Dani Arribas-Bel

What Battersea Power Station taught me about the value of satellite embeddings

Tue, 05 May 2026 09:59:51 +0100

Sometimes, magic finds its way through the forest of math that underpins most of my research. I spent a good amount of time a couple of weeks ago working on our embeddings workshop for GISRUK. We just released a new data product with Google satellite embeddings for small areas in the UK, and we couldn’t wait to take it on the road. This post is a note about one of the most insightful moments I had while preparing the materials but, if you want to check out the entire workshop, it’s all open at:

https://imago-sdruk.github.io/embeddings_workshop/jupyterlite/content/02-Change.html

One of the most appealing characteristics of satellite embeddings is their ability to explore change. Since embeddings map the information in an image to a shared latent space, it doesn’t matter where or when that image was taken, it all gets encoded in the same mathematical “language”. Exploring whether the location of an image is similar or not to that of a different image; or whether such location has changed compared to what it looked like at a different point in time, is relatively straightforward. You can play with the “comparison across space” idea in our Imago UK embedding Explorer. And we played with change across time in this notebook.

The part that got me is the last exercise. We ask the question what area has become the most like Hyde Park between 2020 and 2024? This seems a bit esoteric but, I think, is an interesting one to ask. We know areas change within a city all the time, wouldn’t it be cool to know which ones are becoming more like some specific landmarks? In the workshop, I called it “Hyde Park-ification”, but I may deny this in public… It also turns out this is relatively straightforward to check with embeddings. You can follow all the details, code included, in the notebook. The gist is you calculate how similar all areas are to Hyde Park in both years, take the difference of that distance, and pick your winner as the smallest of those.

Here’s our winner¹:

For the uninitiated reader, this is Battersea Power Station. Which, even if all you know is the first photo in the Wikipedia page I just linked to, you’d be forgiven to think this embeddings sorcery is nonsense. This is as far as you can get from Hyde Park in terms of look and feel. So, where’s the catch?

The catch, of course, is that we are not looking into overall similarity, but change. Our approach looks for areas that, over the period we consider, have become more like Hyde Park. That does not mean they are like Hyde Park. There’s another interesting bit we show in the notebook: Battersea Station, by the standards of the areas we use, has changed very little. Again, this is counterintuitive maybe, but not incompatible with what we’re asking of embeddings in this exercise: you can change very little, but entirely in one particular direction.

As it turns out, this is what happened to our winner. This insight did not come painlessly to me, as I struggled through a good 20/30 minutes of scratching my head about whether I was asking too much of this technology (particularly when it’s aggregated from the pixel to an irregular area). I’m glad I stuck with this and followed it to the bottom. And I’m glad Google keeps funding Google Earth for free, including its time travel feature. Below is a comparison of the area in question between 2020 and 2024²:

If you can’t spot the difference (I couldn’t for the first ten minutes), follow the red arrow (which wasn’t there when I was looking for twenty minutes). The area, broadly speaking, has not changed much (which checks out with our low score of change). It was, and still is, a retired power station turned epicenter of cool south of the river. Mostly chimneys, mostly concrete. But there’s a small part that has changed. A small patch by the river used to be concrete buildings but, in between 2020 and 2024, it was flattened. Instead, it is now a patch of grass where Londoners enjoy the three days a year where the sun shines with all its fury³. You can see some of the before and after photos in this article by the BBC.

I love this example because it captures very well why I’m so excited about embeddings and about making them accessible to more people through data products like our small area one. I knocked out that notebook in about an hour (if you exclude my Google Earth rabbit hole). In that period, I was able to leverage data from seven massive satellite sources (see Figure S2 in this paper for details), across two periods of time on a setup that runs on my iPad’s browser. Just let that sink in. What questions can we tackle that we couldn’t before? More importantly, which ones will we actually consider now that the price of asking is so low? I think we’re about to find out, and I can’t wait.

