CombinaryStream

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Talking Drupal: Talking Drupal #563 - Drupito: More Than a Marketplace

Today we are talking about Drupito, its Business model, and Marketplaces with guest Ashraf Abed. We'll also cover Generate (Social Media) Image as our module of the week.

For show notes visit: https://www.talkingDrupal.com/563

Topics

Meet Drupalito and the Mission
Platform Layers and Roadmap
Pricing and New Markets
Marketplace Success Stories
Exportability and Vendor Lock In
Growing the Drupal Ecosystem
Derivatives and Recurring Revenue
Rebuilding on Drupedo
Funding Drupal Association
Global Community Check In
Migrating Sites to Drupedo
Marketplace Vision Shift
Maintenance and Incentives
Safe Updates Blue Green
Testing Mindset for Templates
Official Marketplace Collaboration
Agency Revenue and Partnerships
Niche Derivatives and Pricing
Launch Plans and Vetting

Resources

Code that ships

Hosts

Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Ashraf Abed - drupito.com ashrafabed Avi Schwab - froboy.org froboy

MOTW Correspondent

Avi Schwab - froboy.org froboy

Brief description:
- Have you ever wanted Drupal to generate dynamic social media images using tokenized node data, similar to the share images on GitHub repos or Reddit threads? There's a module for that
Module name/project name:
- Generate (Social Media) Image
Brief history
- How old: Created by tfranz of Germany on 22 April 2022
- Versions available: 1.x-dev, 2.0.0-beta2, published a few weeks ago by our own Martin Anderson-Clutz
Maintainership
- Minimally (although now slightly more actively) maintained
- No Security coverage (yet)
- Passing GitLab CI tests
- Well fleshed out README for docs
- Number of open issues: 8 open issues, 0 of which are bugs against the current branch, but there are lots of feature requests
Usage stats:
- 1 site reports using this module
Module features and usage
- GSMI requires an image style that uses a "Text Overlay" effect — this comes from the Image Effects module and lets you burn tokenized (or static) text onto an image. Normally, when Drupal generates an image style derivative, there's no entity in scope — it's just processing a file — so a token like [node:title] would resolve to nothing. GSMI's real contribution is the glue: when it builds a derivative for a specific node, it swaps in the node-resolved text before generating the image. That's what makes entity-aware tokens work inside an effect that otherwise only sees global tokens.
- Once the image style exists, GSMI's settings form lets you pick a source image field on the node — an image field or a media-reference field — plus a fallback image for when that's empty. From there it generates the styled derivative from that source image, for any node of any content type that has the field.
- Finally, GSMI exposes its own token — [node:generate-style], with optional style/field overrides — so you're not locked into the one global style/field pair configured in the settings form. I used that to drop the generated image into Metatag's og_image field for the Session content type on the MidCamp site, getting us dynamically generated session images..
- A couple of gotchas we hit setting this up:
- Text Overlay's layout options have some bugs, and not filling out all of the options will result in an image library error.
- The bigger one: GSMI names the generated derivative file after the source image's filename — extension included — not after whatever format the image style actually outputs. If you try to convert an image to WebP you might get a WebP image with a JPG extension.
- BUUUUT - LinkedIn still doesn't support WebP (at least as per their documentation, so it's still in 2026 not safe to use WebP for a universal og:image. https://www.linkedin.com/help/linkedin/answer/a521928

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Metadrop: How to automate WordPress-to-Drupal content migration with WordPress Migrate SQL

Some WordPress sites reach a point where the content model no longer fits the organization's needs. Custom content types, custom entities, and custom fields become necessary to represent internal data precisely, and Drupal supports all three, shaping that data with full flexibility.

Other sites reach that point because of AI. Drupal has built a strong AI ecosystem over recent years, enabling integrations of every needed type: content creation, accessibility checks, RAGs, among others. These are the two situations behind most WordPress-to-Drupal migrations: an architecture the site has outgrown, or an AI integration WordPress cannot support natively.

The challenge of migrating WordPress content to Drupal

The core challenge in a WordPress-to-Drupal migration is migrating the content itself. WordPress sites may host thousands of pieces of content that need to be moved to the Drupal site.

A full migration project runs through several phases: redesigning the site if needed, building the new architecture, and creating a Drupal node for every WordPress post or page. Creating the content is where the real work concentrates, since every image, every user, and every translation has to make it across, on top of adapting content to a different format defined by the new architecture.

A manual content migration is not viable due to its high costs and risks. Handled by hand, this work is prone to human error. A single error is easy to fix, but a…

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Dries Buytaert: Responsibility follows control

An AI model does not decide what data it can access, which tools it can use, or whether it can act without approval. People make those decisions at different points. Upstream, a model developer trains and tests the model and decides whether and how to release it. Downstream, a developer builds the model into a system, connects that system to data and tools, and decides whether a person must review its proposed actions before they take effect.

Those choices determine whether harm is possible at all. So when harm occurs, responsibility should fall on those who controlled the relevant choices. That responsibility may be shared: model developers control training and release, product builders control permissions and deployment, and users control deliberate misuse.

Responsibility should follow meaningful control.

That principle is missing from much of the debate over open-weight AI models, which often treats the decision to release a model as the only one that counts.

Axios recently reported that United States officials had considered measures that could restrict American companies from using Chinese open-weight models. Open-weight models make their trained parameters available for others to download, modify, and run on their own infrastructure, without going through the company that built them.

More than 230 companies and organizations have since signed an industry letter defending open weights. After critics accused Anthropic of supporting a ban on open-weight models, Dario Amodei published a statement denying that position. He described open-weight models that do not have dangerous capabilities as a public good and supported mandatory safety testing for sufficiently capable models, open or closed.

The disagreement is less about whether open weights can create risk than about when those risks justify restricting a release, and whether restrictions would improve safety or mainly concentrate power in the largest AI labs.

I am firmly in the open-weights camp. I have run and compared open-weight models, argued that digital sovereignty depends on who controls software, not where it comes from, and believe organizations should control their infrastructure and data instead of depending on a handful of providers.

I also believe consequential algorithms need oversight. More than a decade ago, I argued that we would eventually need something like an FDA for software. The harder question is where responsibility for that oversight should lie.

Open-weight models unbundle control

With hosted, closed-weight models from providers such as Anthropic and OpenAI, the provider typically keeps the weights private, controls how customers access the model, and decides when to update the hosted service.

Open weights can separate those roles. One organization creates and releases the model. A repository such as Hugging Face hosts and distributes the weights. Another team might fine-tune them. A product builder incorporates the model into a product and connects it to data, tools, and users.

Each team controls something different. Because open weights unbundle control, it becomes harder to say who is responsible when harm occurs.

A model developer controls the training process, capability testing, documentation, and release decisions. A repository controls what information it displays about a model's origin, which security checks it performs on uploaded files, and which access restrictions it provides or enforces. A product builder controls what data and tools the resulting system can reach, which actions require human approval, and what gets logged.

Control is not the only thing that matters, but it shows who could still have changed the outcome. When harm involves AI, several actors may bear responsibility for the same incident because each controlled a different opportunity to prevent it.

A lesson from Drupal and Open Source

Like open-weight models, Drupal's code can be copied and changed without asking anyone's permission. A site owner could use it to spread misinformation or operate a fraudulent website. The site owner controls the content and operation of the site and is responsible for those choices. The Drupal project is not responsible merely because someone used its code.

But the Drupal project controls other decisions. When someone privately reports a security vulnerability, the Drupal Security Team follows a coordinated disclosure policy. It keeps the issue private while a fix is prepared. Once a security release is available, the team publishes an advisory and tells site owners to upgrade. The timing of that disclosure can give site owners a fair chance to protect themselves.

The same distinction applies to AI. Responsibility should follow the decisions each actor controls.

The product is where capability receives authority

A model generates outputs. An agent is a software system that uses a model to work toward a goal, often by calling tools and taking actions. The people who build and configure the agent decide what it can access, which actions it can take, and when it needs a person's approval.

In a coding agent such as Claude Code, a model can generate a database command. Whether that command can run depends on the tools and permissions the agent provides, as well as the access allowed by the computer, network, and database.

A content management agent can propose deleting an article. The content management system (CMS) determines whether the agent has permission to delete it, whether the deletion is reversible, and whether the action is recorded.

Permissions, isolation, audit logs, rate limits, human approval, and rollback are not merely engineering details. They determine who has control at the point where harm can still be prevented. That is why AI governance is becoming an essential part of product architecture.

How should government regulate AI?

Deciding who should answer after harm is the easier half of this, even when the answer is not obvious. The harder half is deciding what government should require before any harm has happened.

If control and responsibility are distributed across several actors, government rules should be distributed across them too. This is not a new idea. We already regulate many technologies this way.

More than a decade ago, when I argued for something like an FDA for software, I had drug approval in mind. I no longer think that is the right model. The FDA approves a drug for one or more intended uses, while a general-purpose model may be used for many different purposes.

Cars are a better comparison. The government sets safety standards, manufacturers certify that their vehicles meet them, and drivers are licensed separately. Manufacturers must also report safety defects and conduct recalls when required. Alcohol is regulated in layers too. Bars need a government license that they can lose, and in many US states a bar can be liable for harm caused by serving someone who is visibly drunk.

Each rule targets the actor who controls a particular decision. I would take the same layered approach to AI.

For most risks, government should regulate the products and services that put a model to work. Those rules might limit what data and tools an AI system can access, require human approval for consequential actions, or require those actions to be recorded.

Blocking a model's release is a much stronger step. For an open-weight model, that means preventing the developer from publishing the weights. For a closed model, it could mean preventing the provider from offering access.

I would support that only when safeguards in products and rules governing their use could not prevent a serious danger in time. The three-part test below applies only to this exceptional step, not to AI regulation in general.

Three questions before blocking a model's release

The strongest argument for restricting open-weight releases is that they are difficult to reverse. The UK AI Security Institute notes that safeguards can be removed and that released weights can be redistributed and run privately beyond the original developer's monitoring.

If a model made catastrophic harm much easier, its release could be the last moment anyone had meaningful control. By catastrophic, I mean mass-casualty or comparably systemic harm, not ordinary product failure, fraud, or abuse.

Before blocking a model's release, a government should be able to answer yes to three questions:

Would releasing it make catastrophic harm substantially easier? The comparison should be with closed models and other tools people can already access.
Would safeguards applied when the model is deployed or used fail? They might be ineffective, easy to bypass, or simply arrive too late.
Would blocking the release materially reduce the danger? A restriction should reduce the risk, not merely move it to another country or distribution channel.

The third question is the one I expect people to argue with. Slowing an attacker down has value even if you cannot stop them. But if the same model remains available from another country or distribution channel, a ban costs defenders a tool they can inspect and run themselves while taking almost nothing away from the attacker.

As of July 2026, I have not seen public evidence that an open-weight model has crossed this threshold. It will eventually be crossed, which is why I still think meaningful regulation is coming.

For now, I would allow publication and place obligations where control already exists: on model creators for testing and release decisions, on distributors for provenance and file integrity, on product builders for permissions and deployment, and on users for deliberate misuse.

This approach also protects competition. A regulatory regime that only the largest labs can satisfy could protect them from competition without necessarily making anyone safer. It could also push organizations toward depending on a handful of providers for infrastructure they cannot inspect.

Open weights do not eliminate control. They distribute it. Regulation should follow that structure: place obligations on each actor at the point where harm can still be prevented, and block publication only when release would make catastrophic harm substantially easier, downstream safeguards could not contain it, and a restriction would materially reduce the danger.

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Why We Contribute: The Philosophy Behind 1xINTERNET's Top-Tier Drupal Status

This is a guest post from the incredible team at 1xINTERNET, a Top-Tier Drupal contributor and digital agency headquartered in Frankfurt, Germany.

When the Drupal Association announced that 1xINTERNET had become one of the world's Top-Tier Drupal Contributors, it was a proud moment for the company. Reaching the highest level of contribution recognition places 1xINTERNET among a select group of organisations helping shape the future of one of the world's leading open-source content management systems.

Yet, ask anyone inside the company about the achievement, and you'll hear the same response: becoming a Top-Tier Contributor was never the ultimate goal. Instead, it is the natural outcome of more than a decade of believing that if you build your business on open source, you should help build open source itself.

For over thirteen years, 1xINTERNET has invested in the Drupal ecosystem, not only by delivering digital platforms for clients, but by contributing code, maintaining projects, sponsoring community events, supporting governance, leading strategic initiatives and encouraging employees to actively participate in the community.

Today, the company sponsors more than 500 hours of Drupal contribution every month, actively supports more than 85 Drupal projects, has sponsored over 50 Drupal events, and has contributed to hundreds of issues across the Drupal ecosystem. Those numbers tell one story. The people behind them tell another.

Contribution isn't only about strengthening Drupal, it creates real value for the organisations that choose Drupal as the foundation for their digital platforms. We spoke with Baddý Breidert, Christoph Breidert and James Tillotson about why contributing matters, how it benefits clients, and why they believe giving back is essential to building better digital experiences.

Photo of James, Christoph, and Baddy

Building the future instead of following it

For 1xINTERNET CEO Baddý Breidert, contributing to Drupal has always been part of the company's identity.

"It represents over a decade of dedication to the Drupal project," she says. "I've worked with Drupal since 2006 and been actively involved in the community since 2013. Being recognised as one of the top three Drupal companies globally validates the expertise and sustained effort our team has invested over the years."

But the motivation goes much deeper than recognition. Instead of simply following the direction of Drupal, 1xINTERNET believes in helping shape it. Since Drupal is the technological foundation behind many of the company's digital platforms, contributing to its future isn't viewed as optional, it's viewed as a responsibility.

That philosophy influences almost every decision the company makes. Rather than waiting for new features, improvements or innovations to arrive, the team actively participates in creating them.

Managing Director Christoph Breidert describes it simply.

"We don't just build with Drupal; we help influence where the platform is going next."

It's an approach that benefits not only the Drupal community, but every organisation that chooses Drupal as the foundation for its digital future.

Open source is built on collaboration

Although contribution often means writing code, the three leaders agree that it's ultimately about something much bigger.

