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Dani Arribas-Bel

Computers, Cities, Data

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    🔗 An LLM Codegen Hero's Journey

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    There’s so much interesting stuff on this piece, it puts a lot of “tangible” into this new way of software engineering. Interestingly, I don’t know how much this workflow translates into data science, where one often needs to get deep into coding to be able to fully define it (aka write a good prompt). But, paraphrasing this piece, maybe this is me being a bit of a “boomer data scientist”…

    Our brains are often ruined by the rules of the past.

    You will start coding defensively:

    • really hardcore test coverage

    • thinking about formal verification

    • using memory safe languages

    • choosing languages based on compiler verbosity to help pack the context window

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    🔗 The End of Programming as We Know It

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    In each of these waves, old skills became obsolescent—still useful but no longer essential—and new ones became the key to success.

    When there’s a breakthrough that puts advanced computing power into the hands of a far larger group of people, yes, ordinary people can do things that were once the domain of highly trained specialists. But that same breakthrough also enables new kinds of services and demand for those services. It creates new sources of deep magic that only a few understand.

    In short, there is a whole world of new software to be invented, and it won’t be invented by AI alone but by human programmers using AI as a superpower.

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    🔗 Generative AI

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    new technologies are now invented so quickly that it is no longer useful to separate them out from one another. They crowd into each other, disrupting what already exists, only for many of them to come back to regenerate and rekindle what has already been invented.

    These systems are moving science from being theory-led to data-led, from deduction to induction, although this does not mean that theory is being dispensed with, only that new ideas can emerge and converge from any or both of these directions.

    generative AI is uniquely suited to designing solutions which improve the human condition, in our own case the quality of life, the sustainability and the prosperity associated with cities. In a previous editorial (Batty, 2024b), I sketched out how generative AI was an early theme in the development of configurational statistics, shape grammars, design methods, pattern languages, and related optimisation models which we published in this journal. In fact, this is likely to herald a revival in ideas about design coming from this area in the next decade.

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    🔗 “Wait, not like that”: Free and open access in the age of generative AI

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    But the trouble with trying to continually narrow the definitions of “free” is that it is impossible to write a license that will perfectly prohibit each possibility that makes a person go “wait, no, not like that” while retaining the benefits of free and open access.

    Often by trying to wall off those considered to be bad actors, people wall off the very people they intended to give access to.

    The true threat from AI models training on open access material is not that more people may access knowledge thanks to new modalities. It’s that those models may stifle Wikipedia and other free knowledge repositories, benefiting from the labor, money, and care that goes into supporting them while also bleeding them dry. It’s that trillion dollar companies become the sole arbiters of access to knowledge after subsuming the painstaking work of those who made knowledge free to all, killing those projects in the process.

    This isn’t just about strain on one individual project, it’s about the systematic dismantling of the infrastructure that makes open knowledge possible.

    Instead, we must ensure that mechanisms are in place to force AI companies to engage with these repositories on their creators' terms.

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    🔗 The rise of ‘Frankenstein’ laptops in New Delhi’s repair markets | The Verge

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    India’s largest e-waste hub — becoming a critical way to source spare parts. Seelampur processes approximately 30,000 tonnes (33,069 tons) of e-waste daily, providing employment to nearly 50,000 informal workers who extract valuable materials from it. The market is a chaotic maze of discarded electronics, where workers sift through mountains of broken circuit boards, tangled wires, and cracked screens, searching for usable parts.

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    🔗 Angelina Jolie Was Right About Computers

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    Without closed source, proprietary Big Tech, there’s no open source, free-for-all Little Tech. Don’t listen to the techno-hippies who claim otherwise; that’s always been the case.

    RISC-V has already done what many thought impossible and made a sizable dent in Arm’s and Intel’s architectural dominance. Everyone from Meta and Google and Nvidia to NASA has begun to integrate it into their machinery.

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    🔗 Satellites are getting too good for forest carbon?

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    There’s a letter in Science today from a bunch of well known remote sensing researchers that make the unusual point that modern satellite resolution is getting too good to be accurate for forest carbon estimation.

    but this resolution (0.3-5m) is too high for mapping forest carbon. Forest carbon has a natural resolution constraint: the size of an individual tree.

    This is a great reminder that, sometimes, good spatial thinking trumps “better” technology. The insight applies much more widely than forest carbon estimation.

