Spot an error? Highlight the words or lines you want to fix — an “edit” button appears, and the panel opens with your selection ready to edit.
Will: All right, hello and welcome everyone to another episode of Waiting to Be Signed, a special interview episode. We are here with Trinity, of course. And today we're joined by Pierre Casadebeig. I hope I got that pronunciation close. How's it going, Pierre? How's it going, Trinity?
Pierre Casadebeig: Going pretty well, actually. Thanks for having me and taking the time for an interview.
Trinity: Thanks for joining us. It's always great to have new artists on, people we haven't spoken to before. And you're not exactly the classic p5 generative artist we typically talk to, so I'm really excited to dig into some of these questions. But Will, I know we have our standard intro question.
Will: Yeah, for sure.
Trinity: I'll give you the honors.
Will: I think if people are familiar with Pierre, they probably know he makes generative art, but he hasn't released any long-form generative art because the language he uses is R -- a statistical modeling language that just doesn't work in-browser. That excludes you from releasing on platforms like Art Blocks or fx(hash). But you have a Tender release coming up on Verse, which is perfect, since they enable pre-selected, curated outputs. Pierre, first, give us the correct pronunciation of your last name, and then introduce yourself -- your background in science, how you got into making art, and how you ended up plotting these wonderful mountains.
Pierre Casadebeig: You were pretty close -- the final G is silent, so it's Casadebeig. I'm not sure how common this is in digital and crypto art, but I have no real background in art beyond what I studied in school and whatever curiosity and research time I've had since. I studied plant science and data science, and I work in a lab trying to understand how crops function and how we can design more sustainable cropping systems. It's a very open lab, a mix of crop scientists and humanities people.
There isn't much direct link between my science background and my art -- maybe only that it's the same language, in a sense. I can use skills from work in my art, but it's actually quite hard to use what I know about plant biology to design art about plants, for instance. As for R itself and how it interacts with platforms: I was exposed to art through data visualization. Thomas Pedersen is well known in that space and did a lot of technical development for graphics -- he came from that same background. Aleksandra Dovančić and Nadieh Bremer also work at that intersection with data, even if not all their work is data-based.
The real limitation for platforms is that I need a bunch of data, and I can't easily get that data onto the blockchain or link to it through the web. That's what's kept me off fx(hash) and similar platforms. As for the language itself, I'm not geeky enough to know for sure, but I believe something like WebAssembly lets you embed different languages in the browser, so it's getting solved. It shouldn't be a permanent limitation.
Trinity: I don't think you quite answered this -- what specific type of plant science do you do?
Pierre Casadebeig: Imagine a crop -- sunflowers, say. You can study and improve them through experiments, but you could spend your whole life just sowing, measuring, and harvesting. Or you can move into modeling, and try to extend what you observe in the field into what could happen theoretically, using mathematical models. That matters a lot given the weather conditions we'll face from climate change and changing farming practices. So there's a strong link between data science, applied mathematics, and crop science. If any listeners have a biology background, it's a great field for this kind of application -- all my in-laws are biologists, actually.
Trinity: But they're more on the marine side of things, fisheries and the like. When you talk about exploring data and extrapolating what could happen -- what's within the realm of possibility -- it really seems to tie into algorithmic art. You start with a simple algorithm, and depending on the hash that's input, you get all these different potential outcomes in a system that can be more or less controlled. Interesting parallels there -- though maybe that's just what it means to work with code and information generally.
Pierre Casadebeig: Right, I think anyone with a background in an applied field -- architecture, systems thinking -- can carry that into generative art, because you're designing systems, designing inputs, balancing what's random against what's controlled. People from very different trades can get into generative art and have a lot of fun with it.
Will: You mentioned in your intro that you weren't sure how common your story was, but honestly, it's incredibly common among the artists we've talked to over the years. A lot of them came from other disciplines -- architecture is a common one, but also computer science broadly. People who already knew how to code, and at some point in their career thought, it might be fun to turn this skill into something more pleasing, more fun, more for me. So many artists we've talked to have only been doing this for one, two, three years -- don't sweat it, you're in good company in the Web3 world.
You mentioned your scientific work touches on environmental and climate change, and I think we can sense some of those themes in your art. How did that play into your decision to get into crypto and NFTs? A lot of artists we know started on Tezos specifically because it was seen as a "green" blockchain -- they had environmental concerns. Ethereum is proof-of-stake now, but there's still a lot of bad PR around crypto, and a few proof-of-work chains still out there. Was that a difficult consideration for you?
Pierre Casadebeig: Like a lot of people, I was mainly exposed to this through Twitter, browsing around. I was following Zancan's work, since he was doing plotter pieces with plants -- a big example for me, combining plotting, crops, and plants. He got into crypto quite early and started releasing work on Tezos and making money from it, so that was one big influence.
The other was Dan Catt. He documented his process very thoroughly -- "I made this many prints this month, I can sell this much, I won't quite break even, but I'll make a small profit." Little by little he started asking questions that resonated with me, like: when do you stop considering it a hobby, and start pushing more time into it as a practice? And of course, once crypto brought in some income, I could buy new things -- a plotter, a computer. Those two people were instrumental in convincing me to go into it.
It was only afterward that I learned about the environmental impact of different blockchains. Tezos has a different consensus system that seemed cleaner, or at least more defensible. But I don't think that was the biggest factor -- it was mostly that the Tezos community was easier to access at first.
Will: Definitely a good community there. Trinity, can you take the next question? I've got some noise outside my door, so I'll go back on mute.
Trinity: We've talked about your art, the science, the blockchain side -- I'd love to hear how you go from working with data to actually making art, especially given that your background is in plant science systems, but the work we know you best for on Verse is mountain ranges. How did you get from cornfields to elevation data, and everything in between? How did mountains become the subject?
Pierre Casadebeig: At first it was really about working with data. Elevation data is very easy to get a lot of -- it's extremely well documented, at least in France and Europe, because there's public funding to gather it for things like architecture, urban design, or flood prevention. So it's high resolution and accessible. And the process of reading that data, modifying it, and exporting a plot -- not a plotter plot, just a graph -- is really close to what I do for my job.
I also don't live far from a big mountain range, the Pyrenees, so it was tempting to take places I knew from hiking and turn them into graphs, and later into plots on paper. I never really considered plants for this. I actually had this conversation with Zancan -- he thought science could help design plant shapes. But scientifically, a field is just huge layers of green, like a big cake -- there's no realism to it. I don't need much precision to predict yield or function accurately. If I add too many details to the model, I end up with too many parameters, and I get them wrong. So you actually have to stay far from reality just to describe a field usefully. That made it useless for art -- there's no real visual output from those models, just numbers.
With mountains, I'm much more interested in incremental change than in making wildly different pieces from week to week. It started with an idea: take a surface from elevation data, cut it, analyze it, and shape the output to resemble my early mountain pieces, which were an imitation of Meridian by Manolo Gamboa Naon -- I really liked that and wanted to try to reproduce it. Once you do that, you start messing with the parameters. Some outputs end up much simpler, and you notice some stand on their own while others could be combined -- and that's how composition enters the picture.
Meridian — Matt DesLauriers
That became another branch of the algorithm: not just representing geography, but creating a synthetic, almost artificial place that still resembles a mountain, but carries more emotion, where you're more involved in shaping the algorithm's output. From there it's about how to lay these forms out on the page, questions of composition. That path led me to discover a lot of authors and guidelines on graphic design -- a whole domain I realized I needed to learn, and I found it fascinating.
Will: I imagine the decision to use data came from your background with R and your scientific experience. But during this exploration, did you ever think about it differently? Zancan makes plant-based landscapes without data, and plenty of other artists do mountains and landscapes without data too. Did you ever consider that you might get even wilder outputs by learning a different language, or by working without data at all? What role does data actually play in your work beyond just providing the foundation for the forms? And when you import data, is it one specific dataset, or are you pulling in the full historical record of mountain range elevations and doing statistical modeling to project where they might go, then playing with those parameters? Are we talking about erosion here? Are we talking about mashing up different datasets to get interesting forms? I'm curious about the actual process.
Pierre Casadebeig: I wanted to stay compatible with fx(hash) or Art Blocks and JavaScript, and there's a path where you lean into noise algorithms instead of real data. But that required a lot more work to get topographical landscapes that were interesting without being realistic. So I had a choice: go deep into learning how to make and modify noise, or work with real data. Even if you start with realistic data, you can import a huge chunk of mountains, sample different places, recombine them — and even though it's "your" material, it gets modified so much that it no longer resembles the original data. I was more at ease with that approach. I could stick with the data and still bring in a lot of randomness, even though it's deterministic at first. I hadn't even thought about the kind of erosion modeling you're describing — that could be another direction entirely.
Trinity: That's really smart.
Pierre Casadebeig: Great ideas.
Will: This is why people love the show. Another way you use data — I noticed this looking at Despair and some of your other projects — is that in addition to adding noise, you're discarding data. You start from a full set and remove parts of it. Is what's removed replaced algorithmically by code you've written, or is it just gone?
Despair — Pierre Casadebaig
Trinity: And is it the same data being removed every time, or does it vary per piece?
Will: How do you decide what to take out?
Pierre Casadebeig: The starting question was: how much data can I remove and still have something that reads as a landscape? I pushed it as far as I could to see whether I'd end up with just ridges, or something more realistic. What gets removed is random. When you start with something deterministic, like real data, whatever you add on top should lean fully into randomness — every level of the process should follow that logic.
You can scan the landscape and choose to remove only the lower parts — in bands, or paths — or keep only the higher path. That's the core of my work: sample a new place, then apply modifiers based on its features — removing, deleting, adding noise — and get a new output.