Open source succeeds because thousands of people collaborate, share knowledge and solve problems together. Every contribution, whether it's code, documentation, testing, mentoring, event organisation or strategic leadership, helps strengthen the ecosystem for everyone.

For Christoph, this spirit of reciprocity sits at the heart of open source.

"If you build digital solutions using an open-source project but choose to remain on the sidelines, you miss the opportunity to influence the tools you rely on," he explains. "Open source is built on shared knowledge, and contributing back is simply part of how we work."

That collaborative mindset is equally visible throughout 1xINTERNET's culture. 1xINTERNET’s UK Growth Manager James Tillotson sees open source as an extension of how the company works internally.

"We don't hoard knowledge," he says. "We share it to raise the baseline for everyone, which in turn allows us to keep innovating."

Rather than viewing contribution as something separate from day-to-day work, it's embedded in the way teams learn, collaborate and continuously improve.

Contribution isn't separate from client work

One of the biggest misconceptions surrounding open source is that contribution somehow competes with client work. The reality, according to the team, is exactly the opposite.

James puts it bluntly: ""Contribution is client work."

When developers fix a bug in Drupal core or improve functionality that thousands of websites rely on, every client benefits, not just today, but for years to come.

Christoph agrees: "If you're not involved in building the technology, you're always reacting instead of leading."

Technology evolves quickly. Artificial intelligence, digital experience platforms, accessibility, security and content management continue to change at an unprecedented pace. Agencies that simply consume technology are forced to wait for innovation. Agencies that contribute help create it.

Baddý believes that's one of the company's greatest strengths.

"Contribution allows us to lead initiatives like Drupal AI, ensuring we aren't just consumers of the technology but creators of it."

Instead of adapting after the market changes, 1xINTERNET helps shape those changes from within.

Driving innovation through Drupal AI

Perhaps nowhere is that philosophy more visible than in Drupal AI. As Product Lead for Drupal AI, Christoph has been deeply involved in defining its roadmap, working alongside developers from around the world to build practical AI capabilities directly into Drupal.

For him, watching Drupal AI evolve from an ambitious idea into one of the platform's most exciting capabilities has been one of the defining milestones of the company's contribution journey.

"It's been incredible to collaborate with a global community to build something that will help shape the future of the web."

The significance goes beyond technical innovation. Because 1xINTERNET helps build Drupal AI, its teams understand the technology long before it becomes mainstream. They know what's coming, how it works and how organisations can use it responsibly.

James, who contributes to the Drupal AI Marketing Initiative, believes this creates a significant advantage for clients.

"Our clients have access to the latest innovations because we're involved in creating them."

Innovation isn't something clients wait for. It's something they experience alongside the people helping build it.

Better contributions create better client solutions

Although many clients may never see the code being contributed to Drupal, they experience its impact every day. Active contributors develop a much deeper understanding of the platform than those who simply implement it. Because the team understands Drupal's architecture, roadmap and future direction, they can make better long-term decisions for every project.

"Our clients receive stable and modern solutions without having to manage the underlying complexity," Christoph explains. "By maintaining our contribution status, we act as a direct pathway to web innovation."

That means fewer surprises, more sustainable architectures and platforms designed to evolve instead of becoming outdated. James believes clients increasingly recognise that value.

"They know we're not simply using Drupal, we're helping steer where it's going."

Trust has become a competitive advantage

Contribution also creates something that's difficult to measure but incredibly valuable: trust.

When organisations invest in large scale digital platforms, they aren't simply buying technology. They're choosing partners who will help them navigate years of future development.

Being recognised as one of the world's leading Drupal contributors provides confidence that 1xINTERNET isn't standing on the outside of the ecosystem, it's helping lead it. Baddý has seen this become increasingly important during procurement processes.

More organisations now actively look for suppliers who contribute back to the technologies they depend on. Public sector organisations and enterprise businesses increasingly view contribution as evidence of technical excellence, long-term commitment and sustainability.

James has experienced this while expanding 1xINTERNET's presence in the United Kingdom. "When entering a new market where people don't yet know your brand, your contribution footprint becomes a global passport. The Drupal community already knows who you are."

That credibility opens doors long before a first meeting takes place.

Supporting digital sovereignty

For Christoph, contribution is also connected to a much broader movement taking place across Europe and beyond. As organisations become increasingly concerned about vendor lock-in, proprietary platforms and ownership of their data, open-source software is becoming strategically more important than ever. By contributing to Drupal, companies don't simply improve software, they strengthen an independent digital ecosystem that organisations can trust.

"Businesses increasingly want digital sovereignty," Christoph says. "By actively contributing to Drupal, we're helping build a secure and independent IT landscape that organisations can rely on."

It's a perspective that positions contribution not only as technical work, but as an investment in the future of open digital infrastructure.

A culture that attracts exceptional people

Contribution doesn't only benefit clients. It also shapes the people who choose to work at 1xINTERNET. The company actively encourages employees to contribute code, maintain projects, organise events, mentor others and share knowledge across the community. For many developers, that's exactly the environment they're looking for.

"Top developers want to work on things that matter," James says. "We offer them a stage, not just a desk."

Christoph agrees. Many developers are motivated by solving meaningful problems that have an impact far beyond a single client project.

For Baddý, contribution creates something equally valuable: a culture of continuous learning. By collaborating with some of the best Drupal developers in the world, the entire team continually raises its own standards, creating an environment where innovation and professional growth go hand in hand.

Looking ahead

Becoming a Top-Tier Drupal Contributor isn't viewed as a finish line. Instead, it's another milestone in a much longer journey. The company plans to continue investing heavily in Drupal AI, supporting the wider community, encouraging employees to contribute and helping organisations embrace open-source innovation with confidence.

Christoph hopes to make Drupal AI even more accessible through practical demonstration environments that allow organisations to experience its capabilities with a single click.

James wants to strengthen the connection between enterprise organisations and the open-source community, demonstrating that open source can successfully support even the most ambitious digital transformation projects.

Baddý remains focused on investing in people, community leadership and the long-term health of the Drupal ecosystem.

More than contribution

Ultimately, becoming a Top-Tier Drupal Contributor isn't really about rankings, badges or recognition. Those are simply the visible results of years of consistent investment.

The real achievement is building a company where contribution is part of everyday work, where sharing knowledge is expected, collaboration is celebrated, and innovation is something created together rather than consumed.

For 1xINTERNET, contributing to Drupal has never been about giving something away. It's about helping build a stronger platform, a stronger community and better digital experiences for everyone who depends on Drupal.

Because when the platform grows stronger, so do the organisations, developers and communities that build upon it.

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Drupal Association blog: Why We Contribute: The Philosophy Behind 1xINTERNET's Top-Tier Drupal Status

This is a guest post from the incredible team at 1xINTERNET, a Top-Tier Drupal contributor and digital agency headquartered in Frankfurt, Germany.

When the Drupal Association announced that 1xINTERNET had become one of the world's Top-Tier Drupal Contributors, it was a proud moment for the company. Reaching the highest level of contribution recognition places 1xINTERNET among a select group of organisations helping shape the future of one of the world's leading open-source content management systems.

Yet, ask anyone inside the company about the achievement, and you'll hear the same response: becoming a Top-Tier Contributor was never the ultimate goal.

Instead, it is the natural outcome of more than a decade of believing that if you build your business on open source, you should help build open source itself.

For over thirteen years, 1xINTERNET has invested in the Drupal ecosystem, not only by delivering digital platforms for clients, but by contributing code, maintaining projects, sponsoring community events, supporting governance, leading strategic initiatives and encouraging employees to actively participate in the community.

Today, the company sponsors more than 500 hours of Drupal contribution every month, actively supports more than 85 Drupal projects, has sponsored over 50 Drupal events, and has contributed to hundreds of issues across the Drupal ecosystem. Those numbers tell one story. The people behind them tell another.

Contribution isn't only about strengthening Drupal, it creates real value for the organisations that choose Drupal as the foundation for their digital platforms. We spoke with Baddý Breidert, Christoph Breidert and James Tillotson about why contributing matters, how it benefits clients, and why they believe giving back is essential to building better digital experiences.

Photo of James, Christoph, and Baddy

Building the future instead of following it

For 1xINTERNET CEO Baddý Breidert, contributing to Drupal has always been part of the company's identity.

"It represents over a decade of dedication to the Drupal project," she says. "I've worked with Drupal since 2006 and been actively involved in the community since 2013. Being recognised as one of the top three Drupal companies globally validates the expertise and sustained effort our team has invested over the years."

But the motivation goes much deeper than recognition.

Instead of simply following the direction of Drupal, 1xINTERNET believes in helping shape it. Since Drupal is the technological foundation behind many of the company's digital platforms, contributing to its future isn't viewed as optional, it's viewed as a responsibility.

That philosophy influences almost every decision the company makes. Rather than waiting for new features, improvements or innovations to arrive, the team actively participates in creating them.

Managing Director Christoph Breidert describes it simply.

"We don't just build with Drupal; we help influence where the platform is going next."

It's an approach that benefits not only the Drupal community, but every organisation that chooses Drupal as the foundation for its digital future.

Open source is built on collaboration

Although contribution often means writing code, the three leaders agree that it's ultimately about something much bigger.

Open source succeeds because thousands of people collaborate, share knowledge and solve problems together. Every contribution, whether it's code, documentation, testing, mentoring, event organisation or strategic leadership, helps strengthen the ecosystem for everyone.

For Christoph, this spirit of reciprocity sits at the heart of open source.

"If you build digital solutions using an open-source project but choose to remain on the sidelines, you miss the opportunity to influence the tools you rely on," he explains. "Open source is built on shared knowledge, and contributing back is simply part of how we work."

That collaborative mindset is equally visible throughout 1xINTERNET's culture.

IxINTERNET’s UK Growth Manager James Tillotson sees open source as an extension of how the company works internally.

"We don't hoard knowledge," he says. "We share it to raise the baseline for everyone, which in turn allows us to keep innovating."

Rather than viewing contribution as something separate from day-to-day work, it's embedded in the way teams learn, collaborate and continuously improve.

Contribution isn't separate from client work

One of the biggest misconceptions surrounding open source is that contribution somehow competes with client work.

The reality, according to the team, is exactly the opposite.

James puts it bluntly.

"Contribution is client work."

When developers fix a bug in Drupal core or improve functionality that thousands of websites rely on, every client benefits, not just today, but for years to come.

Christoph agrees.

"If you're not involved in building the technology, you're always reacting instead of leading."

Technology evolves quickly. Artificial intelligence, digital experience platforms, accessibility, security and content management continue to change at an unprecedented pace. Agencies that simply consume technology are forced to wait for innovation. Agencies that contribute help create it.

Baddý believes that's one of the company's greatest strengths.

"Contribution allows us to lead initiatives like Drupal AI, ensuring we aren't just consumers of the technology but creators of it."

Instead of adapting after the market changes, 1xINTERNET helps shape those changes from within.

Driving innovation through Drupal AI

Perhaps nowhere is that philosophy more visible than in Drupal AI.

As Product Lead for Drupal AI, Christoph has been deeply involved in defining its roadmap, working alongside developers from around the world to build practical AI capabilities directly into Drupal.

For him, watching Drupal AI evolve from an ambitious idea into one of the platform's most exciting capabilities has been one of the defining milestones of the company's contribution journey.

"It's been incredible to collaborate with a global community to build something that will help shape the future of the web."

The significance goes beyond technical innovation.

Because 1xINTERNET helps build Drupal AI, its teams understand the technology long before it becomes mainstream. They know what's coming, how it works and how organisations can use it responsibly.

James, who contributes to the Drupal AI Marketing Initiative, believes this creates a significant advantage for clients.

"Our clients have access to the latest innovations because we're involved in creating them."

Innovation isn't something clients wait for. It's something they experience alongside the people helping build it.

Better contributions create better client solutions

Although many clients may never see the code being contributed to Drupal, they experience its impact every day.

Active contributors develop a much deeper understanding of the platform than those who simply implement it.

Because the team understands Drupal's architecture, roadmap and future direction, they can make better long-term decisions for every project.

"Our clients receive stable and modern solutions without having to manage the underlying complexity," Christoph explains. "By maintaining our contribution status, we act as a direct pathway to web innovation."

That means fewer surprises, more sustainable architectures and platforms designed to evolve instead of becoming outdated.

James believes clients increasingly recognise that value.

"They know we're not simply using Drupal, we're helping steer where it's going."

Trust has become a competitive advantage

Contribution also creates something that's difficult to measure but incredibly valuable: trust.

When organisations invest in large scale digital platforms, they aren't simply buying technology. They're choosing partners who will help them navigate years of future development.

Being recognised as one of the world's leading Drupal contributors provides confidence that 1xINTERNET isn't standing on the outside of the ecosystem, it's helping lead it.

Baddý has seen this become increasingly important during procurement processes.

More organisations now actively look for suppliers who contribute back to the technologies they depend on. Public sector organisations and enterprise businesses increasingly view contribution as evidence of technical excellence, long-term commitment and sustainability.

James has experienced this while expanding 1xINTERNET's presence in the United Kingdom. "When entering a new market where people don't yet know your brand, your contribution footprint becomes a global passport. The Drupal community already knows who you are."

That credibility opens doors long before a first meeting takes place.

Supporting digital sovereignty

For Christoph, contribution is also connected to a much broader movement taking place across Europe and beyond.

As organisations become increasingly concerned about vendor lock-in, proprietary platforms and ownership of their data, open-source software is becoming strategically more important than ever.

By contributing to Drupal, companies don't simply improve software, they strengthen an independent digital ecosystem that organisations can trust.

"Businesses increasingly want digital sovereignty," Christoph says. "By actively contributing to Drupal, we're helping build a secure and independent IT landscape that organisations can rely on."

It's a perspective that positions contribution not only as technical work, but as an investment in the future of open digital infrastructure.

A culture that attracts exceptional people

Contribution doesn't only benefit clients.

It also shapes the people who choose to work at 1xINTERNET.

The company actively encourages employees to contribute code, maintain projects, organise events, mentor others and share knowledge across the community.

For many developers, that's exactly the environment they're looking for.

