edtech – Mike Paul

2,178 Digitized Occult Books: Strange Treasures for Authentic Learning

I want to tell you about a library in Amsterdam housed in a 17th-century building called the House with the Heads, funded in part by the author of The Da Vinci Code, with a collection that was granted UNESCO Memory of the World status in 2022, and whose digital archive you can browse for free from your couch right now.

The Bibliotheca Philosophica Hermetica — the Ritman Library, now housed at the Embassy of the Free Mind — contains roughly 30,000 titles on Western esotericism, mysticism, alchemy, astrology, Kabbalah, Rosicrucianism, and related traditions. In 2018, after Dan Brown donated €300,000 to fund the digitization project (he’d visited the library multiple times while researching The Lost Symbol and Inferno), the library launched what they called, with genuine wit, Hermetically Open: a free, publicly accessible digital archive of its rarest pre-1900 texts. As of 2025, 2,178 books are fully scanned and available online.

The collection includes the Corpus Hermeticum from 1472, Giordano Bruno’s work from 1584, the first printed visual representation of the Kabbalistic Tree of Life from 1516, alchemical manuscripts with intricate hand-drawn diagrams, and hundreds of texts in Latin, Dutch, German, French, and English that blur every boundary we’ve drawn between science, philosophy, theology, medicine, and magic.

My first thought when I found this collection was: this is exactly what I want students to encounter.

Why “Occult” Is the Wrong Frame for This

The word does its work on us. “Occult” conjures Halloween aesthetics and conspiracy theories, and it’s easy to dismiss the whole thing as fringe material with no serious application in a classroom.

That reaction, though, says more about our current assumptions about knowledge than it says about these texts.

For several centuries of Western intellectual history, there was no clean dividing line between alchemy and chemistry, between astrology and astronomy, between hermetic philosophy and natural science. Isaac Newton — who gave us calculus, the laws of motion, and the theory of universal gravitation — spent at least as much of his intellectual energy on alchemy and Biblical prophecy as he did on physics. His alchemical manuscripts are available online too, through Cambridge’s digital library. The man who arguably launched the scientific revolution was also, by any contemporary definition, deeply engaged in occult practice.

This isn’t an embarrassing footnote. It’s actually essential context for understanding how scientific knowledge develops — through the messy, often wrong, often ideologically entangled process of humans trying to make sense of the world with the conceptual tools they have available. The Ritman collection is a primary source archive for that story.

As a doctoral student who has spent years reading about how knowledge is constructed, organized, and transmitted, I find this collection genuinely thrilling. These books are where the medieval and the modern collide. They’re where you can see what people got wrong and what they got surprisingly right, often in the same text, often for reasons that have nothing to do with intelligence and everything to do with the conceptual frameworks available to them.

That’s exactly what I want students to sit with.

What Makes This Useful for Teachers

The collection isn’t neat. It’s multilingual, dense, and built for scholars. That’s part of the point — it’s not pre-digested curriculum content, it’s actual historical material that requires work to interpret. For teachers who believe students should wrestle with primary sources rather than always receiving polished summaries of them, this is a goldmine.

A few ways I’d use this across disciplines:

History and Social Studies — Trace how alchemy became chemistry. Look at how astrology shaped political decisions in early modern Europe. Ask students why the intellectual tradition represented here was systematically excluded from what we now call the history of science, and what that exclusion says about how we decide what counts as legitimate knowledge.

English and Literature — The visual and linguistic strangeness of these texts is remarkable. The archaic spellings, the “long s” that looks like an f, the allegorical imagery, the blend of Latin and vernacular — all of it offers material for close reading and for connecting to the Gothic, Romantic, and magical realist traditions that drew heavily from this well.

Science — Contrast alchemical “recipes” with modern chemical procedures. Examine how flawed models of the cosmos were still generative — the people using them weren’t stupid, they were working at the edge of what was knowable. What does that say about our own current models?

Art and Design — The illuminated manuscripts and alchemical diagrams in this collection are extraordinary visual objects. The symbolic language is dense and codified and genuinely beautiful. There’s serious material here for design history, visual communication, and semiotics.

Philosophy — The Hermetic tradition represents a sustained attempt to synthesize Greek philosophy, early Christian theology, Jewish mysticism, and natural observation into a unified account of reality. That synthesis didn’t work out the way its practitioners hoped. But the attempt itself raises questions about knowledge, interpretation, and the limits of any single framework for understanding the world — questions that don’t go away.

