fidzu : software

MariaDB 13.1 is a rolling release preview, and, as usual, this is the right moment to test what is coming, give feedback, and help us polish the next MariaDB Server release. But this time, there is something really interesting. And by "interesting", I mean: wow! MariaDB 13.1 Preview includes 32 MDEVs with new features and […]

26 Jun 2026 8:11am GMT

Earlier this week, in "Drupal's role in agentic workflows", I argued that Drupal's AI future has two parts: helping people with AI inside Drupal, and helping agents use Drupal from the outside.

So we are splitting Drupal's AI strategy into two workstreams. Inside AI is led by Christoph Breidert, who has been driving that work already. Outside AI, the new workstream, is led by Scott Falconer.

The easiest way to think about the difference: with Inside AI, a person uses Drupal, and Drupal uses AI to help. With Outside AI, a person uses an agent, and the agent uses Drupal.

We launched the Drupal AI Initiative one year ago, in June 2025, with a published strategy. A year later it spans 32 organizations and more than 50 contributors, shipping against a public 2026 roadmap through two paid delivery teams.

So far, most of that work has focused on Inside AI, though much of the foundation also supports Outside AI.

Outside AI will serve three kinds of users:

• Developers new to Drupal. They ask an AI agent to build a website, and the agent chooses what to build on. Agents reach for whatever they can spin up in seconds, so the opportunity is to make Drupal that easy to install, configure, and use.
• Experienced Drupal developers. They already know Drupal is the right tool, and they want agents to take on more of the work. For Drupal agencies, Outside AI should turn AI into a stronger advantage: helping teams move faster, win more work, protect profitability, and get more value from their Drupal talent.
• External agentic systems and workflow automation tools. These systems coordinate work across many tools, but when they touch content, they need a trusted system of record for workflows, permissions, revisions, and publishing. Rather than rebuilding that governance elsewhere, they should call into Drupal.

If we are successful, agents will recommend Drupal to new users, help Drupal developers move faster, help agencies win more work, and use Drupal as the trusted layer for content management and governance.

Thank you to everyone who helped bring the Drupal AI Initiative to this point. Together, the community has turned an ambitious idea into real momentum.

I'm excited about what comes next! Want to get involved? Join the #ai-initiative channel on Drupal Slack.

26 Jun 2026 8:11am GMT

When we started working on the Drupal AI initiative in June 2025, I assumed most AI features would live inside Drupal.

By my DrupalCon Chicago keynote in March 2026, my thinking had changed. I framed the shift as "inside-out" versus "outside-in" and concluded that not every AI capability belongs inside Drupal. Some work is better done outside the CMS, where AI tools can move faster and connect back to Drupal when needed.

Last week, I explored these ideas in more detail in "AI and the great CMS unbundling". That post argued AI is making the CMS less central as a creation tool, but more important as a control layer: the place where content is structured, governed, reused, and published with trust.

This post picks up from there. If Drupal's role as the control layer is becoming more important, it needs to support AI-driven workflows that run inside Drupal, start outside Drupal, and move across both: external workflows that call into Drupal, and Drupal-native workflows that outside systems can safely rely on.

Drupal joins workflows beyond the CMS

First, Drupal needs to work well with tools outside the CMS: coding agents like Claude Code and Cursor, and orchestration platforms like Salesforce Agentforce, n8n, and Activepieces.

I actually showed what this could look like in my DrupalCon Vienna keynote in October 2025. Here is a short clip of that:

It was a proof of concept demo, and we had some fun with it. But the pattern behind the demo mattered more than the demo itself: an external tool drove the work and handed tasks to Drupal, while Drupal returned structured content and state.

Watch the demo, and it will be easy to imagine the same pattern in a real marketing use case. Campaigns usually begin with a marketing brief and span many tools and channels: email, website landing pages, social media, paid media, and more.

An external agentic platform could generate campaign copy and ask Drupal to build a landing page. Drupal could map the copy to the right structured content type, place it into approved components with Drupal Canvas, save a draft, and flag any fields, metadata, or translations still missing before it can publish.

If Drupal reports a missing hero image, the agentic platform could search the digital asset management system (DAM), find an approved campaign image, and hand it back. Drupal could then attach it through its media model, supply or verify the required alt-text, confirm the page meets its requirements, and advance the draft through the editorial workflow.

