Drupal Planet

BloomIdea: Mautic Audiences for Drupal: personalisation built on the segments you already maintain

Your marketing team already knows who the VIPs are. They maintain the segment in Mautic, they send it a campaign every month, they add and remove people as orders and behaviour change. Then somebody asks for a VIP banner on the website, and the website turns out to know nothing about any of it. So "VIP" gets built a second time, as a Drupal role or a checkbox on the user: two definitions of the same audience, maintained by two teams, drifting apart from the first week.

The second problem arrives with the first. Personalisation on Drupal usually means varying the page per visitor, and a page that varies per visitor is a page nobody else can be served, so the banner meant to lift conversion ends up slowing the site down for everyone.

We build and run Mautic-driven marketing for our e-commerce clients, so we kept meeting both problems on the same projects. Mautic Audiences is our answer, now on drupal.org as free software (GPL-2.0-or-later) for Drupal 10.3 and 11, covered by Drupal's security advisory policy. It reads the segments and tags you already maintain in Mautic and turns them into native Drupal primitives: block visibility, Twig, tokens, JavaScript, Views, Search API and, since 1.1, a field.

What editors can build without a developer

Place any block, scroll to Visibility, pick Mautic segment, type vip. Save. That is the whole configuration, and it covers most of what people actually ask for:

  • A VIP banner that appears the moment Mautic adds someone to the segment. No Drupal save, no cache flush.
  • A homepage hero per traffic source: three blocks, one each for newsletter, instagram and paid-search, each gated on its own segment.
  • Hiding what someone already has. The newsletter signup block disappears for visitors already tagged subscriber.
  • Coupon-aware touches for visitors carrying a coupon:* tag, fired client-side so the page itself stays cacheable at the edge.
  • Cross-channel campaigns. Mautic sends the email, and the Drupal page the link lands on recognises the segment it was sent to. One audience definition, two channels.

Anonymous visitors count: someone carrying Mautic's tracking cookie has an audience with no login, which is what lets a campaign link open a personalised page at all. Editors check their work by appending ?ma_preview_segments=vip to any URL, which shows the site as a member of that segment sees it.

Themers get the same decision as a one-liner that brings its own cache metadata, and there is a token for metatag patterns and a JavaScript call for whatever has to happen after the page is served:

{% if is_in_segment('vip') %} <p>Free shipping today, your VIP perk.</p> {% endif %} Content that knows who it is for

The module started by answering "who is this visitor?". Version 1.1 adds the other half, "who is this content for?", in a bundled sub-module.

Add the Mautic audience field to any bundle and editors pick, from the real list of segment names, who a piece of content is meant for. By default that is a targeting hint templates and listings read. Per field, you can turn on Restrict viewing to the selected Mautic segments, and content with a segment picked is then refused to everyone outside it. Content with nothing picked stays for everyone.

A gate that only closes the page is not a gate, so the sub-module also ships a Views filter that keeps whichever rows each visitor is entitled to, and a Search API processor that adds the condition at query time rather than keeping items out of the index. Its README lists what none of it can cover, exports, feeds, custom code that loads entities without an access check, because a promise of restriction with holes in it is worse than no promise.

The interesting part is what it refuses to expose

Segment names and tags are business intelligence: cancelled-subscription, risk:churn-90d, coupon:VIP-50. Ship them to the browser and you have published how you categorise your customers, and sometimes a working coupon code, to anyone with DevTools open.

So the client-side API answers questions instead of listing facts. It will tell you whether this visitor is in a segment you name; it will not tell you which segments exist or which ones they are in. The endpoint that returns an actual list starts empty and returns only what an editor has allowlisted. The field type has no formatter at all, and reading its values takes an administrative permission, which covers JSON:API, REST and Views alike.

It stays out of your render path

Audiences are read from local storage on every render, never from the Mautic API, so a slow Mautic never sits in front of a page, and an outage degrades to an empty audience rather than an error. Cache contexts key on the resolved audience rather than on the visitor, so two people in the same segments share a cache entry and a page with two gated blocks has four variants, not one per visitor. Anonymous personalisation does cost you Drupal's Internal Page Cache on the pages that use it, which docs/cdn.md deals with head on.

Get it composer require drupal/mautic_audiences drush en mautic_audiences

It needs Advanced Mautic Integration for API connectivity, a Mautic instance with API credentials, and PHP 8.2 or later. A bundled sub-module wires Klaro into the resolver, so visitors who have not consented resolve to an empty audience without any code.

The project page, the issue queue and the documentation are at drupal.org/project/mautic_audiences. If you run Drupal Commerce, Commerce Mautic Connect pushes the abandoned carts, customer metrics and coupon tags into Mautic that this module reads back. Issues and merge requests are very welcome.

Security public service announcements: Upcoming critical contributed project security release on September 23, 2026 - PSA-2026-09-21

Date: 2026-September-21Security risk: Critical 16 ∕ 25 AC:Basic/A:User/CI:All/II:All/E:Theoretical/TD:UncommonDescription: 

There will be a security release for a widely used contributed module on September 23, 2026 between 17:00 and 21:00 UTC.

We are announcing this release in advance because the affected contributed module is used on a significant portion of Drupal sites, and the upcoming release will include a significant number of advisories. The advisory with the highest risk score for the release is currently rated as critical.

Drupal core is not affected.

Advisories may be published in batches (a few at a time)

The current rate of advisories may require changes to our practices going forward:

  • The security team may publish advisories individually, at different times inside the window.
  • We will try to publish batches grouped by module.
  • We will announce in Slack when all planned releases for the day are complete.
  • We will release mailing list emails about the security updates together at the end of the window, to reduce the risk of site owners updating multiple times while advisories are still being published.

These changes are intended to make the process easier for the team and to make communication from the team easier to follow.

No special release procedures

The planned update does not require special release procedures.

Coordinated By: 

DDEV Blog: DDEV Snapshots: Checkpoints, Restores, and Seeded Databases

Snapshots have been a beloved feature of DDEV for years, but in v1.25.4 there is so much more.