The cross-disciplinary angle is what I find most powerful. One of the things that frustrated me most in my years as an educator before moving into instructional coaching is how thoroughly we’ve siloed knowledge. Students take chemistry, history, and English as separate things, as if the history of chemistry weren’t fascinating, as if the literary history of science didn’t exist. The Ritman collection doesn’t respect those boundaries because it predates our drawing of them.

The Resource

The collection is free, fully accessible online, and searchable — though the search interface takes some patience. The direct link to the digital catalog is here. I’d recommend starting with the “Digital collection” page, which gives you some orientation before you dive in.

A few things worth knowing:

The majority of texts are in Latin, Dutch, German, or French. English-language texts exist, but aren’t the majority. For classroom use, this is actually an opportunity — translation, context-building, and working with unfamiliar material are valuable skills.
The image quality varies, but the rare and fragile items were prioritized for digitization, so many of the most valuable texts are well scanned.
The broader collection, which includes 30,000 titles and continues to grow, is housed at the Embassy of the Free Mind in Amsterdam. If you’re ever there, it’s worth visiting.

The collection earned UNESCO Memory of the World status in 2022, a designation UNESCO does not hand out lightly. This is genuinely important cultural heritage, now freely available to anyone with internet access. That’s remarkable.

Dan Brown’s novels that led him to the Ritman Library — The Lost Symbol and Inferno — both draw heavily on the kind of Hermetic and esoteric tradition documented in this collection. If you want a somewhat lurid but surprisingly well-researched tour of the ideas, they’re a decent starting point. Brown is not a subtle writer, but he did his homework.

Related on this site: the AI books post covers how knowledge evolves and what it means to think critically about the tools we use — a thread that runs directly through what this collection makes visible.

What Teachers Need to Understand About AI and the Economy — A Reading List

man using laptop wit chat gpt — Photo by Matheus Bertelli on Pexels.com

Here’s something that should be keeping school leaders up at night: 55% of recent graduates report that their academic programs didn’t prepare them to use generative AI tools in the workforce. Not just use AI well — use it at all. We are preparing students for an economy that is reorganizing itself faster than our curriculum review cycles can keep up with, and most schools are responding with either panic or denial.

The World Economic Forum’s Future of Jobs Report 2025 projects that AI will displace 92 million jobs while creating 170 million new ones — a net gain on paper, but that math only works if the people losing the 92 million jobs can access the 170 million new ones. That transition requires education, retraining, and policy infrastructure that does not currently exist at the scale needed. Young workers in AI-exposed occupations are already experiencing shifts in employment. The college wage premium has flattened. Jobs requiring AI skills now command a 56% wage premium over those that don’t — up from 25% just the year before.

This is not an abstract future problem. It is the context in which our students will graduate.

I don’t write primarily about business or economics — this site is about education, technology, and the ideas that shape both. But understanding how AI is disrupting the economy is part of understanding what we are actually preparing students for. The books below are the ones I’d put in front of any educator or school leader who wants to think more seriously about this.

The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman

Get it on Amazon

Suleyman co-founded DeepMind (later acquired by Google) and Inflection AI before becoming CEO of Microsoft AI. He is, in other words, someone who has been building this technology from the ground up and who has had to think carefully about what he was building.

The Coming Wave is his argument that we are facing a genuine inflection point: AI and synthetic biology are advancing faster than governance structures can keep pace with, and the window to build appropriate containment mechanisms is closing. His central concern isn’t that AI is malevolent — it’s that the concentration of power that comes with controlling transformative technology is itself the problem, whether that power sits with corporations, governments, or both.

For educators: the chapter on economic disruption is essential reading. Suleyman doesn’t pretend the transition will be smooth. He takes seriously the question of what happens to people and communities during the displacement phase, which is precisely the phase our current students are entering.

AI Superpowers: China, Silicon Valley, and the New World Order — Kai-Fu Lee

Get it on Amazon

Lee has a unique vantage point: he’s worked at Apple, Microsoft, and Google, and then moved to Beijing to lead Google China before becoming one of China’s leading AI investors. AI Superpowers was published in 2018, and some of the specific competitive dynamics have shifted, but the core argument holds: we are in a global race for AI dominance between two different models of how AI development should work, and the outcomes of that race will have profound economic consequences at every level.