I’m pasting here screenshots because it’s 2026 and YOLO, but you can play with the interactive map on the workshop site. ↩︎
You can play with the feature here. ↩︎
This is a joke. I know it’s, at least, five days. ↩︎

Fri, 01 May 2026 08:56:01 +0100

Happy release day to those who celebrate!!! Today, you can access our derived product that packages Google embeddings for small areas in the UK. Embeddings are changing how we work with satellite data. With this product, we hope it’ll be even easier to access and get started to look at places. If you take them out for a spin, let us know!

https://imago.ac.uk/news/imago-releases-google-satellite-embeddings-at-the-small-area-level-across-uk

Mon, 27 Apr 2026 07:22:50 +0100

Early starts

Fri, 24 Apr 2026 13:24:58 +0100

Happy data release day to those who celebrate!!! We’ve been working really hard (how hard can it be to count clouds? a lot, it turns out…) to bring you all your cloudy needs neatly packaged. If you play with SPF, we’d love to hear from you!

imago.ac.uk/news/imag…

🔗 THE PEOPLE DO NOT YEARN FOR AUTOMATION

Thu, 23 Apr 2026 22:14:54 +0100

Amen.

Metadata

Author: The Verge
Category: podcast
URL: podcasts.apple.com/gb/podcas…

Highlights

[21:51] I’m just saying these things aren’t everything, that not everything about our lives can be measured and automated and optimized. It shouldn’t be. And so the tech industry is rushing forward to put AI everywhere at enormous cost: energy, emissions, manufacturing capacity, the ability to buy RAM, locked into the narrow framework of software brain without realizing they are also asking people to be fundamentally less human. And then they’re sitting around wondering why everyone hates them. I don’t think a couple of haircuts are going to fix it.

Fri, 10 Apr 2026 09:30:35 +0100

Pasa la vida…

Notes from a week in the Bay Area

Mon, 23 Mar 2026 13:01:00 +0100

I’m just wrapping up a week in Berkeley and San Francisco. I came for two reasons, both have been fantastic, and I’m definitely feeling now the post-high blues that good academic interaction tends to induce.

I spent the first few days at the Berkeley Institute for Data Science, where the good folks of PySAL organised the Spatial Data Science Summit. This was a meeting to discuss opportunities and overlaps across the spatial data science community, and beyond, including the wider Python ecosystem for scientific computing. These days, I’m much less active (and that’s a kind way of putting it) in the development of PySAL. But it was nevertheless a fantastic experience. PySAL 1.0 shipped in 2009 and I was there to see it. Since then, it’s been a project and a community that has brought me much of what I am and who I’ve become. And, as the cliché goes, many of the collaborators have become close friends. Clichés are clichés for a reason.

Then I hopped on the BART to San Francisco’s Tenderloin, where geographers were descending left and right to discuss all things Geography at the AAG. I was part of a session brilliantly put together by Elizabeth Delmelle and Geoff Boeing on “problem-driven methods” (as opposed to the seemingly more common “method-driven problem development”) in the context of cities. I presented on satellite embeddings. When I was preparing the slides, I felt a bit uncomfortable because, arguably, I was about to engage in precisely the type of behaviour the sessions sought to avoid: “reaching for this year’s shiny new tool” instead of addressing cities' ‘wicked problems’”. Elizabeth and Geoff prompted us to include a slide at the beginning stating the “big urban problem” we were tackling. I’m so glad they did because it really nudged me to spell out why I think embeddings are more than this year’s shiny tool. My framing revolved around two key arguments. First, there is much more we can (and should!) do to tailor the data we use to the problems we tackle. Second, there is a lot of untapped data to help in that tailoring. In this context, satellites are one of those underutilised sources that can help provide better empirical fit to the questions we care about. And embeddings lower the barrier to access satellite data, making it cheaper to ask questions and explore ideas. I think, in the end, it went well and was well received.

I also participated in a panel organised as part of the discussion we had started in Berkeley earlier in the week around open source in spatial data science research. Serge Rey prompted us to think about existing gaps, low-hanging fruit, and surfaces of overlap. We covered quite a bit of territory, and Serge structured the conversation so there was clear and constant interaction with the audience, which became an “additional panelist”. A lot of fun.

Besides the strictly “work” things, this week also had plenty of space for fun. We (i.e., Rachel, Levi and your truly) attended the AAG Awards ceremony to pick up our Media Achievement award for GLaD. To celebrate it, that afternoon we hijacked a boardroom, stuck a hand-written note in the door that read “recording in process”, and taped the first episode in a long while where we were physically together. And, after all that flurry of activity, the day after, I managed to convince Geoff Boeing and Martin Fleischmann to join me on a walk around very tall trees north of the Golden Gate. Long walks are underappreciated ways to exchange ideas.

With that, the week came to an end. It’s been so much compressed in such a small amount of time and space. I know it’ll take me a few days to unpack, literally and figuratively. This is conferencing at its best: more brain cycles in less days, all away than your usual routine. Academic life the way it should be.