Read on (or watch, or both) to see:

  • Basic use of snapshots
  • Use of a seed snapshot to automatically provide content to a project on first start
  • Committing a seed snapshot into a Git repository
  • Starting/restarting with a seed snapshot
  • Embedding a snapshot (usually for huge databases) into a custom database image
Table of Contents Screencast showing new and old features of snapshots Snapshots are easy!

A DDEV snapshot is a physical, "hot" backup of your database — mariadb-backup/xtrabackup for MariaDB and MySQL, or pg_basebackup for Postgres — not a text-based mysqldump. Because it copies the database's on-disk files instead of dumping SQL statements, it's much faster to create and restore, especially on large databases. All the basics about snapshots are in the docs.

Normally snapshots live in .ddev/db_snapshots/, and the filename encodes the database type and version, for example mariadb_11.8. That's why a snapshot only restores against a matching engine and version — restoring a mariadb_11.8 snapshot into a mariadb_10.11 project will fail.

Snapshots are compressed with zstd by default. --uncompressed skips the decompression step on restore, trading a much larger file on disk for a faster restore. Postgres doesn't support uncompressed snapshots.

Core Commands
  • ddev snapshot --name=<name> — create a snapshot
  • ddev snapshot restore — opens a TUI allowing you to select snapshot to restore
  • ddev snapshot restore <name> — restore a named snapshot
  • ddev snapshot restore --latest — restore the most recent snapshot
  • ddev snapshot restore $HOME/tmp/mysnapshot-mariadb_11.8.zst — restore from an arbitrary path, not from the default .ddev/db_snapshots/
  • ddev snapshot --list (-l) — table of snapshot name, created date, size, database version, and compression; shows a Worktree column when relevant
  • ddev snapshot --cleanup (-C) — delete one snapshot (--name=<name>) or all of them (prompts for confirmation unless -y)
  • ddev snapshot --all (-a) — snapshot all projects (automatically starts stopped projects to accomplish this)

If your project has multiple Git worktrees, snapshots taken from other worktrees of the same repository are available too — by name, with --latest, or through the interactive list.

Snapshots as Migration Checkpoints

Take a snapshot before each step of a migration or update: ddev snapshot --name=pre-migration-step3. If a step breaks something, restore the last good snapshot instead of restarting the migration from scratch. (ddev snapshot restore --latest can be a great technique if you do this religiously.)

ddev snapshot --list becomes a log of checkpoints, and ddev snapshot restore <name> or restore --latest jumps back to any of them instantly.

This builds on the workflow described in DDEV Database Management: snapshot, ddev restart --reset-database, restore. It's also a natural lead-in to seeding a new database volume directly from a snapshot, covered next.

The seed Snapshot

DDEV projects have always automatically created a database named db to help you get started fast. But it's been an empty database, with no content. Now, in DDEV v1.25.4+, the seed snapshot has been added. You can create a snapshot named seed (with whatever content you want) and when somebody starts up a project for the first time, the content on the seed snapshot will automatically be loaded. You can even check the snapshot named seed into your Git repository if you don't object to its size, and it can help folks new to the project to get started that much faster. (The seed snapshot is only used when there is no database; your changes to the database are kept as always.)

To add the seed snapshot to Git:

git add -f .ddev/db_snapshots/seed-* git commit -m "Add default seed db for clean startup" Seeding New Projects with --seed-snapshot

If you want to start a project with an alternate seed snapshot, ddev start and ddev restart accept --seed-snapshot=<name-or-path>, which seeds the database volume from a snapshot instead of the stock seed database. This only applies when there's no existing database — DDEV errors otherwise, telling you to add --reset-database or use ddev snapshot restore.

<name-or-path> can be a short name from .ddev/db_snapshots/ or a full path:

ddev start --seed-snapshot=$HOME/tmp/mysnapshot-mariadb_11.8.zst

This works for every database type DDEV supports, unlike the baked-dbimage technique below, which is MariaDB/MySQL-only — it's restored the same way ddev snapshot restore does, just at volume-creation time.

Combine --seed-snapshot with --reset-database to reseed an existing project in one step:

ddev restart --reset-database --seed-snapshot=<name> -Oy

-O (--omit-snapshot) skips the automatic snapshot save of the database being thrown away, and -y skips the confirmation prompt.

This is the lightweight alternative to baking a seeded database image: no custom image or registry, just a snapshot file — good for local or small-team use where a shared registry is overkill.

Seed Snapshots + --reset-database

Once you have a seed snapshot, ddev restart --reset-database -Oy repeatedly returns the project to that known-good state — handy between test runs.

Building a Seeded Database Image

You can also create a replacement database image that has an alternate seed database built into it. This is especially great for delivering huge databases, as the process can be handled by the image, or an upstream process. All the image building does is copy a base_db.zst or base_db.mbstream into the /mysqlbase/custom directory of the DB image.

For teams that want to share a ready-to-go database via a Docker image registry instead of a snapshot file, build-and-push-seeded-image.sh is an example that builds a real multi-arch (linux/AMD64, linux/ARM64) image with a snapshot baked in:

build-and-push-seeded-image.sh --snapshot=uncompressed-2g \ --output-image=randyfay/uncompressed-2g:v1.25.4 --push \ --base-image=ddev/ddev-dbserver-mariadb-11.8:v1.25.4

This technique relies on mariadb-backup/xtrabackup, so it doesn't support Postgres.

Uncompressed seeds make for a much larger image and a slower push, but a faster, decompress-free container startup. It's worth comparing the actual image sizes to see the bandwidth cost of each trade-off.

Using a Seeded Image via dbimage:

Point a project at the seeded image using dbimage in .ddev/config.yaml (or .ddev/config.local.yaml):

# .ddev/config.yaml or .ddev/config.db.yaml or .ddev/config.local.yaml # Example dbimage dbimage: randyfay/uncompressed-2g:v1.25.4

Then:

ddev restart --reset-database --omit-snapshot -y Examples and resources

This article was edited and refined with assistance from Claude Code.

