Today I am speaking with Thierry Bleau, Co-founder and COO at Playgrounds, a data solutions company leveraging The Graph to provide on-chain data infrastructures and services for data teams, analysts, and engineers. Long-time listeners of the podcast will remember that we featured Thierry’s colleague, Tachi, during Ep. 85.
Playgrounds recently released the Playgrounds API, a novel solution that’s aimed at streamlining the user experience with The Graph’s decentralized network. I invited Thierry to come onto the podcast to talk about this release and to share more about what Playgrounds has been working on over the past year. During this interview, Thierry shares his journey into web3, why he left his job to go to work on Playgrounds, and so much more.
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Thierry Bleau (00:52):
Develop essentially a mini language to give you the ability for you to specify an option and would immediately give you the historical price. And as soon as I saw it, I was like, “I should probably quit my job and start pursuing this.”
Welcome to the GRTiQ Podcast. Today I am speaking with Thierry Bleau, co-founder and COO at Playgrounds, a data solutions company that leverages The Graph to provide on-chain data infrastructures and services for data teams, analysts and engineers. Longtime listeners will already be familiar with Playgrounds, not only because of their activity within The Graph community, but because I featured Thierry’s colleague Tachi during episode 85. Playgrounds recently released the Playgrounds API, the novel solution that’s aimed at streamlining the user experience with The Graph’s decentralized network. I invited Thierry to come onto the podcast to talk about the release, and to share more information about what Playgrounds has been working on over the past year. During this interview, we talk about Thierry’s background, and his journey into web3, why he left his job to go full-time on Playgrounds, and a lot more. As always, we start the discussion talking about Thierry’s educational background.
Thierry Bleau (02:36):
So I studied mathematics and computer science in university, so I got the chance to be exposed to a myriad of domains, AI, dynamical systems, programming languages, software systems. So I was able to have a good bird’s eye view of a lot of technical fields.
This relationship between mathematics and computer science is coming up a lot on the podcast, and maybe it’s because I’m naive and I just don’t understand people with technical backgrounds. But that association, that’s a strong correlation, and it sort of leads people into technology. I mean, is that the right way to think about it?
Thierry Bleau (03:17):
Well personally, I think that they are more two facets of the same coin, as in they are both domains that are held together by logic in that sense. So I would say they are more than related, they are more two ways of thinking about the same thing. So for example, I think of math as a family of languages that more or less build on top of each other.
You essentially use these languages to describe relations between well-defined objects in an unambiguous way. And because the relations and definitions need to be consistent, you can learn a lot about what you are studying just by looking at the language or the structure of the language that you use to describe it. And then you can make clear arguments and conclusions that are if correct irrefutable. On the other hand, computer science is more like specifying ways and manipulate information, and all the implications that arise from it. So you have how hard it is to perform a particular task, or if that task is performable at all. How different shapes of information are better at modeling certain aspects of the real world, or how you can guarantee that you’ll always get the same results. Or another way, maybe a oversimplified way to think about it, is if you consider a mathematical statement as a way to describe a whole map all at once, well then computer science or algorithms rather are specific step-by-step instructions to get from one point to the other within that map.
Well Thierry, longtime listeners of podcast know that I always like to ask this question of people that studied computer science. And I want to know if you ever came across certain topics or specific topics related to web3, blockchain, these types of things. And it’s a way for me just to survey what’s going on in universities across the world. And naturally, as you might assume, the younger the guest is, the more likely it is that they came across this in some way. So how about in your case, did you ever come across any of these things?
Thierry Bleau (05:24):
My last year of university I took a graduate class that was called Algorithmic Game Theory, and so the subject matter was very similar to what I guess web3 would call tokenomics. It’s about setting incentive structures, and making decisions as a collective, and see if at some point you get to some equilibrium.
You also mentioned that you study a little bit of AI, AI in recent memory has just gone on to huge importance, and it’s been picked up in media, and people are using it, and all these different things. I’m curious about what you studied about AI in university, and if given that education you’re surprised by what we’re seeing now with this huge public interest and concern about it.
