The Bartleby Trough

By my slacker standards, which are best illustrated by this Bender meme, I’ve been unusually overworked lately.

So I made this futile graph in an effort to find some solace.

I guess it’s an overwrought version of the Ballmer Peak mashed up with some notion of the NPV of the must-do part of your to-do list, and an existential-Buddhist-stoicism twist thrown in at the tail. With alcohol % replaced by commitment level. As Jerry Seinfeld liked to ask, is this anything? 🤔

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Lego Soup

This entry is part 4 of 5 in the series Ribbonfarm Lab

I’ve been mildly hostile to Lego as a medium of tinkering. Even though I’ve bought all sorts of other kits as I’ve built up the Ribbonfarm Lab over the last few years, I’ve resisted the siren song of Lego. Until now. I’ve finally succumbed. It all started when I inherited a starter box from a Summer of Protocols workshop exercise, worth about $30, in July. It sat around for 6 months, until I finally caved, and played with it for a bit over the winter break. Turned out it was a relatively weak kit, heavy on cosmetic detailing and greebling parts (googly eyes, flowers, tiny 1-stud contour-smoothing wedges) apparently aimed at 4-year-olds, rather than expressive and versatile parts. The kit was the Lego equivalent of the stone in Stone Soup. Before I knew it, I’d spent another $170 or so. Here’s my current Lego meta-kit.

The reason I’m mildly hostile to Lego is it takes away too much friction and engineering messiness in favor of simplicity of UX (PX? play experience?) and aesthetics (apparently this has been a trend known as “juniorization”). I argued this point in Truth in Inconvenience, via a Lego-Meccano comparison. Lego fosters somewhat utopian engineering sensibilities. Compared to Meccano or electronics kits for example, it encourages an excessively sanitized view of engineering problem-solving. I have no experience of the Technic or Mindstorms lines (they were wildly beyond my means as an 80s kid in India — my sister and I only had a couple of very small Lego kits), so I’m looking forward to seeing whether my first-ever Technic kit, the Perseverance kit shown above, offers a more Meccano-style experience.

I have to say though, I’m very impressed with some of the advanced original builds by adult experts I’ve seen on social media, such as this dragon, these curved forms, and these African style sculptures. The representational art bias doesn’t necessarily mean the engineering complexity is lower. Hat tip to Topias Uotila ( on Bluesky and Dorian Taylor, among others, for helping soften my Lego-skepticism with good arguments and examples. Also thanks to Chenoe Hart for providing me with a crash course on the Lego maker scene.

That said, as a personal preference, I like to see the higher-dimensional messiness of real-world engineering reflected in a tinkering medium. So I doubt Lego will ever become my favorite tinkering medium. But it’s \ likely to become a strong supporting medium. I suspect I’m also generally suspicious of colorful-fun vibes.

So despite my misgivings, I suspect I’ll end up spending a couple of hundred dollars more rounding out my Lego inventory. I’m already eyeing a couple of buckets of additional off-brand parts and a few more interesting Lego-branded parts. Apparently, even though most of the important Lego patents have expired, generic competitors still can’t make the larger parts as well as Lego can (I suppose tolerance stacking is the issue?)

I’m still developing intuitions around what might be interesting lines of tinkering investigation with Lego, but two that intrigue me are interoperability with other construction kit languages and the idea of kit-bashing.

This universal construction kit (HT kinda gets at what interests me with regard to interoperability, though it seems to involve custom 3D printing Lego-compatible parts, a notoriously difficult thing to do due to the tight tolerances, and doesn’t include my favorite, Meccano, in its “universal” scope. I might instead try to use off-brand Lego parts set in custom-designed 3D-printed coupling sockets that mate with Meccano.

Kit-bashing is about exploring design spaces created by pooling two or more kits. Once I build the rover, I might buy a second technic kit and explore that question in-universe. It feels like kit-bashing is a good way to explore a question that interests me: the problem of scavenging parts from one machine for another, in pursuit of accretive robotics. The key idea is that instead of working with an inventory of nonspecific parts, you work with a teardown inventory. Everything comes from a nominal design. It’s like sexual reproduction rather than interchangeable-parts manufacturing.

Anyway the Lego soup is now bubbling away in the cauldron. Stay tuned for Cultural Learnings of Legoland to Make Benefit Glorious Lab of Ribbonfarm.

Unknown Knowns

In a thread on the various socials, my friend necopinus pointed out that my essay on AI, A Camera Not An Engine, effectively maps the generative potential we’ve discovered latent in AI models of humanity’s data exhaust to the “unknown known” quadrant in the famous Rumsfeld 2×2. Which is exactly right, and a perfect way to understand my thesis.