Omega8.cc: Two Old Friends Move In

BOA now runs Textpattern and Grav 2 sites inside the Ægir control panel, next to Drupal and Backdrop: install, backup, restore, clone, an upgrade done on a copy while the original keeps serving, then rename and cutover, on platforms which build themselves from the newest official release. It is a promise kept, the Continuity which our thirty years story named and our website has repeated since: Drupal CMS on Ægir, done; Backdrop first-class, done; and now Textpattern, our pick before the Drupal era, whose return that story did promise, together with Grav, which has run our own website for a decade and runs our docs from Git. All of it lives in a Drupal 7 panel, on a Drush 8 which works on every PHP between 5.6 and 8.5 and talks to bee, to the Drush inside every modern Drupal codebase, and now to Grav's own CLI and Textpattern's own installer. Fourteen Drupal core lines and fifteen distributions sit next to them. And before somebody asks: no plans for WordPress ;-)

Drupal AI Initiative: Intelligent Layouts: Drupal Canvas AI and the Context Layer

Reposted from Acquia with permission from Acquia. Authored by Martin Anderson-Clutz (mandclu)

Drupal Canvas AI shifts content platforms from reading to writing, using governed context layers to help enterprise AI agents automate safely.

What changes when an agent stops reading your content and starts writing it?

For the last two years, the conversation about AI and content has mostly been about reading. Retrieval, summarization, a chatbot that answers a question from your knowledge base. That problem is largely solved, and it is solved almost everywhere. Any serious platform can find a relevant paragraph and hand it back.

The shift that actually changes the job of a content platform is quieter. Agents have moved from "answer my question" to "do the work." They are no longer only reading your content. They are starting to write it, assembling pages and populating components and publishing the result. The category we have called content management for twenty years was built for the first job. It was never asked to do the second.

Where the Real Cost Lands

At this point a fair objection turns up: is this not exactly why we keep a human in the loop? It is. Any content platform worth running in an enterprise keeps a person between the agent and the published page. A system that lets an agent push unreviewed work straight to production is not showing you the risk of agentic content. It is showing you that it was never built for the enterprise to begin with.

So the risk worth talking about is not the rogue page. Review catches that. The risk is waste.

A read and a write still fail in opposite directions, and the cost is what separates them. When a retrieval system returns a weak answer, one person spends a moment sorting it out and moves on. When an agent produces a weak write, a layout that misses the brand or a component wired to the wrong relationship or a draft that ignored a business rule, someone has to notice it, correct it, and often send it back to be generated again. Every one of those steps costs the reviewer's time and burns the tokens that produced the work in the first place.

That cost lands on the exact person the agent was supposed to help. The promise of agentic content is leverage, so the specialist spends their time on judgment instead of assembly. A guessing agent quietly reverses the trade. Rather than assembling the page themselves, the reviewer now inspects a draft they did not write. They hunt for the places it went wrong and explain what to change. That is not leverage. It is rework wearing the costume of automation, and it scales the wrong way, because an agent that guesses does not guess once. It guesses across every draft, at machine volume.

This is why context is the whole game. A human in the loop is cheap when the work in front of them is already right, and expensive when it is not. Give the agent structured, governed context and the review step becomes a quick yes. Hand it flat fields and hope, and the review step becomes the job you were trying to automate, now done twice.

Every Generation Answered to a Different Consumer

It helps to look at what actually changed, because the content platform has served three different consumers over its life.

Content Management System Headless and Composable Agentic Content Platform Primary consumer A person in a browser A front end serving a person Software that acts Content flows Outward, to one presentation Outward, to many presentations In both directions How editorial judgment is applied By hand, page by page By hand, page by page Encoded as context up front, confirmed at review Measure of success Editorial autonomy Reuse across channels Safe, accountable machine action

For most of that history, judgment stayed with the editor. The system stored content and rendered it, while a person decided what was true, what was on-brand, and what was ready to publish. Headless moved content to more places, but it did not move that responsibility. A human still stood between the content and the world.

Agentic content management changes that. The agent now does the assembly the editor used to do, which means the judgment the editor brought to the work has to come from somewhere. A platform that carries it, with the business rules and the content relationships and the standards for what good looks like, hands the reviewer a draft that already reflects those things. A platform that cannot leaves every one of those calls for the person to supply by hand, one draft at a time. The judgment does not disappear. It moves back onto the reviewer the agent was supposed to free.

The Distinction the Category Is Missing

Here is the line most tooling blurs, and the reason so many agentic demos fall apart the moment they meet a real enterprise. Content is not context.

Content is what you publish: the page, the article, the product description, the campaign.

Context is everything an agent draws on to produce that content safely. Your brand voice. Your content model. Your business rules, your reference material, and the relationships that connect all of it. Context is what turns a vague prompt into a result the organization would actually stand behind.

Most systems collapse the two. They hand an agent a set of flat fields and hope it infers the rest: the tone, the relationships, the governance that always lived in the editor's head. Guessing works beautifully in a demo and breaks in production, because the fields never carried the context to begin with. The agent was handed the output and asked to reconstruct the reasoning behind it.

Treating context as a governed layer of its own, with clear owners, review, version history, and scope, is what separates an agent that produces a plausible draft from one an enterprise can trust to publish. The prompt gets simpler. The result gets more accountable. The organization decides in advance what the agent is allowed to know and do.

What Structure Actually Buys You

Here the shape of the underlying system stops being an implementation detail and becomes the whole question.

An agent that writes needs things the read era never demanded. It needs to see the page as a structure it can reason about, with components, order, and resolved values, rather than a wall of markup it has to pattern-match. It needs to work inside the governance that already exists, the roles and permissions and workflows the organization spent years getting right, instead of routing around them. And it needs its output to land in a reviewable state, with a record of who initiated the work and what produced it, so a person can still say yes or no before anyone sees it. Structured context is what makes that review a quick confirmation rather than a second round of work.