Thierry Bleau (06:09):
Well, I went to university in Montreal, so that was a big place for AI back then, I think 2017, 2016. And funnily enough, I specialized a bit in natural language processing, but back then the deep learning models weren’t as sophisticated. They weren’t as big as they are today, so you couldn’t have a real almost conversation the same way that you use ChatGPT today. So it would be more used for classifying sentences, gauging the tone of a sentence, stuff like that. But today the AIs are a lot more extracting some shapeless substance from all the texts on the internet, and then using that substance as a pure knowledge that you can then give a shape in whatever way you want. So I knew it was coming, but I was very surprised at how fast it came.
Given your background and the fact that you’ve thought a little bit about this, do you think web3 as an industry is better positioned to sort of leverage AI and its potential than maybe web2, or a world in which web3 doesn’t exist, or blockchain doesn’t exist? I mean, is there any argument to make that these two things emerged at the same time with good reason because they both can leverage each other?
Thierry Bleau (07:30):
I think they are both very different ways of handling information. One is more like dealing with a common very specific context that we share, while the other is more like some kind of background of common knowledge instead of a specific state that we know how much I owe you or you owe me. But in that sense, you could imagine that blockchain could control the interfaces between AIs, and how AI could be used in production. But luckily enough, The Graph in particular I think would be very well-placed to take advantage of it. In the end, you could imagine social media as like a blockchain, but then you can use an Indexer to index what is being said on social media, and then make it available the same way that The Graph makes index blockchain data available.
We’ll have to double-click on that maybe a little bit later in the interview. Before we get to some of those things, I want to go back to your story arc here. So you’re studying computer science, studying mathematics, what did you intend to do with your education after you completed university?
Thierry Bleau (08:44):
Well, I didn’t really intend to do anything, I was more pushed into it. I would say I was very uncomfortable with the fact that before I got to university, almost everything around me felt like black boxes, and every black box like finances, computers, governments, law, all of that. Taken in isolation you can understand what it does, but there didn’t seem to be a completely unified, coherent way to think about all of it at the same time. And if there was, I wouldn’t have the ears to actually hear the way to think about it. So I went into math and computer science, because I felt that it would allow me to meaningfully engage with all of these topics at the same time.
So math and computer science for you was a way to open these black boxes of specialized knowledge or understanding of the world from finance to economics and everything else?
Thierry Bleau (09:43):
Well, when you think about it, almost all of the processes that the world runs on, in the end our effort’s to make them as well-defined as possible to be able to make them reliable. And the more reliable they are, the more you can build on top of them. So you have pyramids of reliability building on top of each other, and I guess that’s basically our society today.
Okay Thierry, so then when was it that you first became aware of or interested in crypto then?
Thierry Bleau (11:22):
Well, I always had an interest in markets. As I described earlier, all of these black boxes, markets was definitely a big one. You see numbers going up and down and people talking about it. And there’s that weird… You have all of these smart people constantly thinking about where the market will go, but they’re always basically wrong. And I found that incredibly fascinating. So I read all the books I could get on investing markets, even structured financial products. But the first time I heard about Bitcoin, to me it was just another asset to be bought and sold. But given that I was mostly on the side trading through my bank account, I could not really engage with it. So I think that was around 2014 maybe. But I couldn’t really engage with it fully. But that was until maybe my first year of university, where I heard of Ethereum and The DAO hack, and I always had an interest in emergence and those kinds of ideas.
And when I learned what a decentralized autonomous organization was, it really spoke to me. And then I saw how it related to software, and how Ethereum wanted to essentially be a medium in which these organizations could live in. That interested me a lot, and so I was able to observe right after The DAO hack the ICO mania started, and I was able to see that up close. But at that time almost everything was… All the money being thrown around was mostly related to white papers. I think that was the first mania that I could see up close, and it stuck with me, especially since I was in college and the college world was very different from what I was seeing on the crypto side.
I want to double-click on your interest in emergence. So I think I have a sense of what you’re referring to there, but if you don’t mind just for me and listeners, how would you explain what emergence is and what your interest is in that particular phenomena?
Thierry Bleau (13:40):
Emergence is a crucial idea when you think about I guess the broader topic of complexity science, and that science mostly deals with how scale essentially changes the very nature of what you’re studying in a observable way. I think the most topical example here is an AI, the more layers that you add some new capacities start to emerge from the sheer number of parameters that you’re dealing with. Another example would be just the patterns that develop just through the on chain interactions that people have.