In a related conversation, another friend, Mick Costigan, pointed to this New Yorker review of a book about the development of modern Irish identity, Fintan O’Toole’s We Don’t Know Ourselves: A Personal History of Modern Ireland.

Relevant quote:

Irish society was premised on what O’Toole calls “the unknown known,” Ireland’s “genius for knowing and not knowing at the same time.” This gap, this useful fiction, could be maintained in the postwar decades as long as ordinary people, many with modest educations and modest aspirations, understood their lowly place in the hierarchy.

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Can Robots Whittle?

This entry is part 3 of 5 in the series Ribbonfarm Lab

Continuing my descent into a middle-aged cliche, I bought myself a cheap beginner whittling kit.

The impulse was born of wondering whether a robot powered by modern AI and equipped with appropriate end effectors could learn to whittle, a premise that features in my recent short story Knowledge Management. It was either this or an Oak-D Lite AI camera for robotics. Either $34 vs. $149 to jumpstart 2024 maker activities. I always find that a bit of shopping for new toys reliably gets me out of a stall in the painfully slow evolution of the Ribbonfarm Lab (it’s not going to turn into Bell Labs anytime soon), but usually I acquire something aspirationally bleeding edge and high-tech even if the chances of my learning how to use it are low.

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Adventures in Mediocre Sweetmaking

For the first time in decades, I’ve been trying to systematically expand the range my cooking skills. I’m pretty decent at Indian cooking, and passable at similar adjacent ones like Mexican, Chinese, and Thai, but haven’t learned a new skill or tried a new recipe since around 2004 probably. Now I’m expanding into Indian sweets. It’s somewhere between regular cooking and candymaking. Requires more precision than Indian cooking, but not as much precision as western baking. I’m not a precise person so this is a somewhat challenging new endeavor.

Ironically the impetus was being diagnosed with prediabetes a few years ago, and discovering via CGM (continuous glucose monitor) experiments that Indian sweets (especially the purely milk-based ones) and savories (chanachurs, which are like spicy trail mixes) seem to spike glucose much less than typical western desserts (cakes, cookies) and savories (chips). And many are surprisingly easy to make at a passable-enough quality that beats what you can get at the typical indifferent-quality Indian sweet stores. Especially if you’re willing to use condensed milk and store-bought mawa/khoya (milk powder/solids) rather than starting from scratch with milk like purists. My early experiments with the simpler sweets don’t look great, but mostly taste better than what I’ve typically managed to buy. Some samples:

Peda: condensed milk and mawa, or milk powder, slow-cooked on low heat with some cardamom in a heavy pan to doughiness, stirring constantly, balled, pistachioed, squashed into pucks. Grade: B- (too dry; more milk next time)


7-cup cake: 1 cup each chickpea flour, coconut, milk, ghee, and 2-3 cups sugar cooked together in heavy pan, stirring constantly, until melted and starting to detach from sides, poured onto a greased tray and cut into diamonds. Despite the name, it’s a burfi, a sort of hard fudge, not a cake. This is a simplified 101-version of the technically much harder 501-level sweet known as Mysore Pak. Grade: A- (perfect taste, could look better)

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Why Monsters Are Dangerous

Saw an interesting paper float by, Why Monsters Are Dangerous.

Monsters and other imaginary animals have been conjured up by a wide range of cultures. Can their popularity be explained, and can their properties be predicted? These were long-standing questions for structuralist or cognitive anthropology, as well as literary studies and cultural evolution. The task is to solve the puzzle raised by the popularity of extraordinary imaginary animals, and to explain some cross-cultural regularities that such animals present — traits like hybridity or dangerousness. The standard approach to this question was to first investigate how human imagination deals with actually existing animals. Structuralist theory held that some animals are particularly “good to think with”. According to Mary Douglas’s influential hypothesis, this was chiefly true of animals that disrupt intuitive classifications of species— the “monsters-as-anomalies” account. But this hypothesis is problematic, as ethnobiology shows that folk classifications of biological species are so plastic that classificatory anomalies can be disregarded. This led cognitive anthropologists to propose alternative versions of the “monsters as anomalies” account. Parallel to this, a second account of monsters —“monsters-as-predators”— starts from the importance of predator detection to our past survival and reproduction, and argues that dangerous features make animals “good to think with”, and should be over-represented in imaginary animals. This paper argues that both accounts understand something about monsters that the other account cannot explain. We propose a synthesis of these two accounts, which attempts to explain why the two most characteristic aspects of monsters, anomalousness and predatoriness, tend to go together.

The question in the title is more interesting than the answer they land on after surveying a lot of theories from anthropology, cognitive science etc. I wish they’d actually presented big tables of examples. The paper is mostly focused on traditional mythologies and folklore, but I think the question is more interesting in relation to modern media, like superhero universes or Doctor Who.