Some of these modifications were designed with a plotter in mind. If you just draw the true data, you get a very smooth line, which isn't very interesting, so you add noise to give the pen small irregularities, like real drawing or etching. Interestingly, in other cases the data was too rough, so you'd smooth it with statistics — or not — and instead use a fountain pen versus a brush. Because the brush's movement lags slightly behind the arm's movement on the plotter, it smooths things naturally. So you get this interaction: you design something at the algorithm level, but translating it to paper through the device adds something else, and it turns out you didn't need to add that at the code level at all. That's the research side of working with a plotter — it's not just a way to print or get your work onto paper, there's a genuinely strange interaction with how you design the code.
Will: It's fascinating — you incorporate data, noise, and other functions, and the final output still feels like a mountain. You start from a factual mountain and end up with a synthetic, algorithmically determined one, but looking at the plots behind you, they're still completely believable. That balance has to be intentional.
Despair — Pierre Casadebaig
Pierre Casadebeig: The one behind me is actually one of the first outputs — it spreads out into these abstract maps, almost like a true map. It gets very hard to know what you can remove. It's easy to add things, much harder to remove them well. That's what I was aiming for with the more brush-like pieces in the first drop.
Trinity: Going back to Despair — I know we want to talk about the upcoming collaboration, but this collection has so much variation built in. Was that variation intentional in the curation? Is it a function of different algorithms being used? How did you arrive at such a broad range of interpretations of mountains? Some pieces are almost photorealistic, some are very similar to Meridian, and Polyline Hiking barely reads as a mountain at all — it's pure interpretation. How did you get to some of these outputs?
Pierre Casadebeig: That was the goal for this work with Verse — I work with data, I make mountains, but with this algorithm the simplest output is the most realistic one, with a large number of lines. From there, depending on the sampling locations and the order in which you chain different functions — filter the dataset first and then remove data, or the other way around, then add smoothing — you get a whole range of variations, just from the parameters of those functions.
I could go very realistic, or, at the other extreme, get down to Polyline Hiking, which is just two lines per location. The idea was to sample 60 locations across the range and put them in columns — no link to geography, except that if you know the place, it's the Pyrenees, with the Atlantic Ocean to the east and the Mediterranean to the west. Cross a transect through the mountain range and you get a transect of altitude, so the first samples are very quiet, there's more noise in the middle, and it's quiet again at the end. That one's more abstract.
Polyline Hiking — Pierre Casadebaig
With Verse, I realized I could do all of that without it being "the same algorithm" — it's hard to even define what counts as one algorithm versus another. It's just functions patched together in different orders, using the same set of tools. For example, one piece is close to the first drop, with a more comic-style look and precise ridges in different frames — but for that one I chose the seeds manually, so which ones would work well together. The algorithm produced the output, but the layout was manual composition. It's like designing a postcard: you make a lot of decisions, but it's hard to articulate the rules behind why the text goes in one spot and not another.
That manual control is one thing for a single piece, but it gets much harder when you want each cell to be generative across, say, 100 outputs instead of a short series. That's the main challenge with the new release: how do you guarantee the iterations remain comparable, that the composition across different cells or pages still holds? I'm still learning this. There's a lot of theory in graphic design — grids and so on — but it's hard to know how to actually operate with it. In the end you use a grid and then rely on instinct. Code doesn't help you learn that part.
Will: Let's talk about the upcoming Verse drop — 128 pieces, in collaboration with Tender. It's been a long time coming; I think your collaboration with Adam was announced back when the Tender Pass first sold, at least eighteen months ago, if not longer. Walk us through that journey with Ridge Regressions. Were there a lot of starts and stops? How many times did you come close to releasing it before the plan changed? What have the last eighteen months been like working on this?
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: Maybe the key thing is that the generative art space moves so fast that if you're not putting out a lot of work, you constantly feel behind. When I first discussed this with Adam, we connected through a common collector who was kind enough to introduce us and say Pierre was interested in doing a release with TENDER. My goal was to do long form, which wasn't the norm for me but felt like the right direction. I said, I want to do that, I need to learn JavaScript, I want to use fx(hash), and I need to find a subject that isn't mountains, because of my dependency on data there.
I had a project on plants, actually, using a different algorithm that output different plant shapes. It's not very mysterious -- a lot of this work is based on mathematical sequences. One approach uses Collatz sequences, which divide or multiply numbers in a sequence, producing numbers that decrease but in a very irregular way. That kind of irregularity is close to what you see in plants -- in the internodes, the little sections of stem between the leaves, whose lengths are highly variable. So it seemed to have the right properties, and it was easy to code and get plant shapes out of it.
I started that in JavaScript, but then thought: I can only do plants, I'd like to add some writing too. My idea was to mix in some calligraphy and render the plant shapes in a very simple, minimalistic way, like calligraphy itself. So I needed to add other components, more graphics. And then you see how it goes -- you want plants and other components, and then it's, how do I put this on the page? Oh, that's not very nice. Maybe I could add some titles, paragraphs, and on and on. It wasn't successful, at least for me. It was missing contrast -- everything was lines, and it's very hard to bring in bold strokes that way. I was also struggling to learn a new language and kept missing what I already knew in R.
I think it was mid-2023 when we decided to just stop, and I said, okay, I'll refocus on my own language and what I know. That was it for that drop. I made some experiments and actually sent Adam a postcard -- one very simple piece done with a brush pen -- and he said, okay, we need to do that. So in this case, it wouldn't be long form. It seemed like that kind of curation wasn't necessarily better accepted -- though he was following the space much more closely than me. There were moments on Twitter where people argued that artists should embrace the digital medium and not imitate paper. But there was also a lot of pushback saying, we can do what we want. More recently it seems more accepted that each person can follow their own path -- with data or not, with a lot of curation or not.
So that, plus the technical possibilities with Verse, made us say, okay, let's go into that and figure it out.
If I can add some precision on why R is interesting to me: on the purely computational side, I'm not sure it's that different -- it might be slower than other languages, but I'm not certain even of that. The two things that really stand out are, first, its focus on functional programming. You don't often write iteration, or at least not using indices or complex calculus-style constructs. You just say, I want to apply a function over all the leaves in my stems, and it reads much closer to natural language. A lot of biologists gravitate to it for that reason -- you can write code, come back six months later, and still understand what you wrote. The other thing -- not little, actually significant -- is that there's a lot of theory in R around visualization, with a clear separation between the data and how you visualize it. Once you've coded the part that generates the data, you can try many different visualizations, and the code changes needed are tiny and very interactive. Both of those were things I missed when experimenting in JavaScript.
Ridge Regressions — Pierre Casadebaig
Will: That answers the question of whether you've experimented with other languages -- sounds like you did quite a bit. If you ever go back to that flower project, you could always consider releasing it under a pseudonym on fx(hash) and see how people take to it. Apparently it's not uncommon for artists to keep secret alt accounts, so keep that in mind.
Continuing on -- you mentioned the design elements you added for this piece. Something that comes up in a lot of the outputs is the paneling, the breaking up of the mountains into almost a comic-book-style composition. But there's also this asemic writing, which I don't think I saw in any of your previous work -- or if I did, I missed it. What role does that play in the theme and exploration you're going for with Ridge Regressions? Why the writing, and also the style -- there's quite a bit of variation, some with thinner plotted lines and some with really thick calligraphy, almost like a single stroke. I'd love to hear about all the different elements you've incorporated, their significance, and how you arrived at them.
Pierre Casadebeig: A broader thing I'm trying to learn is composition. Say you build different systems -- one generates plants, one generates writing, another generates mountains or decorative frames -- and at the end it's tempting to mix them together. The easy way is to ask, what would I do if I had these different elements and needed to put them on a page? That leads you into generative grid design. In this case, I first designed a grid and then tried to match elements into it. That's my basic solution for mixing different systems.
A more recent example is Golan Levin's Yazidographia drop. He explained that all the different parts and organelles of the cells are separate systems that he patched together within a broader system -- the cell. He'd start with two blobs, decide one should be smaller, leave a little space between them, add another element that fits accordingly, and so on -- a way of integrating different systems within a larger one, with each layer interacting with the others. He has a huge practice behind that, so he can figure it out at that level, but for someone still curious and starting out, layering things is the main thing I wanted to experiment with.
As for the writing -- once I had the mountains, I wondered how to bring in other ideas. Some outputs felt complete on their own, with very thin ridges and a lot of emptiness -- some of those could be rendered with a brush pen and stand thicker, standing on their own. Others needed to be complemented, or could at least support additional systems. My scientific background came back here too -- like scientific figures, where you have one figure, one legend, and by the time it's all typeset it looks beautiful, but there's a lot of work behind it that you don't see because the elements were carefully separated.
This also carries over from a previous project with TENDER, where I tried to make each page feel like part of a book. What would the story of a book be -- front pages, intermediate pages, and so on? It seemed like a good number of the outputs could support that kind of page treatment. Others could stand alone. One could support clouds. Again, these are like bigger branches -- variations, but not different enough that they needed to be separated into different drops.
Ridge Regressions — Pierre Casadebaig
Trinity: Looking at the previews on Verse right now, they're actually scans -- not digital outputs, but actual scans of the plotted work. Can you speak to the importance of having this physical element baked into the release from the start, rather than it being purely digital?
Pierre Casadebeig: I remember Dan Catt saying it was lazy, so I'll say that about myself too. But at one point during exploration, the interesting thing is that you code a line, and then when you change the device on the plotter -- using a broad or narrow nib, or a brush pen -- the line completely changes. It's a huge shortcut: you use the same code and get different shapes on paper. But if you lean into that, you lose the sense that the digital version still matches the paper one.
To go back to the Golan Levin example -- he actually coded the paper part too, so that's twice the work. It seems more sensible to me to avoid coding aesthetic effects to simulate ink on paper, and instead let the final output really be the paper itself. Some digital versions end up quite close to the scanned paper, and some are very far from it.
