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Simpleadmindoc is django application that allows you to quickly create help for modules in Django admin. Goal is to be flexible enough, fast to create and easy to integrate.

Middleware that intialize specific locale for admin pages.

Importing and exporting data with included admin integration.

django-cookie-consent is a reusable application for managing various cookies and visitors consent for their use in Django project.

Next Wednesday evening, I'm going to be speaking remotely at the Drupal London meetup.

I was originally going to attend in person, but after injuring my foot last week, I can't travel so will have to join remotely.

I'm going to be giving a talk and demo of automated testing and test-driven development with Drupal as well as some Q&A and pair programming, if time allows and we're able to do so remotely.

RSVPs are still open for the event and hopefully I'll get to attend Drupal London in person in the future.

It was once said over the grapevine that: "Our C++ API documentation has some issues, our QML API documentation has a lot of issues."

And it was true, but that is to change soon! As you might know, there is an ongoing effort to port our documentation from Doxygen to QDoc, and you can help with that.

This is a task that has been adopted by the Streamlined Application Development Experience championed by Nicolas Fella and Nate Graham as part of the KDE Goals initiative.

We would like to invite you to join our porting sprint effort to finish this task. On November 14th at 1PM UTC, we'll be hanging out in the Matrix room working on this. Hope to see you there.

Some prerequisites:

• Ability to use a terminal
• Extra disk space (30GB minimum)
• Some familiarity with APIs

Check out the instructions prepared by Thiago Sueto on how to get started porting a project to QDoc.

Our Debian User Group met on November 2nd after a somewhat longer summer hiatus than normal. It was lovely to see a bunch of people again and to be able to dedicate a whole day to hacking :)

Here is what we did:

lavamind:

• reproduced puppetdb FTBFS #1084038 and reported the issue upstream
• uploaded a new upstream version for pgpainless (1.6.8-1)
• uploaded a new revision for ruby-moneta (1.6.0-3)
• sent an inquiry to the backports team about #1081696

pollo:

• reviewed & merged many lintian merge requests, clearing out most of the queue
• uploaded a new lintian release (1.120.0)
• worked on unblocking the revival of lintian.debian.org (many thanks to anarcat and pkern)
• apparently (kindly) told people to rtfm at least 4 times :)

anarcat:

• figured out he still can't figure out how to do 3D printing and should probably just delegate these prints, currently in his wishlist:
  • k4vp VHF HT radio case (fits on your phone)
  • (tr)uSDX HF radio (fits in your hand!)
  • framework expansion card holder (fits in my bag!)
  • Ortlieb QUICK-LOCK1 compatible inserts (fits on my bike?)
• fixed an RC bug in python-ulid
• sponsored fuzzel 1.11.1+ds-2 upload, resolving a FTBFS on non-x86 arches
• tried to move the sigal packaging forward a little, failing in obscure pybuild land

LeLutin:

• opened an RFS on the ruby team mailing list for the new upstream version of ruby-necromancer
• worked on packaging the new upstream version of ruby-pathspec

tvaz:

• did AM (Application Manager) work

tassia:

• explored the Debian Jr. project (website, wiki, mailing list, salsa repositories)
• played a few games for Nico's entertainment :-)
• built and tested a Debian Jr. live image

Pictures

This time around, we went back to Foulab. Thanks for hosting us!

As always, the hacklab was full of interesting stuff and I took a few (bad) pictures for this blog post:

I find Linux virtualization stack confusing. KVM? ibvirt? QEMU? Xen? What does that even mean?

This post is my attempt at making sense of that all. I don’t claim it to be correct, but that’s the way I understand it. If I badly missed a mark somewhere, please reach out and tell me how wrong I am.

Virtualization primer

Virtual machine is a box inside which programs think they are running on different hardware and operating system. That box is like a full computer, running inside a computer. Virtualization is process of creating these boxes. Virtual machine is called guest, while operating system that runs virtual machine is called host.

Two main reasons to virtualize are security and resource management. Security - because if virtual machine is done correctly, then program inside it won’t even know it’s inside a virtual machine, and that means there’s a good chance it won’t escape to interfere with other programs. Resource management - so you can buy huge server and assign slice of resources to each box as you see fit, and dynamically change it as your needs shift.