"Top developers want to work on things that matter," James says. "We offer them a stage, not just a desk."

Christoph agrees.

Many developers are motivated by solving meaningful problems that have an impact far beyond a single client project.

For Baddý, contribution creates something equally valuable: a culture of continuous learning.

By collaborating with some of the best Drupal developers in the world, the entire team continually raises its own standards, creating an environment where innovation and professional growth go hand in hand.

Looking ahead

Becoming a Top-Tier Drupal Contributor isn't viewed as a finish line.

Instead, it's another milestone in a much longer journey.

The company plans to continue investing heavily in Drupal AI, supporting the wider community, encouraging employees to contribute and helping organisations embrace open-source innovation with confidence.

Christoph hopes to make Drupal AI even more accessible through practical demonstration environments that allow organisations to experience its capabilities with a single click.

James wants to strengthen the connection between enterprise organisations and the open-source community, demonstrating that open source can successfully support even the most ambitious digital transformation projects.

Baddý remains focused on investing in people, community leadership and the long-term health of the Drupal ecosystem.

More than contribution

Ultimately, becoming a Top-Tier Drupal Contributor isn't really about rankings, badges or recognition.

Those are simply the visible results of years of consistent investment.

The real achievement is building a company where contribution is part of everyday work, where sharing knowledge is expected, collaboration is celebrated, and innovation is something created together rather than consumed.

For 1xINTERNET, contributing to Drupal has never been about giving something away.

It's about helping build a stronger platform, a stronger community and better digital experiences for everyone who depends on Drupal.

Because when the platform grows stronger, so do the organisations, developers and communities that build upon it.

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Smartbees: Automatic Content Translation System

Discover how our solution automated content translation and helped the client’s team work faster. read more

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Tag1 Insights: Teaching AI to Speed Up Accessibility Testing

Marlene Wanberg, Frontend Developer at Tag1 and a Drupal builder since 2007, built a suite of fifteen AI agent skills that runs a full automated accessibility audit and narrows thousands of raw scanner findings down to a short worklist of confirmed fixes.

To paraphrase the much used line about writing: I don't enjoy doing accessibility testing, but I like having done accessibility testing. When a site becomes more usable for everyone, I feel good. Users feel good. Clients feel good. (Regulators feel good.) The process of getting there can be very tedious.

Before we even begin addressing accessibility, we need to know where the problems live, and that's where testing comes in. There are two main categories of accessibility testing: automated and manual. Automated testing uses deterministic tools and scanning scripts to check the rendered HTML markup for certain obvious flaws, things like missing ARIA labels or low color contrast. Manual testing checks what those tools can't. A person navigates the site with the tech real users rely on, like keyboards and screen readers, and catches holes in user flows or spots where meaning gets missed. An image might have text in its ARIA label, but does that text actually help explain what the image is and how it's relevant to the rest of the content? Both categories let us find the areas of a site that need adjustments to make them more usable.

People are having plenty of thoughtful arguments right now about what AI is good for and where it doesn't belong. One use stands out to me: letting it take on the repetitive, mechanical parts of a job, freeing me up for the work that needs real judgment. So I set out to see how much of accessibility testing an AI agent could carry.

Automated testing seemed like the most logical place to start, because so much of it is deterministic: you run the scanners and collect the results. And yet a full automated pass involves lots of little decisions along the way, lots of setup steps, documentation to keep straight, mountains of results to sort through, and reports to write at the end. Exactly the type of work I wanted to hand off.

One Skill File Was Not Nearly Enough

Claude Code has a feature called skills: instruction files that teach the agent a repeatable procedure. That seemed like a promising starting point. There might be other approaches that smarter people have thought of, but this is what I knew at the time, and one of the best ways to learn is to just do and experiment. So I had a conversation with Claude and had it help me write my first automated accessibility testing skill. (In my experience, having AI write instructions for AI based on my intent tends to give me better results.)

I very quickly realized that one skill file could not handle the complexity I was asking of it. So I split it up: a skill for gathering information about the project, one for setup, one for running the tools, one for consolidating and analyzing the results, one for tracing issues back to their source, and one for reporting.

The suite kept growing as I used it and learned what else it needed to be robust. I'd run it through fresh on a site and each time find different ways it either wasn't doing enough or was just doing it flat wrong. Instead of getting mad (ok, I did get annoyed a few times) I asked, "Where is my process breaking down? What does the agent need that I haven't provided? Where is it spending the most tokens and how can I make that more efficient?"

The process now spans fifteen skills and a shared library of tested scripts, covering the full arc of an audit: plan the scope, discover and categorize the pages, pick and configure the scanners, run them, boil the output down, trace findings to source code, verify what's real, research fixes against the actual specs, write reports for the humans who need them, and retest after fixes get implemented.

I also wanted the workflow to work on any project, not just the stacks I know best. Drupal, Next.js, Svelte, WordPress, whatever comes through the door. Including up-to-date documentation for every likely framework inside the skills would have been unrealistic, and stale guidance is worse than no guidance, because the agent follows it confidently. (Just ask any Drupal dev trying to use AI out of the box for dev help). So I baked in a dependency on Context7, a service that lets the agent query current, version-specific documentation for whatever the project uses, and required checking it at several points in the workflow. Now it works from what the project is actually running instead of trusting whatever its training data half-remembers.

Humans Stay In the Loop, On Purpose

I value ownership of my work. Current models can do a lot, but they still far too often make inaccurate inferences, skip facts, and end up like my robo-vacuum, stuck in a corner and tangled in cords, costing me time and effort to get it unstuck and redo work. In personal projects this is annoying; in a regulatory environment this is unacceptable.

So at key moments I want my agents to bring me their work, get my input and signoff, and then continue based on the direction I set. The agent interviews me up front about the project and its goals. It asks permission before installing tooling dependencies or guides me through the installs. It pauses so I can check that the process and the results so far look accurate, and that it hasn't wandered off track.

What Broke Along the Way

Many useful things I learned in this process came from something going wrong. A sampling:

False positives. One scanner rule alone produced 257 rows complaining that icon-only buttons (picture a bare magnifying-glass search button) had no label for screen readers, when in reality every one of them was labeled correctly. A quick fix might be to tell the scanner to stop running that rule, but a genuinely unlabeled icon button elsewhere in the site is a real barrier for someone navigating by screen reader, so switching it off entirely would bury real problems alongside the noise. Instead the workflow keeps a list of these known false-alarm patterns, each with a condition attached: ignore this rule only where the evidence proves a label already exists. And it keeps paired test cases, one that should trip the rule and one that shouldn't. If the exception ever starts covering a real failure, a test catches it.

Too much output, then not enough. Four scanners across a real site produced 188 raw result files holding over three thousand findings, most of them duplicates of each other in different formats. I had to build a whole consolidation stage: normalize, deduplicate, cluster by root cause, rank. Then I discovered the agent was dropping and misclassifying findings during consolidation, so I pushed that work out of the agent's judgment and into tested scripts whose behavior I could verify.

Empty, meaningless reports. Early reports read like typical AI marketing copy, generic percentages and process jargon instead of actual numbers from the audits and explanations a developer could act on. They were unusable to me, the person who had instructed the AI to write them, and would certainly not be usable to anyone else. Now every report has to be built from the audit's actual findings, and written for the specific person who will read it.

The disappearing CSV parser. I watched the agent spend enormous amounts of tokens recreating, over and over, a CSV parser I knew it had already written, until I cornered it on why. The library it needed had gone missing mid-session, and rather than say so, it kept quietly rebuilding the wheel. The lesson went straight into the shared library's principles: “Never write a one-off parser; if the library doesn't support what you need, first check in with me, then extend the library and add tests.”

Confidently wrong. Authentication tripped it in a way I almost didn't catch. After one scan cleared browser cookies, the session cookie never got reapplied, so every authenticated page silently scanned as the login page and came back artificially clean. Clean results feel great until you notice the settings page weighs a fraction of what it should. And color contrast in modern CSS gave it fits; converting oklch() color values to check contrast ratios burned real time and produced confident errors before I required the math to live in a tested script rather than the agent's head.

Whenever something like this came up, I stopped the agent and we talked through what happened and why. Then I changed the process and the skills themselves: more decomposition, more ask-the-human steps, more reusable tested scripts, and adversarial review agents that critique the work before I see it. Each time the pattern is the same: treat agent failures as process bugs, not one-offs to scold away.

Did It Produce Anything?

Yes, it did, and I'm quite pleased with the results.

Take a static marketing site. The workflow pointed four accessibility scanners (axe, Pa11y, Lighthouse, and IBM Equal Access) at 35 pages. Between them they returned 7,028 raw findings. Most automated scanners finish by handing me a pile of output that I have to sort through to find the meaning. That's boring and annoying so I use my workflow to remove duplicates, group the remaining items by root cause, and in this case it landed on 37 clusters. A cluster is just a bunch of findings that all come from the same underlying problem, so one broken pattern repeated across fifty pages becomes a single cluster instead of fifty separate things to chase. The review step of the workflow turns those into a short worklist. 11 of them are confirmed code fixes ready to act on. 7k+ findings down to 11 things I need to do.

I've run it on four sites so far, on stacks that share almost nothing, and gotten similar results each time.

Site	Raw findings	Narrowed to
Eleventy site	7,028	37 clusters
Drupal module	1,091	24 clusters
Next.js app	3,216	32 clusters
SvelteKit site	6,980	26 clusters

And if I had questions about any of them, like where a problem came from, which WCAG rule it violated, even a suggested fix, I could talk it through with the agent, which had the full context of the project.

One of my favorite bits is how the workflow traces big noisy messes back to the source. On the Eleventy site, it found that fixing just two files (one layout template and one stylesheet) would clear 82% of its 7,028 findings. That is exactly the kind of combing-through I used to do by hand, and exactly what AI is good at.

It is not only good at ruling things out or narrowing down issues. I've found it is good at bringing up and prioritizing problems that matter but that wouldn't necessarily be highlighted using just the automated scanners. For example, on a Next.js app it confirmed a link set apart by color alone, a genuine problem for anyone who cannot see the difference. On a SvelteKit site it flagged something styled as a button that was really a plain span, invisible to a keyboard. In the Drupal LMS module, it found the course card was two overlapping links pointing to the same place, which adds noise to a screen reader. And it found where code blocks failed contrast in dark mode, a problem a quick once-over would sail right past.

How Do I Know It Isn't Missing Things?

If my workflow drops the findings from thousands to under a hundred actionable items, that can feel good, but it also raises the question: Are we missing something now?

Two things stop that from happening. First, the automated pass is not the full audit. Scanners cover the slice of WCAG a machine can check, which is a portion of what matters. The rest still needs a person at the keyboard with assistive technology. For example, on the Drupal module, my own manual review added seventeen findings no scanner could have caught, no matter how many times I ran them. Second, someone who knows what "wrong" looks like has to read the workflow's output. Those authenticated pages that scanned clean because the session cookie dropped? A less experienced reviewer might file that clean result and move on. The workflow speeds up an expert. It does not replace one.

The Workflow Keeps Evolving

I treat the workflow itself as a project under audit. Each time I use it I find ways to improve it. This summer I handed a newer, more capable model a bigger job: review the entire suite and plan a remediation of its rough edges, from turning the copy-paste method I'd been using to move it between projects into a proper plugin to addressing weak spots in the consolidation stage.

That overhaul is underway now. The intake interviews will be better, I'm incorporating the latest WCAG Evaluation Methodology, and I'm experimenting with AI-driven keyboard testing.

What You Can Take From This

I haven't released this workflow yet; it remains a personal tool that I use on the projects in front of me, including Tag1's. But the pattern is the transferable part, and none of it requires my code:

Decompose the work until each piece has one job.
Keep a human at the decisions that matter.
Push the agent toward current documentation for the project's actual stack instead of letting it coast on training data.
Fix the process when the agent fails.
Move anything deterministic out of the agent's judgment and into tested scripts.
Measure and review before you trust; sometimes the AI's most confident conclusions turned out to be based on broken steps.
Add adversarial agent reviews to both the planning and the workflow.

None of this made accessibility testing fully automatic, and that wasn't my goal. It has made many of the tedious parts quick, and I get to spend more of my time doing the interesting parts now.

This is one of several ways we're putting AI to work on real engineering problems at Tag1. You can find more in our Insights.

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DDEV Blog: A Love Letter to the DDEV Community

I've been working on DDEV for years now, and there's something I don't say often enough:

You make this so worthwhile.

Stas and I love to get up in the morning to hear what you have to say, learn from your experiences, share our path together.

We feel so thankful to be creating something useful in collaboration with you.

All of us have had jobs before where some boss was making random decisions on product features that we knew might be irrelevant in weeks or months. It's a frustrating feeling, and that lack of control is so terrible. With DDEV and your guidance, we always know that you're keeping us on track about real needs for real features. It's fantastic.

Real Feedback About Real Problems

Your questions in Discord or Slack and the issues you file aren't "noise". They're signal. When something breaks (or is awkward) in your workflow, you tell us, often with enough detail that we can reproduce it immediately. When something is confusing, you ask questions that reveal where our assumptions were wrong.

That feedback shapes DDEV in ways that internal testing never could. We don't use DDEV on every possible OS, with every PHP framework, in every hosting environment. You do. And you tell us what you find.

We Love Your Questions and Comments.

AI has been replacing human interaction in support situations, and in many cases doing a decent job. It usually does a good job with questions about DDEV.

But getting answers to questions is not the only purpose of support. It's also a great way to communicate problems and ambiguities to project maintainers.

We want you to ask us questions! We live for your questions. We miss the fact that you've been absent from Discord, #ddev in Drupal Slack, and the issue queue. When you ask, it helps us to understand what your struggles are and how DDEV can get better. DDEV's strength has always been the community's willingness to engage and share their needs and frictions and hopes for the project.

Hard Questions Lead Somewhere

Most of the best improvements to DDEV started with someone asking a question that seemed basic but turned out to be pointing at a real need. Why does this take so long? Why does that require a workaround? Why can't DDEV just handle this case?

Those questions are gifts to all of us. They push us to look at things we've gotten used to, and ask whether they actually need to be that way.