The broader Japanese woodblock print logic is also a big inspiration -- partly for the sheer aesthetic of simple elements and poems, but also for the model of production. From what I've read, there was a separation of roles: one person did the carving, another did the coloring, another handled distribution. I feel more at ease with that kind of system in generative art. The plotter does the printer's work, Adam and the platforms handle distribution. Otherwise, you'd have to be a full-time artist, and even then, having one person do all the layers isn't just difficult -- it's really, really hard, and it has to balance against work-life and family life. This kind of role separation suits me.
Will: I didn't put this in the notes, but I want to ask some market-related questions, because I heard from Adam that you're actually pretty keen on observing the market. You've said yourself that just watching generative art from the sidelines while working on this project, things move so fast. So even though you don't release a lot, you seem to have a good sense of what's going on.
To kick it off: you've only been doing this for a bit more than a year, and some of your initial one-of-ones sold for a lot of money. I don't know how the Ridge Regressions auction will pan out, but some people bought TENDER passes specifically because they were excited to collect your work. What do you think allowed you to break through all the noise so early on?
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: Yeah.
Will: Where do you think your success came from? Random luck? Offering something that looked different? It's on OBJKT, right -- you didn't even put your earliest work on fx(hash), where there are a lot of eyeballs. I find it can be hard to find things on OBJKT. Talk a bit about those early days -- what it felt like to say, I'm going to make some mountains, I'm a scientist but I'm going to make mountains, and then suddenly have people trying to spend thousands of dollars on them.
Pierre Casadebeig: Yeah, that's not at all fueling my impostor syndrome -- not at all. Joking aside, I think it would be interesting for other artists to hear an honest answer to this too, so they can feel more comfortable asking it. In my case, I think it's a mix of things. It's not pure luck, but there's definitely an algorithmic element -- getting retweeted by Zancan, being associated with him, maybe even unconsciously, since we both work with nature and have a similar profile. My posts got shown to some key people without me really doing anything to make that happen. So I put a good amount of that down to luck, without fully understanding the mechanics behind it.
The other part, and I don't have a clean explanation for this, is that representing nature speaks to a lot of people. It almost feels too easy from the artist's point of view -- you make a mountain, and it sells. If I did something abstract, I'm not so sure it would land the same way. So, a bit of both: luck, and nature speaking to a broad audience.
Will: Have you ever considered doing abstract work, or have you only explored these natural themes so far?
Pierre Casadebeig: Yeah, no, I'm very bad at it. Something I use to guide myself when watching my own output is an analogy with music: if you hear a catchy tune, you get excited immediately, but usually I notice that it doesn't get a lot of play over the years. So I try not to keep the things I'm very impressed by at first. I prefer things I understand slowly. In this case, I try to stick to that rule when choosing what to release.
Ridge Regressions — Pierre Casadebaig
Will: Gotcha. Trinity jumped off for a second, but she'll be back.
Pierre Casadebeig: No worries.
Will: The last year has been pretty brutal, right? You've had one drop on Verse, but you've been pretty quiet overall. Looking at your website, it's not like you have dozens of projects up there — it seems like you're very deliberate and intentional with how you release your work. Do you have a philosophy about this? Are you following platforms and seeing which ones have a lot of interest, or is it more a function of having a full-time job and a family, so it's done when it's done, and that's what dictates when you release something?
Pierre Casadebeig: Definitely the second option. When I realized I wanted this to be more than a hobby — in France, in research, you have some facility to take part-time positions — I dedicated one day a week to art and the other four to my job. So I work on art one day a week, and if I can manage two releases a year, that's perfect. That's really the only way to pace myself. It's also easy to not be satisfied with your work — you try a lot of things, you plot a lot of things, and there are more outputs and sketches on my website than there are NFTs.
You also mentioned crypto a while back. One effect I've noticed is that it creates a huge split between the open-source community — people making art on their own, exchanging tips — and people just using crypto to authenticate and distribute their work. The open-source crowd tends to associate that with speculation and can be quick to cut you off for it. So on my part, I decided to rely on very specific, timely releases and not put a lot of weight on NFTs. But the space seems to be getting better with the years. Maybe the crypto slowdown last year helped avoid a flood of PFP projects, and there's been a shift toward taking more time between releases, so it does seem to be evolving in a good way. A lot of people from the p5 community are also releasing on Art Blocks and elsewhere now, which feels like they're accepting crypto too. Definitely positive signals for me.
Will: Have you explored or talked to other platforms? I doubt Art Blocks would work unless you went back to JavaScript, but a platform like Tonic — I don't know if you're familiar with them — every release they do has a physical component. They even did a generative chair last year. Have you explored other platforms, or is it more inbound, people like Jamie from Verse or Adam from Tender coming to you? Do you just sit back and let the requests come in?
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: No, it's really about the people. I knew Adam first — he's very accessible, gives me feedback, and doesn't try to control the interaction too much. That's why I felt comfortable working with him despite not releasing much before. With Verse, I don't even remember if I contacted them or the reverse, but the fact that they proposed physical installations and an exhibition was reassuring — it was the first time my art was hanging on an actual wall, not just something on-chain. Those are the two reasons I've stuck with them, and I'm happy with that for now.
Will: What kind of notes or feedback did you get from Adam? We've had him on the show a couple times, and he's always reluctant to talk too much about his process. To the extent you feel comfortable saying — what did Adam bring to the collaboration that you really benefited from? Did he play a big role in helping you craft those initial p5 projects, get them into a state where you were happy? Did he influence Ridge Regressions in ways you wouldn't have thought of yourself? I'm curious what the collaboration was like for you.
Pierre Casadebeig: There was a difference between this project and the one that wasn't released. On that earlier one, he was very active, saying "this is working, this isn't," giving stricter and stricter guidelines. Eventually there was so much to explore that I just stopped and went back to my own language and expressions. On Ridge Regressions, it was more about reassurance — saying, okay, the stuff with the brush pen, simple as it is, works, don't worry. You always hit a point where you think your work isn't enough, especially seeing other artists progressing so fast, and you think, "I'm not going to release anything." So it helped a lot to have him say, "this path is working."
Ridge Regressions — Pierre Casadebaig
The other thing that helped a lot on the first release was curation. For each style, we generated about 500 outputs, and then went through a curation step shaped partly by geography. It was good to let go and have outside eyes selecting things — that was really valuable.
Will: I want to jump back to data. You've experimented a bit with plants, and said the data wasn't super useful there. You've obviously found a lot to uncover in mountain range and geological data. Do you have a running list of future data sources you might want to explore? What else might appeal to you, or what other potential is out there? I'm thinking of Thomas Lund Peterson, who talked on Ken's podcast last year about going out into the wilderness to collect electromagnetic field data — getting far from cities, basically trying to listen to the atmosphere. There seems to be so much potential here, but it's also a challenging thing for artists to work with. What else has come to mind as a potential area to explore?
Pierre Casadebeig: Yeah, sticking with data — I think it was Matt Delaury with that electro device.
Will: Yes, yes.
Pierre Casadebeig: One option is to go with more detail. There are lasers embedded in planes — LiDAR — that give very precise descriptions; you can even make out individual trees in a forest. That data has the same properties as what I'm working with, just elevation data, three-dimensional, but much bigger to read and slower to process. I'm genuinely trying to work with that. The problem for now is that with so much detail, the outputs get very realistic but not necessarily meaningful yet.
The other direction is what you mentioned — new sources of data. Scientific simulation models, for instance: you can simulate the level of stress on a crop field day by day for different crops, and that's also three-dimensional data. I haven't had time to explore it, but you could also use generative models that produce chunks of data, then patch my process onto that and explore from there.
Ridge Regressions — Pierre Casadebaig
Another option is something like global temperature data — thinking about what story you could tell with it. Maybe properties of the data could change with time, temperature, water level. I tried something with rising sea levels over time in the Pyrenees, but it came out a bit gloomy — I'm not sure it's something people want to easily see, or whether "time passes and we're going to flood" is too simple a message. So that one didn't quite work. But there are still many possibilities within the constraints of data.
Trinity: Cool.
Will: We usually wrap up with a few rapid-fire questions to get to know you better. One we like to ask: what do you listen to while you work? What kind of music do you like? And feel free to throw in any media recommendations — I actually really enjoyed a French film this year called After the Fall.
After the Fall — Mikkel Hartmann
Pierre Casadebeig: I don't know if you—not in it, but yeah, them too.
Will: It was really good, and a lot of it was in English, which was great for me. What do you like to listen to while you're coding?
Pierre Casadebeig: That's a tricky question — hard for me to say what my top ten is. But honestly, a lot of things. Mainly electronic music, ranging from mainstream like Radiohead to shoegaze, something like Moon Duo. I just start something, and if the music doesn't fit, I change it until I find the mood for the day or the coding session. Sometimes, not often, I'll use noise-canceling to just get silence and focus. But usually I'm coding with music.
Will: Gotcha — electronic music and shoegaze, those two come up a lot. There's something about coders liking that repetitive quality — it's there, but it's not distracting, and you can just chill and work.
Pierre Casadebeig: Yeah, it happens with classical music too — Steve Reich, for instance. I think the common pattern is: as soon as it's repetitive enough, with no lyrics you can latch onto, it hits the right spot of abstraction.
Will: Another one we sometimes ask, since you've made some money from your sales — any artists you collect that you want to shout out? Have you explored much on the collecting side?
After the Fall — Mikkel Hartmann
Pierre Casadebeig: I don't collect much. The key pieces I have are from Zancan, and from Emily Edelman — she makes a series called Asemica.
Will: Yes.
Pierre Casadebeig: And recently one from Golan Levin. Beyond that, the things I'm drawn to are just unreachable — Meridian, of course.
Meridian — Matt DesLauriers
Will: We actually just talked about the Golan Levin release last night, on our weekly episode where we cover new drops. I've seen a lot of big artists talking about how much they love that release, but when you look at the market reaction, it sold out and did fine, but underneath, I think, the value a lot of artists expected. Where do you think the disconnect is between artists who really appreciate it and collectors who bought it but now can't seem to sell it for a profit?