Virtual machine pretends to be the entire computer, but most discussions revolve around CPU. Each CPU architecture has a specific set of instructions, and programs generally target only one of them. It is technically possible to translate instructions from one architecture to another, but that’s usually slow. Most modern CPUs provide extensions that allow virtual machines to run at speed comparable to non-virtualized installations. Using these extensions is only possible when the guest and the host target the same CPU architecture.

Conceptually, there is no significant difference between virtualization and emulation. Practically, emulation usually refers to guest thinking it has different CPU architecture than host, and to virtual machine pretending to be some very specific physical hardware - like a video game console. Virtualization in turn usually refers to specific case where guest thinks it has the same CPU architecture as the host.

KVM

KVM is part of the kernel, responsible for talking with hardware. As I mentioned before, most modern CPUs have special extensions that allow guests to achieve near-native performance. KVM makes it possible for Linux host to use these extensions.

QEMU

QEMU is weird, because it’s multiple different things.

First, it can run virtual machines and pass instructions from guest to KVM, where they will be handled by virtualization CPU extension. So you can think about QEMU as user space component for KVM.

Second, it can run virtual machines while emulating different CPU architecture. So you can think about QEMU as user space virtualization solution, which can run without KVM at all.

Third, it can run programs targeting one CPU architecture on a computer with another CPU architecture. It’s like lightweight virtualization, where only single program is virtualized.

Finally, it is set of standalone utilities related to virtualization, but not directly tied to anything in particular. Most of these programs work with disk image files in some way.

QEMU is probably the hardest part to understand, because it can do things that other parts of the stack are responsible for. This results in some overlap between different components and encountering program names in contexts where you would not expect them.

I think this overlap is mostly a historical artifact. The oldest commit in QEMU git repository is dated 2003, while KVM was included in Linux kernel in 2007. So it seems to me that QEMU was originally intended as software virtualization solution that did not depend on any hardware or kernel driver. Later Linux gained drivers for virtualization CPU extensions, but they were useless without something in user space that could work with them. So instead of creating completely new thing, QEMU was extended to work with KVM.

Technically, the journey towards understanding Linux virtualization stack could end here - you can use QEMU to create, start, stop and modify virtual machines. But QEMU commands tend to be verbose and long, so people rarely use it directly.

libvirt

I have ignored that so far, but KVM is not the only virtualization driver in kernel. There are many, some of them closed-source.

libvirt is intended as unified layer that abstracts virtual machine management for application developers. So if you would like your application to create, start, stop or delete virtual machine, you don’t have to support each of these operations on KVM, QEMU, Xen and VirtualBox - you can just support libvirt and trust it will do the right thing.

libvirt doesn’t do any work directly, and instead asks other programs to do it. System administrator is responsible for setting up the host and ensuring virtualization may work. This makes libvirt great for application developers, who can forget about details of different solutions; but most of libvirt users are in less fortunate position, because they have to take a role of system administrator.

libvirt will talk with QEMU, which in turn may pass CPU instructions to KVM. Some sources online claim that libvirt can talk with KVM directly. libvirt itself perpetuates this confusion, as KVM is listed alongside QEMU on the main page, giving the impression they are two equivalent components. But detailed documentation page makes it clear - libvirt only talks with QEMU. It can detect KVM and allow user to create “hardware accelerated guests”, but that case is still handled through QEMU.

It’s also worth noting that libvirt supports multiple virtualization drivers across multiple operating systems, including FreeBSD and macOS.

virsh

virsh is a command line interface for libvirt. That’s it, there’s nothing more to it. If it was created today, it might have been called libvirtctl.

Vagrant

Vagrant abstracts virtual machine management across operating systems. It’s similar to libvirt, in the way that it allows application developers to target Vagrant and leave all the details to someone else. And just like libvirt, Vagrant doesn’t do anything on its own, but pushes all the work to tools lower in the stack.

Vagrant primary audience is software developers. It allows to succinctly define multiple virtual machines, and then start and provision them all with a single command. These machines are usually considered disposable - they might be deleted and re-created multiple times a day. If you want to manually modify the virtual machine and keep these changes for longer period of time, Vagrant is probably not a tool for you.

