Dani Arribas-Bel Profile Photo

Dani Arribas-Bel

Computers, Cities, Data

Links

    ∞

    🔗 How Markdown took over the world

    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

    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.

    ∞

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

    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

    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.

    ∞

    🎧 From Data Dump to Data Product

    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

    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

    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

    Highlights

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

    1. 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.

    1. 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

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

    Metadata

    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]

    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.

    ∞

    🎧 The Vergecast on RAM

    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)

    Metadata

    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

    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

    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.

    ∞

    🎧 The Verge wrap up and forward

    Two end-of-year linked episodes of the Vergecast are good fun:

    Both are Joanna Stern from the Wall Street Journal, and The Verge’s very own Nilay Patel and David Pierce. This is end-of-year podcasting at its best. It got me thinking what’d be my boring, mild, and spicy predictions for 2026, though not sure I have much to add. Perhaps for another one in GLaD…

    ∞

    🔗 World Urbanization Prospects 2025

    Cities are (still) a pretty cool thing…

    Metadata

    • Author: Population Division of the United Nations Department of Economic and Social Affairs (UN DESA)
    • Category: pdf
    • URL: population.un.org/wup/asset…

    Highlights

    1. The world has become increasingly urban; more people live in cities today than in towns or rural areas.

    2. The number of “megacities” (10 million inhabitants or more) continues to grow; over half are in Asia.

    3. More people live in small and medium-sized cities than in megacities; many of these smaller settlements are among the fastest growing, especially in Africa and Asia.

    4. Growth of the world’s city population between now and 2050 will be concentrated in seven countries.

    5. City population growth is uneven; most cities are growing, but thousands have shrinking populations.

    6. Towns are home to more than a third of humanity and are critical for sustainable development.

    7. As the world’s rural population approaches its peak size, it faces unprecedented challenges.

    8. The expansion of built-up areas is outpacing population growth worldwide.

    9. The Degree of Urbanization methodology reveals the world is more urbanized than national statistics suggest.

    10. Sustainable development requires integrated planning that treats cities, towns and rural areas as interconnected and interdependent.

    ∞

    Metadata

    Highlights

    If I’m right, this spending is unsustainable. I was in the tech industry during the dot com boom, and I saw a similar dynamic with Sun workstations. For a couple of years every startup needed to raise millions of dollars just to launch a website, because the only real option was buying expensive Sun servers and closed software. Then Google came along, and proved that using a lot of cheap PCs running open-source software was cheaper and much more scalable. Nvidia these days feels like Sun did then, and so I bet over the next few years there will be a lot of chatbot startups based on cheap PCs with open source models running on CPUs. Of course I made a similar prediction in 2023, and Nvidia’s valuation has quadrupled since then, so don’t look to me for stock tips!

    Nobody ever got fired for buying IBM, and nobody’s going to get fired for investing in OpenAI.

    ∞

    🔗 The climate action that matters is in the global south, argues an architect of the Paris agreement

    Metadata

    Highlights

    The era when American politics could make or break global climate co-operation is over. The world is no longer waiting for Washington. This time the global south is leading the way.

    ∞

    🎧 The company at the heart of the AI bubble

    This is a very interesting look into the more physical aspect of AI (data centers) but also to the even more ethereal one (finance) behind the recent spectacular growth, and whether it is a good (economic) idea.

    podcasts.apple.com/gb/podcas…

    ∞

    🔗 The City As Text

    Neat paper by a great gang.

    Metadata

    • Author: Jonathan Reades, Yingjie Hu, Emmanouil Tranos, Elizabeth Delmelle
    • Category: pdf
    • Document Tags: paper
    • URL: www.nature.com/articles/…

    Highlights

    This Review seeks to ground this opportunity in an introduction to the kinds of text and tools available to researchers, providing examples of the state of the art in urban research while contextualizing these applications in the broader framework within which this interest in textual data evolved.

    ∞

    🔗 AI as Normal Technology

    Finally made it to read (listen) to this one, after seeing it referred to by seemingly everyone whose views on AI I respect. Well worth the time. Particularly the first part (regulatory and risks leave me a bit colder), it provides such a useful framing to about AI as a rather than the technology.

    Metadata

    Highlights

    AI as normal technology is a worldview that stands in contrast to the worldview of AI as impending superintelligence.

    […] we assume that, despite the obvious differences between AI and past technologies, they are sufficiently similar that we should expect well-established patterns, such as diffusion theory to apply to AI, in the absence of specific evidence to the contrary.

    ∞

    🔗 Beyond the Machine

    Metadata

    Highlights

    Thinking of AI as an instrument recenters the focus on practice.

    In other words, instruments can surprise you with what they offer, but they are not automatic. In the end, they require a touch. You use a tool, but you play an instrument.

    We may not be in AI winter, but I am hoping for an AI autumn. Autumn is amazing; the air cools, the mania of summer dissipates, things slow down.

    Here’s Eno again from earlier this year: “I can see from the little acquaintance that I have with using AI programs to make music, that what you spend nearly all your time doing is trying to stop the system becoming mind numbingly mediocre. You really feel the pull of the averaging effect of AI, given that what you are receiving is a kind of averaged out distillation of stuff from a lot of different sources.” An average email or line of code is fine. Average art isn’t.

    ∞

    scikit-learn has always been an example in many ways of how to do many things well (apis, open source, community). One more reason to the list.

    Metadata

    Highlights

    And two decades later, we have won. Open source is everywhere. Statistical algorithms raise billions of dollars. But what good will this free software, these algorithms, have been if an Elon Musk can buy their vector of action and transform it into a fascist machine. This victory is bitter.

    And it is these battles that today’s medal rewards. I have always been wary of individual distinctions. Success is rarely the work of a single person. We need more collective effort and fewer heroes, less ego.

    ∞

    🎧 How Silicon Valley enshittified the internet

    Man, Cory Doctorow is on fire on this one.

    —-

    Decoder: How Silicon Valley enshittified the internet, Oct 30, 2025

    podcasts.apple.com/gb/podcas…

    … “And so, you know, one day Mark Zuckerberg arises from his sarcophagus and says, harken to me, brothers and sisters, I know I told you that the future was arguing with your most racist uncle using this text interface, but actually, I’m going to transform you and everyone you love into a legless, sexless, low-polygon, heavily surveilled cartoon character so I can imprison you in a virtual world I stole from a 25-year-old cyberpunk novel. I call it the metaverse, right? And that’s end stage enshittifcation, the giant pile of shit.”

    ∞

    🔗 Why You Should Write Every Day (Even if You’re Not a Writer)

    Metadata

    Highlights

    When you write, you can’t handwave. You can’t bluster and obfuscate your own ideas into oblivion. When you’re alone with a blank page, there’s nobody to rescue you with a charitable interpretation.

Older Posts →