Pierre Casadebeig: I really have no idea what makes people collect and expect a profit, except maybe picking up on social trends and thinking, "lots of people seem into this, so there must be more demand than supply." As for collectors not being interested enough — it seems like huge formats, abstract work, and color are still very appealing. I'm not passing judgment, but I often picture huge abstract pieces on blank walls in huge houses — very appealing. Outside of that, I'm not sure what the mainstream of collectors wants.
Animated pieces are interesting, especially in this case, because Golan spent so much time animating all the little elements and making them interact with each other. But I have no idea how you'd display that. You could print them, but I'm not sure people get excited about having a "weird cell" on the wall. And digital frames — I'm not sure the technology is there yet to support that properly.
Will: I have a Samsung Frame, and it's very good at displaying static pieces if you put in the work to get them in the right resolution and size. It doesn't intelligently resize the art to fit, so it's actually a really annoying process to get your art up there. And if you want to run something live off the native browser, it's really bad—the best way is to mirror a computer up to the screen. We're still really behind on that.
Pierre Casadebeig: I'm not embracing it too much myself, but I can definitely see it becoming something. If there's development in how digital art gets shown—beyond the virtual gallery model—I think it'll draw more people in. I spoke a bit with Zancan about interacting with the community; he does that a lot to get feedback and pick up on trends. Even though I'm not into that myself, I think it's a really peculiar thing about Web3 art specifically that could be translated into the work itself. That'll be interesting to follow over the next few years.
Will: Looks like Trinity isn't coming back—she had a work emergency. Let's do one or two more and wrap up. Another question we like to ask: who would you like to hear us interview? Who would excite you as a guest on Waiting to Be Signed?
Meridian — Matt DesLauriers
Pierre Casadebeig: I'd need to read through the podcast's history to know better, but I've listened to a bit of Marcel Schwittlick. Did you ever get the chance to speak to Matt DesLauriers? I'm curious about him—he has some really striking work, and he's also very active in the open source community, giving a lot back while balancing that with crypto. I've never met him, and I'd love to hear him speak about it.
Will: Not yet—he's definitely on the list. Especially after hearing him on Ken's show, he sounds like a really interesting guy to talk to. We haven't reached out yet. Sometimes it takes us a few months to get warmed up and figure out how to build an episode—though we got yours done quickly. But yes, he'd be an amazing guest.
Pierre Casadebeig: Also Aluan Wang—he makes a lot of interactive art. He doesn't post reactions much, but when he does, I really enjoy his thoughts. I wonder how he sees the movement, since a lot of people here have been around since way before Web3. That's the hard part for newcomers: acknowledging all the development that came before, learning it, and finding our place within that. I'm very curious about the people who've been here a long time.
Will: We've both collected some of his work on fx(hash), but no, we haven't had him on either—he'd be a great guest. Thank you for those suggestions. Lastly, tell us what else might be coming up from you, other than Ridge Regressions, which comes out February 6th on Verse. Anything else you're working on that you want to get us excited about? I guess this is one of your two releases of the year, maybe?
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: Maybe, yeah.
Will: Maybe toward the end of 2024—maybe you haven't even started it. But if there's anything you want to tease, please go ahead.
Pierre Casadebeig: There's this thing called plant blindness. In a landscape, you hear a lot of animals—mammals, insects, birds—and think, okay, there's a lot of life here. But really, you're standing in a forest full of plants, and you just don't see them. I'm trying to stick with that idea—that we need to showcase more plants and find a good way to do it. I'm still trying to link plants and calligraphy, because I really like that aesthetic. We'll see—hopefully by the end of this year I'll have something out.
Will: Did you ever see the video of someone recording plant sounds—a plant making noise at a frequency people tend to ignore, but that you can actually technically hear? It seems similar to what you just said about people tending to ignore plants.
Pierre Casadebeig: Do you know what that is? There are a lot of different things happening. One is about water movement in plants—with the right devices, you can detect it, especially during drought, when bubbles form in trees. That's really just listening to them draw water from the soil, no special meaning behind it. The other is interaction with pollinators—leaves vibrate, and there's some kind of recognition based on sound. It's a tricky subject, because it's easy to anthropomorphize and say the plant "sings" or "communicates," when it might just be adaptation and randomness. I'm usually very cautious with this kind of news, but there are still ways living things communicate that we don't directly grasp. There was also the idea of mother plants and saplings exchanging signals—but when the research went further, they found no real link. They're connected, but there's no actual exchange of nutrients.
Will: I've seen so many weird plant science things on TikTok. There's one where if you put a plant in a room with random lights, the plant predicts where the light is going to be. Have you seen this one?
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: No, not at all.
Will: I'll find it and send it to you on Discord. I think they're trying to prove it's either plant intelligence or some kind of quantum link between plants and light—without training, they'll grow toward a future light source that's being determined randomly, with statistically significant consistency in picking right versus wrong. We can talk about that next time.
Pierre Casadebeig: Yeah, you can tell me if it's real or not.
Will: I sent it to my brother, who's a PhD, but I don't think he actually looked into the paper.
Pierre Casadebeig: In short, I think cybernetics originally was just a definition of taking in different signals, integrating them, and reacting—a form of reaction and intelligence. If you stick to that and say plants are interacting with different signals, there is a form of intelligence there, even memory. But if you push the idea too far, you get all these wild theories. Still, definitely a cool topic.
Will: Mushrooms are aliens, all that stuff. Well, Pierre, it's been awesome to have you on. We're really looking forward to your upcoming release on Verse, and to seeing something from you at the end of 2024 too. It's been great getting to know you—hope you enjoyed it. I think that's a wrap.
Ridge Regressions — Pierre Casadebaig
Pierre Casadebeig: Thanks so much, Will, and Trinity too. It was definitely enjoyable, and easier than I expected to explain all this. Thanks for the thoughtful questions.
Will: It's fun to talk science. That's it for this one—we'll be back soon with another episode. Thanks for listening, everyone. Bye-bye.
Pierre Casadebeig: Thanks a lot. Bye. Always listen—we're waiting to be signed.
Speaker A: All right, hello and welcome everyone to another episode of Waiting to Be Signed, a special interview episode. We are here with Trinity, of course. And today we're joined by Pierre Casadebeig. I hope I got that pronunciation close. I don't know. I actually should have asked you before the episode started. Yeah, that's fine. How's it going, Pierre? How's it going, Trinity?
Speaker B: Yeah, it's going pretty well, actually. Thank you very much. And yeah, thanks for having me and taking this time for an interview.
Speaker C: Thanks for joining us. It's always appreciated to have new artists on or people that we haven't spoken to before. And you're not necessarily the p5 classic gen artists that we typically talk to. So really excited to dig into some of these questions that we have for you. But I know that, Will, we have our standard intro question.
Speaker A: Yeah, yeah, yeah, for sure.
Speaker C: I'll give you the honors.
Speaker A: Well, I mean, I think if people are familiar with Pierre, they probably know that, well, he makes generative art, but he hasn't released any long-form generative art because the language that he uses is R. It's a statistical modeling coding language. That just doesn't work in browser, right? And so it kind of excludes you from being able to release things on like Art Blocks or fx hash. But you have a Tender release coming up that's going to be on Verse. Perfect, right? Because they enable you to do these pre-selected curated outputs. But yeah, Pierre, first of all, why don't you give us all the correct pronunciation of your last name and then also introduce us to yourself and tell us a bit about your background in science and how you got into making art and and plotting and these wonderful mountains that you've been making.
Speaker B: Yeah, actually you are pretty close. It's just the last G of my name is not pronounced, so it's Casadebeig. So, all right. Yeah, and I'm not sure how common it is in digital art or in digital and crypto interaction, but I have no real background in art except for what I studied in school and the big curiosity that is left here and time to do research on art. So I learned plant science and data science, and I'm working in a lab and trying to understand how the crops are functioning, how we can design more sustainable cropping systems. And in the lab, there is a mix between crop scientists and humanities, actually. So it's a very open lab. There is not so much link between the science background and the art itself. May only be because this is the same language. I learned from work and I can use for art, except that it's really hard to use things that I know in biology to design or to produce art related to plants, for example. And also when you mention R and the interaction of this language with a platform and with the arts, it's more like I was exposed to arts through data visualization. I think other people are maybe a bit in this trend, more Daniel Navarro, Thomas Pedersen actually is quite well known and He also did a lot of development, technical development for, for graphics. So he came from this background. Aleksandra Dovančić, Nadia Bremer also have this kind of interaction with data, even if not all their works are database-based. In fact, it was more this part which was limiting for platforms that I need a bunch of data and I couldn't include it in the blockchains or make links through the web and through this data. So this is more the limiting parts to release on fxhash or whatever. And for the language itself, I'm not geeky enough to know, but there is something like WebAssembly and it seems like you can embed different languages also in web. So it's getting done with error. So it shouldn't be the main limitations. Yeah.
Speaker C: I don't know if you provided this answer in there. I think it was somewhere, but what types of science do you do?
Speaker A: Yeah, specific plant science. What is it?
Speaker C: What specific types of plant science? What are you— what do you work on?
Speaker B: Yeah, you try to imagine a crop, let's say sunflower crop, for example, and you can study sunflowers and improve them by doing experiments, but you can end up doing that all your life, just sowing, measuring, harvesting. So You can go into modeling and try to expand what you see in the field to what could happen in theory using mathematical models. And so this is quite a big thing because all the weather conditions we're going to get because of the climate, because of the change of practice of the farmers. So there is actually strong links between data science or at least mathematics, applied mathematics and crop science. Could be also a way to pull, if there are any listeners into this background and saying that, hey, biology also is a really good field of applications in All my in-laws are biologists, so.
Speaker A: Oh.
Speaker C: But they're more on the marine side of things.
Speaker B: Okay.