This is a pattern that Drupal will need to support well. External platforms can coordinate work across the broader digital stack, but Drupal should remain the system that assembles, validates, governs, and publishes the content.

External workflows call Drupal-native workflows

While the larger campaign workflow may live outside Drupal, there is an equally important case for Drupal-native workflows.

External agents are good at coordinating work across systems. But once a workflow needs to act on Drupal content or data, it should use Drupal's native content model, permissions, validation rules, moderation states, revisions, and publishing workflows rather than recreate them outside of Drupal.

That is where projects like ECA, FlowDrop, and Maestro become more important. They let Drupal turn site-specific processes into repeatable, multi-step workflows that outside systems can call.

For example, any of these three systems could power a single Drupal workflow that validates a draft, coordinates translations in five languages, assigns reviewers, and publishes the content only after all required approvals are complete.

Depending on the task, these internal Drupal workflows can be deterministic, AI-assisted, or fully agentic. Drupal can support that today.

An external agent should not have to manipulate Drupal from the outside, field by field or function by function. It should be able to ask Drupal to execute a Drupal-native workflow through a single external API call.

That is the other half of what I showed in the demo video above. Drupal was not just exposing content to an external agent. It was exposing custom Drupal-native ECA workflows that an external automation tool called.

That was powerful last fall at DrupalCon Vienna, but as agentic workflows become more common, this pattern will only grow in importance.

The best end-to-end experience will win

There is a natural tendency to debate what should live where. Should AI happen inside Drupal, or outside of it? Should workflows be Drupal-native, or managed by external platforms?

Those are useful questions, but the answer is not universal. AI and workflows should live where they create the best end-to-end experience. Sometimes that will be inside Drupal, sometimes outside Drupal, and often across both.

What "best" means depends on the use case, the user, and the requirements for speed, reliability, security, governance, cost, and human review. There will be best practices, but no single approach fits every case.

Who is doing the work shapes what "best" looks like:

• A developer may prefer an external coding agent like Claude Code.
• A marketer may prefer a Drupal-native experience that keeps them close to previews, translations, moderation, and publishing.
• A campaign manager may need an external workflow that coordinates email, social media, analytics, DAM, and CMS.

Work will move across systems and people. The best end-to-end experience will come from doing each step where it can be done best, while making the handoffs feel invisible.

A head start is not a plan to win

Understanding Drupal's role in these future workflows gives us clarity about where Drupal needs to go. Drupal needs to get better at supporting external orchestration, Drupal-native workflows, and the real-world hybrids that combine both.

What makes Drupal interesting is that it already has so much of the hard, unglamorous infrastructure these workflows need: structured content, granular permissions, revisions, rollback, JSON:API, and plenty more. These are exactly the capabilities agents need, and they're genuinely difficult to build well from scratch or retrofit.

Conceptual clarity and a strong foundation are wonderful things, but they are not enough to win.

When I asked whether AI coding agents would recommend Drupal, the issue was not Drupal's capability. It was whether agents could get from prompt to a working Drupal site quickly and easily enough. As I wrote in "Friction, abstraction and verification", agents tend to choose the path that gets them to a real, verified result with the least friction.

For experienced Drupal teams, the challenge is different. They have already chosen Drupal. They do not need an agent to recommend it. They need agents that help them build, validate, and ship quality work faster.

To turn Drupal's advantage into adoption, we need to improve both paths: helping agents choose Drupal for new builds, and helping existing Drupal teams ship better work faster. Both depend on making it easier for agents to work with Drupal from start to finish.

That means Drupal needs to expose the best practices, site context, tool definitions, constraints, and precise validation agents need to take the right actions and verify the result.

A lot of this is already underway across Recipes, Site Templates, Drupal AI, Drupal Canvas, ECA, FlowDrop, Maestro, MCP, and CLI improvements, to name a few.

But the goal is bigger than any one project. Drupal needs these efforts to add up to a clear, coordinated path for agents: from setup to connection, context, governed action, validation, recovery, and launch.

Special thanks to James Abrahams, Jürgen Haas, Randy Kolenko, Scott Falconer, and Shibin Das for their review and contributions to this blog post.