The section on job displacement is where this book becomes most directly relevant to educators. Lee argues that routine cognitive work is the most vulnerable to automation — not just manual labor — and that the categories of work that will be protected are those requiring creativity, empathy, and complex human judgment. That framing has direct implications for what we teach and why.

Read this alongside The Coming Wave for a richer picture of the geopolitical and economic forces shaping the AI landscape.

Prediction Machines: The Simple Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

Get it on Amazon

Three economists from the University of Toronto built their framework around a deceptively simple claim: AI is, fundamentally, a technology that makes prediction cheaper. When prediction gets cheaper, the value of the things that complement prediction — judgment, action, data — increases. When prediction gets cheaper, the value of things that substitute for prediction — routine rule-following, low-stakes decision-making — decreases.

This framework is useful for educators because it maps directly onto a question we should be asking about curriculum: what are we teaching students that will be substituted by cheap AI prediction, and what are we teaching them that will be complemented by it? The answer has real implications for what genuinely rigorous education looks like in an AI economy. Prediction Machines is the most analytically useful book on this list for thinking through those questions.

The Age of AI: And Our Human Future — Henry Kissinger, Eric Schmidt & Daniel Huttenlocher

Get it on Amazon

An unusual collaboration: a former Secretary of State, a former Google CEO, and an MIT computer scientist thinking together about what AI means for how human societies understand the world. The book is less about the economic disruption and more about the epistemological one — the way AI systems generate outputs that humans can use without understanding how those outputs were produced, and what that does to decision-making in business, government, and education.

The argument that lands hardest for me as an educator: we have spent centuries building institutions of learning around the transmission and evaluation of human knowledge. AI is producing a new kind of knowledge — statistical, pattern-based, extraordinarily capable, and fundamentally alien to how human minds work. What does education mean in that context? This book doesn’t fully answer the question, but it asks it more precisely than most.

Power and Prediction: The Disruptive Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

Get it on Amazon

The follow-up to Prediction Machines, published in 2022, moves from “here’s what AI does to economics” to “here’s how organizations and institutions will be restructured by it.” The core new argument: AI doesn’t just automate tasks; it disrupts the decision-making systems in which those tasks are embedded. That disruption creates power shifts — between professions, between institutions, between incumbents and challengers.

The education implications are direct. The authors discuss healthcare and legal services as sectors being restructured by AI-driven prediction, and the analysis applies equally to education. What happens to the teacher’s role when AI can provide personalized feedback faster and at greater scale? What happens to credentialing when AI can assess competencies that diplomas approximate? These aren’t comfortable questions, but they’re the right ones to be asking now rather than after the disruption has already happened.

The Question Underneath All of These Books

The books above are written primarily for business leaders, policymakers, and economists. That’s who they were designed for. But they all circle around a fundamentally educational question: what kind of people do we need to develop, and what do we need to prepare them for, in an economy being reorganized by AI?

Self-Determination Theory gives us part of the answer — humans are most resilient and most capable when they have genuine autonomy, a sense of competence, and meaningful connection. Those psychological needs don’t get automated. They get more important as the tasks around them do.

The Connectivist framing that the network is where knowledge lives is also useful here: in an economy where AI can provide information faster than any human, the competitive advantage lies in the quality of your connections — to ideas, to people, to problems worth solving — and in your capacity to navigate those networks with judgment. That’s what education in an AI economy should be building.

These books don’t answer those questions for us. But they describe the problem with enough precision that we can start asking the right ones.

Related on this site: the AI books post covers the books I’d recommend for understanding what AI actually is — how it works, what it can and can’t do, and what the most credible researchers think about its implications. That’s a companion list to this one.

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Google’s Stitch Update: “Vibe Design” and the Shrinking Distance Between Ideas and Tools

Google recently announced a major update to its experimental design tool, Stitch. If you haven’t heard of it before, Stitch is an AI-powered interface design tool—but this update signals something bigger than just new features.

Google is now describing Stitch as an “AI-native software design canvas”—a space where users can move from an idea to a high-fidelity interface using natural language, images, or even voice.

That shift in language matters.

What’s Actually New in This Update?

Stitch isn’t new, but this version pushes it in a different direction. A few highlights stand out.

First, Stitch is no longer framed as a traditional design tool. Instead of starting with wireframes or components, users are encouraged to begin with intent—what they want to build, how it should feel, and what it should accomplish. In practice, that means you can describe a goal and generate a working interface almost immediately.