Speaker C: And fisheries and things like that. But when you're talking about kind of that exploration of the data and kind of the extrapolation of what could happen, what is within the realm of possibility, it really seems like you can apply, and I'm making a big leap here, to kind of like the algorithmic art Where you start with, here's a simple algorithm and, you know, based off of the hash that's input into it, you could see all these different potentialities occur in a system that can be more controlled or less controlled. So really cool tie-ins there, but maybe that's just working with code and information overall. Very fascinating.
Speaker B: Yeah, I think that if you have some people have information in like an applied science, like architecture and also like system thinking. It kind of works for generative art because you can design systems, design a part of inputs, balance the parts that are random or controlled. So I think that in different trades, you could get involved in generative art and have a lot of fun on that.
Speaker A: You know, in your intro, you said that you weren't sure if your story was common, but I think it's actually incredibly common based on all the different artists that we've talked to over the years. A good number of them came from other disciplines. Architecture, like you said, being a common theme, but also just computer science in general. People who knew how to code and then somewhere along the lines in their career, they thought, oh, it might be fun to learn how to convert this ability, this skill into something that's more pleasing, more fun, right? Like a little more for me. Yeah. So many artists that we talked to have only been doing it for 1, 2, 3 years. So don't sweat it there. You're in good company here in the Web3 world. But, you know, you kind of alluded to some of your scientific work having to do with like environmental change, climate change, right? And I think we can sense some of those themes in your work. Perhaps we can talk more about that. But how did that kind of play into perhaps your decision to come into crypto and NFTs? I mean, so many artists that we know like started on Tezos, for example, because it was like a green blockchain. They had environmental concerns, but then obviously Ethereum now is proof of stake, but I think there's still a lot of bad PR around crypto and blockchains, and there's still a few proof of work chains out there. So was that kind of a difficult path for you? Did it come into play at all in consideration?
Speaker B: Yeah, in this case, I think for us, a lot of people, it was mainly exposed to Twitter and browsing. And I was following the work from Michael Zancan since he messed with the plotters and the plants. So of course it is a big Example for me because he has two really interesting things: the plotting and the crops and the plants. And he began quite early to go into crypto and to release something on Tezos and to get money from that. So yeah, I think this is one big thing. The other maybe would be like Dan Cat. He very thoroughly documented how he was proceeding with his art. He said, "Okay, I made so many prints per month. I can sell this much. So this month I'm not going to break even. I make a small profit." And then Little by little, he start to ask a question that resonated with me, like, when did you consider it's not a hobby anymore? That you are going to push more time into your trade, actually. And also, of course, the change by crypto and say, okay, I began to sufficient money to to buy new things, such as a plotter or computer. So yeah, these two people were very instrumental into saying, okay, I should go into that. And it just afterwards. that I learned about the interaction of blockchains and environments. And as you mentioned, yeah, Tezos has different consensus systems and seems very, not clean, but at least correct on that. But I'm not sure it is a big aspect. It's just that the community on Tezos was easier to access with at first. Yes.
Speaker A: Definitely a good community here. But Trinity, if you can take the next question, because there's some babies going by my door here. So I'm going to go back on mute.
Speaker C: You know, we've talked about your art, we've talked about the science, we've talked about blockchain. I'd love to hear about how you kind of come up with leveraging the input of data into some of the art, especially given that you work with plant science systems a little bit more. But the art that we know you the best for on Verse especially is with mountain ranges. Can you talk a little bit about how you get from cornflowers, to elevation changes and everything that goes in with that. How did you come across that as an interesting topic for art consideration?
Speaker B: I think at first it was working with data. So the one way to get a lot of data very easily is about this kind of elevation data. It's very documented, at least in France or in Europe. There are a lot of public fundings to get this information because it's useful to other people like architecture or design of the cities or whatever. develop flood prevention. So they're very high resolutions and accessible. And actually just the process to read data and to modify it and to export a plot, not a plot for the plotter, but just a graph, is really, really close to what I was doing for our job. So yeah, it seems like this part of data was really accessible. Of course, I don't live very far from a big mountain range, from the Pyrenees. So this is also tempting to just to More like cartography, to change the places I knew from hiking into graphs and later into plots on paper. Yeah, so I didn't consider plants on that. And on plants, I remember having a bit of this discussion also with Michael Zancan, that it seems like he also thought that science could help to design plants or shapes. But for science, I mean, a field is just like you imagine, like big layers of green things, like a huge cake. There is absolutely no realism. I do not need to be very precise to accurately predict the yield of the functioning, actually. And if I get too many details in the model, I have too many parameters and I get them wrong. So you have actually to be very far from reality to just describe the field. So it was absolutely not useful for making art because it, uh, I don't think for the— there is some scientific model where you can see the visualize the output of plants or the field. It's just numbers, actually. For the mountains, uh, The artist that that can make let's say works different from each other, from week to week or from month to month. I'm much more into incremental changes. Let's start from this idea. I get some surface from elevation data. I cut them, analyze them, and then get the output to resemble the early mountains I made, which was like an imitation from Meridian from Madelaurier. I really like this, so I really want to. Try to reproduce that. And then once you make that, you try to mess with the parameters. You end up with some outputs that are very much more simple. And you say, okay, some are standing by their own. Some could be mixed with each other. You try to get into composition. So this is another branch for the algorithm. And then imagine you can not only represent the geography, but more like a synthetic or at least artificial place that still resembles a mountain, but With more emotion and with more, you get more involved in what is actually the output of the algorithm. And from that you can say, okay, maybe I can put different on the page. How can I lay them out on the page? What about composition? And this is all this kind of path that is driving me. I have questions about composition and I just discover a lot of authors and guidelines on graphic design. I say, okay, this is a whole domain I need to learn. And actually that's very interesting.
Speaker A: I imagine the decision to use data came from your background with R and just your scientific experience. But as you were exploring this, did you think about— like, you mentioned Zancan, right, who makes his plant-based work and his landscapes but without data? And there's many other artists who do landscapes, mountains, and stuff without data. During this whole exploration, did you ever consider, like, oh, if these are the types of things I want to make, perhaps I could make even crazier, like, wilder outputs if I learned a different language, or even not even within R, but without using data. I mean, I guess I'm— what I'm trying to get at is like, what role do you think the data plays in your work specifically beyond just being a basis for like the foundation of the forms? And when you're importing the data, are you importing like one specific dataset or are you importing like all of the historical data of these like mountain range elevations and then trying to do statistical modeling to project where they might go and then kind of play with those parameters? So. Because I don't know, are we talking about like erosion here? Are we talking about mashing up different datasets and trying to just get really interesting forms? I'm just really curious to understand more about the process.
Speaker B: I try a bit not to use data to be compatible with fxhash or Art Blocks or JS, but you get into the like noise algorithms and you can work on that. But there was much more work needed to get some not realistic but interesting topographical landscapes. So I said, okay, either I go this way and learn a lot about how making noise and to modify and all. And the other thing is that even if you start with realistic data, as you said, you can import a huge chunk of mountains, sample different places, recombine them at the end. Even if it's your material, it gets modified, modified, and don't resemble too much the data. So I kind of thinking I'm more at ease with that and okay, stick with the data and I'm still can bring a lot of randomness with that, even if the, of course, it's deterministic at first. And I didn't even think about what you described about tracking some modeling to make erosion of modification. So yeah, this is, this could also be another—
Speaker A: There you go.
Speaker C: That's really smart.
Speaker B: Great ideas. Yep.
Speaker A: This is why people love the show. So another way that you use the data though, I saw when I was looking at your past work with Despair and for some of the other projects. So in addition to adding noise, you're discarding data, right? So you're starting from a full set and then removing it. So it's being removed and then is it being replaced algorithmically by some code that you've written or is it just being fully removed and it's gone?
Speaker C: And is it the same data being removed every time or is it like per piece?
Speaker A: So how do you decide, how does it work like when you're taking out data from the start?
Speaker B: Yeah, I think the starting question was how much data I can remove and still have some kind of landscape at the end. So we were just trying to push the button very far to see if I get some ridge only or more realistic landscape. And the part that is removed is random. Working with data, you start with a very deterministic thing and then the things you're going to add should be really into randomness. And so all the levels should be like that. So the, yeah, in this case, you can scan the landscape and find where you can remove only the lower parts, like in bands or in path or whatever, or only keep the higher path. So yeah, it's the core of my work is you sample a new place and then according to the features of this new place, you apply some modifiers like removing, deleting, adding noise, and you get a new output on that. Some modifications were designed having a plotter in mind for the end. Like if I just draw the true data, I have a really smooth line and it's not very important. So you just add noise so that the pen makes like small irregularities as you get if you were drawing or etching this. Interesting thing also is in some cases it was the inverse. The data was a bit too rough. So you can smooth it with statistics or not, but you smooth it. And then, for example, it's okay if you use a fountain pen, for example, and then you use a brush. And because the movement of the brush kind of lags behind the movement of the arms on the plotter, the Smooth things is really natural. So you get this interaction when you think about something at the algorithm level, and then because it was translated into paper with the device, it added another things, and it turned out that you did not need to add that at the code level. This is the research part with the plotter. It's not only a way to print, of course, but not only a way to get your work on paper, but There is some weird interaction with how you design the code on that.
Speaker A: It's so fascinating because you're doing this process, you're incorporating data, you're incorporating noise, and I'm sure other functions as well. But then the final output still feels like a mountain. You're starting from a factual mountain and then creating a synthetic or algorithmically determined mountain, but it's still, despite all that process, looks— I'm looking at the plots behind you, for example. They look absolutely believable. And I think that's got to be part of the balance, right?
Speaker B: The one behind me has actually one of the first outputs, like spreading and having these kind of maps with abstract things like a true map. But I'm sure a lot of people will say that, but it gets very hard to know what you can remove. It's easy to add things and not to remove that. So yes, this is for the more brush-like that are in the first drops. This is what I was Aiming for.