So Thierry, are you saying emergence is the study of patterns or consistent activities derived from really complex systems or something like that?
Thierry Bleau (14:34):
Well, it’s funny the fact that the intertwinedness of collective behavior in the end creates new behaviors that you can identify on its own.
Connect those dots for us, if you don’t mind. That emergence idea is super interesting, of course. And then you said because of your interest in that you had an aha moment with Ethereum and DAOs, and what they could do. So connect those dots for us, how does emergence relate to DAOs and your discovery of Ethereum?
Thierry Bleau (15:06):
When you think about decentralized autonomous organizations, in your mind you can think about them as living and breathing organisms. You have people coming in and out of them, but in the end the spirit of the organization lives on. But in the particular case of DAOs, that would be true for any organization. But in this specific case of DAOs, they are almost cybernetic entities, where you have groups of people behaving together and directing this whole collective in one way or another, but it all happens in software. At the very least, it’s represented in software.
Thierry, as you described also your interest or familiarity with crypto, beginning with Bitcoin, you saw it as a speculative asset. And then you became aware of Ethereum, you stumbled across DAOs, and you started to build some conviction for the underlying tech I guess, or the disruptive nature of what’s happening in web3. Talk us through that a little bit, because that’s another common thread. People become aware of crypto via Bitcoin, they see it as a speculative asset. And then at some point they become aware of Ethereum, and it’s a light bulb moment for them where they see something different. Talk to us about that experience for you.
Thierry Bleau (16:23):
I would say that it wasn’t exactly a light bulb for me. It was more of a very gradual process while I was in university, and a bit after too. But mostly I came to the conclusion that I didn’t really feel that blockchains were technological resolutions, as much as they were more a re-imagining of how administration is done, if the internet and computers were at the core of this new way of doing administration. So accounting, finance, legal agreements, all of that, if you redesign it from scratch with the internet at its core, changes a lot about how organizations work, how they interact with each other, and how communication happens across them. So if we consider what happened before, kind of an example here, double-entry bookkeeping. If we consider what happened before double-entry bookkeeping, where people would basically do their best by writing lists of stuff to keep track of inventory, as soon as the operation becomes too complex, you start making mistakes, and these mistakes propagate across the system pretty quickly and they become impossible to correct.
So when double-entry bookkeeping became a thing, you essentially had a way to check your math as you were doing it, and so you could catch errors pretty quickly and stamp them out. And that allowed essentially the creation of organizations that are a lot bigger in scale, you can think of nation states, or the East India Company. But with blockchain, basically everything is automated, even the math checks itself as the transactions are processed, and also you divorce any one organizations from these processes. So the processes can outlive the organizations that created them, so the complexity of what can be achieved is almost limitless. In that sense, I think blockchains will keep growing, and their histories will keep expanding by us just essentially doing business on top of them. And The Graph’s ecosystem as a whole will essentially serve as a way to contextualize, interpret and model all of that information and actionable knowledge.
Well Thierry, I appreciate that overview and a lot of interesting things there we’re going to talk more about. But before we do, and longtime listeners of the podcast know I’m about to ask this, but do you see web3 as a natural evolution, a next step in the evolution of how we build and use technology? Do you see it as a revolution maybe against the ways in which web2 failed us or some of the sins of web2, so to speak? How do you contextualize the emergence of web3?
Thierry Bleau (19:18):
I wouldn’t say it’s a revolution in technology, but I would say it’s a revolution in administration really. As I said earlier, it was more of a revolution in how we operate and organize around specific common goals. It’s really how we keep track of stuff, how we argue stuff based on what we keep track of, and essentially the thing that keeps track of how things grow. And so I think it’s mostly an iterative process on top of everything that came before. So that’s why contracts in particular, you organize and operate software in a way that allows you to smash through the limits of scale as an organization. So I think we’ve seen a lot of the parallels in history, as I described with double-entry bookkeeping. But in the end, at the tech level, I think it’s mostly a new way to operate software.
Thierry, let’s go back to your story and talk about what you did after university. So what can you tell us about what you did?