None of that can be added after the fact. A platform that stores content as flat, disconnected fields cannot suddenly expose relationships it never modeled. A platform with shallow governance cannot suddenly supervise an agent it was never designed to hold. These are properties of the foundation. Either the structured content and the mature governance are already there, or you are trying to pour a footing under a building that already stands.

For years, careful content modeling and granular governance were treated as overhead, the slow and unglamorous work that held teams back. The agentic era inverts that. The same rigor is now what lets a team move quickly and safely at once, because it is exactly what an agent needs in order to act without guessing.

Where This Gets Real: Drupal Canvas

Plenty of tools can turn a prompt into a layout now. That trick is becoming table stakes, and it is the wrong thing to be dazzled by. The real question is whether the layout that comes back is on-brand, relevant, usable on the devices your audience actually reaches for, and built on terms you control. The answer depends on the foundation under the prompt, and that is what Drupal Canvas is designed around.

Four differences show up the moment you move past the demo.

The first is how layouts get built. Canvas assembles them from Twig-based single-directory components, React-based code components, and Drupal blocks, so you work with the component technologies your team already knows rather than adopting one proprietary format wholesale.

The second is where the context comes from. Because Canvas can ground its work in the Context Control Center, the agent is not inventing your brand from a prompt. It is working from the voice, content model, business rules, and relationships your organization has already curated and approved. That is the difference between a layout that is merely plausible and one that is on-brand and relevant.

The third is the model underneath. Through a provider-agnostic AI layer, Canvas is not wired to a single vendor's model. You use the one that suits the task in front of you, and you change your mind later as the field moves, without re-platforming to chase whatever shipped this quarter.

The fourth is where the output can go. Coupled or decoupled, you manage the content once and render it across the front ends and devices your audience uses, so a traditional site and a headless build stay open to you from the same system.

Any one of these helps on its own. Together they are the distance between generating a layout and producing one you can put into production: on-brand because it is grounded in your context, usable anywhere because the output is ambidextrous, and built with whatever model best fits the work.

The Engine Behind It: The Drupal AI Initiative

None of this is a single product feature or a one-vendor bet. It comes out of the Drupal AI Initiative, the funded and coordinated effort in the Drupal community to make the platform both a great place to build with AI and a safe place for agents to act. That initiative is the engine behind the capabilities that put Drupal in front on the things that matter here: structured content an agent can reason about, governance it has to respect, a provider-agnostic model layer, and the freedom to publish coupled or decoupled.

The work runs on two fronts. One brings common AI features directly into Drupal so they operate together instead of as disconnected add-ons. The other makes Drupal legible and callable to agents and tools working from outside, measured against an Agent Readiness scorecard that keeps the progress honest. Because it is happening in the open, on standards-based foundations, the improvements compound for everyone building on Drupal rather than accruing to one company.

The Context Control Center is a good marker of the pace. It turns the context an agent can draw on into a governed content entity, with ownership, workflow, revisions, translations, and scope, and its first stable release is expected in the days ahead. That moves the grounding layer from promising to production, which is the exact piece most platforms are still treating as a roadmap.

If you want to see where this is heading, DrupalCon Rotterdam has two AI Summits dedicated to it. I will be presenting at the AI Dev Summit, and my colleague Scott Falconer will present at the Enterprise AI Summit, one track for the people building with these tools and one for the people who have to answer for them in production.

Design It In, Do Not Patch It On

Faced with a fast-moving category, the tempting move is to wait for a winner and buy in later. The trouble is that the properties that matter here do not arrive as an upgrade. Structured content, relationship-aware data, a governed context layer, model choice, and the freedom to render coupled or decoupled are either in the foundation or they are not.

So the question for a content team is not which AI feature to switch on. It is harder and more useful than that. When an agent stops reading your content and starts writing it, does your platform still carry the judgment that used to live with your editors? Content answers to a person. Context is what lets software act in their place. The teams that see the difference, and that build on a foundation treating context, structure, and governance as first-class concerns, are the ones who will let agents do real work while keeping a hand on what ships.

File attachments:  Image1.png

Electric Citizen: Keeping Your Website Fit

Launching your site isn’t the end of the work. That’s the bad news.

Even after you settle back into your day-to-day, your website still needs you. There’s still so much you can and should do.

The good news: the work spreads out across the year, and the effort rises and falls. But measuring your data and making steady improvements is needed to keep your website effective long after launch.

Omega8.cc: The Valet Key

Every login on a BOA account is a key of one kind, and the ring holds six of them: your own panel login and your oN.ftp shell open everything; a colleague's login to the Ægir panel holds one of two roles, Client, which can do with a site what you can short of deleting it, backups and the admin login link included, or the new Site operator, the valet key of the control panel, which verifies, clones, migrates, flushes caches and runs updates on the Client's Drupal and Backdrop sites and cannot reach a copy of the database, the admin account or the delete button, not even from the Backups tab; and a colleague's shell login comes as one of two, files and themes, or whole codebases with Composer and Drush. Every task records the login which queued it. Drush is the database, so the codebase key belongs with the Client role and never beside a site operator, and the roles are a list shipped with BOA, re-asserted every night, readable to the permission on one page.

Talking Drupal: Talking Drupal #570 - Laravel & Marketing PHP

Today we are talking about Laravel, Marketing, and The PHP Foundation with guest Matt Stauffer. We'll also cover Formdazzle as our module of the week.