26 Jun 2026 8:11am GMT

Review: Platform Decay, by Martha Wells

Series:	Murderbot Diaries #8
Publisher:	Tor
Copyright:	2026
ISBN:	1-250-82701-9
Format:	Kindle
Pages:	245

Platform Decay is the eighth book in the Murderbot science fiction series. You absolutely should not start here, but you also don't need to remember the specifics of the previous books.

As the story opens, Murderbot and a friend (the identity of whom is a spoiler for previous books) are infiltrating a Corporation Rim torus, a massive space station that encircles a mined-out planet. (Like most science fiction megastructures, this is more space than the plot really requires.) Murderbot's mission is to exfiltrate some of Dr. Mensah's family members who have become entangled in corporate shenanigans. The corporates are eager to get revenge for the events of System Collapse, not to mention the other times Preservation Station has upended corporate plans. Murderbot's job is to stop them.

The group, in addition to one of Dr. Mensah's partners, includes an older woman and a young child. Murderbot is analytical and of course not at all emotional about children, which is reliably a good time. Also, the older woman is gruff, stubborn, and thoroughly enjoyable.

There are, of course, complications that lead to picking up more children and going through rather more of the torus than Murderbot wanted to explore. Each section of the torus is run by a different corporation and has a different constructed environment and visual aesthetic, so there are a lot of opportunities for fights, daring escapes, and incidental trouble.

Also, well:

So I had installed a mental health module. I know, I was surprised I did it too.

After the events of System Collapse, University Medical decided that Murderbot needed a bit more metal health support.

The only reason I agreed to it was that the mental health module didn't actually try to adjust my processing or core programming or anything; it just monitored my organic neural tissue. When my neural tissue started to generate weird chemicals and whatever, it would ping me to "check in with my emotional state." Seriously, I could have coded that myself.

(I told Dr. Bharadwaj that, and she said, "Would you have ever coded that yourself?" which was totally unfair and also correct. I would never have done that.)

Speaking as someone whose neural tissue sometimes generates weird chemicals and whatever, I sympathize.

The specific form this module takes is periodic "emotion check" parentheticals throughout the narration, which I found utterly delightful.

I ran that through risk assessment and it produced the equivalent of a shrug.

(Emotion check: Shrug sigil right back at you, you piece of shit.)

This is otherwise an extended action movie sort of a book, much like several of the early novellas. There are no major political or interpersonal developments here and the usual cast (apart from Murderbot) is mostly absent. Instead, we get an extended, dangerous journey through a corporation-controlled habitat, mixed with Murderbot trying to interact with humans in a way that minimizes its annoyance while being hopefully reassuring. It's competence porn with awkward but surprisingly heartfelt emotional bonding, not that Murderbot in any way wants to bond or would appreciate that description.

I doubt this will be anyone's favorite entry into the series since there are none of the big reveals or major leaps of character development there have been in the past few books. But, like all Murderbot books, the narrative tone is wonderful and all of the small asides and little moments of character interaction are an utter delight. If you've gotten this far in the series, you know what I mean and you'll be as happy to read more of it as I was. There is a part of me that is hoping for some major plot development, and I always want to see more of ART (who has no significant role in this book), but Wells has the narrative style down so perfectly that I would read and enjoy a book about Murderbot doing just about anything.

If you're this far in the series, you probably don't need a review, and since this is an action-heavy adventure rather than a character growth novel, I don't have a lot more to add. There's a new short Murderbot novel out and you want to read it. Recommended to everyone who enjoys the series.

Rating: 8 out of 10

26 Jun 2026 3:23am GMT

I saw that there was some argument over how much slower slot access is than struct access, so I just decided to measure it naively. I made a two slot sruct and a CLOS version of a CONS cell with car and cdr slots and I ran LTAK using regular lists, `lists' made from CLOS conses, and `lists' made from structs. Here are the results:

D:\repositories\clos-benchmark>sbcl --script run-benchmarks.lisp
Benchmark: ltak over native cons cells, CLOS my-cons nodes, and my-cons-struct nodes
Inputs: x=15 y=9 z=4 repeats=35

Scenario                   min-ms     mean-ms      max-ms      ratio
--------------------------------------------------------------------
native standard               0.129      0.146      0.186
clos standard                 1.346      1.365      1.475       9.37x
struct standard               0.172      0.175      0.179       1.20x
native optimized              0.068      0.069      0.073
clos optimized                0.411      0.414      0.419       6.04x
struct optimized              0.068      0.069      0.073       1.01x

In this naive use case, structs are same as native cons cells, but CLOS objects are one ninth the speed of a struct or cons cell if you just use it unoptimized, and one sixth the speed if optimizations are turned on.