Sun, 22 Mar 2026 21:16:45 +0100

We’re recruiting for two PhD positions at Liverpool with the Imago team and based at the GDSL:

Pixel2Policy, led by Elisabetta Pietrostefani

This project investigates how satellite-derived indicators can be co-produced with policymakers and communities to better reflect real needs and lived realities. Bridging Earth observation, participation, and policy, it aims to make satellite data more inclusive, credible, and impactful in decision-making.

Space2Health, led by Ron Mahabir

Space2Health explores how satellite imagery can be transformed into meaningful measures of environmental exposures linked to health. The project relies on recent AI technologies to develop and evaluate satellite-derived indicators, examining how data choices and analytical approaches shape health-relevant evidence for research and decision-making.

Both open until April 13th, get in touch for any queries.

The GLaD Podcast recognised with AAG award

Mon, 16 Feb 2026 13:04:56 +0100

This happened, the GLaD podcast is now the “award-winning” GLaD podcast:

https://www.aag.org/2026-aag-awards-recognition/

Reproduced here for posterity:

AAG Media Achievement Award

The 2026 Media Achievement Award is presented to Drs. Daniel Arribas-Bel, Rachel Franklin and Levi Wolf, the co-creators and hosts of the Geography, Life + Data (GLaD) Podcast. This podcast is celebrated for enhancing the understanding of geography by exploring the intersection of our discipline with data science, public life, and academia—or, as their episode intro says, “geography, life, geography life, and data. Launched in 2023, the GLaD Podcast and its predecessor series have produced over 50 episodes, amassing over 8,000 downloads, over 15,000 views on YouTube, and attracting more than 5,000 listeners worldwide. The podcast is renowned for its ability to simplify complex topics—such as spatial data science and urban analytics—through an engaging and accessible conversational style. It effectively breaks down barriers for students, early-career researchers, and non-specialists. Recognized as an invaluable educational resource, it has been integrated into graduate seminars and serves as a platform to humanize leading scholars. The podcast offers candid, practical advice on academic challenges like job searching and conference navigation, fostering a supportive community. GLaD’s continued independent production underscores the creators’ commitment to bridging the gap between academic research and the wider public.

Like most things worth your time in life, we did not set out to do a podcast (the “I’m in a band” of the XXIst Century…) for the awards, but it does feel very good to be recognised, mostly by people listening to it every month, and now with this too.

Thank you, thank you, thank you to everyone who’s clicked on the Play button at some point. And Thank you, Thank you, Thank you to the kind souls who nominated us for the award. Like I’ve said elsewhere, whatever they tell you, what academics really crave is peer recognition, and this feels pretty close to it!

Fri, 13 Feb 2026 17:39:02 +0100

New episode of the one and only #GLaDpodcast. For this one, we dust off our oracles to speculate what 2026 (whatever is left of it anyway) has in store for Geography, Life, Geography Life, and Data! Come for the hot takes, stay for Polymarket-style futures contracts!

Tue, 10 Feb 2026 23:32:18 +0100

This was a lot more fascinating than I thought it would. Come for the craze around OpenClaw, stay for the background story of the mind behind it.

Metadata

Author: The Pragmatic Engineer
Category: video
URL: www.youtube.com/watch

Highlights

Then it will send you a message on WhatsApp, and suddenly you talk on WhatsApp, making this flow easy. That was hard. Yeah, even coming up with the idea that you’re not editing the configuration, because the agent can edit its own configuration. You don’t have to update anything because the agent can update itself. You can literally ask your bot to update itself, and it will fetch itself and update itself, coming back with new features. Planning the technical giveaway, so far that’s the magic.

“I would recommend them to be infinitely curious. Yes, it’s going to be harder to enter this market. It’s absolutely going to be harder, and you need to build things to gain experience. I don’t think you need to write a lot of code, but you need to explore. There’s a lot of open source that is complex, that you can check out and learn from.

I added a bootstrap file to explain the model that is now being born to create an identity and a soul where the values of the user are, and the model will be like, “Hello! Who are you?” “Who am I? What’s my name?” You know, and I’ve watched people do it, and that’s where the magic starts.

You don’t have to update anything because the agent can update itself. You can literally ask your bot to update itself, and it will fetch itself and update itself, coming back with new features. Planning the technical giveaway, so far that’s the magic. That’s

You don’t have to update anything because the agent can update itself. You can literally ask your bot to update itself, and it will fetch itself and update itself, coming back with new features. Planning the technical giveaway, so far that’s the magic.