For show notes visit: https://www.talkingDrupal.com/570

Topics
  • What Is Laravel
  • Writing Laravel Books
  • AI and Technical Writing
  • Laravel Versus CMS
  • Drupal as Framework
  • Integrating Laravel and CMS
  • Laravel and Symfony
  • Marketing Modern PHP
  • Laravel Community Marketing
  • Jigsaw and Onramp
  • Drupal Marketing Lessons
  • Onboarding Focus in Laravel
  • Laravel BDFL Changes
  • Onboarding And Docs
  • Drupal Framework Perception
  • What PHP Foundation Does
  • Marketing PHP Vs Laravel
  • AI Answers And Positioning
  • Cross Ecosystem Collaboration
Resources Guests

Matt Stauffer - mattstauffer.com

Hosts

Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Amber Matz - tugboatqa.com [amber himes matz](https://www.drupal.org/u/amber himes matz)

MOTW Correspondent

Bernardo Martinez - bernardm28

  • Brief description:
    • This week's module is Formdazzle, a developer tool that makes theming Drupal forms easier.
    • Drupal's Form API is a powerful abstraction, but when you want to target one specific field, label, button, or form wrapper, the default Twig template suggestions can be limited.
    • The module works by taking information Drupal already knows about the form, like the form ID, element type, and element name, and using that to generate more targeted Twig template suggestions.
    • For example, in a Drupal View with exposed filters, you may want to style the Reset button differently from the Submit button. By default, Drupal renders both buttons through the same input–submit.html.twig template, which makes it difficult to customize them independently. This module lets you assign different templates to individual form buttons—such as Submit, Reset, or Filter—based on their action, type, and other properties.
    • This module has no configuration. Just enable the module and it starts working and look at the twig debug comments including extra template suggestions.
  • Module name/project name:
  • Brief history
    • How old: created in 13 September 2019 by johnalbin
    • Versions available: ^10.1 ^11 ^12
  • Maintainership
    • Actively maintained
    • Last release was 1 September 2026, currently the module has two maintainers Stephen Mustgrave and John Albin.
    • The module includes both test and security coverage.
  • Usage stats:
  • Module features and usage
    • There's no configuration. Just enable the module and it starts working, including with Views exposed forms and Webform.
    • Formdazzle automatically adds more specific theme suggestions based on the form ID, element type, and element name.

Droptica: Drupal content modeling for answer coverage: fields, not prose

Prices, specifications and proof buried in body copy are hard to compare, filter or reuse across pages, feeds and AI answers.

Drupal content modeling stores those facts as fields, connects related records with entity references and leaves prose for explanation. Maciej Lukianski walks through buyer-question audits, product and service field tables, migration stages and coverage reports that turn one edit into every output.

Droptica: Why Drupal sites get read and cited by AI

AI crawlers already fetch product pages during live conversations, but citations stay rare. Drupal sites cited by AI need one fact in fields, then the same value on the page, in JSON-LD, in feeds and through JSON:API or MCP tools.

Maciej Lukianski walks through what fetchers need, which Drupal modules cover Markdown, llms.txt and MCP Server today, and where configuration still decides whether a bot can quote your catalogue.

Tag1 Insights: Optimizing Drupal Core CI

Take Away Nathaniel Catchpole, Tag1 Senior Performance Engineer and Technical Lead, Drupal core framework and release manager and contributor, walks through how Drupal core's CI pipelines dropped from around 55 minutes to 5 to 7 minutes, and the installer and test optimizations now reducing the CI minutes behind the Drupal Association's infrastructure costs.

At DrupalCon Vienna, Tim Lehnen presented on the main costs for running Drupal.org. Around 50% of the total cost of running drupal.org, or approximately $1.5m, is infrastructure costs. A significant slice of infrastructure costs comes from drupal.org's self-hosted GitLab, and in turn much of that cost is due to GitLab CI for Drupal core and contributed modules.

Figure 1: Drupal Engineering activities compared to the various funding sources

Drupal core is the single biggest project in terms of CI minutes, both due to the sheer number of tests as well as the level of activity in Drupal core issues, with hundreds of commits per month and activity on thousands of issues and Merge Requests ("MRs").

Figure 2: Drupal CI minutes by project, July 2026, core issue forks are treated as separate projects

Since Drupal originally moved to Gitlab CI from our previous Jenkins-based CI infrastructure in 2023, we've been working on reducing the time taken for Drupal core test runs.

The primary end goal of this work was to reduce the wall time for pipelines on MRs. These started at around 55 minutes when we originally moved to Gitlab CI (approximately the same as they were on Drupal CI), and now generally finish in 5-7 minutes. The 55 minute runtimes already relied on previous known optimizations like using a ramdisk for both the database and filesystem, applied to GitLab runners. GitLab does not support this out of the box.

Five minute turnaround times on pipelines have made a huge difference to Drupal core velocity. Whereas core contributors used to push to a branch, then go and eat lunch or dinner while waiting for the pipeline to finish, there's now barely enough time to make a cup of coffee, let alone drink it.

Figure 3: Contibutors recognizing and commenting the time improvement of waiting for the pipeline to finish

However, the bulk of the initial gains we made to core CI pipeline performance was in wall time, with much less impact on CPU minutes. This is now starting to change, as we're finding ways to reduce the CPU minutes while also keeping wall times as short as possible.

Reducing Pipeline Wall Times

We reduced pipeline wall times via the following approaches.

Concurrent Test Running and Parallel CI Jobs

Drupal's run-tests.sh has supported running tests concurrently for a long time. We added support for Gitlab's parallel test runs, splitting test groups with thousands of tests into smaller groups so that they can be run on multiple test runners at once. For example Drupal's functional test group is executed in 8 parallel jobs, at 15 concurrency, with a CPU request of 10 per job. This runs 150 test classes at a time on 100 CPUs. By running smaller sized individual jobs, there is also a higher chance of them fitting into test runners that become available rather than requiring a new AWS instance to be spun up.

Slowest Tests Run First

Tests are always run slowest first. Drupal's test runner has supported a #slow group for a long time, so that very slow tests can be run first. We now also order tests by the number of methods, so that tests with more methods, which overall tend to be slower, run first too. This is critical for other optimizations to be effective. If a single class takes three minutes to run, starting it at the beginning when the rest of the tests can also be completed in three minutes means the entire test run can be finished in three minutes. But if that job started last, the job could take six minutes, with just that one test being run for half the time, leading to slower wall times and idle CPUs.

