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How we’ll fight the platform war against Big AI - Anil Dash
Anil Dash, 2026/06/25

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The only reason why there's even such a thing as a 'platform war' against AI is that the companies pushing AI are already, for the most part, platforms. Microsoft. Google. X/Twitter. The reason there's even a need for a war has nothing to do with AI in particular, it's because we've for the most part caved to the big platforms already. Anyhow. Anil Dash offers a few suggestions. First, get in front of it by using "open tools or interfaces that aren't controlled by the Big AI companies" to access AI services. Keep hold of the ability to "seamlessly switch between different AI providers on the fly." Where you can, use Non-commercial LLMs. And on a less useful note, he recommends getting angry.

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Does this help someone learn?
David Hopkins, David Hopkins / Education & Leadership, 2026/06/25

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When someone says the question should be, "does this help someone learn?" my response is usually to ask, "what do you mean by that?" Part of that is the philosopher in me, but the rest of it is a concern they're focused on teaching to specific outcomes or teaching to the test, neither of which really has the interests of the learner in mind. David Hopkins doesn't exactly take that path, but he is very concerned to make us aware that learning isn't always fun and that design should not increase cognitive effort. Standard instructivist stuff. It's as though learning is a search problem: "They find what they need, understand it and move forward with confidence." I appreciate the desire to get the message across. But that's not 'learning' in any meaningful sense.

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What Edtech Got Right, What It Missed, and What AI Changes Now (Jennifer Carolan, Reach Capital)
Allison Dulin Salisbury, The Humanist, 2026/06/25

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"The narrative of our field is very much a before-and-after Gen AI story." That's how this article starts, and how it's framed - though the interviewee, Reach Capital's Jennifer Carolan, is very firmly fixed on the traditional classroom environment. "I'd tell every edtech builder to become a substitute teacher. Shadow a teacher. Sit in a classroom for three days in a row—spoiler: it's boring and exhausting, and it isn't the teacher's fault, but the factory-like structure of it all." I don't think that ed tech ill succeed by solving the problems faces in the current classroom environment. Students - just like the startup founders being coached by Reach - should get out of the building. That's why the field isn't a before-and-after GenAI story. Not so long as the AI is intended to emulate a teacher

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Paradoxes of Openness: Power, Reciprocity, and the Governance of Scholarly Infrastructures
Katja Mayer, The Politics of Open Infrastructures, Open Book Publishers, 2026/06/25

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This is a chapter from a larger book, the whole of which is worth looking at. This summary doesn't do this complex chapter justice. The first paradox is power: "The central issue is no longer only access to scholarly outputs, but who controls the infrastructures through which scholarly communication is organised." This is why I have historically argued for decentralized open access, rather than platform centrlization. The second paradox is 'reciprocity', where proponents of openness have opposed text and data mining (TDM) because " the same openness that dismantled subscription barriers also created conditions under which scholarly content could be recomposed as a scalable input for platform economies" (readers should remember my many years of advocacy for a non-commercial clause to prevent just this). The third is governance: "smaller data infrastructures now find themselves overwhelmed by automated scraping requests, forced to absorb the operational costs of large-scale harvesting while lacking the resources to govern, limit, or benefit from such use." This is why (and my colleagues from EDUCAUSE three decades ago may remember) why I argues for distributed resources and aggregation, rather than federation.

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LLMs and performative productivity
Jim Nielsen's Notes, Jim Nielsen's Notes, 2026/06/24

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It's as though people believe there's a learning 'off switch' (though it only exists in other people). Like this: "A junior who made a mistake is one step closer to being a senior; a junior who let an LLM make a mistake (and had the LLM fix it for them) has probably learned nothing." What would justify this conclusion, authored by John Collinsworth (Nielsen doesn't link to it, though he quotes it extensively - boo, hiss)? The junior will still learn, but will learn a different thing. Nobody 'learns nothing' - human brains don't shut off like light bulbs. What's really happening here is that we're mking a value judgement, specifically, that the lesson learned from doing it by hand is more important than the lesson learned by doing it with AI. This 

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Sublimation
Julian Stodd, Julian Stodd's Learning Blog, 2026/06/24

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The first think I thought of when I read this post from Julian Stodd was simulated annealing and Boltzmann machines. Why? Because Stodd describes crystals as structures that "represent the lowest energy configuration," which is what Boltzmann machines try to do with neural nets. Stodd compares crystals to organizations, and here the metaphor needs rescuing a bit. He writes, organizations "build structure for greatest efficiency, value, predictability, replicability, and of course, ease of control, and potential for oversight and measurement," but that they are "systems trapped at one energy level, dreaming of the next." Sure, they can change state - they can melt into fluid and merge with something else, or they can sublimate into their individual atomic components (a.k.a. people). But they can change energy level, but (just as with matter and neural nets) they need to go through an annealing process, a 'hardening through fire', as it were. 'Move fast and break things' is a clumsy attempt at just such a process; in neural networks they just increase and decrease the bias (ie., sensitivity, not prejudice (that's a different meaning of 'bias')) of individual entities.

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There Are No Instances in atproto
Dan Abra, overreacted, 2026/06/24

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What's great about this article is that it really clearly defines the distinction between Mastodon and Bluesky, arguing that the question "where are the instances?" is a Mastodon question, and doesn't really apply to Bluesky. But I think it's misleading in three major ways. First, there are instances in Bluesky, but there are different types of them: 'atproto hosting' and 'apps'. The fact that there are so few 'atproto hosting' instance is an issue. Second, it under-describes the ATmosphere architecture by leaving out the 'personal data store' (PDS) entirely. And third, it argues that atproto is just like RSS and Google Reader. But RSS is a simple text file any person can create in a text editor (so, for that matter, is a PDS, sort of), while a 'atproto hosting' instance is a very large and complex piece of software that consumes massive resources. So too are the apps, which for some reason, must handle "the whole Atmosphere". And the whole argument raises the question: why can't we simply have PDS readers as apps? What is the whole 'atproto hosting' infrastructure buy us, except control over our identity?