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    🔗 Formalising the urban pattern language: A morphological paradigm towards understanding the multi-scalar spatial structure of cities

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    urban patterns, the recurring configurations or arrangements of urban elements (Marshall, 2004), to reduce complexity and summarise the characters of the urban form.

    pattern language represents a set of guidelines or solutions for reoccurring design problems derived from historic and contemporary urban environments.

    urban landscape as a series of patterns of different urban elements at varying scales

    The urban pattern language conceptual framework is built upon two fundamental hypotheses

    firstly, that distinct patterns of different urban elements exist at various scales are not arbitrary but follow specific rules,

    and secondly, the rule or relationship between the diverse patterns is unique with potential as a reflection to the cities' particular background and needs.

    it is important to identify the scales of these patterns, whether the focus is on an overarching city blueprint or the intricate design of a specific neighbourhood

    The language could be interpreted as the rule or the relationship between the patterns coming together to form the solution.

    Note: This is an interesting way of summarizing the information on metrics across scales and the two cities.

    The quest to correlate urban form with emergent dynamic data has taken centre stage over the past decade.

    [Note: Good set of references of papers linking urban form with other phenomena to follow.]

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    🔗 Turning Numbers into News: the Economist’s Sondre Solstad on Data Journalism and Mapping the Ukraine War

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    Dr Solstad described how sometimes he starts from the question, asking himself what information is missing, why it is missing, and what he can do to get it

    he considers it his job to deliver information that is both useful to the reader and told in an engaging way which respects readers’ time and attention.

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    🔗 AI’s Future: Not Always Bigger

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    Are they supporting a small number of wealthy companies in Silicon Valley? Or are they open to a new army of software developers and software users? Are they a billionaire’s toy for achieving science fiction’s goal of human-level intelligence? Or are they designed to enable practical work that’s highly distributed, both geographically and technologically? The data centers you build so that a small number of companies can allocate millions of A100 GPUs are going to be different from the data centers you build to facilitate thousands of companies serving AI applications to millions of individual users.

    The big question, then, is how these models will be used. What happens when AI diffuses through society? Will we finally get “relentlessly human” applications that enrich our lives, that enable us to be more creative? Or will we become further enmeshed in a war for our attention (and productivity) that quashes creativity by offering endless shortcuts?

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    🔗 The Unbelievable Scale of AI’s Pirated-Books Problem

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    generative-AI chatbots are presented as oracles that have “learned” from their training data and often don’t cite sources (or cite imaginary sources). This decontextualizes knowledge, prevents humans from collaborating, and makes it harder for writers and researchers to build a reputation and engage in healthy intellectual debate.

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    🔗 Stitch by stitch, Rose Girone kept her family going

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    Her instructions for life remained the same. Anything you could fix with money was not a problem. Nothing was so very bad that something good couldn’t come of it. Don’t sweat the small stuff. Her recipe for longevity was good children (you had to be lucky with that. She had the best child in the world), and lots of dark chocolate. Most important, though, was always to have a plan.

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    🔗 Malka Older on narrative disorders in the not so distant figure. Late to the party, but this piece is so insightful, such an elegant way to bring a bunch of trends and seemingly disparate phenomena under the arc of narrative.

    firesidefiction.com/the-narra…

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    🔗 Cellophane Oasis

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    […] the “convenience stores” of those olden says, were far more romantic steppe oases, colorful caravanserais where sunburned merchants could swap stories and trade goods, as well as buy their camel fodder and egg salad sandwiches?

    Yet aren’t modern convenience stores much the same?

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    🔗 The Guardian reviews Nord

    www.theguardian.com/food/2025…

    It’s part Austin Powers shag palace, part Mos Eisley Cantina from Star Wars. It really is groovy, baby.

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    🔗 JupyterGIS

    blog.jupyter.org/real-time…

    Baking a bit of geo right on top of Jupyter “ is a great idea for both the Jupyter and geo worlds. Plenty of room to leverage the geo data science py-stack!

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    🎧 Writing a novel about, really about, climate change

    Interview with Steven Markley, author of my very most favorite book of last year. Pretty wonky and and insightful conversation on the next couple of decades for climate, politics, and society.

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    🗞️ Making Satellite Data Mainstream: Gaps, challenges, and opportunities for remote sensing experts [Industry Profiles and Activities] URL

    By Aravind Ravichandran on IEEE Geoscience and Remote Sensing Magazine (11 December 2024).

    Plenty of interesting points here. My favorite one is the focus on the boring but necessary aspects required to push adoption beyond the “usual suspects”. Very much an Imago theme.

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    🔗 Pluralistic: Apple's encryption capitulation (25 Feb 2025)

    Apple’s decision to disable a key security feature for UK users highlights the risks of prioritizing profit over customer privacy in the context of the controversial “Snooper’s Charter.”