Optimize or Split Up the Very Slowest Tests

In some cases we have had individual test classes that took more than 10 minutes to run. For these very slow running tests, we've split them into smaller test classes so that they can be run in parallel, and/or optimized the test set-up requirements so that no individual test takes longer than a full run.

Reducing CPU Time for Test Pipelines

With these techniques, we've been able to balance CPU requests and concurrency across the various core test types, so that every job finishes within approximately 3-4 minutes. This has given us a solid framework for keeping pipeline wall times to a minimum while allowing us to adjust CPU requests and concurrency for individual test types to match the scope of core's overall test coverage. As far as we know there are no longer obvious optimizations to make via tweaking concurrency and test running order.

While we've been working on optimizing the tests themselves, in recent months focus is increasingly shifting in that direction as the best way to further optimize test runtimes, but more importantly, reduce CI minutes and the resulting infrastructure cost for the Drupal Association overall.

Test Types

Drupal core started with only one type of test: SimpleTest 'functional tests' that require a full Drupal install into a separate site that the tests are then run against. Over time with the adoption of PHPUnit, we've added unit tests, 'kernel tests' which include a full dependency injection container but don't do a full install, functional JavaScript tests which use a real browser, and build tests which allow creation of a completely separate code base in its own directory. There is an ongoing effort to convert functional tests to kernel and unit tests where this can be done without losing test coverage, with the recent addition of http request testing to kernel tests making many more tests eligible. Converting a functional test to a kernel test can reduce the time it takes by 3/4, so for the tests where this is possible it's one of the most effective ways to make gains, although the conversions have to happen test by test across dozens or hundreds of test classes.

Over the past couple of years there has been a concerted effort to improve Drupal core performance. Many runtime performance improvements don't necessarily make a lot of difference to test runtimes as a whole. But because functional and functional JavaScript tests install a full Drupal site and request real pages, anything which improves installer or cold cache performance tends to have an outsized effect on test runs. Installer performance generally doesn't affect production sites (because they're already installed!) and cold cache performance is often not a priority for production sites because it tends to affect a low percentage of overall requests, however as well as CI times, it can also make huge differences to the user experience for new users as well as improving responsiveness after deployments and cache clears.

Installer Improvements for Functional Tests

In 11.2.0, we changed module install to support installing multiple modules at once without a separate dependency injection container rebuild between each module. Instead of doing 50 or 60 container rebuilds during an install, we do more like 11 or 12. This took tens of seconds off Drupal installs, whether via the UI, Drush, or during test runs.

Figure 4: Installing multiple modules in Drupal 11.2

Source: Figure 4: Installing multiple modules in Drupal 11.2.

In Drupal 11.4, we made container rebuilds during the installer more conditional, reducing container rebuilds during a functional test from 11 to 8.

Recently, I've been looking at whether it would be possible to reduce the 8 remaining container rebuilds further, without necessarily an expectation that there would be much room for improvement, and found some. With all of those changes, some of which are not committed yet, we should be able to get down to an absolute minimum of 2 container rebuilds in tests. While some of the optimizations are test-specific, a real-life Drupal install of the minimal profile takes less than 2 seconds.

This investigation also uncovered further possible performance improvements in the installer.

While the combination of these changes probably saves only around 5 seconds at most from an install during a test run, this saving is multiplied by every install that occurs, with thousands of Drupal installs on every test run, this adds up to several minutes of CI time.

This has already allowed us to reduce the total CPU request for functional tests from 128 to 80 with no increase in wall time. We expect to be able to reduce the CPU request for both functional and functional JavaScript tests further once more optimizations land.

Kernel Test Performance Improvements

Kernel tests in general run much faster than functional tests, however there is still a per-method overhead which is a lot higher than unit tests. We are looking at adding an option to kernel tests to share the database state between test methods which will remove a lot of that overhead. This in turn will allow us to re-use the dependency injection container between methods. As we move functional tests to kernel tests, this should increase the impact of that change on resource usage even more.

Re-Evaluating On-Commit Pipelines

Drupal core has daily, weekly, and on-commit jobs on its branches, as well as those that run on individual MRs. In looking at the information we get from those jobs, we realised that the on-commit jobs, which on average run several times per day, and run the full test suite against multiple different database types (Mysql, MariaDB, SQlite, PostgreSQL) don't necessarily give us information that we can't otherwise get from MR, daily and weekly runs. For release branches, we need immediate post-commit feedback in case something is unexpectedly broken, which sometimes happens when two independent commits are fine individually, don't have merge conflicts, but break when combined anyway. However, we're in the process of trialling running our development branches without on-commit pipelines whatsoever. This should reduce CI minutes for core purely via running pipelines less often, on top of the in-pipeline optimizations above.

Reducing Wall Time for Contrib CI Runs

While individual contrib projects are not the biggest user of CI minutes, there are thousands of contributed projects. Several of the performance optimizations for the installer, functional tests, and kernel tests will apply to contributed module tests too, since those have to install core the same way as core tests do.

Additionally, there has been recent work to switch contrib's gitlab_templates shared pipeline definitions to running concurrent tests by default. Contrib tests previously used raw phpunit which runs each test sequentially with an option to switch to concurrent test running via run-tests.sh; the default flipped to run-tests.sh by default in September 2026. Because contrib tests should also benefit from core's 'slowest test first' strategy, this should compress pipeline times in contrib and it may have a positive impact in reducing CI minutes overall if runners are able to complete jobs in a shorter time with the same CPU request.

Effect on the Drupal Association's Hosting Costs

Taken together, these changes lower the cost of running core's CI run by run, through shorter wall times, fewer CPU minutes, and fewer pipelines overall. As Figure 2 shows, core is the single biggest consumer of CI minutes on drupal.org, so that work is aimed at the largest single driver of the GitLab CI costs behind the Drupal Association's infrastructure bill.

What that adds up to on the bill itself is a separate measurement, and will take longer to validate. Total cost depends not only on the cost per run but on how many runs happen, and core activity (commits, issues, and merge requests) is holding steady or rising. So the effect on the DA's hosting costs has to be read from same-month comparisons year over year, or averages across several months, rather than any single snapshot. We’ll be keeping a close eye on this as the latest round of changes are committed.