On Linux, Vagrant usually works with libvirt, but may also work with VirtualBox or Xen.

VirtualBox

VirtualBox is full virtualization solution. It consists of kernel driver and user space programs, including one with graphical interface. You can think of VirtualBox as something parallel to all that we’ve discussed so far.

The main reason to use VirtualBox is ease of use. It offers simpler mental model for virtualization - you just install VirtualBox, start VirtualBox UI and create and run virtual machines. While it does require a kernel module, and kernel is notoriously bad at breaking modules that are not in tree, VirtualBox has a lot of tooling and integrations that will ensure that module is built every time you install or update kernel image. On distributions like Ubuntu you don’t have to think about this at all.

Technically speaking, libvirt supports VirtualBox, so you can use libvirt and virsh to manage VirtualBox machines. In practice that seems to be rare.

Xen

Xen is another virtualization stack. You can think about it as something parallel to KVM/QEMU/libvirt and VirtualBox. What sets Xen apart is that instead of running inside your operating system, it runs below your operating system.

Xen manages scheduling, interrupts and timers - that is, it manages who gets access to computer resources, when, and for how long. Xen is the very first thing that starts when you boot your computer.

But Xen is not an operating system. So right after starting, it starts the guest with one. That guest is special, because it provides drivers to hardware and is the only one with a privilege to talk with Xen. Only through that special guest virtual machine you can create other virtual machines, assign them resources etc.

Xen is one of the oldest virtualization solutions, with first release back in 2003. However, it was included in kernel only in 2010. So while many people prefer Xen and think favorably of its architecture, it seems to lost the popularity contest to KVM and QEMU.

Xen provides the tools required to manage guest virtual machines, but you can also manage them through libvirt.

Summary

I thought to put a visual aid in place of summary. Each column represents a possible stack - you generally want to pick one of them. Items with dashed line style are optional - you can use them, but don’t have to. Click to see the bigger image.

In basically every engineering organization I’ve ever regarded as particularly high functioning, I’ve sat through one specific recurring conversation which is not – a conversation about “complexity”. Things are good or bad because they are or aren’t complex, architectures needs to be redone because it’s too complex – some refactor of whatever it is won’t work because it’s too complex. You may have even been a part of some of these conversations – or even been the one advocating for simple light-weight solutions. I’ve done it. Many times.

When I was writing this, I had a flash-back to a over-10 year old post by mjg59 about LightDM. It would be a mistake not to link it here.

Rarely, if ever, do we talk about complexity within its rightful context – complexity for whom. Is a solution complex because it’s complex for the end user? Is it complex if it’s complex for an API consumer? Is it complex if it’s complex for the person maintaining the API service? Is it complex if it’s complex for someone outside the team maintaining it to understand? Complexity within a problem domain I’ve come to believe, is fairly zero-sum – there’s a fixed amount of complexity in the problem to be solved, and you can choose to either solve it, or leave it for those downstream of you to solve that problem on their own.

Although I believe there is a fixed amount of complexity in the lower bound of a problem, you always have the option to change the problem you're solving!

That being said, while I believe there is a lower bound in complexity to contend with for a problem, I do not believe there is an upper bound to the complexity of solutions possible. It is always possible, and in fact, very likely that teams create problems for themselves while trying to solve a problem. The rest of this post is talking to the lower bound. When getting feedback on an early draft of this blog post, I’ve been informed that Fred Brooks coined a term for what I call “lower bound complexity” – “Essential Complexity”, in the paper “No Silver Bullet—Essence and Accident in Software Engineering”, which is a better term and can be used interchangeably.

Complexity Culture

In a large enough organization, where the team is high functioning enough to have and maintain trust amongst peers, members of the team will specalize. People will begin to engage with subsets of the work to be done, and begin to have their efficacy measured against that part of the organization’s problems. Incentives shift, and over time it becomes increasingly likely that two engineers may have two very different priorities when working on the same system together. Someone accountable for uptime and tasked with responding to outages will begin to resist changes. Someone accountable for rapidly delivering features will resist gates between them and their users. Companies (either wittingly or unwittingly) will deal with this by tasking engineers with both production (feature development) and operational tasks (maintenance), so the difference in incentives isn’t usually as bad as it could be.