Speaker C: Going back to Despair, because I think that this is a really interesting piece. I know that we want to talk about your upcoming collaboration, but in this particular work, and you talked about different styles of visual interpretation for each piece, this collection, Despair, it has a ton of variation built into it. Was that part of the intention behind the, like, the curation? Is it because of different types of algorithms being there? Like, how did you get to such a broad range of visions of mountains. Some of them are just so like mountainous and like almost look photorealistic. Some are very similar. One is very similar to Meridian. Polyline Hiking has almost no relation to the visualization of a mountain. It is like very interpretive. Can you talk about like how you get to some of these particular outputs?
Speaker B: Stating it obviously, it was like my goal for this work with Verse and saying, okay, I work with data, I make mountains. But with this algorithm, the simplest output is the more realistic one with a large number of lines and kind of realistic. And then from that and the different sampling locations, if you patch different functions in different orders, first filters the dataset and then remove data or the other way, and then add smooth things, you can get a range of variations. And only the parameters of this different functions that are creating these different outputs. And yeah, it was some kind of description and say, okay, I can go very realistic. And at the end, I can only get 2 in the kind of polyline hiking, only 2 lines per location. So the idea was I just sample 60 locations in the range and then put them in columns. So it's no link to geography, except if you know the place, actually the Pyrenees, there is the Atlantic Ocean at the east and Mediterranean Sea at the west. So if you cross a transect to the mountain range, you get a transect of altitude so that the first samples are very quiet and there are more noise in the middle and then quiet at the end. This is more abstract. Yeah. So at the moment of Verse, it was like, okay, I can do all that with not the same algorithm. It's pretty hard to define what is an algorithm and what is not. It's just function and you patch them together in different orders, but with the same set of functions, the same set of tools, actually, I can do that. And then it was, for example, there is one which is quite close to the first drop where I can like more comic style and very precise ridges in different frames. But this one I choose manually which seeds could go together. So it was composed. It was an algorithm that produced the output, but then the layout and everything was manual. A bit like if you want to, let's say, to draw a postcard, you make a lot of decisions, but it's very hard to explain what are the rules behind that, why you are putting the text in these parts left behind the main design, for example. So it was easy to get manual control and to get, not easy, but to get these outputs. And if I want each cell to be more generative, not long series, but let's say 100 outputs, it gets harder. And so this is the main work from the recent release on this one. How can I guarantee that the different iterations could be more or less comparable? Does the composition between the different cells or pages are still valid? Also something I'm learning. There's a lot of theory in graphic design, but very hard to know how to operate. They talk about grids, lots of theory, but then at the end it's just use a grid and then you use your instinct to play the things, to play the things on the grid. So it's not helpful so much to, to learn code on that. But yeah, just learning on that also.
Speaker A: Well, let's talk about the upcoming Verse drop. It's 128 pieces. This is in collaboration With Tender, it's been a long time coming. Actually, I think that your collaboration with Adam was advertised in the very beginning when the Tender Pass was first sold, which was at least 18 months ago, if not longer at this point. So why don't you walk us through a little bit of that journey with Ridge Regressions, working with Adam? Were there a lot of starts and stops along the way? Was it like, how many times did you come close to releasing it? And then all of a sudden it's like, oh, we're changing the plan. Like, just can you kind of lay out for us what the 18 months or so has been like working on this?
Speaker B: Yeah, maybe the key things is like generative art place is moving so fast. So if you can't put a lot of work on that, you constantly feel behind. And so when I discussed with Adam first, we connect through a common collector. So it was very kind for him to present to Adam and say, okay, Pierre is interested to do a release and to push some work with TENDER. My goal was, okay, it was long form, which was not the norm, but at least very present. So I said, I want to do that. I need to learn JS. I want to use fx hash. And I need to find a subject which is not mountains because of the dependency of data. And so I had some project on plants actually with a different algorithm that output different shape of plants. It's not very mysterious. A lot of works are based on mathematical sequence. There is one which has Collatz sequences, which just divide or multiply numbers in a sequence and you get a sequence of numbers decreasing, but in a very irregular way. And this kind of sequence is very close to what you see in plants, in internodes, in the little portion of the stems between the leaves. Their length is very variable. So it seems like it had good properties to do that. So it was easy to code and easy to get shapes of plants on that. So I started on that a bit in JS. And then I say, okay, I can do only plants. I'd like to add some ratings. And my idea was to mix a bit of calligraphy and to render the shape of plants in a very simple way, minimalistic way, like calligraphy also. So I need to add other components like graphics. And then you see how it goes. You want plants and other components. Then you get, how can I put that on the page? Oh, it's not very nice. Maybe I could add some titles, paragraphs, and then on and on. It was not successful, at least for me. It was missing like contrast. Everything was lines. It's very hard to bring broad lines in that. I was also having difficulties to learn another language and kind of missing the thing I knew with R. Maybe I think it was mid-2023 last year where we just decided to stop and I say, okay, I refocus on my language and the thing I knew. And that was it for the drop. I made some experiments and actually I sent just a postcard to Adam, which was one very simple with a brush pen. And he said, okay, we need to do that. Okay. So in this case, it won't be long form. It seems like the curation was not better accepted, but probably following that much closer than me. But there are some places where some moments in Twitter where the people are like, okay, artists should size the digital medium and should not imitate the paper on other mediums. And then there is a bunch of feedback and say, okay, we can do what we want. And So it seems like more recently it was more accepted so that each person could follow his own path with data or not, with a lot of curation or not. So yes, that and technical possibilities with Verse could, okay, we go into that and analyze that. And if I can just add precision now on why error is interesting is that I think on the just computational stuff, I'm not sure it's very different. be slower than other languages, but I'm not sure for that. But the 2 main things that are very nice is a kind of focus of functional programming. And it means like you don't write too often iteration, or at least not using index, or at least complex calculus stuff. You just say, okay, I want, for example, I want to apply a function over all the leaves in my stems. And you, it's very closer to natural languages. So this is one thing that a lot of biologists are into that because they say, okay, I can code and still Read this code and even 6 months later, catch back and understand what I've written. And the other little thing is that, not little, but there is a lot of theory into visualization and a clear separation between the data and how you visualize it. So once you code the part that generates the data, you could try a lot of visualization and the change of codes are very, very tiny and very interactive. So both things I was missing here actually to experiment.
Speaker A: I mean, I think that answers the question of have you experimented with other languages, and it sounds like you did quite a bit. If you ever go back to that flower project, you could always consider releasing it under a pseudonym or something on fx hash and seeing how people take to it. It's, uh, not an uncommon practice we're learning that artists having alt accounts that they keep secret, so keep that in mind. So continuing on, you know, you were talking a bit about the design elements that you added For this piece in particular, and something that comes up in a lot of the outputs, are the paneling, the kind of breaking up of the mountains and almost doing a comic book style paneling execution on the compositions. But then also this asemic writing, which I was looking through, I don't think I saw in any of your previous work, or if I did, I missed it. But what role does that play in the theme and the exploration that you're trying to get across here with Ridge Regressions? Like, Why the writing and also the style, right? There's quite a bit of variation in the style. Some of them are more thinner plotted lines and some of them are just like really thick calligraphy, almost like they'd be just one single stroke. So yeah, I'd love to hear more about all the different elements you've incorporated, their significance, and kind of how you stumbled into them.
Speaker B: Maybe the broad things I'm trying to learn is about composition. Say you make different systems, one generates plants, one generates writings, the other generates mountains or decorations like frames and all. And, uh, and at the end, you, it's tempting to mix them together. The easy way is to get the, what would you do if you have these different elements and put them on a page? And then you get into this kind of grid and generative grid design. And, uh, in this case, I first, uh, designed a grid and then tried to match elements in this grid. So this is my basic solution to mix different systems, but, uh, maybe another example, recent example is the recent drop from, uh, Golan Levin about, uh, Yazidographia and, uh, And in his case, he explained that all the different parts and organelles on the cells are different systems. And he patched them together within a broader system, which is the cell, and solved some problem and say, okay, I start the cell with different 2 blobs, saying, and then one is smaller, or there is a little space between the 2, and I add one element, and then the next one could be fitted this way. So there is a way Also to integrate different systems within the larger systems, each layers interacting together. But hey, I think he has a huge practice, so he can figure this thing out. But when you not start, but you're curious, layering things out is the main things. So I wanted to experiment on that. And about the different writings, once you got the mountains, I wonder how to put some ideas. It seems like some outputs were very called by themselves. Because there are very thin ridges and all this emptiness. So some could be rendered with a brush pen and get more thick or stand by themselves. And other ones need to be complemented, or at least, yes, could support additional systems on that. Maybe in this case, the scientific background came back, but like scientific figures, you have one figure, one legend, and you try to— at the end, when it's all typeset, it seems all beautiful. There is a lot of work behind that and you only see one figure. Without really thinking, separating the elements. Yeah, so this is remains from a previous project with Tanner's when I tried to get into each page could be part of a book. But in this case, what would be the story of a book? Would there be like front pages, intermediate pages and such? So it seemed like a whole bunch of the outputs could support this kind of page output. The other one could stand out. One could support some clouds. So Again, this is like bigger branches, but not different enough so that it couldn't be separated in the drop.
Speaker C: And I think, you know, looking into this, the previews that are on Verse right now, they're actually scans. It's not digital outputs. It's like actual scans of the plotted work. Can you speak a little bit to the importance or the reasoning behind having this physical element really just baked into the release from the get-go? And it not just being something that's purely digital.