But this new world needs people that can bridge both areas.

Even, you know, even when I get a pull request, I’m actually more interested in the prompts than in the code. I ask people to please add the prompts, and some do, and I read the Prompts more than I read the code, because to me this is a way higher signal of like how did you get to the solution? What did you actually ask?

🔗 How Markdown took over the world

Wed, 04 Feb 2026 23:42:56 +0100

Sobre very good Internet History lesson, full of delicious details that corroborate the idea the web is built more on the nice part of human beings than the not so nice.

Metadata

Author: Anil Dash
Category: rss
URL: anildash.com/2026/01/0…

Highlights

But it’s important for everyone to know that the Internet, and the tech industry, don’t run without the generosity and genius of regular people. It is not just billion-dollar checks and Silicon Valley boardrooms that enable creativity over years, decades, or generations — it’s often a guy with a day job who just gives a damn about doing something right, sweating the details and assuming that if he cares enough about what he makes then others will too.

Wed, 28 Jan 2026 12:26:17 +0100

Yesterday, we (the Imago team) gave a workshop on satellite embeddings for a room with about 50 people from different corners of the UK Government. It was fantastic in every respect (with the possible exception of Github bringing down our website because it thought it was receiving a DDoS attack lol). It’s so exciting to see these ideas and technology move at the speed of light from state of the art research to Government. Anything we can do at Imago to facilitate that transfer, we’re here for that!

If you’re curious, materials are available at:

imago-sdruk.github.io/EMBED2Soc…

And if this looks interesting to your and/or your organisation, do get in touch!

🔗 Earth Embeddings: Towards AI-centric Representations of our Planet

Fri, 23 Jan 2026 22:35:26 +0100

Very very timely paper that captures the current zeitgeist in EO and AI. If nothing else, it serves as a fantastic introduction to one of the technologies that I think(/hope) will help the most bring imagery to the masses in the coming years.

Metadata

Author: eartharxiv.org
Category: article
Document Tags: paper
URL: eartharxiv.org/repositor…

Highlights

Earth embedding vectors emb are produced by a family of embedding functions E that map continuous location inputs (i.e., longitude, latitude with optionally elevation, and time) into a d-dimensional vector space:

Figure 2: Earth embeddings provide different functions: (1) They compress high-dimensional data into a lower-dimensional vector format. (2) They fuse together different geospatial data modalities, from different types of images to text and tabular data. (3) They can interpolate to unseen spatiotemporal locations, where raw data is missing. (4) They are interoperable with other AI foundation models, such as LLMs, through aligned embedding spaces.

as explicit models, extracting embeddings from raw data (e.g. satellite imagery) associated with a location (emb ∼ Eexplicit(datalocation))

implicit models, returning embeddings from only location inputs (emb ∼ Eimplicit(location)).

Earth embeddings map places and times that share similar properties closer together in embedding space.

GeoFMs are large-scale modeling and learning frameworks, whereas Earth embeddings constitute the interoperable, location-indexed data outputs that can be stored, shared, or queried independently of the model that created them.

We posit that Earth embeddings will emerge as the dominant format of geospatial data in the AI age

ways in which users can employ Earth embeddings for prediction, conditioning, simulation, and search

Call to action: Advancing analyses and applications with Earth embeddings.

• Evaluating and benchmarking Earth embeddings

• Explainable and interpretable Earth embeddings

• Learning planetary processes with Earth embeddings

Earth Embedding Models: Explicit Feature Extraction versus Implicit Neural Representation

Challenges and opportunities for improving Earth embeddings.

• Model capacity

• Spatio-temporal heterogeneity

• Data curation and scaling

• Learning objective

The research agenda we outline is fundamentally interdisciplinary: Earth embeddings will rely on feedback from domain scientists, e.g. in ecological, geological, oceanographic, and atmospheric sciences, that incorporate Earth embeddings into their analyses and from data practitioners apply- ing Earth embeddings in their workflows and products.

Wed, 21 Jan 2026 18:29:07 +0100

A bit late to post about (though not to publish!), we have a new episode of the #GLaDpodcast out. If nothing else, be enticed by the title (the oldest profession in Geography?!?!?!); if something else, delight in Anthony Robinson’s views on maps, AI, and microwave ovens!