But the CLOS instance is more functional than the cons cell in mimics. For instance, I could add a slot to the class and all the instances would be lazily updated with the new slot. I can also subclass the CLOS class and the selector functions will continue to work. Finally, I can redefine the CLOS closs while I'm developing it and all the instances will be uppdated. THe machinery to keep all this running is costing us our factor of 9.

But this might be worth the cost if we are running on a netwerk where the bulk of the time will be transmitting the answer down the pipe once it is computed. Taking a few extra milliseconds to compute the answer might be worth the convenience features of CLOS.

25 Jun 2026 4:11pm GMT

A new small package of mine just hit CRAN. The tl package wraps the (also very new) rspdlite package (announced last week) to offer a lightweight and consistent logging interface from both R and C++ that is also 'tiny, fast, capable' thanks to rspdlite.

The rspdlite announcement is a good place to get a first glimpse at that package; the upstream spdlite repo has all the details (for the C++ side of things). With tl we follow the same idea that our [spdl][spdl] package introduced: a simple consistent interface via just the tl:: prefix and the appropropriate logging level. In other words tl::debug("Alert -- foo is at '{}'", foo) will work from both R and C++ (given a variable foo, and in the case of C++ an extra semicolon). Just give it a try, and see how it goes. The package is still young and small.

The NEWS entry for this release is also very simple and just announces that we have a release. More details are in the ChangeLog and the GitHub repo.

Changes in version 0.0.1 (2025-06-17)

• Initial CRAN upload

This post by Dirk Eddelbuettel originated on his Thinking inside the box blog. If you like this or other open-source work I do, you can sponsor me at GitHub.

23 Jun 2026 1:45am GMT

The seL4 organisation on GitHub uses git-repo to manage multiple source repositories, and so there are a large number of projects to get your head around when figuring out the ecosystem.

As an experiment, I have taken the various manifest files across the org, and constructed a graph based on how frequently each pair of repositories is mentioned in a manifest together. See below:

[This may render badly when syndicated outside of my blog; and also on small screens. And probably large screens. I've attempted to make sure there's a non-JS fallback - on my site with JS enabled, if you hover over a node, it should highlight connected nodes.]

The colouring of the nodes is mostly manual; I experimented with graph clustering algorithms but have not found a satisfactory result so far. Still, some clusters are obvious:

• Kernel - the seL4 microkernel proper. This often but not always co-exists with the main cluster of core libraries, but it is pulled away slightly by the verification and microkit manifests.

• Verification - the verification repositories (l4v, HOL, graph-refine, polyml, isabelle) form a very distinct group. These are connected only to the seL4 microkernel itself, which is the only component formally verified.

• Microkit - microkit is a newer operating system framework that does not use CAmkES, so stands apart from the rest of the pack. I chose to scope this work to the seL4 org, so the LionsOS ecosystem and sDDF which are maintained by Trustworthy Systems are not shown. Also not linked is rust-sel4, because this modern world isn't using git-repo in the main to manage its repositories.

• RefOS - I'd not come across refos before, but it appears to be an example OS from 2021 built on the seL4 kernel.

It's quite hard to pull apart the CAmkES framework and the core libraries; there are definitely some which are more associated with VM management, but the overall shape of this co-occurence data is a messy ball in the middle with some outliers in orbit. One observation is that camkes is correctly identified as more peripheral than camkes-tool, which contains the actual core CAmkES code.

Reflecting on this approach, in hindsight I'm surprised that using co-occurences worked as well as it did - there was no attempt to actually inspect the code and find direct mentions of other code e.g. library header dependencies. As the newer microkit effort largely eschews git-repo, better results might be found by actually taking that more detailed approach, so that graph edges could represent real dependencies between two packages. Additionally, this could allow diving into the various libraries held in the different 'libs' repos, to get a more granular graph of relationships between them.

However, I think I spent more time on making it possible to render graphviz graphs easily on my blog than actually gaining any insight into the codebase!

21 Jun 2026 3:36pm GMT

Back in the day, the US government had a program called SBIR (Small Business Innovation Research) that funded small businesses to do research and development. I recall sitting in our dorm in college, reading through a giant printed catalog of SBIR grants just to amuse ourselves by brainstorming solutions over bad pizza.