Drupal AI Initiative: AI at DrupalCon Rotterdam

Written by Duncan Worrell (dunx)

DrupalCon Rotterdam is almost here. Alongside two dedicated AI summits and the main conference keynote, the program is stacked with high-value AI content for developers, strategists, and leaders alike. Whether you're looking to push agentic workflows, scale digital governance, streamline content operations, or keep your AI integrations trustworthy, here is a complete breakdown of the top AI sessions to help you optimize your schedule.

Full schedule at https://events.drupal.org/rotterdam2026/schedule
Tickets at https://events.drupal.org/rotterdam2026/registration-information

All session times are local CEST.

Summits

In addition to the main DrupalCon event, there are two AI-specific summits being held catering for two very different audiences.

Enterprise Drupal AI Summit

An executive-focused event for CXOs, Heads of Digital, and enterprise leaders connecting with curated Drupal AI partners. Hosted on the historic former ocean liner, SS Rotterdam. 

Date & Time: All day Monday, 28 September
Event details here: https://summit.enterprisedrupal.eu/schedule.html

AI Dev Summit

Getting Drupal developers up to speed on AI coding tools, AI in PHP/Symfony/Drupal frameworks, Canvas, and Drupal CMS innovations.

Date & Time: All day Monday, 28 September
Event details here: https://events.drupal.org/rotterdam2026/ai-dev-summit

Keynote

For many, the DriesNote by Drupal founder Dries Buytaert is the week’s highlight. Expect a keynote packed with the latest AI roadmap updates, architectural reveals, and live technical demos.

Date & Time: Tuesday, September 29, 2026 - 10:30 to 11:45

DriesNote will live stream on YouTube if you can’t make the event in person.

Sessions

Every session is likely to mention “AI” but we expect these sessions to be focused on AI. 

Unblocking AI: Why Programmes Stall at Pilot Stage, and How to Move Past It

Research and strategies for moving AI initiatives past the pilot phase to deliver real-world impact.

Date & Time: Tuesday, 29 September 2026, 13:00 – 13:10
Speakers: Amanda Falshaw (AI Enablement Lead at Reading Room) & Megan Harvey (Reading Room)

Open Intranet: A Drupal-Based Digital Workplace

Features AI-assisted content creation as part of an open-source Drupal intranet workspace.

Date & Time: Tuesday, 29 September 2026, 13:15 – 13:25  
Speaker: Maciej Łukiański (CEO and Co-founder of Droptica)

Culture Mapping the Human Side of AI — A Workshop to Meet Your People Where They Are

Leadership and organizational change management required to guide teams through fast-moving AI adoption.

Date & Time: Tuesday, 29 September 2026, 13:30 – 14:15  
Speaker: Timi Csontos (Culture Consultant/Freelygive)

Reviewer-Friendly AI: A Practical Drupal Contribution Workshop

Practical AI-assisted workflows designed to turn ideas into high-quality, review-ready open-source contributions.

Date & Time: Tuesday, 29 September 2026, 13:30 – 14:15  
Speaker: Scott Falconer (Senior Principal Software Engineer at Acquia)

Why Your AI Agent Hallucinates: Engineering Lessons From a Production Workflow Builder in Drupal

Engineering reliable, trustworthy AI agent integrations in Drupal using modules like AI, ECA, and agentic tools.

Date & Time: Tuesday, 29 September 2026, 13:30 – 14:15  
Speaker: Shibin Devadas Kakanat (Backend Pro Lead at Factorial)

Context Control Center: Bringing AI Context Natively to Drupal CMS

Structuring, scoping, and natively managing AI context within Drupal CMS for downstream agents and tools.

Date & Time: Tuesday, 29 September 2026, 14:25 – 15:10  
Speakers: Emma Horrell (User Experience Manager University of Edinburgh and UX Research Lead for Drupal CMS) & James Abrahams (Technical Director at Freelygive)

Encoding Expertise: How UX Research Powers Human-First AI

Applying UX research methods to train and ground AI content tools to output domain-specific quality.

Date & Time: Tuesday, 29 September 2026, 14:25 – 15:10  
Speaker: Aidan Foster (Senior UX Strategist at Kanopi Studios)

From Shadow AI to Sovereign AI Infrastructure

Addressing data security, compliance, provider selection, and cost control as AI adoption scales.

Date & Time: Tuesday, 29 September 2026, 14:25 – 15:10  
Speaker: Michael Schmid (Head of Technology and Co-Founder of amazee.io)

AI-assisted site migration to Drupal Canvas: a real-world case study

Testing AI coding agents on live projects to automate complex site migrations into Drupal Canvas, examining real metrics, wins, and limitations.

Date & Time: Wednesday, 30 September 2026, 10:45 – 11:30  
Speakers: Wolfgang Ziegler (Architect, Founder of drunomics) & Jeremy Chinquist (Project Manager at drunomics)

AI-Augmented Accessibility: From Automated Alt-Text to Governance Gates

Automating inclusive governance and identifying accessibility errors early by bridging code, humans, and AI workflows.

Date & Time: Wednesday, 30 September 2026, 10:45 – 11:30  
Speaker: Mike Gifford (Senior Accessibility Strategist at CivicActions)

Nobody Asked for a CMS: From Content Management to Answer Delivery in the Age of AI

Adapting content architecture for direct answer delivery to AI systems while increasing Drupal’s strategic value.

Date & Time: Wednesday, 30 September 2026, 10:45 – 11:30  
Speakers: Tomi Mikola & Ulla Koho (both digital strategists and content architects at Wunder)

Clean Code in the Age of AI: Writing for Humans When Machines Write the Code

Maintaining human readability, software architecture, and clean code standards when using AI generators.

Date & Time: Wednesday, 30 September 2026, 11:40 – 12:25  
Speaker: Len Swaneveld (Senior Drupal Developer at iO)

One Brand Voice from 35 Sources: How We Rebuilt VisitEurope.com and How Drupal AI Unifies Its Tone

Unifying 35 national voices into a cohesive travel brand using generative AI integrated into Drupal.

