packages became available for AlmaLinux Kitten 10, and today we are happy to announce that x86_64_v2 EPEL support is now available for AlmaLinux 10 Stable as well.

The EPEL package builds for AlmaLinux OS 10 stable are now complete and ready for use!

How It Works

We monitor the EPEL repository for the latest stable packages, grab their SRPMs, and rebuild them using the AlmaLinux Build System. These rebuilt packages are signed with a dedicated GPG key and then released to a dedicated repository.

Linux users have always preferred AMD over Intel – at least, this is what I’ve seen and read since the beginning of my Linux journey in 2014. TUXEDO Computers confirms once again that it is listening to feedback, as Linux users continue arguing against Intel and demanding more AMD hardware. Therefore, the German-based hardware manufacturer has unveiled the TUXEDO InfinityBook Pro 14 Gen10 Linux Laptop with more red power! So, what does this new generation of elegant Linux laptops offer?

The post All AMD, No Intel: TUXEDO InfinityBook Pro 14 Gen10 Unveiled appeared first on Linux Today.

In an interesting turn of events, Linus Torvalds and Bill Gates meet each other for the first time at a dinner invite. What would have they talked about? Any guesses?

This photo also made me realize how quickly Torvalds has aged in the past few years 😔

💬 Let's see what else you get in this edition

• Kubuntu also dropping Xorg support.
• Hyprland working on a paid plan and not everyone being happy about it.
• KDE's new setup tool.
• Void Editor with open source AI to tackle Cursor supermacy.
• And other Linux news, tips, and, of course, memes!

📰 Linux and Open Source News

• Kubuntu is also set to drop Xorg in favor of Wayland. Fedora, Ubuntu and now Kubuntu. I can see more distros following this trend in the near future.
• KDE plans a new setup tool to welcome users after a fresh installation.
• Hyprland is planning to launch a paid premium tier and that decision has led to heated discussion in the communities.
• Murena Find launches as a Qwant-based search engine.
• Zed Editor's new debugger has arrived with multi-language support.
• Kingfisher is MongoDB's new open source real-time secrets scanner.

Fedora plans to ditch 32-bit support completely. This will impact Steam and Wine.

Fedora Looks to Completely Ditch 32-bit Support

Fedora plans to drop 32-bit packages completely.

It's FOSS NewsSourav Rudra

🧠 What We’re Thinking About

Accessibility on Linux is being taken for granted.

It’s True, “We” Don’t Care About Accessibility on Linux

What do concern trolls and privileged people without visible or invisible disabilities who share or make content about accessibility on Linux being trash without contributing anything to projects have in common? They don’t actually really care about the group they’re defending; they just exploit these victims’ unfortunate situation to fuel hate against groups and projects actually trying to make the world a better place. I never thought I’d be this upset to a point I’d be writing an article about something this sensitive with a clickbait-y title. It’s simultaneously demotivating, unproductive, and infuriating. I’m here writing this post fully knowing that I could have been working on accessibility in GNOME, but really, I’m so tired of having my mood ruined because of privileged people spending at most 5 minutes to write erroneous posts and then pretending to be oblivious when confronted while it takes us 5 months of unpaid work to get a quarter of recognition, let alone acknowledgment, without accounting for the time “wasted” addressing these accusations. This is far from the first time, and it will certainly not be the last.

TheEvilSkeletonTheEvilSkeleton

🧮 Linux Tips, Tutorials and More

• What’s a shim file, and why does your Linux distro need it when dealing with UEFI secure boot?
• You can easily beautify Xfce desktop with these themes I suggest.
• Did you know you could tweak the file manager in GNOME desktop and extend its features?
• Quick tip on using dark mode with VLC.

Fast, pretty, and actually helpful. Btop++ nails system monitoring.

Btop++: Linux System Monitoring Tool That is Definitely Better than Top

A sleek terminal-based system monitor that gives you detailed insights to your resources and processes.

It's FOSSAbhishek Prakash

👷 Homelab and Hardware Corner

Abhishek boosted his Raspberry Pi's performance with this simple tweak. However, this is not a trick you should use often.

This Simple Change Improved the Performance of My Homelab Running on Raspberry Pi

My struggling Raspberry Pi got a performance boost by a small change in the memory configuration.

It's FOSSAbhishek Kumar

Spotted this 'glow bot' smart AI assistant on Kickstarter. A cool desk companion with a futuristic vibe, only if you have money to spare. It is not open source. I hope someone starts a similar open source project soon, as this is an interesting concept to have customized pixel animation that reacts according to interaction.

✨ Project Highlight

Don't like Cursor's proprietary nature? You can try Void instead.

Void Editor Is Shaping Up Well: Is it Ready to Take on Cursor and Copilot?

Looking for a privacy-first AI code editor? Void’s got you covered.

It's FOSS NewsSourav Rudra

📽️ Videos I am Creating for You

A rare Linux game review from us in video format. There is a text version, too. If you like it, we will cover more indie games that can be played natively on Linux.

🧩 Quiz Time

Can you guess all the Shell Built-in commands?

Guess the Shell Built-ins: Crossword

Time to exercise those grey cells and correctly guess these popular shell built-ins in this fun crossword for Linux users.

It's FOSSAbhishek Prakash

💡 Quick Handy Tip

In the Konsole, you can view file thumbnails. To accomplish this, first enable "Underline files" in a profile you use in Konsole via Menu → Settings → Configure Konsole → Profiles → Your Profile → Edit → Mouse → Miscellaneous → Underline files.

Now, perform Menu → Settings → Configure Konsole → Thumbnails → Enable thumbnails generation. Also, set an activation key to hold while hovering your cursor, I used the Shift key to demonstrate below.

That's it. Now, when you press Shift and hover your mouse over a file, a thumbnail will appear!

🤣 Meme of the Week

I feel like a mentor 👨‍🏫

🗓️ Tech Trivia

Microsoft was incorporated on June 25, 1981, in the state of Washington, following its founding by Bill Gates and Paul Allen in 1975.

One more fun fact, Linus Torvalds and Bill Gates recently met at a dinner hosted by Microsoft Azure's CTO, Mark Russinovich.

🧑‍🤝‍🧑 FOSSverse Corner

Hotmail is a name I haven't heard in quite some time now. One of our FOSSers is not happy with it.

Hotmail, the final straw. An icy rant from the polar regions

I’m not sure if this rant even belongs to these here pages, but I leave it to a Moderator to correct me and I promise I will stay in line later. Then again - i am seriously p…d off! I’ve had a @hotmail account since they first went online — long before many of you here, brothers and sisters on these pages, were even born. Back then, I was a Windows user, and Hotmail was far better than what my internet provider could offer. We had dial-up modems using landlines, and you still had to physica…

It's FOSS Communityaudun_s

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Read more of this story at Slashdot.