Second, Google introduces the idea of “vibe design.” While the phrasing might feel a little buzzword-heavy, the concept is straightforward. Rather than trying to get a design right on the first attempt, users can explore multiple directions quickly and refine toward a stronger result.

Third, the updated Stitch includes a design agent that works alongside the user. This agent can reason across the entire project, suggest changes, and help explore different directions simultaneously. It shifts the process from step-by-step construction to something closer to collaboration.

Another notable addition is the introduction of DESIGN.md, an agent-friendly markdown file that captures design rules and structure. This makes it easier to move designs into other tools or continue development with AI systems without starting over.

Finally, Stitch now supports instant prototyping of user flows. Instead of static screens, users can connect interfaces and immediately experience how someone would move through the app. That ability to test ideas quickly changes the pace of iteration.

Why This Matters for Educators

At first glance, this might seem like a tool built for designers or developers. But the implications for classrooms are more immediate than they appear.

For years, we’ve asked students to design solutions to problems—create a product, propose an innovation, build something meaningful—but those ideas often remain abstract. They exist in slides, posters, or written descriptions.

Tools like Stitch begin to close that gap.

Students can take an idea—such as a tool to help track progress in Algebra 1—and generate a working interface in minutes. From there, they can evaluate it, revise it, and improve it. The work becomes more tangible, and the feedback loop becomes faster.

That shift from describing an idea to interacting with it has real potential to deepen thinking.

The Bigger Shift Underneath

What Stitch represents is part of a broader change in how creation works.

The more technical aspects of building—layout, structure, and basic interaction design—are increasingly handled by AI. That doesn’t eliminate the need for skill, but it does change where the most important thinking happens.

Instead of focusing primarily on execution, the emphasis shifts toward clearly defining problems, making intentional design decisions, and evaluating whether something is actually useful.

Those are the kinds of capacities we want students to develop, but they’re often overshadowed by the mechanics of building something from scratch.

A Quick Reality Check

This doesn’t automatically lead to better learning.

If we simply replace “make a slideshow” with “generate an app,” we haven’t meaningfully changed the task. The tool itself isn’t the innovation. The thinking behind how it’s used is what matters.

Used thoughtfully, however, tools like Stitch can support faster iteration, more visible thinking, and more authentic design work.

Try This in Your Classroom

If you’re curious about what this might look like in practice, you don’t need a full unit redesign to get started. A simple activity can open the door.

Start with a question tied to your content:

“What would a tool that helps students master this unit actually look like?”
“How could we design something that makes feedback more useful?”
“What would help someone learn this concept more effectively?”

Have students work individually or in small groups to:

Define the purpose of their tool
Describe the user (another student, themselves, a teacher)
Generate a design using Stitch or another AI interface tool
Review the result and critique it

Then push their thinking:

What works about this design?
What doesn’t?
What would you change to make it more useful?
How does it connect to what we know about learning?

The goal isn’t to build a perfect product. It’s to move students into a cycle of idea → prototype → critique → revision, which is where deeper learning tends to happen.

Final Thought

Google describes this update as helping users “close the gap from idea to reality in minutes rather than days.”

That may sound ambitious, but it reflects a real trend.

As that gap continues to shrink, the question for educators isn’t whether students can build things. It’s what we ask them to build—and whether those tasks are worthy of the tools now available to them.

Everyday Objects Are Unrecognizable at Super Macro Scale

Posy takes us on a grand adventure into the world of the incredibly small and ridiculously close.

Prepare to sit in awe of everyday objects from an entirely new perspective.

Funny, we should probably try to look at the world from different perspectives more often…

We must build AI for people; not to be a person

My life’s mission has been to create safe and beneficial AI that will make the world a better place. Today at Microsoft AI we build AI to empower people, and I’m focused on making products like Copilot responsible technologies that enable people to achieve far more than they ever thought possible, be more creative, and feel more supported.

I want to create AI that makes us more human, that deepens our trust and understanding of one another, and that strengthens our connections to the real world. Copilot creates millions of positive, even life-changing, interactions every single day. This involves a lot of careful design choices to ensure it truly delivers an incredible experience. We won’t always get it right, but this humanist frame provides us with a clear north star to keep working towards.

Some thoughts from Mustafa Suleyman on building AI that doesn’t convince people that AI is a human, or needs rights. Or is a god.