🎧 From Data Dump to Data Product

Mon, 19 Jan 2026 00:07:31 +0100

So many common points and arguments that really resonate here and make me more hopeful for Imago. The discussion of data as infrastructure, invisibility as success, and thinking really hard about how to make sure “it”’s not only here now, but tomorrow and the day after are points that’ll stay with me. And it’s also great to find more people who’re thinking creatively (not only from the tech side of things) to ensure the world has more collective-ness around data. Most recommended listen.

Metadata

Author: MapScaping
Category: podcast
Document Tags: audio
URL: podcasts.apple.com/gb/podcas…

Highlights

The way I frame it is like, the game is figuring out how to lower the cost of asking questions.

🔗 21 Lessons from 14 Years at Google

Sat, 17 Jan 2026 13:57:13 +0100

There’s probably a limit to how many “lessons” blog posts one should read but, every once in a while, they’re helpful. Many of these resonate with me after a similar amount of time in research and academia.

Metadata

Author: Addy Osmani
Category: rss
URL: www.oreilly.com/radar/21-…

Highlights

The “best tool for the job” is often the “least-worst tool across many jobs”—because operating a zoo becomes the real tax.

Abstractions don’t remove complexity. They move it to the day you’re on call. Note: “Every augmentation is an amputation” Senior engineers keep learning “lower level” things even as stacks get higher. Not out of nostalgia but out of respect for the moment when the abstraction fails and you’re alone with the system at 3am. Use your stack.

Teaching is debugging your own mental models.

If you win every debate, you’re probably accumulating silent resistance.

Model curiosity, and you get a team that actually learns.

Your job isn’t forever, but your network is. Approach it with curiosity and generosity, not transactional hustle.

Deleting unnecessary work is almost always more impactful than doing necessary work faster. The fastest code is code that never runs.

The engineer who treats their career as compound interest, not lottery tickets, tends to end up much further ahead.

The engineer who truly understands the problem often finds that the elegant solution is simpler than anyone expected.

First do it, then do it right, then do it better.

🔗 The year of technoligarchy

Sat, 10 Jan 2026 11:05:43 +0100

Molly White is on fire for this new year’s first dispatch…

Metadata

Author: Molly White
URL: www.citationneeded.news/the-year-…

Highlights

They know it. The technoligarchs aren’t confident their hold will last. That’s why they’re dismantling oversight, rushing through favorable legislation, securing pardons, amassing wealth — grabbing everything they can reach right now.

An economy built on stripmining its populace cannot be sustained.

The industry that promised it would free us from captured institutions has captured them itself.

When the economy they’ve hollowed out seizes up, when the markets they’ve destabilized implode, when the legitimacy of the institutions they’ve captured evaporates, and when everyday people suffer, their names are on all of it.

📺 deftones: private music & more with zane lowe [apple music]

Mon, 29 Dec 2025 21:31:42 +0100

With their new album, I’ve been listening to some interviews. This one is particularly nice (at over 1h, probably only for hardcore fans). Besides making me feel a bit younger again, there’s something nice in seeing a bunch of friends who’ve gone through all the ups and downs of the rockstar life still sticking together to do what they like.

2025 in reads

Mon, 29 Dec 2025 14:18:14 +0100

It’s that time of the year (again). Last year was the second time and, if I manage a third one this year, we can start calling it a tradition. And I quite like traditions like this. Before we get into the good stuff, one clarification. The sharp eye will have noticed I did drop “book” in this year’s title. This is all but unintended. In part, it is a bit of ego caressing¹, in part it might be something a little more meaningful. Earlier in the year, I signed up for Reader and, later on, I picked up an Onyx Boox. All of a sudden, the whole of the internet was presented to me like a book I could take to bed without distractions. That’s taken its toll on actual books², but it’s not been for nothing. I have read more long-reads and discovered more perspectives than I’d have been able by focusing only on books. So, this year, I’m giving them a spot on my review by highlighting the ones that have made a mark as of December (way too many to list wholly, but you can see a sample in my link blog). With that out of the way, enough of intros, links and footnotes (you can see a full list of books and links at the bottom), let’s do it.