The events depicted in this movie are fictitious. Any similarity to any person living or dead is merely coincidental.

When we get a bunch of folks from far-flung corners of an organization in a room, fire up a slide deck and throw up some asperiational to-be architecture diagram in order to get a sign-off to solve some problem (be it someone needs a credible promotion packet, new feature needs to get delivered, or the system has begun to fail and needs fixing), the initial reaction will, more often than I’d like, start to devolve into a discussion of how this is going to introduce a bunch of complexity, going to be hard to maintain, why can’t you make it less complex?

In a high functioning environment, this is a mostly healthy impulse, coming from a good place, and genuinely intended to prevent problems for the whole organization by reducing non-essental complexity. That is good. I'm talking about a conversation discussing removing lower-limit complexity.

Right around here is when I start to try and contextualize the conversation happening around me – understand what complexity is that being discussed, and understand who is taking on that burden. Think about who should be owning that problem, and work through the tradeoffs involved. Is it best solved here, or left to consumers (be them other systems, developers, or users). Should something become an API call’s optional param, taking on all the edge-cases and on, or should users have to implement the logic using the data you return (leaving everyone else to take on all the edge-cases and maintenance)? Should you process the data, or require the user to preprocess it for you?

Carla Geisser described this as being reminicent of the technique outlined in "end to end arguments in system design", which she uses to think about where complexity winds up in a system. It's an extremely good parallel.

Frequently it’s right to make an active and explicit decision to simplify and leave problems to be solved downstream, since they may not actually need to be solved – or perhaps you expect consumers will want to own the specifics of how the problem is solved, in which case you leave lots of documentation and examples. Many other times, especially when it’s something downstream consumers are likely to hit, it’s best solved internal to the system, since the only thing that can come of leaving it unsolved are bugs, frustration and half-correct solutions. This is a grey-space of tradeoffs, not a clear decision tree. No one wants the software manifestation of a katamari ball or a junk drawer, nor does anyone want a half-baked service unable to handle the simplest use-case.

Head-in-sand as a Service

Popoffs about how complex something is, are, to a first approximation, best understood as meaning “complicated for the person making comments”. A lot of the #thoughtleadership believe that an AWS hosted EKS k8s cluster running images built by CI talking to an AWS hosted PostgreSQL RDS is not complex. They’re right. Mostly right. This is less complex – less complex for them. It’s not, however, without complexity and its own tradeoffs – it’s just complexity that they do not have to deal with. Now they don’t have to maintain machines that have pesky operating systems or hard drive failures. They don’t have to deal with updating the version of k8s, nor ensuring the backups work. No one has to push some artifact to prod manually. Deployments happen unattended. You click a button and get a cluster.

On the other hand, developers outside the ops function need to deal with troubleshooting CI, debugging access control rules encoded in turing complete YAML, permissions issues inside the cluster due to whatever the fuck a service mesh is, everyone needs to learn how to use some k8s tools they only actually use during a bad day, likely while doing some x.509 troubleshooting to connect to the cluster (an internal only endpoint; just port forward it) – not to mention all sorts of rules to route packets to their project (a single repo’s binary being run in 3 containers on a single vm host).

Truly i'm not picking on k8s here; I do genuinely believe it when I say EKS is less complex for me to operate well; that's kinda the whole point.

Beyond that, there’s the invisible complexity – complexity on the interior of a service you depend on. I think about the dozens of teams maintaining the EKS service (which is either run on EC2 instances, or alternately, EC2 instances in a trench coat, moustache and even more shell scripts), the RDS service (also EC2 and shell scripts, but this time accounting for redundancy, backups, availability zones), scores of hypervisors pulled off the shelf (xen, kvm) smashed together with the ones built in-house (firecracker, nitro, etc) running on hardware that has to be refreshed and maintained continuously. Every request processed by network ACL rules, AWS IAM rules, security group rules, using IP space announced to the internet wired through IXPs directly into ISPs. I don’t even want to begin to think about the complexity inherent in how those switches are designed. Shitloads of complexity to solve problems you may or may not have, or even know you had.

Do I care about invisible complexity? Generally, no. I don't. It's not my problem and they don't show up to my meetings.