Thierry Bleau (20:22):
So I essentially graduated at the same time that COVID started, so I’m pretty grateful that I didn’t have to go through the university experience while COVID was a thing. But I was lucky enough also to get a job at a company that aggregated crypto data. So it was essentially my job to get familiar with as many protocols as possible, and see how we can get data from it, and make it available through the platform. And eventually DeFi Summer happened, and I witnessed firsthand the fever dream that was that period. Where you had the burst of yield farming, the SushiSwap vampire attack, the new FOOD coins topping up every week. And I think it culminated with the Uniswap airdrop, from what I remember people didn’t really believe it at first. Some people were speculating about maybe someday some protocol just airdropping money on everyone that used it, but the fact that it was Uniswap that did it, and they did it in such a surprising way, that was pretty amazing.
It’s a complicated time for a young person to graduate from university at the time of a global pandemic, so I imagine that was pretty stressful. But you were fortunate in the sense that you had some early interest in crypto, graduated and went right to work in the industry. Once you got settled in, did you think, this is the place for me, this is exactly what I want to work on, these types of problems in this type of industry?
Thierry Bleau (21:59):
Yeah, for sure. I came across a problem that I knew I wanted to solve, which was at the time almost every data provider was mostly focused on centralized exchanges, because DeFi wasn’t developed enough for people to be able to get data from it. If there were, it was essentially custom APIs made by the protocols themselves that you had. Or maybe Infura, you could get some on-chain data from there.
But other than that, it was mostly data providers aggregating data from all of these centralized exchanges, and modeling it, and making it available for traders, analysts, you name it. And I realized that the shape of the data accounted for a lot of how much I liked the data provider. Depending on how they shaped the data, you could immediately see what was available to you by just reading the documentation, and you could imagine what kind of operations you could do and what kind of analytics, what kind of charts you could do. And I knew that that was coming, that would be definitely a problem for on-chain data. It’s essentially finding the right ways to shape it that corresponds to how people think about the activity that is happening on-chain.
Hi, this is Thierry with Playgrounds, if my conversation with the GRTiQ Podcast has been helpful to you, then please consider supporting future episodes by becoming a subscriber. Visit grtiq.com/podcast for more information, that’s grtiq.com/podcast. Thanks for listening.
Well Thierry, you and I are speaking today because eventually you make your way to Playgrounds and go to work with the team there. And longtime listeners of podcast know that for episode 85, I interviewed Tachi. And Playgrounds has been doing a lot of cool stuff in this space. We’re going to double-click a lot on that. But before we do, what’s the backstory between you finding the team and helping launch Playgrounds?
Thierry Bleau (24:06):
So while I was working at that data aggregator, I stumbled upon The Graph, and I thought it was very interesting, but I didn’t know exactly what to do with it yet. Given the problem that I wanted to solve that I described earlier, as soon as I saw The Graph, I was like, “Well, the problem is almost solved.” So immediately I went to my friend, Stopher, the CTO and co-founder too. At that time, we were very interested in that protocol called Open, which was essentially a protocol for on-chain options. But something that is very necessary to be able to truly operate within an options setting, is you need to know what was the historical price of that option. And so I went to my friend, it was like, “Well, I know that The Graph indexes on-chain data, and we need historical option prices, so maybe there’s something to do there.”
And he came back essentially the next day, and he had developed essentially a mini language to give you the ability for you to specify an option, and would immediately give you the historical price. And as soon as I saw it, I was like, “I should probably quit my job and start pursuing this.” But it was not Playgrounds, what we started was Protean-Labs. And we pursued that language, we even got a grant from The Graph to pursue it, but we realized that not everyone wants to learn a new language. Actually, it’s pretty hard to do, and the benefits weren’t that clear. So we decided to rewrite it in Python, a language that almost all data people will be pretty familiar with. And we took all of what we learned from the language, which was a bit closer to functional programming and used pretty advanced features to make everything seamless, but you still had to learn it, which was pretty difficult.
So we were working on this language, and we were trying to find use cases for it. One day I stumbled upon the Olympus DAO Discord, and I went into their data channel and I just posted, “Oh, hey, how do you guys fetch on-chain data? Do you know about subgraphs? How do you operate?” And Tachi is the one who responded, and we immediately hit it off. And this is essentially when I realized that the language we were developing was probably not the best.