This year, there’s not been a clear winner on non-fiction, like last year. There’s been a few really good ones that I’ve returned to in a less obsessive way over the subsequent months. I greatly enjoyed What if we get it right? and it might be one of the few instances where I think the audiobook is probably a better medium. It’s set up as interviews with a bunch of people from all corners of life on what “getting it right” with the climate would look like. The audiobook contains the actual interviews, not a readout of them. Not all of them touched me in the same way, but enough of them made me think in slightly different ways about climate change. I also really liked How infrastructure works. It’s less of a thrilling read, more of a slow, boring in the best possible sense of the word (calm, undistracted) one. If you stick to it and get past some aspects of the tone, there’s plenty to like and learn on the other side. One of them was the insight of how the challenge in infrastructure for the XXIst Century is going from a world where resources felt unlimited but energy limited to one of “infinite energy and finite materials”. And the core message of the book is also one to retain with you: that infrastructure is, at its best, shared care, humanity coming together to care for each other at scale. Try to find that elsewhere.

It was also a good year of fiction. No five stars but plenty of fours. I got around (pun intended!) to read Orbital and thought it was very well written but, perhaps, not more. I liked Delta-V because it came at a time where I was really in the mood for good, “hard” sci-fi, it’s pretty much that and it delivers at it. And speaking of traditions, I returned to the Slough House series for my yearly fix, ahead of the TV version. All in all, most very satisfactory, almost none much of a surprise. The closest I came to it was Bluebird, Bluebird, which was a new author to me and a new detective series set in contemporary East Texas. It was a nice mix of good detective execution and a great context setting in terms of race, culture and idiosyncracies of that corner of the world.

And now for the ”new” medium, the Internet. According to Reader, I’ve highlighted (my measure of whether a text had anything I wanted to remember or not) 262 items this year. Many of them were technical, irrelevant by now (AI!) or not that memorable. A few of them have managed to stick to my brain in ways that have surprised me. About half of them were about AI in one way or another, there was sooo much of it this year, some of it was even good! I read a lot of Cory Doctorow, so it’s no surprise something from him made the list. I’m a bit saturated of “enshitification” at this point, but his new big idea, Reverse Centaurs and how AI is being designed to be helped by humans rather than the other way around is as interesting as it gets, I imagine we’ll hear more of it in 2026. I finally got around reading (listening to!) AI as Normal Technology and can confirm all the chatter around it is justified. I enjoyed particularly the first of the three parts, which focuses on the economics of the technology and how it will (or not) spread throughout the economy. This is econ 101 but, sometimes, you “just” need the basics well understood. Speaking of understanding well, Neal Stephenson has some thoughts too (and a new newsletter!). From his post on AI I took the “every augmentation is also an amputation” quote (which was actually from Marshall McLuhan), and it’s returned to me every time I read a new shiny app feature description that promises to make me so much more productive. Harper Reed is one of the few folks occupying the shared space in a Venn Diagram with people excited about AI and people I like and respect, and his “codegen” hero’s journey is equal parts insightful and fun(ny). A bit of a technical one, so indulge me but, if you are into satellites, the TerraWatch newsletter is a treat in your inbox. His recent take on why privatising basic Earth Observation is a terrible idea is a good entry point. To wrap up, two very different and very personal reads that involve absolutely zero AI or technical stuff. I don’t know how I came across Mike Monteiro, but his newsletter has stuck in my read digest. I imagine it’s the combination of honesty, rawness and the occasional insight. His recent post about flying to bury his father (after a very complicated history) is all those three condensed and multiplied by several x’s. And the final one, a very apt one at that, is by Craig Mod. I’ve read a lot of him (no less his phenomenal book on walking in Japan) this year, so it’s also no surprise he makes the list. This one is a retrospective on the house in Tokyo he’s selling. But it’s really a meditation about all the time he’s passed there, what’s meant to him, and how the house was both context and sometimes even content to much of it. It’s beautiful.

And that does it for this year! If you(‘ve) read any of the above, remember next time you see me one of the few things I like more than reading is talking about reading. If there are others I should read, hit me up with recommendations. Life’s too short to fill it with mediocre reads.