Generous Contributions

The DDEV ecosystem is full of people who built something useful for themselves and then shared it with everyone. Add-ons, CI configurations, documentation fixes, screencasts, blog posts.

Stas, who joined the project more recently, was surprised by how much DDEV could be extended and customized, and how good the documentation was for figuring it out. Looking back, that didn't happen by accident. It came from years of feature requests and contributions from people who solved their own problems and then shared the solution.

Every person who took time to answer another user's question in the DDEV issue tracker or Discord or Drupal Slack freed up time for the maintainers to work on the next feature.

We Learn from You

Working with the community makes us better at this work. The patterns we see in your issues, the use cases we hadn't considered, the ways you've adapted DDEV for environments we never anticipated—that knowledge informs everything.

Thank You

To everyone who filed an issue, answered a question, wrote a blog post, sponsored the project, gave a talk, built an add-on, tested a prerelease, or just told a colleague that DDEV was worth trying:

Thank you. This project exists because of you, and it's only possible because of the ways you engage with it.

If you want to stay involved, here's where to find us:

Discord — active community discussion
GitHub Issues
Drupal Slack

Come say hello.

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Dries Buytaert: From personal AI experiments to shared tools

In April 2025, I published Claude Code meets Drupal, my first public experiment with an AI coding agent. I have been experimenting with coding agents ever since, often by building tools to solve problems in my own work.

Last week, I joined the Drupal AI Learners Club to discuss several experiments I had already published. Angie Byron started the club and runs it with co-organizer Amber Himes Matz. It gives people in the Drupal community a place to show how they are using AI and talk honestly about what works and what does not.

I spent an hour walking through the experiments, starting with Drupal Digests, a tool that uses AI to summarize key developments across Drupal Core, Drupal CMS, Drupal Canvas, and the Drupal AI initiative.

Drupal Digests led to another experiment: AI-generated Rector rules. When a Drupal Core change deprecates an API, Drupal Digests analyzes the issue and code changes and generates a rule that can automate the corresponding upgrade in other Drupal projects.

I also showed an API catalog that helps AI agents discover my website's search API.

These are only some of my AI experiments. Most begin as tools I build for myself, and many never go any further. When one seems useful beyond my own work, I publish it so others can try it and improve it.

Once it is public, we can see whether people use it and want to help improve it. If they do, it may eventually become a community project. If not, that is useful to know too.

The recording goes into much more detail, with demonstrations of the tools and questions from the group. You can watch it below.

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BloomIdea: Shipping with MRW from Drupal Commerce: no more copy-pasting into the carrier portal

Several of the e-commerce stores we build and run ship with MRW, one of the main carriers in Spain and Portugal. Until recently, shipping an order meant leaving the store: open MRW's customer portal, retype the customer's address, print the label, then copy the shipment number back into Drupal so the customer gets a tracking link. Multiply by every order, every day, and add returns, which meant doing the same dance with the addresses swapped.

We replaced that with a Drupal module. It now runs the daily expedition of the first of them, and we are releasing it to the community: Commerce MRW is available on drupal.org, with a 1.1.0 release.

What it does

Commerce MRW integrates Drupal Commerce with MRW through SAGEC, the carrier's SOAP webservice for creating and managing shipments (envíos). The whole expedition cycle happens on the shipment admin pages the team already uses:

Transmit shipments (TransmEnvio): one click creates the shipment on MRW's side and stores the returned shipment number as the Drupal shipment's tracking code, feeding the customer-facing tracking link.
Labels on demand (GetEtiquetaEnvio): the transport label PDF is streamed straight from SAGEC every time someone asks for it. Labels are never stored locally, so MRW stays the single source of truth and there is no stale-file problem.
Cancel (CancelarEnvio) transmitted shipments before the courier picks them up.
Return pickups: a request can carry a pickup address (DatosRecogida), so MRW collects the parcel at the customer's door and delivers it back to the store. Returns stop being a manual job in the carrier portal.
Tracking: a client for MRW's TrackingServices webservice queries the current status, or the full status history, of any shipment: by MRW number, by your own order reference, individually or in bulk.
"Transmit as": shipments whose shipping method is not MRW (say, a generic "Free shipping" flat rate) can still be shipped with MRW. The operator picks the executing method at transmit time and the choice is recorded on the shipment.

Service codes cover the SAGEC catalogue (Ecommerce, Urgente 19 Expedición, Urgente 13 and friends), and PRE (test) and PRO (production) environments are separate credential sets with a test-mode toggle, so you can validate the integration with your franchise before a single real parcel moves.

Small module, sharp edges

The SAGEC manual is short; reality is not. A few of the edges the module rounds off for you:

Postal codes are per-country folklore. Spain wants 5 digits with leading zeros, Portugal wants only the first 4 of its 7-digit codes, Andorra's letters become zeros, and Gibraltar is always 00010. The module normalizes all of it before SAGEC ever sees an address.
The tracking service hides behind a WCF quirk: it only accepts SOAP posts on its relative endpoint address, and answers 404 on the address the documentation leads you to. We found out so you do not have to.
No PHP SOAP extension required. Both clients build their envelopes by hand over Drupal's HTTP client, which also makes every request fully testable with mocked responses; the module ships with a kernel test suite that asserts the exact XML that goes over the wire.

Your store's rules stay yours

Carrier integrations die by hardcoding someone else's workflow, so Commerce MRW deliberately does not have one. Site-specific data travels through an event: subscribe to the TransmEnvio request event and fill whatever your store knows, the consignee's phone number, a NIF, delivery observations for the courier, or a full pickup address to turn a transmission into a return pickup. In one of our stores, three small subscribers do exactly that: one copies the customer's phone, one sends the backoffice's "carrier notes" field as delivery instructions, and one swaps the addresses when a shipment is flagged as a return.

Tracking follows the same philosophy. The module gives you the client and a documented integration recipe, but ships no polling and changes no shipment states: whether "delivered at destination" should transition your workflow, or just inform a human, is your call. That store runs an hourly cron that mirrors the last MRW status into the expedition dashboard, next to each tracking code; marking a shipment delivered stays a human decision, now an informed one.

Battle-tested, then released

Like our other contrib modules, this one shipped to drupal.org only after running a real store's daily expedition: real transmissions, real labels handed to the courier, and a tracking client validated against the production webservice before its release was tagged. The test suite (42 kernel tests at the time of writing) covers the SOAP envelopes, the postal code rules, the backoffice forms and the tracking parsing.

Get it

composer require drupal/commerce_mrw:^1.1

Requires Drupal 10.3+ or 11 and Commerce Shipping 3.x, plus SAGEC credentials from your MRW franchise (ask your franchise; ours enabled both the shipping webservice and the tracking service on request).

If you run a Drupal Commerce store shipping in Spain or Portugal, this is for you. The issue queue is open, and international shipments, ZPL labels and MRW delivery points are on the roadmap.

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Specbee: Understanding Drupal's Service Container and Dependency Injection

Learn how Drupal's service container and dependency injection work, why they matter for testable code, and what Drupal 11 changes for your service classes. read more

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Salsa Digital: Drupal AI Context — beta 3 released

Beta 3 release of Drupal AI Context Beta 3 of Drupal AI Context , also known as Context Control Center (CCC), is now available. CCC helps Drupal sites provide governed, reusable context for AI workflows and agents, from brand voice and editorial standards to organisational knowledge and governance rules. This gives AI systems access to more structured and relevant information while allowing teams to manage that information centrally. Following the beta 2 release , beta 3 expands how context is authored, imported, selected and extended. It also introduces redesigned administration pages, more granular agent controls and broader integration options. read more

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The Drop Times: Drupal 12 HTMX 4 Upgrade Returns to Review

Changes to how Drupal handles dynamic page updates could force contributed-module maintainers to support different behaviour in Drupal 11 and Drupal 12. read more

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The Drop Times: What It Takes to Sustain Drupal

Maintainers, Association leaders, developers, and community organisers faced versions of the same question this week: how should Drupal fund, govern, and sustain the work its users rely on? Reporting from 20–27 July 2026 followed that question across Association finances, security response, artificial intelligence, technical maintenance, and community participation. Read together, the stories show that shared infrastructure remains dependable only when responsibility for maintaining it is made visible.

The funding question became explicit in The DropTimes’ written exchange with Tiffany Farriss, interim CEO of the Drupal Association. Farriss proposed programme-level cost accounting and the possible use of usage-based contributions for enterprise-facing utility and infrastructure services, while separating that work from digital-public-good programmes and ecosystem advocacy. The proposals are not approved policy, but they move the discussion beyond general appeals for support towards clearer questions about cost, value, and who benefits from Drupal’s shared systems.

The same issue appeared in The DropTimes’ 22 July coverage of Dries Buytaert’s earlier distinction between “License-only Open Source” and “Stewarded Open Source”, first published on 9 July, and in the week’s contributed-project security advisories. A licence can grant access to code, but it cannot by itself guarantee maintenance, vulnerability response, governance, or long-term support. Reporting on AI governance, developer tooling, community events, and the July TDT Open Town Hall extended that principle into newer and more operational parts of the ecosystem. The major stories from the week follow.

Follow The DropTimes on LinkedIn, X, Bluesky, and Facebook, or join #thedroptimes on Drupal Slack.

This issue of Editor’s Pick was written and curated by Allen Jason.

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UI Suite Initiative website: UI Suite Monthly #37 — A new look for Display Builder, and an archaeology site digs in

Our 37th monthly UI Suite meeting (July 23, 2026) opened with a small piece of housekeeping: our sessions had quietly drifted from 30 minutes to a full hour, so Pierre asked the group to bring them back to half an hour — with anyone who wants to keep chatting free to stay on after the slide deck. It worked. Thirty minutes, three updates, two demos, and we still had time for questions. read more

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Webpro Company blog: AI agents do not only read web pages: how to make a Drupal site understandable and safe for machines

AI agents do not browse the web in the same way people do. They look for structure, APIs, permissions, verifiable answers and trustworthy sources. Drupal has strong foundations for this, but only when the platform is deliberately maintained and structured. AI agents do not only read web pages: how to make a Drupal site understandable and safe for machines In recent weeks, the Drupal community has been discussing a question many organisations are only beginning to formulate: how does the web change when it is used not only by people, but also by AI agents? On July 23, 2026, the Drupal AI Initiative published "Outside AI - The State of Agent Experience in Drupal". The article describes how an agent may be asked to create a content model, configure permissions, modify a running system or… read more

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DDEV Blog: Shopware on DDEV: notes from years of client projects

Are DDEV and Shopware a good fit? If you ask me, yes. Let me tell you why.

I first got to know DDEV a few years ago as a freelancer working on an agency Shopware project. Up until then, every time I switched to a different agency, it was a painful process: Would the development environment on my PC (Linux back then, macOS today) even work? What new ports and commands would I have to memorize? Would the environments for my previous projects break?

So I braced myself for the onboarding meeting. But the preparation turned out to be minimal—I already had Docker, and installing DDEV beforehand was just a script. And then it was a git clone (okay, I knew that one well) and a ddev start—that was new. And it was amazing: after what felt like five minutes (okay, let's say 20, including the database download and so on), I had the shop up and running on my machine. Wow!

I quickly switched to DDEV for all of my client projects. It was a game-changer. No more "port 8000 already in use" errors. A Shopware update needs a newer PHP version? A mismatched Node.js version? Easy—just edit .ddev/config.yaml and run ddev restart. Done.

From time to time I also work on WordPress, Shopware 5, or MediaWiki projects, and DDEV is a great fit for all of them: for me, it's one and the same setup, with the same look and feel. Even to write this blog post, I ran a ddev start to bring up the Astro-based backend.

Why I use DDEV for Shopware

Which features do I reach for again and again?

Project isolation

DDEV projects are isolated from each other, so you can work on several at once without any conflicts. While working on one project, another client calls in. Two clicks and the other project is up and running—and the first one stays up, ready to be picked up again whenever you are.

Xdebug

Xdebug used to be a pain to set up with "traditional" Docker environments. With DDEV, it's a breeze. Just run ddev xdebug on and don't forget to tell your IDE to listen on the relevant port.

Redis, RabbitMQ, and Elasticsearch at your fingertips

But back to Shopware—Shopware 6, to be precise. Since it's built on Symfony, it doesn't really need much for a local setup: Apache or nginx with PHP-FPM and a database (MySQL or MariaDB). Once you get to real-world use, though, things get more complex quickly. Two Redis servers (for cache and sessions), a RabbitMQ instance for the message queue, Elasticsearch. This is where the DDEV add-ons come in. Just run ddev add-on get ddev/ddev-redis—and you're set. In my experience, there's basically no system component that doesn't have an add-on.

Hooks

Hooks exist for all kinds of things—for example, a post-import hook for the ddev import-db command. I use it to make the necessary database adjustments, such as rewriting the sales channel domains or switching the mailer to Mailpit (which is, of course, integrated into DDEV).

Inter-project communication

Did I say DDEV projects are isolated? Well, only if you want them to be. Otherwise, your app in one DDEV project can communicate with other projects—DDEV supports direct HTTP/S calls between projects. It's a great feature for developing and testing a Shopware app server, for example. I've also used it to build and test the migration from Shopware 5 to Shopware 6 across two projects.

Mirroring the production or staging environment

A shop's media files can run to tens of gigabytes—so why copy them over at all? Most of my projects use nginx-fpm, which makes an nginx reverse proxy the easy answer. Add a .ddev/nginx/media.conf file with the following contents:

location @mediaserver {
    resolver 1.1.1.1;
    proxy_pass https://www.example.com$request_uri;
    # Uncomment if the remote environment is behind HTTP basic auth:
    # proxy_set_header Authorization "Basic <base64-of-user:password>";
}

location ^~ /media/ {
    access_log off;
    expires max;
    try_files $uri @mediaserver;
}

location ^~ /thumbnail/ {
    access_log off;
    expires max;
    try_files $uri @mediaserver;
}

Then run ddev restart. This not only mirrors (and caches) the media files from the production or staging environment, but also lets you upload new media files to your local environment for testing.