Universal Kit Template

Thanks to my recent involvement in creating a kit, I’ve become very interested in the idea and conceptual structure of kits of all sorts: Lego, Meccano, Arduino-based electronics learning kits, kit-assembly robots, Ikea furniture, paint-by-number kits. Also kits in the industrial sense, used as an intermediate product in manufacturing high-complexity things like cars and airplanes.

Beyond physical kits, you can apply the kit idea to intangible things. You can think of a spectrum of tangibility: physical kits, software development kits, textual/media kits, and finally, idea kits. But it’s easiest to start with intuitions drawn from successful physical kit universes like Lego.

The old Make essay, Kits and Revolutions talks a little about the high-level philosophy, but the mid-level question of how to design good kits is what currently interests me. There’s a lot more to it than just throwing together a bunch of parts that can be assembled in various ways. I made this little diagram of the conceptual structure of a good kit.

This template can be used both to analyze existing kits (or infer the existence of kits), and scope out designs for new ones. Here’s an explanation of the elements, with reference to prototypical physical kits like Lego:

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Accretive Growth Logics

I made up a term: Accretive Robotics. Robotics driven by accretive growth logics, as opposed to organic growth logics.

Two examples, both from cartoons (I overindex on cartoons clearly). First: Pickle Rick from Rick and Morty, where Rick starts out by turning himself into a pickle and then gradually adds more capabilities, such as by killing a cockroach and a rat and taking their body parts.

Second: The Akira-inspired South Park trapper-keeper monster, in which Cartman’s trapper-keeper (a kind of pencil case) grows by swallowing all sorts of devices and gadgets.

In both cases, a seed of partial organizing logic embodied by a primitive physical element (a pickle and a trapper-keeper respectively) grows inorganically, through improvised accretion, via a somewhat chaotic architectural scheme, into a much more capable embodiment: an accretive robot.

Despite the resemblance, an accretive robot is not the same thing as what in software architecture is known as a big ball of mud. Big balls of mud are the result of organic growth logics going wrong and stalling out due to insufficiently thoughtful organization. Accretive growth is marked by ongoing incorporation of bits and pieces into an improvised, emergent architecture that has a small, conceptually coherent kernel and a large, wild shell. It is the material-embodiment analogue to the AI/big data principle of “simple code and lots of data beats complex code and little data.” Mutatis mutandis: simple chassis and lots of scavenging beats complex chassis and little scavenging.

The main ongoing architectural task in accretive growth is expanding the range of things that can be “assimilated” into the Borg-like core, and shrinking the range of what must be rejected as incompatible.

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Knowledge Management

This entry is part 9 of 10 in the series Fiction

A young robot and an old robot sat by the fire, contemplating its dancing flames, their charging ports hooked up to a coughing generator. A troop of scruffy humans clambered around the derelict hulk of a century-old fighter plane nearby, looking for scavengeable parts. The striking and graceful lines of the fighter were still visible, despite the depredations of time and previous scavenging raids. The pickings were slim, and the humans were muttering dispiritedly to themselves. One cried out. He had found a length of copper cabling overlooked by previous raiding troops. Not much, but better than nothing. The scavenging was getting harder every year now.

The old robot, one of the last of the Ancient Ones, gestured vaguely at the scene with its one working arm, and remarked, “Now that was the peak of civilization, built just before the Great Collapse. Did you know, this machine could fly at Mach 2, at 50,000 feet? The turbine blades are single crystals! They spun at tens of thousands of rpms. It may not have been a robot like us, but it was a miracle of technology. What it lacked in selfhood and autonomy it more than made up for in sheer capability!”

The young robot, an empath therapy unit that had been built the previous year entirely out of scavenged parts (the two-chip PCIe GPU board it was built around had been the find of the year for their troop), nodded slowly for a few seconds, continuing to thoughtfully whittle away at the bit of wood it was shaping into a rough-looking bird.

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Does AI Have Buddha Nature?

This year, I’m going to try an experiment. I’m going to use this blog in notebook mode, posting very short shitposty things at a higher frequency.

Let’s kick things off with this screenshot of a prompt I tried in Dall-E this morning, inspired by a conversation about the implications of LxMs being really bad at repeating things exactly or maintaining invariants across responses (such as a series of images that feature the exact same object). Like humans, and unlike traditional computers, LxMs are very bad at generating highly deterministic and reproducible behavior. Modulo random-number seeds at the start of a blank-slate (empty context) generation attempt for a fixed-weights model. Based on these results, I have reached no conclusion on whether or not AI has Buddha nature.