What’s more complex? An app running in an in-house 4u server racked in the office’s telco closet in the back running off the office Verizon line, or an app running four hypervisors deep in an AWS datacenter? Which is more complex to you? What about to your organization? In total? Which is more prone to failure? Which is more secure? Is the complexity good or bad? What type of Complexity can you manage effectively? Which threaten the system? Which threaten your users?

COMPLEXIVIBES

This extends beyond Engineering. Decisions regarding “what tools are we able to use” – be them existing contracts with cloud providers, CIO mandated SaaS products, a list of the only permissible open source projects – will incur costs in terms of expressed “complexity”. Pinning open source projects to a fixed set makes SBOM production “less complex”. Using only one SaaS provider’s product suite (even if its terrible, because it has all the types of tools you need) makes accreditation “less complex”. If all you have is a contract with Pauly T’s lowest price technically acceptable artisinal cloudary and haberdashery, the way you pay for your compute is “less complex” for the CIO shop, though you will find yourself building your own hosted database template, mechanism to spin up a k8s cluster, and all the operational and technical burden that comes with it. Or you won’t and make it everyone else’s problem in the organization. Nothing you can do will solve for the fact that you must now deal with this problem somewhere because it was less complicated for the business to put the workloads on the existing contract with a cut-rate vendor.

Suddenly, the decision to “reduce complexity” because of an existing contract vehicle has resulted in a huge amount of technical risk and maintenance burden being onboarded. Complexity you would otherwise externalize has now been taken on internally. With a large enough organizations (specifically, in this case, i’m talking about you, bureaucracies), this is largely ignored or accepted as normal since the personnel cost is understood to be free to everyone involved. Doing it this way is more expensive, more work, less reliable and less maintainable, and yet, somehow, is, in a lot of ways, “less complex” to the organization. It’s particularly bad with bureaucracies, since screwing up a contract will get you into much more trouble than delivering a broken product, leaving basically no reason for anyone to care to fix this.

I can’t shake the feeling that for every story of technical mandates gone awry, somewhere just out of sight there’s a decisionmaker optimizing for what they believe to be the least amount of complexity – least hassle, fewest unique cases, most consistency – as they can. They freely offload complexity from their accreditation and risk acceptance functions through mandates. They will never have to deal with it. That does not change the fact that someone does.

TC;DR (TOO COMPLEX; DIDN’T REVIEW)

We wish to rid ourselves of systemic Complexity – after all, complexity is bad, simplicity is good. Removing upper-bound own-goal complexity (“accidental complexity” in Brooks’s terms) is important, but once you hit the lower bound complexity, the tradeoffs become zero-sum. Removing complexity from one part of the system means that somewhere else – maybe outside your organization or in a non-engineering function must grow it back. Sometimes, the opposite is the case, such as when a previously manual business processes is automated. Maybe that’s a good idea. Maybe it’s not. All I know is that what doesn’t help the situation is conflating complexity with everything we don’t like – legacy code, maintenance burden or toil, cost, delivery velocity.

• Complexity is not the same as proclivity to failure. The most reliable systems I’ve interacted with are unimaginably complex, with layers of internal protection to prevent complete failure. This has its own set of costs which other people have written about extensively.
• Complexity is not cost. Sometimes the cost of taking all the complexity in-house is less, for whatever value of cost you choose to use.
• Complexity is not absolute. Something simple from one perspective may be wildly complex from another. The impulse to burn down complex sections of code is helpful to have generally, but sometimes things are complicated for a reason, even if that reason exists outside your codebase or organization.
• Complexity is not something you can remove without introducing complexity elsewhere. Just as not making a decision is a decision itself; choosing to require someone else to deal with a problem rather than dealing with it internally is a choice that needs to be considered in its full context.

After reviewing an early draft of this post, Mikey Dickerson described what I was trying to say here back to me as "if you squeeze one part of the water balloon it goes somewhere else", which is a metaphor I've become attached to. Mikey also described these asides as being a Dr. Bronner's label, which I'll own.