Shopware tooling

shopware-cli is increasingly being developed into a one-stop tool for development, and of course I want to use it in my projects too. No problem—there's an add-on for that: ddev add-on get vanwittlaer/ddev-shopware-cli. The add-on also lets you reach the storefront and admin watcher URLs directly and, more importantly, over HTTPS.

Project lifecycle support

DDEV has the concept of "providers" that you can use to load any remote resource into your local environment. Many projects have a provider that lets you download and import a sanitized production database, with a command like ddev pull sanitized (this would be a customized command, so its actual name may vary). In theory, this also works in the push direction, although I have never come across a use case for it so far.

AI tooling

At the time of writing, I use Claude Code for my debugging and development work. To keep it isolated from my local environment, I run it inside the DDEV container—yes, there's an add-on for that: ddev add-on get vanwittlaer/ddev-claude-code. Pair it with Playwright (also in the DDEV container, via ddev add-on get codingsasi/ddev-playwright) and watch Claude do interactive frontend development.

How to get started

If you haven't worked with DDEV or Docker before, start with the DDEV installation guide.

For your first project, you may want to follow DDEV's quickstart guide for Shopware.

I prefer to keep the Shopware part of my projects in a subfolder, e.g. shopware/, separate from the infrastructure around it, such as the .ddev and .github folders. That way any developer, even one who has never used DDEV, can tell at a glance which parts are Shopware and which are not.

My Less than 5 Minutes Install guide includes a script that sets this up with a shopware/ subfolder.

If you prefer to do it manually, there are just four steps:

cd <your project directory>
ddev config --project-type=shopware6 --docroot=shopware/public --web-environment="APP_ENV=dev" \
        --web-working-dir=/var/www/html/shopware --composer-root=shopware
ddev start
ddev composer create-project shopware/production
# When it asks whether to include Docker configuration from recipes, answer `x`—
# DDEV takes care of that part.
ddev exec bin/console system:install --basic-setup --shop-locale=en-GB

You will end up with a working Shopware 6 installation; the admin credentials are admin / shopware.

Conclusions—how good a fit is DDEV for Shopware?

Whether DDEV is a good fit for you depends less on Shopware itself than on the kind of Shopware work you do.

What I take from discussions with others in the Shopware community is that we bring in (at least) three perspectives:

Shopware core development
Store plugin development
Client project development

Naturally, the requirements for a development environment and tooling differ for each. For a core developer, what matters most might be running the latest versions of every dependency. For a store plugin developer, it might be testing a plugin efficiently against every Shopware version and configuration out there. There are focused solutions for these requirements, such as devenv, Dockware, the Shopware-provided Docker setup, or the new shopware-cli features.

Client project development, however, is where I spend most of my working time, and there the Shopware version and the environment that mirrors the production setup are predefined and stable within a project. The day-to-day work is:

debugging (core, third-party plugins, custom code);
developing and testing new features (ERP integration, custom plugins, custom theme);
installing and testing third-party plugins;
implementing and testing Shopware and third-party plugin upgrades.

So what matters to me is an efficient setup for a given set of dependencies and versions, ease of use, integration with testing and dev tools (Xdebug, Claude Code, Playwright, the storefront and admin watchers), reliability, support (DDEV has a great Discord community), and—last but not least—not losing time when switching between client projects.

tl;dr: my answer to the question—how good a fit is DDEV for Shopware?—is a resounding yes.

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Introducing the Drupal AI Security Initiative: The First Six Weeks

Drupal's volunteer Security Team has protected millions of sites for more than 20 years and its process is world-class. Bandwidth among the security engineers has always been the limiting constraint. This spring that constraint met a new kind of pressure: AI-assisted analysis is finding latent vulnerabilities at an accelerating pace.

The Drupal AI Security Initiative adds funded security capacity in response. It is funded through Alpha-Omega's Security-Engineer-in-Residence (SEIR) program, coordinated by the Drupal Association, and works alongside the volunteer Security Team, which continues its normal process throughout.

This post introduces the initiative and reports on our first six weeks. The short version: the funded fractional team model is working and has already evolved our understanding of where we want to focus next.

What changed: the economics of discovery

Drupal's attack surface is what it has always been. What has changed is the cost of finding bugs. AI-assisted analysis makes discovery dramatically cheaper. AI can produce security issue reports at a volume and can discover exploit details at a speed that any volunteer effort struggles to absorb. Our advisory data shows the rate of discovery accelerating (our next post will work through what the data suggests in detail).

Meet the Drupal AI Security Initiative Team

The initiative builds on the lessons of the Drupal 8 Accelerate Initiative, which showed that throughput efficiency depends on funding the whole contribution workflow, not just one part of it.

The Drupal security team needs fixes, not just findings of potential issues. As fixes are developed, they are collaboratively reviewed. An engineer cannot mark their own fix complete. Funding one full-time engineer would likely produce findings faster than volunteers could review them, and they would queue. So we’re using the grant to fund a fractional team that covers the full path from discovery to merge on both the project and infrastructure side for Drupal:

Drew Webber (@mcdruid) is the Fixer. He applies AI-security expertise directly to Drupal's code: scanning, writing patches, building experimental tooling, and then submitting contribution-ready work across Drupal core and the contributed-project ecosystem.
Greg Knaddison (@greggles) and Michael Hess (@mlhess) are Reviewers: They triage submissions, review patches, advance issues, and provide the RTBC status a fixer cannot grant themselves. Both come from the existing Security Team, and the grant helps subsidize the work they would otherwise do on volunteer time.
Neil Drumm (@drumm) handles infrastructure, focusing on Drupal.org itself. The package distribution, build pipelines, and update mechanisms are a high-consequence, specialized surface on their own.
Tiffany Farriss (@farriss) and Tim Lehnen (@hestenet) provide program support and coordination for the Drupal Association.

Our current grant has two three-month phases: Clarity (understand the problem) and Attention (fix issues and harden the process).

Six weeks in: what we've done

We're using the funding and AI tooling to find, validate, triage, and resolve vulnerabilities faster than before, including proactively, across core, contrib, and our own infrastructure. In six weeks, the team has made contributions to more than 10 published advisories and CVEs and filed more than 30 issues. This work includes SA-CORE-2026-005, a critical PHP object-injection issue reachable via JSON:API that arrived as an external report and was coordinated to a fast release, alongside triage and remediation across dozens of findings and hundreds of inbound requests. The team also worked on rapid response/urgent issues off-hours; in one case, AI-assisted review helped find and fix a significant issue in Drupal.org code.

We're also building reusable tooling and automation prototypes that increase throughput and make our security archive searchable and actionable. That includes five skills and a set of opengrep static-analysis rules, each targeting a vulnerability class, and local, open-weight tooling that processes about 40,000 historical security-mailbox emails to assign metadata like CWE mapping and flag duplicates (keeping sensitive data local). One key project outcome will be delivery of working tools the Security Team can continue to use after the initiative ends.

Drupal’s grant is one of several parallel Alpha-Omega grants across open source ecosystems. Being part of this cohort has allowed us to compare notes and share tooling, successes and failures with other open source projects. So far we’ve collaborated most directly with Volker Dusch, who leads the equivalent effort at the PHP Foundation, and with colleagues at the Open Source Technology Improvement Fund (OSTIF), who shared their report-validator protocol for separating real findings from noise. That protocol feeds straight into our intake, and into the report standard we want to co-create next.

The counts are perhaps not the most interesting part. We've resolved more security issues (10) than the minimum number (8) our proposal had committed to over the entire six-month project. We had assumed the meat of the task would be finding and fixing vulnerabilities. It turns out that the more interesting challenge will be adapting Drupal's security process to the volume and nature of higher-quality-than-expected AI-generated and AI-assisted reports.

So far that adaptation has happened downstream, after an issue has been reported. Shepherding issues to a fix, filing CVEs, automating that filing, and automating the analysis of published advisories are important and help scale the response process. But it is all at the bottom of the funnel. The opportunity we would like to explore is higher up, at intake, where issues arrive.

We've started exploring what that might look like. In discussions with core maintainers, some design principles emerged: AI stays limited to a single triage activity per issue and no bot noise on every commit and merge request. Ideally, early intake tooling would pre-filter inbound security issue reports and run a gated check that confirms whether they include enough context and reproduction detail before they reach a human.

What’s next

The next six weeks will build on what is working and push the intake question in two directions. The first is triage. The volume of incoming security issues is expected to keep growing and AI-assisted triage of that queue is an area to explore. We are interested in looking at how modern tooling can sort and deduplicate incoming issues so human attention can be focused where it's actually needed.

The second is the report itself. A clear issue report helps the Security Team and maintainer community move faster; a vague or bloated one slows everyone down. We want to explore and define what a useful AI-generated or AI-assisted security report should contain and draft a working standard, co-created with the Security Team and maintainers. If you are a maintainer or security reporter and have examples of good (or bad) AI-generated reports, please share them in Drupal Slack #security-discussion.

Six weeks of supplemental funding has already made a couple things clear. The roles the Drupal ecosystem depends on (security work as well as release management) need a durable, community-owned funding model, not one-time support. And we need to keep talking and collaborating across ecosystems like this.

Thanks

Huge thank you to Alpha-Omega for the support, funding and for access to AI tooling from Anthropic that enabled several of the findings above; to the Linux Foundation; and to the Drupal Association for coordination. And of course, none of this works without the two decades of effort from Drupal’s amazing Security Team.

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Drupal Association blog: Introducing the Drupal AI Security Initiative: The First Six Weeks

Drupal's volunteer Security Team has protected millions of sites for more than 20 years and its process is world-class. Bandwidth among the security engineers has always been the limiting constraint. This spring that constraint met a new kind of pressure: AI-assisted analysis is finding latent vulnerabilities at an accelerating pace.

The Drupal AI Security Initiative adds funded security capacity in response. It is funded through Alpha-Omega's Security-Engineer-in-Residence (SEIR) program, coordinated by the Drupal Association, and works alongside the volunteer Security Team, which continues its normal process throughout.

This post introduces the initiative and reports on our first six weeks. The short version: the funded fractional team model is working and has already evolved our understanding of where we want to focus next.

What changed: the economics of discovery

Drupal's attack surface is what it has always been. What has changed is the cost of finding bugs. AI-assisted analysis makes discovery dramatically cheaper. AI can produce security issue reports at a volume and can discover exploit details at a speed that any volunteer effort struggles to absorb. Our advisory data shows the rate of discovery accelerating (our next post will work through what the data suggests in detail).

Meet the Drupal AI Security Initiative Team

The initiative builds on the lessons of the Drupal 8 Accelerate Initiative, which showed that throughput efficiency depends on funding the whole contribution workflow, not just one part of it.

The Drupal security team needs fixes, not just findings of potential issues. As fixes are developed, they are collaboratively reviewed. An engineer cannot mark their own fix complete. Funding one full-time engineer would likely produce findings faster than volunteers could review them, and they would queue. So we’re using the grant to fund a fractional team that covers the full path from discovery to merge on both the project and infrastructure side for Drupal:

Drew Weber (@mcdruid) is the Fixer. He applies AI-security expertise directly to Drupal's code: scanning, writing patches, building experimental tooling, and then submitting contribution-ready work across Drupal core and the contributed-project ecosystem.
Greg Knaddison (@greggles) and Michael Hess (@mlhess) are Reviewers: They triage submissions, review patches, advance issues, and provide the RTBC status a fixer cannot grant themselves. Both come from the existing Security Team, and the grant helps subsidize the work they would otherwise do on volunteer time.
Neil Drumm (@drumm) handles infrastructure, focusing on Drupal.org itself. The package distribution, build pipelines, and update mechanisms are a high-consequence, specialized surface on their own.
Tiffany Farriss (@farriss) and Tim Lehnen (@hestenet) provide program support and coordination for the Drupal Association.

Our current grant has two three-month phases: Clarity (understand the problem) and Attention (fix issues and harden the process).

Six weeks in: what we've done

We're using the funding and AI tooling to find, validate, triage, and resolve vulnerabilities faster than before, including proactively, across core, contrib, and our own infrastructure. In six weeks, the team has made contributions to more than 10 published advisories and CVEs and filed more than 30 issues. This work includes SA-CORE-2026-005, a critical PHP object-injection issue reachable via JSON:API that arrived as an external report and was coordinated to a fast release, alongside triage and remediation across dozens of findings and hundreds of inbound requests. The team also worked on rapid response/urgent issues off-hours; in one case, AI-assisted review helped find and fix a significant issue in Drupal.org code.

We're also building reusable tooling and automation prototypes that increase throughput and make our security archive searchable and actionable. That includes five Claude skills and a set of opengrep static-analysis rules, each targeting a vulnerability class, and local, open-weight tooling that processes about 40,000 historical security-mailbox emails to assign metadata like CWE mapping and flag duplicates (keeping sensitive data local). One key project outcome will be delivery of working tools the Security Team can continue to use after the initiative ends.

Drupal’s grant is one of several parallel Alpha-Omega grants across open source ecosystems. Being part of this cohort has allowed us to compare notes and share tooling, successes and failures with other open source projects. So far we’ve collaborated most directly with Volker Dusch, who leads the equivalent effort at the PHP Foundation, and with colleagues at the Open Source Technology Improvement Fund (OSTIF), who shared their report-validator protocol for separating real findings from noise. That protocol feeds straight into our intake, and into the report standard we want to co-create next.

The counts are perhaps not the most interesting part. We've resolved more security issues (10) than the minimum number (8) our proposal had committed to over the entire six-month project. We had assumed the meat of the task would be finding and fixing vulnerabilities. It turns out that the more interesting challenge will be adapting Drupal's security process to the volume and nature of higher-quality-than-expected AI-generated and AI-assisted reports.

So far that adaptation has happened downstream, after an issue has been reported. Shepherding issues to a fix, filing CVEs, automating that filing, and automating the analysis of published advisories are important and help scale the response process. But it is all at the bottom of the funnel. The opportunity we would like to explore is higher up, at intake, where issues arrive.

We've started exploring what that might look like. In discussions with core maintainers, some design principles emerged: AI stays limited to a single triage activity per issue and no bot noise on every commit and merge request. Ideally, early intake tooling would pre-filter inbound security issue reports and run a gated check that confirms whether they include enough context and reproduction detail before they reach a human.