Date & Time: Wednesday, 30 September 2026, 11:40 – 12:25  
Speakers: Krisztián Kása & Zsófia Alföldi (both Project Managers at Brainsum)

AI Best Practises

Leveraging Drupal’s structured architecture to build optimized environments for AI Agents running inside and outside CMS boundaries.

Date & Time: Wednesday, 30 September 2026, 12:30 – 12:40  
Speaker: James Abrahams (Technical Director at Freelygive)

The New Front Door: How AI Agents Are Driving Drupal's Growth

How autonomous AI agents act as primary decision-makers selecting, building, and verifying Drupal systems.

Date & Time: Wednesday, 30 September 2026, 12:45 – 13:30  
Speaker: Scott Falconer (Senior Principal Software Engineer at Acquia)

Drupal AI Product Update: From Foundation to Agentic Workflows

Official product update from the Drupal AI Initiative leadership on building production-ready Agentic CMS capabilities.

Date & Time: Wednesday, 30 September 2026, 13:40 – 14:25  
Speakers: Niels Aers (CTO/AI Tech Lead at Dropsolid) & Dr. Christoph Breidert (CEO and Founder of 1xINTERNET)

From Brief to Review-Ready in 60 Minutes: A Governed AI Campaign Workflow in Drupal 11

Generating governed, high-quality draft campaign pages straight from PDF briefs in minutes without code tickets.

Date & Time: Wednesday, 30 September 2026, 13:40 – 14:00  
Speaker: Kieran Cott (Executive Creative Technology Director at Delete Agency)

How can we improve Drupal's visibility in ChatGPT, Gemini, and Claude?

Open discussion on improving how LLMs describe, evaluate, and recommend Drupal to users.

Date & Time: Wednesday, 30 September 2026, 13:40 – 14:25  
Speaker: Larissa Tropp (Digital Marketing & Growth Specialist at 1xINTERNET)

Dismantling and Reassembling a Drupal Migration with AI

Leveraging AI tools to simplify, re-architect, and map legacy un-typed data into clean destination bundles during migrations.

Date & Time: Wednesday, 30 September 2026, 14:45 – 15:30  
Speaker: Roberto Peruzzo (Principal Architect and Founder of Sparklingboys)

AI Mid-Life Crisis: failure stories from the silent majority

Honest post-mortems on AI project failures and pragmatic ways to navigate rapid technological shifts.

Date & Time: Wednesday, 30 September 2026, 14:45 – 15:30  
Speakers: Dieter Blomme (Drupal Architect at Dropsolid) & Valery Lourie (Lead Software Engineer at EPAM Systems)

Scolta: Drop-in AI Search Without a Search Server

Running lightweight, client-side search powered by Pagefind with an AI layer for query expansion and summaries.

Date & Time: Wednesday, 30 September 2026, 14:45 – 15:30  
Speaker: Jeremy Andrews (CEO and Founder of Tag1 Consulting)

SDCs, Canvas, and the Agent That Builds With Them

Training AI agents to generate Single Directory Components, insert them into pages, and verify browser rendering.

Date & Time: Wednesday, 30 September 2026, 14:45 – 15:30  
Speaker: Matt Glaman (Principal Software Engineer at Acquia)

AI Assists, Humans Decide: Agentic Workflows in Drupal - A Drupal AI Hackathon Story

Agentic translation and governance workflows developed for the European Commission across 24 languages.

Date & Time: Wednesday, 30 September 2026, 16:00 – 16:45  
Speakers: David Galeano & Adam Nagy (both work in the DIGIT department at the European Commission)

AI-Powered Site Building in Drupal: From Blank Site to Styled Pages in a Conversation

Enabling non-technical users to build, style, and structure complete Drupal sites via conversational prompts.

Date & Time: Wednesday, 30 September 2026, 16:00 – 16:45  
Speakers: Francesco Pesenti & Francesco Quagliati (both are Developer Advocates and Solution Engineers at Platform.sh)

From Drupal Content to AI Answers: Learnings from EPSY

Designing constrained AI search engines over standard chatbots to deliver structured content answers.

Date & Time: Wednesday, 30 September 2026, 16:00 – 16:45  
Speaker: Antonella Picarella (Head of Digital Communications & Content Strategy at BFF Banking Group)

From SEO to GEO: What Changes, What Doesn’t

Strategic shifts from Search Engine Optimization to Generative Engine Optimization as AI engines handle discovery.

Date & Time: Wednesday, 30 September 2026, 16:00 – 16:45  
Speaker: Wouter De Bruycker (Digital Marketing Strategist at Dropsolid)

From local voices to global impact: Powering World Cancer Day with Drupal and AI

Translating and moderating hundreds of high-volume personal stories across 60+ languages using AI tools.

Date & Time: Wednesday, 30 September 2026, 17:00 – 17:45  
Speakers: Charles Andrew Revkin & Diego Fernando Costa (both part of the digital communications team at the Union for International Cancer Control (UICC), which runs World Cancer Day)

How Marketers Win in an AI‑First Search World (AEO & GEO Playbook)

Practical tactics for Answer Engine Optimization (AEO) and maintaining content discoverability in AI platforms.

Date & Time: Wednesday, 30 September 2026, 17:00 – 17:45  
Speakers: Reena Tripathi (Digital Marketing Manager at OpenSense Labs) & Anubhav Gupta (CEO/Technical Architect at OpenSense Labs)

Whether you’re coming to DrupalCon Rotterdam to build with AI, figure out how to govern it, or understand where it is taking Drupal next, there is a lot to choose from. From the two Monday summits through the DriesNote and a packed slate of sessions, AI is clearly woven throughout this year’s programme. Check the full schedule, plan around the sessions that matter most to you, and we’ll see you in Rotterdam.

File attachments:  DrupalCon_Rotterdam_2026___Drupal_Events.png

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