Next time you’re sitting through a discussion and someone starts to talk about all the complexity about to be introduced, I want to pop up in the back of your head, politely asking what does complex mean in this context? Is it lower bound complexity? Is this complexity desirable? Is what they’re saying mean something along the lines of I don’t understand the problems being solved, or does it mean something along the lines of this problem should be solved elsewhere? Do they believe this will result in more work for them in a way that you don’t see? Should this not solved at all by changing the bounds of what we should accept or redefine the understood limits of this system? Is the perceived complexity a result of a decision elsewhere? Who’s taking this complexity on, or more to the point, is failing to address complexity required by the problem leaving it to others? Does it impact others? How specifically? What are you not seeing?

What can change?

What should change?

#655 – NOVEMBER 12, 2024
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Tiny Great Languages: Assembly

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Django Bugfix Release Issued: 5.1.3

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PyCon US 2025 (Pittsburgh) Call for Proposals

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Quiz: Beautiful Soup: Build a Web Scraper With Python

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Articles & Tutorials Opposite of Cloud Native Is…?

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This was PyCoder’s Weekly Issue #655.
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Not entirely sure how people use fluxcd, but I guess most people have something like a flux-system flux kustomization as the root to add more flux kustomizations to their kubernetes cluster. Here all of that is living in a monorepo, and as we're all humans people figure out different ways to break it, which brings the reconciliation of the flux controllers down. Thus we set out to do some pre-flight validations.

Note1: We do not use flux variable substitutions for those root kustomizations, so if you use those, you've to put additional work into the validation and pipe things through flux envsubst.

First Iteration: Just Run kustomize Like Flux Would Do It

With a folder structure where we've a cluster folder with subfolders per cluster, we just run a for loop over all of them:

for CLUSTER in ${CLUSTERS}; do pushd clusters/${CLUSTER} # validate if we can create and build a flux-system like kustomization file kustomize create --autodetect --recursive if ! kustomize build . -o /dev/null 2> error.log; then echo "Error building flux-system kustomization for cluster ${CLUSTER}" cat error.log fi popd done Second Iteration: Make Sure Our Workload Subfolder Have a kustomization.yaml

Next someone figured out that you can delete some yaml files from a workload subfolder, including the kustomization.yaml, but not all of them. That left around a resource definition which lacks some other referenced objects, but is still happily included into the root kustomization by kustomize create and flux, which of course did not work.

Thus we started to catch that as well in our growing for loop:

for CLUSTER in ${CLUSTERS}; do pushd clusters/${CLUSTER} # validate if we can create and build a flux-system like kustomization file kustomize create --autodetect --recursive if ! kustomize build . -o /dev/null 2> error.log; then echo "Error building flux-system kustomization for cluster ${CLUSTER}" cat error.log fi # validate if we always have a kustomization file in folders with yaml files for CLFOLDER in $(find . -type d); do test -f ${CLFOLDER}/kustomization.yaml && continue test -f ${CLFOLDER}/kustomization.yml && continue if [[ $(find ${CLFOLDER} -maxdepth 1 \( -name '*.yaml' -o -name '*.yml' \) -type f|wc -l) != 0 ]]; then echo "Error Cluster ${CLUSTER} folder ${CLFOLDER} lacks a kustomization.yaml" fi done popd done

Note2: I shortened those snippets to the core parts. In our case some things are a bit specific to how we implemented the execution of those checks in GitHub action workflows. Hope that's enough to transport the idea of what to check for.

You’ll often need to format and round a Python float to display the results of your calculations neatly within strings. In earlier versions of Python, this was a messy thing to do because you needed to round your numbers first and then use either string concatenation or the old string formatting technique to do this for you.

Since Python 3.6, the literal string interpolation, more commonly known as a formatted string literal or f-string, allows you to customize the content of your strings in a more readable way.

An f-string is a literal string prefixed with a lowercase or uppercase letter f and contains zero or more replacement fields enclosed within a pair of curly braces {...}. Each field contains an expression that produces a value. You can calculate the field’s content, but you can also use function calls or even variables.

[ 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 ]

In 2004, I was living in London, and decided it was time I had my own little virtual private server somewhere online. As a Debian developer since the start of 2000, it had to be Debian, and it still is… This was before “cloud” as we know it today. Virtual Private Servers (VPS) was a … Continue reading "My own little server"

DrupalCamp Berlin 2024 united 150+ Drupal enthusiasts, showcasing AI innovations, inspiring talks, and Dries Buytaert’s vision for the new “Drupal CMS.” From Splash Awards to collaboration highlights, dive into the full recap of this dynamic event!