And so we decided to port it to Python, and today we brought all of the features that we thought were interesting, and we rewrote it in Python, which is today subgrounds. So with this new Python library, you could essentially scan the shape of a subgraph, and then take the entities from it and make them available from within your Python program. So whenever you want to write queries, you could just use these objects in a very intuitive way, and produce and execute these queries, and have the responses well formatted into a neat table, formatted into Pandas DataFrame. That is usually the main format that data science use to perform all kinds of analytics.
Thierry, for listeners that may not be familiar with Playgrounds, what it is, how it works, can you just give us a brief introduction, help us understand what the team’s building?
Thierry Bleau (27:49):
So we’re a team of engineers with the mission to essentially make on-chain data as accessible and intuitive as possible. And we decided essentially to build everything on top of The Graph, because naturally as the main indexing layer for blockchains, it was pretty natural to do so. And so, as I said, we have have the Python open source library that we released, that essentially is a very Pythonic way to interface with subgraphs. And more recently, we released the Playgrounds API, which is essentially another portal to the decentralized network that would allow you to query it without the need for a crypto wallet or managing GRT.
Who’s the target user for Playgrounds then? You’re building on top of The Graph, The Graph has Indexers and developers that are using it, so who is Playgrounds trying to target and serve?
Thierry Bleau (28:47):
So I think it would be useful for anyone who wants to get data quickly, but especially for those who aren’t necessarily crypto native and come from a more traditional business world, and don’t have the experience with on-chain interactions. Or where organizations don’t really have the setup to deal with tokens, whether that’s accounting wise or regulatory wise.
And normally in order to get data from a decentralized network, you would have to acquire the GRT and load it into the billing contract. And more recently, even though The Graph came out with the banks of service, where you could essentially pay for GRT with a credit card, and the GRT would be allocated to you. But we wanted to simplify it even more where you could start getting data from the network with just an email address, and so you wouldn’t have to think about having a crypto wallet managing GRT. So in that sense, we recuperate a bit of the experience of the hosted service on the decentralized network. And you can start for free as soon as you create an account, you get 5,000 queries a month. And so that’s a great way to get started and access the most comprehensive so-and-so [inaudible 00:29:56].
Well, listeners know I already had Tachi on the podcast before, but I’ve been keeping an eye on the Playgrounds team and super curious about what you’re working on and building. And this recent release of the Playgrounds API certainly caught my attention. If you don’t mind, can you just double-click on what the Playgrounds API is, and why people should be excited or interested in what you’re doing?
Thierry Bleau (30:16):
Well, so it’s our way to increase the accessibility of The Graph Network by packaging its offering in a form that is closer to the experience that you would have with a more traditional data provider. The ultimate goal is to boost the adoption of the decentralized network to a new audience, and a more analytics and data-focused audience, instead of the usual dapp developer and protocol developer. And it’ll become even more useful when The Graph starts adding more services to the network, like the Firehose and Substreams, and eventually maybe some AI stuff. So users will be able to leverage all of that world of data services with the same way that they would get data from any other service like [inaudible 00:31:00].
For listeners that are interested in trying it out, checking out what you guys are building, what’s the best way for them to do it?
Thierry Bleau (31:06):
The best way if you’re familiar with Python, it would be to install subgrounds and start querying any subgraph that you want. And if you want to query the decentralized network, I would encourage you to go to the playgrounds.network website, and create an account, and you can get started querying immediately.
Thierry, I think I asked this question to Tachi, I certainly want to ask it to you. It’s this question about The Graph becoming somewhat of a platform for innovation. So you sit there, and I know Tachi said this, that Playgrounds is building on top of The Graph. And it sort of then makes The Graph a platform which builders and innovators can use to create new business models and do some of the cool things you’re doing. So what does that say about how The Graph fits in the web3 and its importance for the industry?
Thierry Bleau (31:57):
Well, I think The Graph will essentially become a crucial layer for every blockchain. And a indexing mechanism is necessary for any blockchain, even though the blockchains themselves are super transparent. The composability of the smart contracts and being able to use the same tokens in different contracts, and how the transactions can chain a lot of these functions, you have no real way, unless you’re very technical, to parse out all of that information. And so The Graph allows you to capture some parsing logic into a recipe, that then makes the results of that recipe available to anyone. And so in that sense, you can imagine that any service that uses blockchain data could be built on top of The Graph.