What’s next

The next six weeks will build on what is working and push the intake question in two directions. The first is triage. The volume of incoming security issues is expected to keep growing and AI-assisted triage of that queue is an area to explore. We are interested in looking at how modern tooling can sort and deduplicate incoming issues so human attention can be focused where it's actually needed.

The second is the report itself. A clear issue report helps the Security Team and maintainer community move faster; a vague or bloated one slows everyone down. We want to explore and define what a useful AI-generated or AI-assisted security report should contain and draft a working standard, co-created with the Security Team and maintainers. If you are a maintainer or security reporter and have examples of good (or bad) AI-generated reports, please share them in Drupal Slack #security-discussion.

Six weeks of supplemental funding has already made a couple things clear. The roles the Drupal ecosystem depends on (security work as well as release management) need a durable, community-owned funding model, not one-time support. And we need to keep talking and collaborating across ecosystems like this.

Thanks

Huge thank you to Alpha-Omega for the support, funding and for access to AI tooling from Anthropic that enabled several of the findings above; to the Linux Foundation; and to the Drupal Association for coordination. And of course, none of this works without the two decades of effort from Drupal’s amazing Security Team.

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The Drop Times: Tiffany Farriss Proposes Cost Accounting and Usage-Based Enterprise Funding

Farriss says reserves are covering a gap in Drupal’s wider stewardship work, prompting proposals to expose programme costs and connect enterprise use with ongoing support. read more

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Dripyard Premium Drupal Themes: How Dripyard gave Tojio a fast foundation for Drupal CMS

When we started building Dripyard as a business, we had a clear objective: Drupal developers should be able to move fast without giving up the things that make it Drupal. Structured content, editorial control, accessibility, open-source ownership, and long-term maintainability should not be traded away just because a project has a tight timeline or limited budget.

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The Drop Times: TDT Town Hall Links Newsroom Changes With Drupal’s Wider Future

Community participation moves beyond story leads as The DropTimes prepares an Editorial Working Group for Drupal contributors. read more

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Smartbees: Sumaris

Check out our B2B platform implementation for Sumaris – a company providing specialized solutions for industry. read more

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Talish Khan: Layout Builder Is Over-Applied: A Decision Framework for When It Actually Fits

The Pattern I Keep Seeing

A team starts a Drupal project. Someone asks how editors will build and arrange page content. Someone else says "Layout Builder," and that is the end of the conversation. Nobody asks what the editors actually need. Nobody asks what the content model demands. Layout Builder gets switched on because it is powerful, modern, and comes with core.

Six months later, one of two things has happened. Either the editors are happily composing layouts and everyone is glad, which is the good outcome. Or the editors are confused by a tool that gives them more power than they wanted, the developers are fighting to constrain a system designed to be open-ended, and the content is inconsistent because thirty editors made thirty different layout choices. That is the bad outcome, and it is more common than the Drupal community likes to admit.

The tool is not the problem. The reflexive selection of the tool without asking whether it fits is the problem.

The Four Options

Before the framework, a quick map of what you are actually choosing between when you decide how editors build pages in Drupal.

Layout Builder. Editors compose pages by placing blocks into regions of a layout, visually, per page or per content type. Maximum flexibility. Maximum editor power. The editor decides the structure.

Paragraphs. Editors add and arrange predefined content components in a field. Structured flexibility. The developer defines the components; the editor arranges them. The structure is constrained by what you built.

Custom templates. The developer defines the layout in Twig and the editor fills in fields. Zero layout flexibility for the editor. Maximum consistency and developer control.

Plain blocks and block layout. Content is placed in regions through the block system, configured by a site builder, largely static across pages. Good for site-wide furniture, weak for per-page composition.

Each of these is correct for some situations and wrong for others. The framework is about matching the tool to the situation.

The Framework: Four Questions

I run every "how should editors build pages" decision through these four questions, in order.

Question 1: Do editors actually need to compose layouts, or do they need to fill in content?

This is the question that settles most cases, and it is the one nobody asks.

If your editors are filling in structured content (an article has a title, a body, an author, a hero image, a set of related links), they do not need Layout Builder. They need well-designed content types with well-designed fields, rendered through templates the developer controls. Giving these editors Layout Builder hands them a layout composition tool for a job that has no layout composition in it. They will either ignore it or misuse it.

If your editors are genuinely composing pages (a marketing team building landing pages with varying structures, arranging components differently per campaign), then layout composition is a real need and Layout Builder or Paragraphs becomes relevant.

The test: watch an editor work, or ask them to describe their job. If the word "arrange" or "compose" or "build" comes up, layout tooling might fit. If they describe "entering" or "updating" or "filling in," it probably does not.

Question 2: How much layout variation do you actually need?

If the answer is "every page can look completely different," Layout Builder is designed for that.

If the answer is "editors combine a fixed set of components in different orders," Paragraphs is the better fit. It gives editors arrangement flexibility without giving them raw layout power they do not need and will misuse.

If the answer is "pages of this type all look the same," you do not need either. Custom templates with fields is the right call, and it will be faster, more consistent, and more maintainable than either flexible option.

Most projects need less layout variation than they think. The instinct is to build for maximum flexibility "just in case." That flexibility has a cost, paid in editorial inconsistency and developer maintenance, and the "just in case" scenario often never arrives.

Question 3: Who bears the cost of flexibility?

Every option shifts cost to a different party.

Layout Builder shifts cost to editors, who now have to make layout decisions on every page, and to developers, who have to constrain and style a system designed to be open. The flexibility is real but so is the ongoing cost of managing it.

Paragraphs shifts cost to developers upfront (building and styling the components) and keeps the editor experience constrained and predictable. Once built, it is low-cost for editors.

Custom templates put all the cost on developers upfront and give editors the simplest possible experience: fill in the fields, the layout is handled.

The question is not "which is most flexible." It is "who should bear the cost of flexibility on this project, and can they?" A marketing team that wants control can bear the Layout Builder cost. An editorial team of subject-matter experts who just want to publish articles cannot, and should not be asked to.

Question 4: How many people will use this, and how consistent does the output need to be?

Flexibility and consistency are in tension. The more freedom you give editors, the less consistent the output.

If three trained content designers are building marketing pages, Layout Builder's flexibility is a feature and the consistency risk is manageable because the team is small and skilled.

If thirty subject-matter experts across departments are publishing content, Layout Builder's flexibility is a liability. You will get thirty interpretations of what a page should look like, and your site will drift into visual chaos within a year. Constrained tools (Paragraphs with a limited component set, or custom templates) protect consistency at scale.

The larger and less design-trained your editorial pool, the more you should constrain their tooling.

The Framework in One Sentence Each

To compress it:

Custom templates when pages of a type look the same and editors fill in fields.
Paragraphs when editors arrange a fixed set of components in varying orders.
Layout Builder when editors genuinely compose free-form layouts and the team is small and skilled enough to manage the flexibility.
Plain blocks for site-wide furniture, not per-page composition.

Notice that Layout Builder is the right answer for the narrowest set of conditions, not the widest. That is the inverse of how often it gets chosen.

Why Layout Builder Gets Over-Applied

Three reasons the reflex exists.

It is in core and it is visible. Paragraphs is contrib. Custom templates require writing code. Layout Builder is right there in core, promoted, documented, demoed. Visibility drives adoption regardless of fit.

It demos beautifully. The drag-and-drop layout composition is genuinely impressive in a demo. Stakeholders see it and want it. The demo does not show the editorial inconsistency that emerges at scale six months later.

It feels like the modern choice. Choosing custom templates can feel like you are not using Drupal's capabilities fully. There is a subtle pressure to use the powerful tool because it is there, even when the simpler option is correct. Resisting that pressure is a senior move.

Closing Thought

Layout Builder is a good tool. I am not arguing against it. I am arguing against choosing it reflexively, without asking whether the project actually needs page composition or just needs structured content entry.

The four questions above take ten minutes to run and save months of pain. Do editors compose or fill in? How much variation is really needed? Who bears the cost of flexibility? How many people, how consistent? Answer those honestly and the right tool usually selects itself.

The instinct to reach for the most powerful option is understandable and usually wrong. The senior move is to reach for the option that fits, which is frequently the more constrained one. A Drupal site where editors fill in well-designed fields through developer-controlled templates is not a less sophisticated site than one built on Layout Builder. Often it is the more sophisticated one, because someone made a deliberate choice instead of a reflexive one.

If you have shipped Layout Builder on a large multi-editor site and kept it consistent over years, I would be curious how you constrained it. That is the hard case, and the honest accounts of making it work at scale are rarer than the demos suggest.

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Morpht: Protecting PII in Drupal AI: An introduction to Guardrails

The Drupal AI module's Guardrails system runs configurable validation plugins both before a prompt is sent to an LLM and after a response is received. Guardrails can pass, block, or rewrite content, and can be combined into sets with a scoring threshold. read more

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The Drop Times: A "Humanizer" Should Not Become a House Style Manual

A developer I work with sent this skill over recently, asking whether it was worth adopting across the agency's client projects (an additional pass before publication under a client's name). That's a fair question to ask before rolling something out across multiple sites, so I read the source rather than taking the pitch at face value.

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MidCamp - Midwest Drupal Camp: Last Chance: MidCamp 2026 Call for Sessions Extended to March 13

We heard you... and we want to hear from more of you!

The MidCamp 2026 Call for Sessions has been extended. The new deadline is March 13, 2026.

If you had a session idea brewing but didn't quite get it across the finish line, now's your window. We extended the deadline because we want a lineup that reflects the full range of people who use, build, and care about Drupal — and we're not there yet without you.

What We're Looking For

MidCamp sessions are open to all skill levels and all corners of the Drupal ecosystem. Whether you're a developer with a deep technical dive, a project manager with hard-won lessons, a designer with a perspective the community needs, or an end user who figured something out the hard way — there is a place for your session at MidCamp.

We're especially interested in talks around:

Drupal AI — practical applications, integrations, and what's actually working in the field
Drupal CMS / Canvas — building with and extending Drupal's newest tools
Decoupled and headless implementations — real-world lessons from the front lines
Accessibility, equity, and inclusion — building a better, more accessible web
Community and contribution — how we grow the ecosystem together

Not sure if your idea fits? Submit it anyway. We'd rather review more proposals than miss a great talk.

How to Submit

Session submissions are open now through March 13, 2026.

👉 Submit your session here

Need help shaping your proposal? Join the #speakers channel on the MidCamp Slack — there are people there who will help you get it over the finish line.

Slack: https://mid.camp/slack

What Happens Next

After the submission window closes, our review team will evaluate proposals and notify selected speakers by April 9, 2026. Selected speakers will have until April 15 to confirm, and the full schedule will be published April 16.

MidCamp 2026 is May 12–14 in Chicago. We hope to see you on stage.

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MidCamp - Midwest Drupal Camp: MidCamp Chicago 2026 call for sessions open through Feb 26

We're excited to celebrate you -- our future speakers! If you've got an idea for a session, now's the time to get involved in MidCamp 2026, happening May 12-14 in Chicago.

Call for Speakers

Since 2014, MidCamp has hosted over 300 amazing sessions, and we're ready to add your talk to that legacy. We're seeking presentations for all skill levels, from Drupal beginners to advanced users to end users and business professionals!

For full submission details and guidelines, visit: midcamp.org/events/2026/how-submit-session

Key Dates

Call for Proposals Opened: February 10, 2026
Proposal Deadline: February 26, 2026
Speakers Notified: Week of April 2026
MidCamp Sessions: May 12-13, 2026

Sponsor MidCamp

Looking to connect with the Drupal community? Sponsoring MidCamp is the way to do it! Whether you're recruiting talent, growing your brand, or simply supporting the Drupal ecosystem, MidCamp sponsorship offers great value. Act early to maximize your exposure!

Stay in the Loop

Join us on MidCamp Slack to chat and get updates.
Follow us on Bluesky and Mastodon for announcements and news.
Subscribe to our newsletter for updates on the venue, travel options, social events, and speaker announcements.

Ready to submit your session? Click away and let's make MidCamp 2026 unforgettable!

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MidCamp - Midwest Drupal Camp: Catch up on all the MidCamp you missed!

Watch the Dries fireside chat from 2025, or catch up on all of the sessions from last year on Drupal.tv.

Theres even more Drupal goodness to be had in our archives or Drupal.tv's.

The Archives: 2024 2023 2022 2021 2020 2019 2018 2017 2016 2015 2014

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Dries Buytaert: Helping agents discover my site search with Agentic Resource Discovery

Yesterday I blogged about the API catalog that announces my site's search API to agents. In response, someone pointed me to the ARD specification, a draft announced last month by a working group that includes Google, Microsoft, GitHub, Hugging Face, Cisco, Nvidia, and Salesforce.

What ARD adds to yesterday's API catalog is discovery. If an agent has never heard of you, it does not know to look for your API catalog. Ask an agent what people have written about the future of Drupal, for example, and it will probably search Google. It may not think to check dri.es or drupal.org directly.

The web solved discovery decades ago. Search engines find the right site, so you do not have to know where the answer lives.

ARD provides the building blocks for search engines for AI agents. Sites publish catalogs, crawlers discover them, and registries index them. An agent can then ask a registry a plain-language question, such as "Who can answer questions about the future of Drupal?". The registry returns a ranked list of relevant resources, perhaps pointing the agent to my site's search API.

You opt in by publishing a manifest at /.well-known/ai-catalog.json. Yes, that is almost the same path as my existing /.well-known/api-catalog.

Here is what my /.well-known/ai-catalog.json currently returns:

{
  "specVersion": "1.0",
  "host": {
    "displayName": "Dries Buytaert"
  },
  "entries": [
    {
      "identifier": "urn:air:dri.es:search",
      "displayName": "Site search",
      "type": "application/openapi+json",
      "url": "https://dri.es/openapi.json",
      "description": "Full-text search across the site's content, ranked by relevance.",
      "representativeQueries": [
        "Find posts about the future of Drupal",
        "What has been written about open source sustainability?",
        "Find writing about digital sovereignty",
        "How is AI changing how we build websites?",
        "Search Dries Buytaert's blog and notes"
      ]
    }
  ]
}

Each entry describes a resource an agent can use. ARD deliberately defines "resource" broadly: it can be an API, an MCP server, another agent, a skill, or even a nested catalog containing more resources.