Another year, another Drupal Camp, and I'm once again delighted by the community, its great atmosphere, and all the things showcased in the talks, informal meetings, hallways, and social events. The motto "Come for the software, stay for the community" remains as relevant as ever. This year, I noticed quite a few new faces. During the closing session, there was a raffle just for first-time attendees at Drupal Camp, and I counted about thirty people in the raffle, so at least 15% were newcomers who, I hope, felt that motto. Additionally, there was significant attendance at the Forcontu introduction to Drupal 10 course, partly because this year the Spanish Drupal Association found the right strategy to bring in students from various institutions. 

As for me, I believe the community remains healthy and strong.

The sessions

Since I still haven't mastered the art of being in two places at once and attending multiple talks simultaneously, I was only able to see a few and hear opinions on some others, so the selection I provide below is incomplete and biased, but I believe it's still interesting.

Drupal future

This year, Drupal Starshot initiative has been launched, aiming to create a new entry point for Drupal, which would be the default download from Drupal.org instead of the Drupal core. The…

Leveraging AI for Optimized Drupal Development: A New Era of Efficiency bhavinhjoshi Tue, 11/12/2024 - 14:12

In the world of web development, staying ahead means being willing to embrace innovation. One of the most transformative forces in technology today is Artificial Intelligence (AI), and its integration with Drupal development is proving to be a pivotal moment.

Why AI?

AI brings automation, precision, and speed to processes that were traditionally time-consuming and manually intensive. In the realm of Drupal development, these qualities unlock unprecedented possibilities, streamlining workflows and enhancing the overall quality of outcomes.

Automated Code Generation

One of the most practical applications of AI in Drupal development is automated code generation. AI tools, like machine learning algorithms, can automate repetitive coding tasks, freeing developers to focus on refining and innovating rather than getting bogged down in routine work.

Imagine AI-powered assistants generating initial code drafts or suggesting code snippets based on programming patterns. This capability can drastically reduce the time developers spend searching for syntax or debugging, accelerating the development cycle and increasing productivity.

Enhanced Security

AI's predictive capabilities allow for the identification and mitigation of potential security vulnerabilities before they become actual threats. In a Drupal environment, this proactive approach translates into safer websites and peace of mind for both developers and clients.

AI can analyze patterns in data traffic, detect anomalies that might suggest a security breach, and even suggest patches or updates to address these vulnerabilities swiftly.

Personalized User Experiences

The demand for personalized user experiences is growing, and AI is poised to meet this challenge head-on. By analyzing vast amounts of user data, AI can predict and tailor content delivery to individual preferences, enhancing engagement and retaining users on Drupal sites.

For instance, AI algorithms can determine the best times to display certain types of content or recommend products, ensuring users are met with relevant information every time they visit a site.

Efficient Data Analysis

Integrating AI with Drupal isn't just about improving front-end experiences, it also offers powerful data analysis capabilities. Developers can gain deep insights from user interactions and site performance metrics, leading to more informed, data-driven decisions that enhance site functionality and user satisfaction.

AI can detect patterns in user behavior, suggest optimizations for content delivery, and even predict future trends, enabling developers to remain agile and responsive to changing user needs.

Challenges and Considerations

While AI offers numerous benefits, it's essential to approach its integration with a clear understanding of its limitations and potential drawbacks. Navigating these challenges effectively requires a commitment to responsible AI usage and continuous learning. It's crucial to stay updated with the latest advancements and best practices. AI models can sometimes produce biased or unexpected results, making it important to have human oversight in place.

Ethical considerations are key. Developers need to ensure that AI tools are used to enhance the user experience while respecting privacy and data protection standards. Transparency with clients about AI interventions and data usage builds trust and aligns with ethical standards.

Training and skill development remain essential. As AI technologies evolve, so should the competencies of those who work with them. Investing in training ensures that teams are equipped to harness AI’s full potential effectively and responsibly.

In summary, AI is not just an add-on, it’s a transformative element that can elevate Drupal development to new heights of efficiency and innovation. By embracing AI thoughtfully, developers can not only streamline processes but also create more secure, personalized, and engaging digital experiences for users.

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