Speaker B: Yeah. I remember also Dan Catt saying it was lazy, so I'm going to say that also for me. But yeah, at one moment, uh, during explorations actually, uh, the interesting thing is that you, you just code a line and then when you change the device on the plotter using a broad or narrow nib or brush pen, the line completely changes. So it's, it's like a huge shortcut. You, you only use the same code and you get different shapes on the paper at the end. But if you do that, and I'm really willing to do that, you also lose the fact that the digital version is no match anymore for the paper one. So actually, to take again the example from Golan Levin's, he actually coded the paper part. And so it's twice the work. So yes, it seems more sensible to me to say my works try to avoid coding aesthetic things to render like ink on papers. So the final output is really the paper itself. Some digital are quite close and some are very far from the, from what you see from the scanned paper. And the broad Japanese woodblocks logic is also get very much inspired by that, in part because the sheer aesthetic of simple elements and poems, but also because of the model. From what I read, it was very interesting. There was separation between one people was doing the carving, the other one was coloring, and then there was some people doing the distribution and Yeah, I'm more at ease with the system in generative arts. For example, in this case, the Plotter is doing the printer work. Adam or platforms are more into the distribution. Otherwise, yeah, I'm sure you have to be a full-time artist. And even with that, having one people doing all the stuff, the layers, is just not insane, but really, really hard. So also like a balance between the work life, the work art life, and the family life. It seems it suits me to get this kind of role separation.
Speaker A: I didn't put this in the notes, but I'm gonna ask some market-related questions for you because I heard from Adam that you are actually pretty keen on observing the market. And I, you know, you even said that just watching generative art on the sideline working on this project, things move so fast, right? So even though you don't release a lot, it seems like you have a pretty good idea of what's going on. But to kind of kick it off, I'm curious, you know, Like you said, you've only been doing this for a little more than a year or so, and some of your initial one-of-ones sold for like a lot. They're worth a lot of money. And I don't know what these, the Ridge Regressions, where the auction's gonna pan out, cuz they're gonna be done with an auction style. But some people in Tender bought Tender passes specifically because they were excited to collect your work. So what do you think was the thing that allowed you to kind of break through in all the noise early on?
Speaker B: Yeah.
Speaker A: Where do you think you found your success? Like, do you think it was just random luck? Do you think it was that you were offering something that just looked different? 'Cause it's on OBJKT, right? So it wasn't like you even put your earliest stuff on fx hash where there was a lot of eyeballs. Like, I find it can be very hard to find stuff on OBJKT. So maybe you can just talk a little bit about those early days of what it felt like to say, I'm gonna make some mountains. Like, I'm a scientist, but I'm gonna make some mountains. Then all of a sudden having people trying to spend thousands of dollars on them.
Speaker B: Yeah, that's not at all fueling my imposter syndrome. Not at all. But no, no, no joking. But yeah, I think it would be interesting to get this answer for different artists also to become more comfortable. But in my case, I think that it's a mix between— it's not luck, but of course algorithmic things happening in Twitter. You get retweeted by Zancan. I was associated with him, even maybe Unconsciously, just because we both do things by nature and I have the same profile, my posts are shown to some key people without me noticing. So definitely I put that on luck without knowing much what would happen behind that and the part that we understand. So I get a lot of luck on that. And the other is, I think, and I have no clear explanation, but representing nature is, it speaks to a lot of people. It seems too easy from the artist's point of view because you say, as you say, you make a mountain, it sells.
Speaker C: Yeah.
Speaker B: If I would do some abstract stuff, I'm not so sure. Yeah, so a bit of both, luck and having nature that speaks to a broader of people.
Speaker A: Have you looked at doing abstract stuff ever? Did you ever consider going down that route, or have you only explored these natural themes so far?
Speaker B: Yeah, no, I'm very bad at it. Something I'm using to guide me when watching my own output is taking an analogy with music. If you hear catchy tunes, you get very excited. You like that immediately. And usually I notice that it doesn't get a lot of play over the years. So I'm trying not to keep the things I'm very impressed from in my mod first. I prefer things I understand slower. And in this case, I try to stick to this rule to choose what would I output or not.
Speaker A: Gotcha. Trinity jumped off for a second, but she'll be back.
Speaker B: Yeah. No worries.
Speaker A: So, you know, the last year has been pretty brutal, right? Like you've had, I think, your one drop on Verse, but you've been pretty quiet overall. Like just looking at your website, it's not like you have dozens of projects up there, right? It seems like you're very deliberate and intentional with how you release your work. So do you have a philosophy about this? Like, are you following platforms and seeing which ones seem to have a lot of interest, or is it more just a function of I have a full-time job, I have a family, so it's done when it's done. And then that's what dictates when you get to release something.
Speaker B: Yeah, definitely the second option. When I realized that I want this work to be more than a hobby, actually in France and in research, you have some kind of facility to take part-time jobs. So I dedicate one day to do art and the other 4 to— yes. So I work on that on one day. So, and I say, I work one day and if I can do 2 releases per year, that would be perfect. So that's actually the only way to pace myself. And yeah, it's also easy to not be satisfied with your work. You try a lot of things and you plot a lot of things. And I think there are more outputs and things on my website than NFT or whatever. Also, you mentioned that a while ago when we were talking about crypto. Yeah. One effect I noticed is that it makes a huge split between open source community, people doing art by their own and exchanging tips, and just using crypto as a way to authenticate your works and just to be distributed. And people associate, the people from the, let's say, open source communities associate that with speculation and all, and you get quite easily cut on. So on my part, I say, okay, I won't to-do box. So I prefer to rely on very timely and specific release and not to put a lot of hunting on NFT. But it seems that the space gets better and better with the years. Maybe the slowdown of crypto last year was interesting to avoid the PFP project or whatever. And also the movement to say, okay, maybe we need more time between release. So it seems definitely evolving in a good way. All the people associated to p5 also are releasing works either on Art Blocks and all. So it seems like they are accepting to be teachers and also to use crypto. So yeah, definitely positive signals for me.
Speaker A: Have you explored or talked to any other platforms? I mean, probably Art Blocks would not work unless you did decide to go back to JavaScript, but a platform like Tonic, which I don't know if you're familiar with them, but every release they do has a physical component. They've even done a generative chair in the last year. So I'm just curious if you've explored, or is it more inbound, like people coming to you, like Jamie from Verse or Adam from Tender? Do you just sit back and let the requests come in?
Speaker B: No, no, no, it's not so. No, just because this is people, I knew first Adam. He's very accessible. He gives me feedback also and doesn't try to control as much the interaction. So that's why I didn't release a lot of things. But so I'm very comfortable with that. And with Verse, I don't remember actually I contacted them, but the fact that they proposed physical installations and like an exposition, it was so like reassuring for me. I say, okay, this is the first time it was my art hanging on some walls, the actual walls, they're not something on crypto. So this is just the 2 reasons I stick with them and I'm okay for now.
Speaker A: What were some of the notes or feedback that you got from Adam? Because, you know, we've had him on the show a couple of times and obviously we're in tender And he's always very reluctant to talk too much about the process, you know? So to the extent that you feel like you can say, what were the things that maybe Adam brought in the collaboration that you felt you really benefited from? Was like really good feedback. Like, did he play a big role in trying to help you craft those initial p5 projects, like in helping you try to get them into a state where you were happy? Did he influence this Ridge Regressions project in a way that you felt like, oh man, like I never would have thought to do this or that. And I'm, you know, just curious what the collaboration was like for you.
Speaker B: Yeah, I think there were differences with the one that was not released, where he was very active and saying, okay, this kind of thing is working, this kind of thing for me it's not. So more and more strict guidelines. And at the end, there was so much possibility to explore that I just say, okay, I stop and get back to my language and expressions. And on the second, it's more like comforting and saying, okay, You know, the stuff with the brush pen and very simple, it works. Don't worry. You have always the case when you think your work is not enough. You can try different things and you see all the works for the different artists that are progressing very fast and you say, okay, I won't release anything. So in this case, say, okay, this path is working. And the second thing that helped a lot with the first release is about curation. For each style, as you said, we generated about 500 in some cases for each style and then on that step of curation only because of the effect of geography. And yeah, I think it was also good to let go and say, okay, I have external people having these eyes and selecting things. That's very interesting.
Speaker A: I want to jump back to data. You know, you've obviously experimented a little bit with plants. You said that the data wasn't super useful when exploring plants as a subject. Obviously, you've found a ton to uncover with mountain range and geological data. Have you ever looked at anything else or do you have like a notebook of, um, you know, future data sources that you might want to explore and see how they can convert into art? Like what else might appeal to you or what are some of the other potentials of data? 'Cause I'm just thinking of like, uh, was it Thomas Lund Peterson who went on Ken's podcast last year and talked about how he goes like out into the wilderness and collects electromagnetic field data on his own, like getting far away from cities and like trying to like listen to the atmosphere basically and stuff like that.
Speaker B: And so.
Speaker A: There seems to be so much potential for it, but it's also, I think, a challenging thing for artists to work with. So I'm just curious, like, what else has maybe come up in your mind as a potential area to explore?
Speaker B: Yeah, sticking to data, I think it was Matt Delaury that has this kind of electro device.
Speaker A: Yes, yes.
Speaker B: Yeah, one option is to go with more details. There are also some kind of lasers embedded in planes. It's called LiDAR. When you get very, very precise description and even you can see the different trees in the forest, for example. In this case, they have the same properties as the data I'm working with, just elevation data and 3-dimensional, much more big to read and longer to process, but the same properties. So actually I'm really trying to do that. For now, it's the problem that so many details, some, yes, and the outputs are very realistic, but not meaningful yet. So this is one part. And the other is what you mentioned, to try new other sources of data. In this case, yeah, some scientific model during the simulation. For example, you can simulate, let's say, the level of stress the crop field for every day in different crops. So you can also use 3-dimensional data on that. And I wonder, I didn't take time to explore that, but you can also use new generators for data. And then you have big generators that generate and make a chunk of data. And then I can patch the process I'm using and explore from that. And also another sources like global temperatures. You can also thinking about what story you could say with that. Maybe I can make properties of the data change with time, with temperature, with water level. I try something with moving the water level, the sea level with time in the Pyrenees and see what happened, but it's a bit gloom. So I'm not sure it's something that should be easily seen or it's a too simple message to say, okay, we see, you see the time passes and we're going to get flood. So no, it's not interesting. But yeah, many possibilities still with the constraint of data.