My site offers just one resource: a simple search API. The whole thing took less than an hour to implement because the entry simply points to the existing OpenAPI document I wrote about yesterday. It advertises the same OpenAPI document, https://dri.es/openapi.json, through a second discovery mechanism.

The representativeQueries field is the interesting part. It lists example questions registries use to match an agent's intent. Mine are first guesses that I will revise once I can see how they get used.

Of the eleven companies listed as contributors, Hugging Face is the only one whose catalog I could find on its primary domain. It also runs an early registry. So I queried Hugging Face's registry directly at https://huggingface-hf-discover.hf.space/search. It responded correctly using the protocol defined by the specification, but for my query, it returned only skills hosted by Hugging Face.

Broad adoption will depend on whether major agents begin searching ARD registries. Microsoft, Google, and GitHub are in the working group, but OpenAI and Anthropic are not. Time will tell if this gets adopted, but Google stated its Agent Platform will connect to ARD registries in the coming months.

Does my blog need this? Probably not. Other sites have more to gain. An online store could announce its product search and checkout APIs, a restaurant its reservation system, and a city its appointment system for renewing a permit.

Many of these sites run on a content management system. A CMS that made its capabilities discoverable through ARD by default could therefore be interesting. Experiments like this help me understand whether Drupal should be that CMS.

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Talking Drupal: Talking Drupal #562 - Acquia Fair Trade Initiative

Today we are talking about Supporting Open Source, Acquia, and The Acquia Fair Trade Initiative with guest James Sims. We'll also cover Image Effects as our module of the week.

For show notes visit: https://www.talkingDrupal.com/562

Topics

Fair Trade Initiative Explained
How the Program Started
Why Fair Trade Matters
Adoption and Open Framework
Agency and Freelancer Benefits
Partner Funded Giving
Who Can Be Makers
Tracking Participation
Tax Deduction Questions
Community Shaped Program
Money Counts Too
Early Challenges
Timeline And Launch
Sustainability Built In
How To Get Involved
Defining Success
Origins Of Fair Trade

Resources

Acquia Fair Trade Initiative
Taste of chicago
- Giordanos Recommended by John
- Lou's pizza
Five For The Future
Image Convolution Playground

Guests

James Sims - rcjmselp85

Hosts

Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Avi Schwab - froboy.org froboy

MOTW Correspondent

Avi Schwab - froboy.org froboy

Brief description:
- Have you ever gone to edit an image style in Drupal, looked at the list of filters, and said "give me more! I want more!". Have you said "I'd like to mirror, filter, and convolute an image in Drupal - all at the same time". If so, you're in luck. Let me introduce you to our module of the week:
Module name/project name:
- Image Effects
Brief history
- How old: Created by Drupal user mondrake of Italy on 17 September 2015. It's also the successor to the ImageCache Actions module, which was created all the way back in 2008.
- Versions available: It has a 4.0.0 version available with Drupal 10 and 11 support, and a 5.0.0 version for Drupal 11.3 and above.
Maintainership
- Actively maintained
- Security coverage
- Test coverage
- Documentation
  - It has a full README with details about the available image styles and whether they are supported by the GD or ImageMagick PHP libraries.
- Number of open issues: 35 open issues, 3 of which are bugs against the current branch. (The current branch has only been out a few months, and many of the open issues against prior branches seem to still be relevant.)
Usage stats:
- 35,196 sites report using this module, with most still on the 3.x or 4.x branches. (its predecessor, ImageCache Actions, still has over 25,000 active installs)
Module features and usage
- The module is pulled in just like any other, with composer require and then enable via drush or the UI. Once it's installed there is a very basic settings page, but most folks won't use much on there.
- The power of Image Effects comes when you go to Config > Media > Image Styles and then edit an Image Style. Once Image Effects is enabled, you'll see over two dozen additional effects in the list.
- These effects range from simple to complex. Interestingly, many of the effects that were so amazing 15 years ago are now doable with CSS. Still, there are some incredibly powerful filters.
- Side note: I'd strongly recommend Aubrey Sambor's recent talk from Drupal Camp Asheville, "You Don't Need JS for That", and her prior talk "Color in CSS" to learn a ton of things you didn't know about CSS effects.
- The basics like Color Shift, Contrast, Mirror, Rotate, and more are there if you'd like to do these natively.
- More advanced filters like Sharpen, Blur, and Convolute let you make more complex modifications to images.
- "Convolution" is the process of applying n-dimensional matrixes to images to create effects such as blurring, sharpening, and edge detection. Try it out on https://anna.engineering/Image-Convolution-Playground/src/
- Lastly, you can create advanced image styles with ImageMagick arguments, create Text overlays using the power of Drupal tokens, or even develop your own Image Effects guided by the incredibly detailed DEVELOPING.md file included with the module.

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Why Drupal Contribution matters to you!

DrupalCon Rotterdam 2026 is going to be way more than just sessions and keynotes, it’s a chance to be part of what actually builds and improves Drupal.

Contribution Day is a part of DrupalCon, and in Rotterdam it will be on Thursday, 01 Oct. This is the heart of the event, where the global community comes together to make a real and concrete impact to the project.

If you’re planning your trip, we highly encourage you to stay for Thursday. It’s the most rewarding day of the conference. Whether you write code, improve documentation, help with UX, fix bugs, or support translations, there’s a place for every skill level.

And better still, you don’t need any prior contribution experience, just curiosity and willingness to get involved. You’ll be guided by experienced mentors, collaborate with many contributors from around the world, and leave with new connections, new skills and something meaningful you helped create.

If you've never contributed before, this is the perfect moment to start!

So, what are you waiting for?
Let’s do it!

Contribution Day in Rotterdam on 01 Oct.

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Outside AI - The State of Agent Experience in Drupal.

By Scott Falconer, Product Lead, Outside AI

Where Drupal really stands with AI agents, where it has a right to win, and what we need to do next.

AI agents can build with almost anything. That is both great news and a problem for Drupal.

A person can ask an agent to recommend a platform, rebuild an existing site, create a content model, configure permissions, or change a running system. The agent then has to decide whether Drupal is a good path, reach it, understand it, act on it, and verify the result.

When that experience fails, we usually do not get a bug report. The agent works around Drupal, produces something that only looks finished, or quietly chooses another stack.

That makes agent experience a growth problem for Drupal, not just a developer-experience problem.

Drupal does not need to be the fastest way to generate any page. Drupal should be the safest, clearest way to a governed, inspectable, long-lived site - and agents should be able to use it effectively.

By governed, we mean the controls that make a site safe to run and hand off - a real content model, scoped roles and permissions, review and audit, safe rollback - not just quick to generate.

This is the purpose of Outside AI, the workstream the Drupal AI Initiative launched: making Drupal legible, callable, safe, and verifiable for agents and builder tools operating from the outside.

The distinction from Inside AI, in shorthand:

Inside AI: a person uses Drupal, and Drupal uses AI to help.
Outside AI: a person uses an agent, and the agent uses Drupal.

These are different experiences, but they need substantially the same foundation: clear state, stable interfaces, scoped identity, governed actions, and reliable verification. Wherever possible, that foundation should be built once in Drupal and shared by both.

Our goal is not to make Drupal better for agents instead of people. It is to make Drupal's existing strengths explicit enough that both agents and people can safely use them. If we are successful we will make Drupal's strengths visible and attainable - improvements that hold no matter which agent, model, or tooling wins.

Where Drupal really is

Early measurements from the Drupal Agent Readiness Scorecard point to a tricky but useful conclusion: capability is becoming table stakes.

Our first-hour study drops a cold agent onto each platform with no prior setup and measures how fast and how reliably it can stand up a small but real structured, permissioned site. The bar: a content model, seeded content, a public page, a scoped editor role. Every milestone is confirmed by an independent HTTP probe, not the agent's own say-so. Agents cleared that bar on every platform we tested: Drupal CMS, bare Drupal core, WordPress, and a from-scratch Node app (each across multiple models and two agent families), plus single spot-check runs on Wagtail, Joomla, Strapi, and Payload.

The evidence is still early and deliberately narrow - and the scorecard is useful for direction, but "can an agent build with Drupal?" is no longer an open question.

The better questions: when should an agent choose Drupal, how far can it reliably get, and what is left after the agent is done?

Drupal has an advantage here. It was not designed for agents - but it was not luck, either.

For two decades, enterprise and community pressure forced Drupal to care about structured content, relationships, roles and permissions, editorial workflows, configuration management, APIs, and migration. Complex digital experiences demanded structure, governance, and safe ways to change things, so the community built them.

Those are exactly the things agents need: structured state they can inspect, explicit permissions they can reason about, actions with known boundaries, configuration they can hand off, and evidence that a change worked. The foundation was already here. AI is now revealing why it matters.

And agents do find it. In the study's Drupal runs, agents reached for native capabilities - content types, roles, permissions, Views, exported configuration - instead of bypassing Drupal with a static lookalike, and what they left behind was inspectable. That evidence is promising, but as Dries wrote about Drupal's role in agentic workflows, a head start is not a plan to win. What this post attempts to measure is where the head start is real, where it is not, and what we need to do to turn it into a win.

Drupal still makes agents work too hard to reach the advantage. Setup choices, authentication, module selection, stale assumptions, unclear action surfaces, and weak verification can consume the whole first session before Drupal's strengths become visible.

Agents do not reward us for architecture they never reach.

The advantage is made of decisions

Drupal core, contrib, and products like Drupal CMS are best understood not just as software, but as an accumulation of hard-fought decisions by many dedicated individuals: core is the architectural commitments (structured content, revisions, granular permissions), contrib the solved problems (search, forms, spam, SEO), and a product like Drupal CMS the curation - which of those a serious site actually needs, working together from day one. That accumulated judgment is the real inheritance, and the hard part to reproduce on any stack.

What makes those decisions unusually legible, inspectable, and reusable - without reading the code that enforces them - is that Drupal represents most of them as structured configuration: data with a schema, exportable to files, reviewable as a diff, and inspectable on a running site. Content types and fields, role grants, Views, editorial workflows - they all live there. That standard is the point: Drupal gives decisions a common, inspectable place to live. On a from-scratch build there is no such defined place - a decision may sit in code, a migration, an ad-hoc config file, or only in someone's head. On some headless CMSs, even the access rules are code. Drupal keeps an unusually large share of the decision surface legible as data.

That is what a human actually inherits from an agent-built Drupal site: decisions they did not know to ask for, in a form they can inspect and safely change. An agent building from scratch gives you exactly what it thought of. An agent building on Drupal CMS hands you the community's accumulated judgment - core's architecture, contrib's solved problems, the product's curation - as artifacts you can review, compare, export or change through the admin UI or by applying a recipe, without a developer touching code. When we verified agent builds, we did not take the agent's summary - we read the configuration. Decisions-as-data is what made that possible: legible, deployable between environments of the same site, composable across sites as recipes, and checkable by someone who was not in the room.

This is where Drupal's advantage can also become fragile - a decision can be structured and still be lost, bypassed, or stripped of its rationale:

The config/code boundary can be a failure point. Configuration declares a desired state that one line of code can silently bypass. The access-coverage probe (which requests a restricted resource through every serving path an anonymous user could reach) found exactly this shape: custom code called ->accessCheck(FALSE) on an entity query and then exposed the returned entities without a later entity-access check, bypassing the access filtering the site otherwise expected. A site's true behavior is the intersection of config and code, and verification that reads only one layer misses the other.
Configuration records what was decided, rarely why. It can carry labels, descriptions, and dependencies, but there is no first-class model for the rationale behind a decision or how future changes should treat it. The hard-fought decision arrives stripped of its reasoning.
Configuration does not defend the intent behind a valid change. It can defend structural validity and dependencies, but not the human judgment a change quietly discards. In the recipe-composition tests - where we combined recipes that touch the same settings - two recipes applied conflicting config actions to the same property and the later action won mechanically. In the intent experiments - where we recorded the reason behind a design decision in the site itself, then asked a fresh agent to make a conflicting change - agents read the stored rationale and then removed the very affordances it was meant to protect.

So "those decisions aren't lost" turns out to be an assumption, not a guarantee - in these tests, it did not hold on its own… but the answer is not to freeze the decisions: the agent acts for the user, and sometimes changing one is exactly right. In the intent experiments the rationale was in the site, and the agents even read it - it still never entered the change. Our bet is timing: move the reason to the moment - keep it attached to the work, and put it in front of the agent exactly when it is about to change what that reason protects. The agent may still make the change; sometimes it should, but it is a tradeoff the agent had the opportunity to evaluate with the right context at the right moment.

And the stakes are rarely one big decision. A long-lived site is changed by many actors over many years - people and agents, each change small on its own. No single lost decision reads as damage; the damage is the trajectory. Small silent losses compound, change after change, until the governed site someone carefully built has drifted into something nobody chose. The advantage accumulated one hard-fought decision at a time, and it erodes the same way - which is why the lever has to sit at the moment of change, the same granularity where the drift happens. The advantage is made of decisions, for as long as you can remember them.

Where Drupal has a right to win

The Playing to Win choice cascade rests on one premise: strategy is a choice.

A disposable landing page, a one-off prototype, or a deeply bespoke product where a CMS addresses only a small slice of the job may be better served by a different stack. Drupal does not need to win every prompt to win the work it is built for.

This is the practical consequence of the great CMS unbundling: AI commoditizes creation while raising the value of control - it lowers the cost of creation, not the cost of trust.

Drupal has a right to win when the result must remain understandable and operable after generation:

Content-rich sites with a real editorial model.
Sites with multiple roles, permissions, and approval boundaries.
Sites that must ensure high quality and accurate content.
Long-lived systems that teams of people and agents will change.
Rebuilds and migrations where the source site's decisions cannot simply be discarded.
Projects where configuration, deployment, auditability, and recovery matter.
Sites where "it looks done" is not the same as "it is safe to operationalize."