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Down the Web Rabbit Hole With a Hammerhand
Alan Levine, CogDogBlog, 2026/06/24

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This is a longish discussion of McLuhan's idea of a tool as an extension of the self, which then of course can be applied to media. Today, it can be applied to AI. So when we say 'AI is a tool' we're not limiting our imagination - "the ceiling of what tool literacy can imagine" - we are instead inviting ourselves to explore how a human and an AI can be (dare I say it?) a system. Now I'm not sure Levine meant his discussion exactly that way, but this is what follows from the text. And I question both McLuhan and that reading. I used to consider the question of whether a social network is an extension of the neural network (which is essentially the Siemens interpretation of connectivism). But I see them as two separate networks, with a process of communication (essentially, emergence and recognition) between them. The McLuhan argument is a nice metaphor. But it should not be taken literally.

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Industry to Educators: Teach Human Skills, Not Just AI
Mike Kentz, How We Frame Machines, 2026/06/23

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The headline here is that "a year after 250 CEOs demanded mandatory AI education, industry leaders are zeroing in on the durable 'human' skills they can't hire for." That sounds good, but as always, I would warn against depending on industry leaders to define what should be taught. For one thing, it's often wrong. But more importantly, their advice is intended to benefit them, not the students. Consider this: "what we need is to figure out how to teach the human skills – how to teach future-proof skills that set an employee up for success no matter what domain they find themselves in." Why would this be important. Well, it could be because technology is changing so rapidly. But from where I sit, it's just as likely that employers want to hire human cogs they can quickly 'retrain' and slot into positions the employees never expected to be doing when they were hired - a lot like the way Facebook transferred software engineers into data-labeling peons.

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This Week in Student Success
Glenda Morgan, On Student Success, 2026/06/23

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I'm not sure 'disjuncture' is the best word for the contradictions being described by Glenda Morgan in this article on the differences between what teachers think they are delivering and what students think they are receiving. But the point is still valid. For example, "When asked whether they were incorporating real-world projects into their courses, between 58% and 73% of faculty said they were, depending on whether workforce readiness was a high priority for them. Yet only 26% of students reported completing a real-world project in a course." Considering the difference in employment between those who did, and did not, receive workplace experience, you can see ho important this is.

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3 Fresh Ideas for Structuring Professional Development
Jennifer Gonzalez, Cult of Pedagogy, 2026/06/23

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If you wanted to make a keynote feel more like indoctrination than an opportunity to stretch the mind, I can't thing of a better method. The '3 fresh strategies' come strain from that playbook: first, facilitated pre- and post-keynote sessions, to frame it; second, a curated Q&A session to screen participant interaction; and third, mandatory poster sessions. Now I admin, I might have been a bit jaded, after reading the sixteen Principles of Learning authored by presenters Jenn White and Josh Kurzweil. I mean, maybe this approach is good for children (though I doubt it). But speaking for myself, I would find such a tightly regimented professional development event to be a form of torture.

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Multi-Agentic EdTech: The Promise and the Costs
Michael Feldstein, e-Literate, 2026/06/22

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Michael Feldstein offers a useful summary of what 'multi-agentic AI' might look like in education: "Imagine a team of learning designers, each of which has particular skills and is assigned to perform a particular task on the way to designing a course. Imagine also that they have workflow and communications tools so that one designer can know when there's work waiting from another designer." He also touches on the cost of the multi-agentic workflow, especially across institutional boundaries, as there's no real way to moderate it - costs are shifting from flat-rate to per-token, where users are charged for content both input and output, the scale of both of which are determined by the individual agents.

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There are too many JavaScript schema libraries, so support only one
Aaron Harper, Inngest Blog, 2026/06/22

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This is rather technical, but is an example of building compatibility in the 'interfaces' between different application domains. "Standard Schema does not standardize error formatting, metadata, defaults, JSON Schema generation, or schema introspection. If you need those, you still have library-specific work to do.... There are too many JavaScript schema libraries. The fix isn't fewer libraries. It's an interface they can all agree on."

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Transforming AI models into useful model organisms
Mariya Toneva, The Transmitter, 2026/06/22

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I have talked in the past about the similarity between neural networks and human brains (as recently as a few days ago) and while there is overlap, they are not the same, and this article is careful to draw the distinction. At the same time, there is a lot of overlap, and we can learn from that. "By moving away from seeing AI models as finished computational models of the brain and instead leveraging them as model organisms that we can perturb and evolve, we move closer to cognitive neuroscience that doesn't just describe the brain but truly understands its mechanics."

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How the Open Knowledge Format can improve data sharing
Google Cloud Blog, 2026/06/22

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The Open Knowledge Format (OKF) is "an open specification that formalizes the LLM-wiki pattern into a portable, interoperable format. This is a vendor-neutral, agent- and human-friendly standard for representing the metadata, context, and curated knowledge that modern AI systems need." It's also useful for other purposes. "If you've used Obsidian, Notion, Hugo, or any of the LLM wiki patterns that have emerged over the past year, the shape will feel familiar. OKF formalizes the small set of conventions needed to make these patterns interoperable."Here's the GitHub repo. Via Shubham Saboo.

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Visualization experiments
Matthias Melcher, x28's New Blog, 2026/06/22

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Interesting extension of Matthias Melcher's diagramming tool. I too use WinDirStat (I recommend it if your hard drive is too full). At some point I need to write an article about the two-pane layout.  

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