Fiction

Delta-V by Daniel Suarez
Rose/House by Arkady Martine
La asombrosa tienda de la señora Yeom by Kim Ho-yeon
Foundry by Eliot Peper
Simplicity, by Mattie Lubchansky
London Rules by Mick Herron
Orbital by Samantha Harvey
Bluebird, Bluebird by Attica Locke
Ice’s End by P. Finian Reilly

Non-Fiction (* for audiobooks)

What If We Get It Right?* by Ayana Elizabeth Johnson
How Infrastructure Works by Deb Chachra
A City on Mars* by Kelly and Zach Weinersmith
The Anxious Generation* by Jonathan Haidt
Techno rebels - The renegades of electronic funk by Dan Sicko
China in ten words by Yu Hua (with translation from Allan H. Barr)
Mood Machine* by Liz Pelly
The Illegals by Shaun Walker
Things Become Other Things by Craig Mod

Internet reads

The Reverse-Centaur, by Cory Doctorow (and my post on it)
AI as Normal Technology, Arvind Narayanan and Sayash Kapoor (and my post on it)
Remarks on AI from NZ, by Neal Stephenson
An LLM Codegen Hero’s Journey, by Harper Reed
Why “Science-as-a-Service” doesn’t work for Earth Science by Aravind Ravichandran (and my post on it)
Studio Goodbye, Studio Hello, by Craig Mod
How to bury your father, by Mike Monteiro

According to Goodreads, I have, after all, read less books and less pages than last year. To be exact, 5,751 instead of 6,671, and 18 books over last year’s 19. I need to tell myself this is for a Reason! ↩︎
For the record, I do think there’s value in actual books. The general criticism of “kids these days” is that they take too many pages to deliver the message. That’s the whole point! They force you to make space to think about an idea in ways you can’t with almost any other medium. ↩︎

🎧 The Vergecast on RAM

Fri, 26 Dec 2025 23:23:58 +0100

Good overview of the forces behind the spike in the price of RAM, and some musings on what’s ahead (spoiler: it’s not great).

The Vergecast RAM Holiday Spec-Tacular

🔗 Pluralistic: The Reverse-Centaur’s Guide to Criticizing AI (05 Dec 2025)

Fri, 26 Dec 2025 21:45:22 +0100

Metadata

Author: Cory Doctorow
Category: rss
URL: pluralistic.net/2025/12/0…

Highlights

Start with what a reverse centaur is. In automation theory, a “centaur” is a person who is assisted by a machine. You’re a human head being carried around on a tireless robot body. Driving a car makes you a centaur, and so does using autocomplete. […] And obviously, a reverse centaur is machine head on a human body, a person who is serving as a squishy meat appendage for an uncaring machine.

Obviously, it’s nice to be a centaur, and it’s horrible to be a reverse centaur. There are lots of AI tools that are potentially very centaur-like, but my thesis is that these tools are created and funded for the express purpose of creating reverse-centaurs, which is something none of us want to be.

🔗 Why "Science-as-a-Service" Doesn't Work for Earth Science

Thu, 18 Dec 2025 16:54:05 +0100

Very important, if sobering, piece on the TerraWatchSpace newsletter on why “handing off to industry” is not a great idea for Earth Observation (EO) for basic science. In some ways, I see parallels with the discussion in the social sciences around how traditional sources (think decadal censuses, but also large surveys, etc.) could potentially be replaced by new sources such as mobility from phones or, for that matter, modern uses of Earth Observation. Don’t get me wrong, I am more excited than most about the potential of new data in the social sciences (imagery in particular!). We do need more data than a drop every ten years to not fly blind through everything that happens between release points (which is a lot). The bit that makes me very uneasy here is the replace, rather than complement. Without the census, satellites and phones are fairly close to useless for social scientists, and the reasons are very similar to why commercial EO needs large, public, and free programmes like Sentinel and Landsat.

Metadata

Author: Aravind
Category: rss
URL: newsletter.terrawatchspace.com/why-scien…

Highlights

Jared Isaacman, President Trump’s nominee for NASA Administrator has articulated a compelling vision: “NASA needs to constantly be recalibrating to do the near impossible, what no one else is doing - and the things they figured out, they hand off to industry.”

Earth Science Data Is Infrastructure, Not a Service

Infrastructure requires institutional commitment that transcends market cycles and political administrations. It requires transparency, neutrality, and guaranteed long-term access. It requires optimization for societal benefit rather than profit margins.

Tue, 16 Dec 2025 11:37:08 +0100

Just in time for a cozy listen this coming break, a new #GLaDpodcast episode! This time, we welcome the one and only Serge Rey to talk all things open, open source, and academia! Come for the code, stay for the stories of lives changed!

Tue, 16 Dec 2025 11:32:38 +0100

With all the rush last month, I forgot we released a new #GLaDpodcast episode. We finally gave in to the GeoAI craze and went straight to the source. Join us for a conversation with Krzysztof Janowicz, who’s been laying the grounds for what today is called “GeoAI” for longer than you can think of!