.

So, I got curious the other day: what does the SBIR landscape look like now?

I can tell you right now: do not even try to read an SBIR solicitation on your local machine. You are opening yourself up to a world of absolute, unmitigated pain.

You might think, what harm could there be in simply opening a file?

Well, in the modern compliance panopticon, any manipulation of digital information that comes from the govenment has the potential to spawn CUI (Controlled Unclassified Information). CUI is basically a digital pathogen; once you download that file, *anything whatsover* derived from it, including notes and metadata, instantly becomes CUI by association. The moment you read an SBIR on your computer, you've infected your system, rendering you subject to a nightmare of Byzantine federal regulations.

These days, the amount of beurocratic red tape surrounding CUI is insane. To even look at the file legally, you need a dedicated, air-gapped machine completely disconnected from the internet, conforming to a massive, expensive slew of NIST standards covering everything from hardware-level encryption to strict access controls. Alternatively you could contract with a cloud company that offers a pre-certified "CUI-compliant" environment.

And assuming you actually shell out the cash and jump through the hoops to set up this digital containment zone just to read a PDF, you must meticulously audit and account for every single action you take in its presence. Under current federal auditing logic, you are explicitly assumed to be attempting to defraud the government unless you can produce a mountain of paper proving otherwise. Want to bring in a partner to bounce ideas around? You can't just "know a guy." You have to navigate a labyrinth of federal subcontracting regulations.

I had intended on amusing myself by reading some SBIRs and daydreaming about solutions that might involve Lisp (an impossibility in the modern enterprise stack for entirely separate, depressing reasons). Instead, I quickly discovered I did not even own the physical hardware required to even read an SBIR without running afoul of federal regulations.

I wanted to read some clever and inspiring engineering proposals. I ended up reading a lot of very dry and boring compliance regulations.

18 Jun 2026 11:48am GMT

Last year I wrote some Lisp related AI apps. There was a syntax highlighter that used the LLM to determine how to colorize and highlight syntax, and a prompt refiner that takes a wimpy LLM prompt and creates more elaborate prompt from them.

I took the apps down last week. They were `vibe coded' and therefore approximate and had bugs (but that's to be expected), but they had a security hole where you could hijack the LLM processing with your own prompt turning my app into an open relay using my API key. Last week I discovered that my AI spend on video creation was becoming serious. This is odd because I never create AI video. It turned out that my app was being hijacked by a proxy in Luxembourg and was generating videos on my dime.

So I shut down the apps. I knew they had the potential of being abused, and I was willing to tolerate a small amount of abuse, but it didn't occur to me that syntax highlighter could be hijacked to generate gigabytes of video at my expense. Future applications will be careful to obtain the API key from the user.

01 Jun 2026 7:00am GMT

All video recordings from FOSDEM 2026 that are worth publishing have been processed and released. Videos are linked from the individual schedule pages for the talks and the full schedule page. They are also available, organised by room, at video.fosdem.org/2026. While all released videos have been reviewed by a human, it remains possible that one or more issues fell through the cracks. If you notice any problem with a video you care about, please let us know as soon as possible so we can look into it before the video-processing infrastructure is shut down for this edition. To report any舰

25 Apr 2026 10:00pm GMT

Are you ready for another challenge? We're excited to host the second yearly edition of our treasure hunt at FOSDEM! Participants must solve five sequential challenges to uncover the final answer. Update: the treasure hunt has been successfully solved by multiple participants, and the main prizes have now been claimed. But the fun doesn't stop here. If you still manage to find the correct final answer and go to Infodesk K, you will receive a small consolation prize as a reward for your effort. If you're still looking for a challenge, the 2025 treasure hunt is still unsolved, so舰

29 Jan 2026 11:00pm GMT

With FOSDEM just a few days away, it is time for us to enlist your help. Every year, an enthusiastic band of volunteers make FOSDEM happen and make it a fun and safe place for all our attendees. We could not do this without you. This year we again need as many hands as possible, especially for heralding during the conference, during the buildup (starting Friday at noon) and teardown (Sunday evening). No need to worry about missing lunch at the weekend, food will be provided. Would you like to be part of the team that makes FOSDEM tick?舰

26 Jan 2026 11:00pm GMT