Sadly, we’re already having those discussions.

You Might Be Trying to Replace the Wrong People with AI

I was at a leadership group and people were telling me “We think that with AI we can replace all of our junior people in our company.” I was like, “That’s the dumbest thing I’ve ever heard. They’re probably the least expensive employees you have, they’re the most leaned into your AI tools, and how’s that going to work when you go 10 years in the future and you have no one that has built up or learned anything?

So says Matt Garman, CEO of Amazon Web Services. A better question to ask: What do you mean, you don’t want to teach your high school students how to use AI to help them write code and solve problems more efficiently?

We live in weird times when people constantly retreat to what came before and avoid any intention of moving on.

Life is the future, not the past.

Black, Latino & Low-Income Kids Felt Better Doing Remote School During COVID

group of people taking photo — Photo by Rebecca Zaal on Pexels.com

The dominant story about COVID-era school closures has been simple: remote learning hurt kids’ mental health. And for many, that’s true. National data show American teens reported more loneliness and more suicidal thoughts between 2019 and 2023, with isolation during lockdown often cited as the culprit.

But a new study complicates that narrative. Researchers analyzed survey data from more than 6,000 middle schoolers during the 2020–21 school year and found a striking divide:

White and higher-income students were significantly happier and less stressed when attending school in person.
Black, Latino, and low-income students often reported the opposite—feeling less stressed and sometimes even happier when learning remotely.

In other words, remote school wasn’t universally worse. For some groups, it offered a reprieve from stressful in-person school environments, from health risks during the pandemic, or from inequities baked into the classroom experience.

The findings don’t suggest remote school is “better” overall. Academic setbacks during closures were real and disproportionately hurt the very students who sometimes felt mentally healthier at home. Instead, the study is a reminder that school isn’t a neutral space. How students experience it depends deeply on race, income, and environment.

As the researchers note, it’s not enough to flatten the pandemic into a single story of harm. Different groups of students experienced it differently—and will need different supports moving forward. If schools want to be places where all kids can thrive, they’ll need to reckon with why in-person learning left some students more stressed than staying home.

Steve Wozniak Never Sold Out

I gave all my Apple wealth away because wealth and power are not what I live for. I have a lot of fun and happiness. I funded a lot of important museums and arts groups in San Jose, the city of my birth, and they named a street after me for being good. I now speak publicly and have risen to the top. I have no idea how much I have but after speaking for 20 years it might be $10M plus a couple of homes. I never look for any type of tax dodge. I earn money from my labor and pay something like 55% combined tax on it. I am the happiest person ever. Life to me was never about accomplishment, but about Happiness, which is Smiles minus Frowns. I developed these philosophies when I was 18-20 years old and I never sold out.

– Steve Wozniak via Slashdot

Teaching the Unmappable: Why Color Defies Easy Charts

For centuries, scientists, artists, and philosophers have tried to pin down a “perfect” way to map color. But here’s the problem: color isn’t just physics, and it isn’t just perception—it’s both. Try to squeeze it into a neat geometric model, and you’ll quickly realize it refuses to stay put.

That’s what makes French video essayist Alessandro Roussel’s latest ScienceClic piece so fascinating for educators. He takes us from Isaac Newton’s prism experiments all the way to modern models of hue, brightness, and saturation. Along the way, he shows why there isn’t just one map of color, but many. Each communicates something different about how humans experience this slippery phenomenon.

So what’s the classroom connection?

In art: Students can compare different models of color—Newton’s circle, Munsell’s tree, the modern RGB cube—and reflect on how each changes the way we think about mixing, matching, or designing with color.
In science: Teachers can use these models to illustrate how physics collides with perception. Why do two people see the “same” red differently? How does light wavelength interact with the human eye and brain?
In interdisciplinary projects: Color mapping opens doors to conversations about how humans create systems to explain the unexplainable. It’s a perfect bridge between STEM and the humanities.

And then comes the kicker for students who think we’ve “solved” everything already: scientists recently managed to engineer a new, so-called impossible color called ‘olo’—a shade outside the traditional visible spectrum.

It’s a reminder that color isn’t just a solved equation or a finished wheel. It’s a living, shifting puzzle that still invites curiosity, wonder, and experimentation.

Imagine giving your students that as a challenge: If color can’t be mapped perfectly, what’s your best attempt?