Speaker C: Cool.
Speaker A: Usually we wrap up these episodes by doing a few rapid-fire questions just to get to know you a little bit better. One of the ones that we like to ask our guests is, what do you listen to while you work? What kind of music do you like? And you could even also do any like media recommendations that you might have. You know, I actually really enjoyed a French film this year called After the Fall.
Speaker B: I don't know if you Not in it, but yeah, them too.
Speaker A: That was really good. Although a lot of it was in English, which was great for me. Yeah. What do you kind of enjoy? What do you like to listen to while you're coding?
Speaker B: That's a very tricky question. Usually it's very hard for me to say, what's your top 10 or what's your list? But no, actually a lot of things. I think mainly electronic music ranging from mainstream like Radiohead to more things like shoegaze, something like Moon Duo, for example. Something like, you just start and if the music not fits, you change it until you find the mood for the day or for the coding session. So it's really hard to get more details on that. But yeah, usually I'm listening music. Yeah, sometimes, not often, I just use a noise-canceling device just to have a lot of silence and to focus. But usually coding with music. Yes.
Speaker A: Gotcha. Electronic music, shoegaze, those are 2 that come up A lot. I think there's just something about coders who just, they like that kind of repetitive, it kind of helps you, it's there but it's not kind of thing and you can just chill and work.
Speaker B: Yeah, it's happened within classical music and Steve Reich also on that. Yes, I think maybe the common pattern will be like, as soon as it's repetitive enough and not lyrics you can relate to and you just hit the right spot of abstraction. Yes.
Speaker A: Another one that we sometimes ask, you know, since you've got some crypto from your sales, is there any artists that you collect that you want to shout out? Like, have you explored much on the collecting side?
Speaker B: I do not collect much. I think the key things I get is from Zancan, from Emily Edelman. She makes a series called Asemica.
Speaker A: Yes.
Speaker B: And recently the one from Golan Levin. But except that, yeah, the things I'm drawn to, they're just unreachable, like Meridian, of course. So yeah.
Speaker A: We just talked about the Golan Levin release last night. So we recorded our weekly episode. We do a weekly episode that's just talking about new drops and stuff. And so I'm curious, like I've seen so many big artists talking about how much they love that release. And then when you look at the reaction to it, you know, it sold out, it did fine, but it's underneath, I think, the value that a lot of artists expected it to go to. Where do you think the disconnect is between artists who are really appreciating it and collectors who seem to have collected it and are now trying to sell it, realizing that they can't get a profit out of it?
Speaker B: Yeah, I really have no idea what people collect and accept to make a profit, except if they pick up the social trends and see, okay, lots of people seem to get into that, so maybe there is more demand than offer. But yeah, I'm not sure about the— when you say the collectors weren't interested too much, It seems like still the huge formats and the abstract things and the colors are very, very appealing. It's not a judgment, but I often see, yes, huge abstract things in huge houses with blank walls and all. So it's very appealing. But as soon as you're out of that, I'm not sure what the mainstream of collectors are. So for that, it's very cool. Animated things, they're interesting, especially in this case, because Golan spent so much time animating all the little elements and make them interacting with each other. But I not have the slightest idea how to display that. We say that you can plot them, but in this case, I'm not sure people get excited to have a weird cell on the wall. And the framing and the digital frames, I'm not sure the tech is here to support that.
Speaker A: It's not. I have a digital frame. I have a Samsung Frame, and it's very good at displaying static pieces if you put in the work to get them in the right resolution and the right size. It doesn't intelligently resize the art to fit. It's actually a really annoying process to get your art up on there. But then if you want to run something live off the native browser, it's really bad. So the best way to do it is to like mirror a computer up to the screen. We're still really behind, I think, on—
Speaker B: Yeah, but even I'm not, not embracing it too much, but I can definitely think that it's going to become something. If there is some development into the way to show digital art except the virtual library or whatever, I think it will draw more people into that. Also, I spoke a bit with Zancan, but not only, but he also mentioned the specifics to Web3 about interacting with the community. He does that a lot to get some feedback, to pick up off trends. And yeah, both these things, even if I'm not into that, I think they're really peculiar things for Web3 art, for Web3 at least, that could be translated into art. And yes.
Speaker A: Yeah.
Speaker B: Part of it would be really interesting to follow for the next years.
Speaker A: Looks like Trinity is not going to come back. She had a work emergency. So let's just do one or two more and then we'll wrap up here. Another question that we like to ask is, who would you like to hear us interview? Who would excite you as a guest on Waiting to Be Signed?
Speaker B: I need to read the history of the podcast to know better, but I listened a bit of Marcel Schwittlick. Did you have the occasion to speak to Matt Delaurier? I wonder because he, yeah, he has some really, of course, of these works. He's also very active in the open source community and gives a lot to that, try to balance crypto. So I'm really curious about, I didn't, never met him. I'm curious to, for him to speak about.
Speaker A: Not yet. He's definitely on the list. And especially after hearing him on Ken's show, it's like, oh, he sounds like a really interesting guy to talk to. So we still haven't reached out and It takes us sometimes a few months, as you've— well, actually, we got yours done really quickly, but sometimes it takes us like a couple months to get warmed up and figure out how to build an episode. But yes, he would be an amazing guest for sure.
Speaker B: Yeah. And also Aluan Wang, he makes a lot of interactive art, but yeah, he also seems very— not on the reaction a lot. He posts sometimes. Usually I really enjoy his thoughts. So yeah, I wonder also how he sees movement and A lot of people are here actually since way before the Web3. And yes, this is the hard part for newcomers to say, okay, to acknowledge all the development that were before, to learn that and to find our place between that. So yeah, in the past, I'm very curious on the people here for a long time.
Speaker A: We've both collected some of his work on fxhash, but yeah, no, we haven't had him on either. He'd be a great guest. Thank you for those. So lastly, lastly then, I will invite you to tell us what else might be coming up from you other than Ridge Regressions, obviously, which is coming out February 6th on Verse. Anything else that you're kind of working on on the side that you want to get us excited about? I guess this is one of your 2 releases of the year, maybe.
Speaker B: So maybe, yeah, yeah, yeah.
Speaker A: Maybe it'll be towards the end of 2024. Maybe you haven't even started it, but yeah, if there's anything else you want to kind of give us a tip on or get people excited about, please go ahead. Yeah.
Speaker B: Yeah, no, really, just to say that plants, there is these things called plant blindness in a landscape. You hear a lot of animals, mammals, insects, birds, and you say, okay, there is a lot of life here. And in fact, the first thing is that you are in a forest and they're full of plants, but you do not see them. So yeah, I'm definitely trying to stick on that and say, okay, we need to showcase more plants and to find a good way. I'm still trying to link plants and calligraphy because I'm really liking this aesthetics, but we'll see maybe, yes, hopefully this year at the end of year, if I get out for this work.
Speaker A: Did you ever see, I'm sure you saw this, I don't know if it was last year or the year before, but the video of the person who recorded the plant sound, a plant making noise, that they just kind of happen at a frequency that people tend to ignore, but you could actually technically hear it, but just for some reason, I think it's kind of similar to what you just said, people tend to ignore plants or not consider it, but like, what is that?
Speaker B: Do you know what that is? Yeah, there are a lot of different things happening. I know there is one way about the movement of water in the plants that with different devices you can learn, especially when there is drought and bubbles within that for trees, for example. So one way is just to listen to them draw water from the soil without a special meaning on that. And the other is like interaction with a pollinators, trying to understand that the leaves vibrate and there is some kind of recognition based on sound on that. It's of course a really tricky subject because it's easy to get anthropomorphism and say, okay, it's beautiful, the plant sings or communicates, whereas it's just maybe just adaptation and randomness. And so it's, for this kind of news, I'm usually very cautious, but still there are some different ways from living things to communicate that we do not grasp directly. There was one with the mother plants and the little saplings that would exchange. Yes, actually, when the research goes further, there is no link between the two. They're just connected, but no exchange of nutrients or whatever.
Speaker A: I've seen so many weird plant science things on TikTok every now and then. Like, the other one is like, if you put a plant in a room and like do random lights, like the plant predicts where the light is going to be. Have you seen this one?
Speaker B: No, no, no, no, not true.
Speaker A: I'll find it, I'll send it to you on Discord. But I think it's so— they're kind of trying to maybe prove that it's either plant intelligence or like some kind of a quantum link between plants and light, where without training they'll like grow towards a future light source that's being determined randomly, and with, with, uh, some statistically significant consistency, like it picks right versus wrong. So we can talk about that next time.
Speaker B: Yeah, yeah, yeah, you can tell me if this one's real or not.
Speaker A: I sent it to I said to my brother because he's a PhD. I don't think he actually looked into the paper or anything about that.
Speaker B: But yeah, in the short answer, yes, I think there is a— cybernetics at first was just a definition when you get different signals and you integrate them and you react. It's a form of reaction and intelligence. And if you stick to that and say, okay, the plants are interacting with different signals, there is of course a way of intelligence, memory even. But yeah, if we push the cursor a bit too far, you get all these wild theories about, yes. So yes, definitely a cool topic.
Speaker A: Mushrooms are aliens, you know, all of that stuff. All right. Well, Pierre, it's been awesome to have you on. We're really looking forward to your upcoming release on Verse. Looking forward to seeing something from you at the end of 2024 as well. It's been great to get to know you. Hope you enjoyed. And I think that's it. I think we're done.
Speaker B: Yeah. Thanks so much, Will, and Trinity also. It was definitely enjoyable and more easier than I think to explain. Yes. Thanks for the honest question also.
Speaker A: It's a really nice Yeah, it's fun to talk science. Well, that's it for this one. We'll be back again soon with another episode. Thank you all for listening. Bye-bye.
Speaker B: Thanks a lot. Bye. Always listen. We're waiting to be signed.
Change log
—Initial transcript — auto-transcribed (AssemblyAI) and readability-edited.