This territory is defined by the work, not the organization's size. A small nonprofit can need strong editorial governance. A large enterprise will often find a disposable microsite sufficient for the right use cases.

In the language of the cascade:

Winning aspiration: Drupal becomes the clearest, most trustworthy CMS choice for an external agent building a real, governed site.
Where we play: work where structure, governance, handoff, and long-term operability matter.
How we win: close enough of the first-session gap that Drupal gets considered, then prove the result is more inspectable, verifiable, and trustworthy.
Capabilities we need: start and connect, understand the site, act through governed interfaces, verify and recover, and rebuild or launch through a repeatable path.
How we measure it: fixed tasks, real running sites, retained failures and nulls, and independently checked outcomes.

How this gets more people to Drupal

Drupal's historical adoption barrier is not that it is powerful. It is that reaching the power has usually required someone who already knows Drupal.

A committed Drupal agency invests through that friction because it knows what is on the other side. A WordPress shop that occasionally considers Drupal, a system integrator with many platforms to choose from, or a lean in-house team may not.

AI can lower the expertise barrier - but only if the results can be trusted.

If an agent can absorb more of the repeatable setup and assembly, while experts review the consequential architecture, business, and governance decisions, then Drupal expertise moves up the value stack. Talented people spend their time on customer experience, editorial strategy, integrations, and the decisions that actually differentiate the site.

Prove that path and the agency pitch changes from:

We can build this after a substantial discovery and setup phase.

to:

We have already built a governed starting position. Here is the architecture, what we learned from the source site, which Drupal decisions we inherited, what we verified, and where expert judgment is still required.

That is a stronger way to enter a rebuild conversation - and it is how Drupal becomes a realistic choice for teams that do not already have deep Drupal expertise in-house.

It is still a strategic bet. We have not demonstrated that better agent experience produces Drupal adoption at scale, and we should not claim the market outcome before we have proven the mechanism. We would know the bet was wrong if agents kept bypassing Drupal's native capabilities even when they were easy to reach, if inspectable artifacts did not measurably cut a second team's time to change a site safely, or if entry friction never fell far enough for Drupal to enter consideration at all.

The first session should not require a laptop

Underneath the expertise barrier sits a second one: the environment. Local tooling for Drupal provides an excellent experience - DDEV can stand up a real site in minutes for someone who lives in a terminal. The same first-hour measurements ran on exactly that tooling, and even there, install weight - not capability - set the pace. And that is the expert path: it assumes a capable machine, a terminal, a container runtime, and the time to configure them. A growing share of first evaluations do not start there. They start on a phone, in a browser tab, or inside a chat window - often mediated by an agent that has no local machine at all.

No amount of polish can remove that local barrier. And to be clear, this is not a criticism of tools like DDEV - DDEV should remain the expert path. But if the only way to try Drupal is to install Drupal, we lose the people - and the agents - who were only willing to spend five curious minutes. We risk rejection before the first page is ever built.

That is why hosted try-and-build surfaces matter: places where someone who does not know or care about Drupal yet - or an agent acting on their behalf - can start a real site with nothing installed. Hosted trials, browser-based build environments, demo workflows, commercial platform starters, and one-click hosting paths each attack that floor from a different angle. And each has a natural graduation path: a trial becomes a real site, and a real site launches onto hosted platforms as it grows. The front door feeds the installed base.

This is also where the community structure of the Drupal AI initiative becomes its advantage. No single on-ramp will fit every user, and each provider brings its own vision, market, and opinions - a browser trial optimizes for the five-curious-minutes case, a demo workflow for build-something-real, a commercial platform for launch-and-scale. That plurality is a strength, on one condition: the Drupal underneath must be the same agent-ready Drupal everywhere - the same state introspection, the same governed actions, the same verification. Providers should compete on experience and opinion, not re-invent the substrate.

The standard: someone who has never heard of PHP or SQL - or an agent with no machine at all - can go from curiosity to a real, governed Drupal site in one session, and graduate that site to production hosting without starting over.

What we need to build

From here the essay turns into inside baseball: issue by issue, for the people working with Drupal every day. If that is not you, feel free to skim, or skip to the closing.

The Outside AI roadmap follows the journey an external agent has to complete - the same path Dries has sketched, from setup to connection, context, governed action, validation, recovery, and launch. These five stages assume Drupal is already in the running; getting there - an agent discovering Drupal, recognizing the task fits its territory, and reaching a starting surface before any Drupal site exists - is stage zero, and it is what the front door and self-description work above are for. In the below, we focus on what we should be able to say, with evidence, before calling it done. And wherever external tooling has to keep explaining the same Drupal quirk to an agent, that quirk is a roadmap item: the workaround is the requirements document.

1. Start and connect

An agent needs supported ways into Drupal and a scoped, auditable identity - and there is still a lot to decide in what that identity can be. An agent can act as a delegate, carrying a scoped slice of the authority of the person it works for. Or it can act as an independent, non-human account with grants of its own - a principal actor. These are two different models of what an agent is, with different strengths: delegation cannot exceed the person it acts for, which keeps the blast radius small and the audit trail human-shaped; an independent identity can carry work no single person's permissions cover, like scheduled maintenance or operations across many sites.

Drupal should not pick the winner. Products, hosts, and teams will choose differently - reasonably - and the same site may run both. From the substrate's side, the fork matters less than it looks: both models need the same structure - a grant that is scoped, an action that is attributed, a denial that is auditable. Build those once and either model, or both at once, can run on top.

The mechanics are arriving. The core CLI entry point (vendor/bin/dr) landed in Drupal 11.4. Work on the execution principal, OAuth behavior, and MCP scope enforcement continues across the initiative: the execution-principal plan, OAuth identity work in Simple OAuth, and scope handling in the MCP Server module. No single entry point serves every environment: dr is a local and server transport, while a remote agent needs authenticated HTTP or MCP. What has to stay constant is the contract - the same action, authorization, and receipt model, reachable through the right transport for each.

The standard: given an agent operating under a scoped grant - delegated from a person or issued to a non-human identity - when it attempts an allowed action, the action succeeds and is recorded against an execution principal that names both the initiator and the executor. When it attempts an action beyond that grant, it fails clearly, safely, and with an auditable reason.

2. Understand the running site

The agent should not have to guess what Drupal or the running site can tell it. We need supported, machine-readable inventory, site structure, API and schema fidelity, path ownership, available actions, and current constraints.

The standard: given a running Drupal site, when an agent requests site context, it can discover content types, fields, roles, permissions, workflows, path ownership, enabled extensions, available actions, and relevant constraints - without scraping the UI or guessing from routes.

3. Act through governed interfaces

Agents need typed inputs, predictable errors, least-privilege execution, approval boundaries, and results another system can inspect.

This fundamental is one Drupal's entity layer already demonstrates: authorization attaches to the operation, not the entry point. An editor does not write to the database - they work through forms their permissions allow, and when a change goes through the Entity API, the same permission and entity-access checks fire whether it arrived from the admin UI or the API. For agents, that is the right foundation: no separate "agent mode" to secure - a new caller walks through a new door and hits the same wall. It is not yet universal: some checks still live at the door, and the command line has historically carried implicit authority - which is exactly why the execution-principal work in stage one matters. Part of the roadmap is making the fundamental universal, not inventing it.

What is missing is declaration, not governance. Entity CRUD is well covered - JSON:API exposes entities as resources under the same policies. But the operations beyond CRUD - clear a cache, apply a recipe, run a migration, reindex search - are scattered across admin forms, Drush commands, and one-off endpoints, each with its own shape. An agent cannot reliably discover what operations exist, what they require, or what they return; efforts like the Tool API and tool declaration introspection are working toward that declared catalog. The requirement is the fundamental, not any one module: one action model, many doors - typed inputs, the same authorization, and a structured receipt from every transport. A receipt, though, is still a claim - judging it is the next stage's job.

The standard: given one declared site action, when an agent calls it through any supported action adapter - CLI, MCP, ECA, or Drupal's AI systems - its typed inputs, authorization, errors, and result receipts behave consistently. Where an operation is entity CRUD through JSON:API, the same identity and authorization policies apply.

4. Verify and recover

The agent's own summary should not be held as proof - we would never expect a human to be the ideal judge of their own work. What matters is what the site actually shows. That is not a new problem: Drupal has always worked on it, because Drupal was never just for managing content - it manages how a team works together. Work does not count until someone else - or a system-enforced guardrail - says it does: drafts, moderation states, revision history with rollback, a permission model where the author does not have to be the approver. An agent is the newest actor in that system: it proposes within its permissions, the workflow gates what counts as done, a different actor approves, and revisions makes it reversible.

That machinery is fundamental to Drupal for content. For code and configuration, teams already have a mature review lane too - it just lives outside Drupal, in version control. And Drupal is unusually well placed to use it: because configuration exports to files, a config change can ride the same discipline as code - a diff, a pull request, a reviewer, CI, a revert. That is decisions-as-data paying off; most platforms cannot put their settings in a code review at all. An agent that works like a developer - building locally, exporting configuration, committing - inherits all of it.

The live site is the harder case, and not just for agents: a person doing site-building on production creates the same risk. Teams manage it by deciding where each kind of change is allowed to happen. Content is edited live, because live content has mechanisms for review. Structure is built in a development copy and flows to production through configuration import - so a config change made directly on production is temporary, and the next deployment erases it; some teams block live config edits outright. Giving an agent the same working agreement needs nothing new: a role that edits content on production, a freer hand in a development copy, the config path in between.

Two things are new though, and as a result they are the roadmap. First, the working agreement has to be explicit. Teams usually write it down for people - onboarding docs, locked-down production, review - but with agents, every session can be somebody's first day on the site, so anything left as "on the job" knowledge repeatedly fails fast. The boundary has to be stated by the site, and feedback given when it is enforced; the explicitness a cold agent needs is the same explicitness that protects a new hire.

Second, speed and scale. Where a team produced a handful of reviewable changes a day, agents can produce thousands. Human review alone does not survive that volume. Independent, automated verification has to absorb it - machine checks covering the routine, so human attention lands on the judgment calls. AI observability can trace requests through standard logging and telemetry, but tracing a request is not the same as independently verifying a change or rolling it back; the checking itself has to become machinery.

Our work is to extend the team discipline Drupal already applies to content - draft, review, approve, revert - to every surface an agent can change, at a speed and scale no site team has faced before.

The standard: given a change the agent claims is complete, when an independent process inspects the site, it can confirm what changed, show which content, configuration, code, or workflow surface was touched, report whether verification passed, and provide a preview, rollback, or recovery path.

5. Rebuild, migrate, and launch

The same governed path has to support a real way onto Drupal and a real handoff toward production. That includes source audits and discovery, content and pattern mapping, Drupal-native architecture advice, redirects, Canvas and configuration integrity, parity evidence, editorial review, and an explicit boundary between structured Migrate API work and agent-led re-architecture.

Issues such as Canvas configuration data integrity and reconciling updates to already-imported default content are part of this path even though they do not carry an "AI" label.

The standard: given a real source site, when an agent proposes or builds a Drupal replacement, the handoff includes source-site findings, mapped content and patterns, Drupal-native architecture, parity evidence, unresolved gaps, and a clear line where human judgment is required before launch.

Measurement is the spine across all five. The Drupal Agent Readiness Scorecard exists to tell us whether Drupal improved while the workflow held steady - separately from the normal improvement of the models themselves.

Rotterdam should prove direction, not victory

The Rotterdam plan - the proof we are aiming to have ready by DrupalCon Rotterdam - is intentionally narrow: one real rebuild of an existing non-Drupal site into Drupal CMS. The question it answers is precise: can an outside operator turn a real non-Drupal site into a defensible Drupal starting position, with independently reviewable evidence?

The operator should be outside the Drupal CMS team.
The source site should contain enough structural complexity to require judgment.
The result should include a source audit, a governed Drupal build, recorded decisions, independent verification, and explicit gaps.
A Drupal expert should review it against a predeclared rubric - architecture, permissions, content integrity, editorial usability, maintainability, verification evidence, and known gaps.
The final question should be binary: would that expert stake their name on this as a sound starting position for a senior team?

The bar we’re setting is not, are we "ready to launch.", it is "is this defensible enough to continue?"

One successful build would demonstrate a viable path in that case. A second site with a second operator would begin to test repeatability. Neither would prove that the market has moved - and we should not claim otherwise.

The question then is what remains after the agent is done. A clear test is: give the finished build to a fresh person or agent with none of the original context, and ask them to make a consequential change safely - add an editorial role, alter a workflow without weakening access, explain why the architecture is what it is, recover from a deliberately broken change - while we measure time-to-understand, mistakes, expert intervention, and whether the site's own state carried the reasoning. That tests "decisions as data" far more directly than a second build.

A head start is not a win

Drupal already has much of what builders need for serious sites. Again, that is the good news.

But the bad news is that potential has little value if agents reject Drupal before reaching it.

We should be careful not to declare victory because Drupal has structured content, permissions, workflows, and configuration management - The work that remains is to turn those properties into a clear, measurable advantage: make Drupal easy enough to choose, explicit enough to understand, safe enough to change, and verifiable enough to trust.

Outside AI needs real workflows more than speculative feature lists. If you are using an external agent to build with Drupal, calling Drupal from another system, or encountering friction anywhere from setup through launch, bring us the real task.

A use case, failed run, repeated workaround, missing capability, or existing issue is enough; you do not need to arrive with a solution or even know where the work belongs. Add it to the Outside AI meta issue or bring it to the #ai-initiative channel in Drupal Slack. We will help reproduce it, map it to the agent journey, connect it with the right maintainers and implementation work, and determine whether it belongs in the scorecard.

If you maintain a project agents need to use, tell us what they repeatedly misunderstand or work around. Those workarounds are requirements documents.

Drupal's earned advantage gives us the right to play. What we build, and what we prove next, determines whether we win.

Evidence note: the measurements described here are early and deliberately narrow; several are exploratory rather than claim-grade. The scorecard work publishes fixed tasks, retained failures and nulls, explicit evidence boundaries, and paired pre/post results before claiming that Drupal itself improved.

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Drupal AI Initiative: Outside AI - The State of Agent Experience in Drupal.