One thing I’ve been thinking about, Thierry, is the amount of data on blockchains and the utility of it. So I think my question is, do you feel like there’s enough data held on chains to sort of populate or seed a whole new industry of innovations and applications? Or is it pretty confined right now in terms of its evolution, and maybe most of the data being related to just DeFi?
Thierry Bleau (33:13):
Well, I think there will be enough data to go around, for sure. But I think most importantly, that same data could be looked at from so many perspectives, that I think entire industries could pop up just from one of these perspectives. You can imagine that for DeFi in particular, the same data could be treated as financial activity, but on the other hand, you could also look at it as a user behavior or product analytics and that kind of stuff.
Thierry, you’ve alluded to it a couple times. I just want to double-click again on this important theme, which is how important is the problem that The Graph is addressing? Can there be a web3 without this indexing query layer like The Graph?
Thierry Bleau (34:04):
I don’t think so. If we draw a comparison with the traditional world, when a company goes through its financial operations, it’s essentially dealing with two separate systems. On one hand, they use the banking system to actually perform the payments and move money around. But on the other hand, you have the accounting world where they deal with the receipts, invoices and all that kind of stuff, but both really don’t speak to each other. And so that’s why there’s an entire aspect of accounting that is dedicated to essentially reconcile both. And if we bring that back to blockchain, you can imagine that it’s essentially having the banking system but without the accounting. And so there’s no real way to actually understand what is happening and how we got there without something like The Graph to make all of the current state of blockchain, but especially its history, available to anyone.
And the last question I want to ask you is about milestones or things you’re watching for. So as web3 tries to gain wider adoption, more traction throughout the world, what are you paying most close attention to, or what are you watching for?
Thierry Bleau (35:18):
I’m looking for essentially big companies dipping their toes with smart contracts, and the big brands essentially minting NFTs. Or on the other hand, having financial institutions moving some of their operations on-chain, like PayPal with their new Stablecoin that they launched. Or the protocols that essentially tokenize treasury bills, and essentially bridges the traditional world with the new world, where you can essentially perform automated operations on these treasury bills in a way that was probably impossible before.
Well Thierry, now we’ve reached a point in the podcast where I’m going to ask you the GRTiQ 10. These are listener favorites because it gives them a glimpse more into you and the things that you’re interested in. But I also hope that these questions help listeners learn something new, try something different, or achieve more in their own life. So Thierry, are you ready for the GRTiQ 10?
Thierry Bleau (36:17):
What book or article has had the most impact on your life?
Thierry Bleau (36:32):
I would say The Listening Society. It’s a book that outlines a philosophy that I think is pretty compatible with the future that blockchains put forth. I would recommend everyone to read.
Is there a movie or a TV show that you think everybody should watch?
Thierry Bleau (36:48):
The Expanse, one of the best sci-fi shows out there I think.
If you could only listen to one music album for the rest of your life, which one would you choose?
Thierry Bleau (36:58):
I’d say anything by Amadou & Mariam.
What’s the best advice someone’s ever given to you?
Thierry Bleau (37:04):
You can achieve a lot just by showing up, there are a lot of people who are willing to take someone under their wing. So just showing up.
What’s one thing you’ve learned in your life that you don’t think most other people have learned or know yet?
Thierry Bleau (37:18):
Words are only windows to meaning. And I think that has been essentially proved by the recent advances in AI with ChatGPT and stuff, where it can be stretched and squeezed, and nudge you to adopt a certain perspective or another.
What’s the best life hack you’ve discovered for yourself?
Thierry Bleau (37:38):
I only use social media for work.
And how about this one, Thierry, based on your own life observations and experiences, what’s the one habit or characteristic that you think best explains why people find success in life?
Thierry Bleau (37:51):
Liking something enough to essentially be constantly thinking about it, and deliberately and diligently improve your skill around it.
And the final three questions are complete the sentence type questions. So the first one is, the thing that most excites me about web3 is?
Thierry Bleau (38:06):
Make bureaucracies keep up with the rest of society.
And if you’re on X, formerly Twitter, you should be following?
Thierry Bleau (38:14):
Patrick McKenzie. He is an advisor at Stripe, and he has a lot of interesting takes in his blog, Bits About Money.
And the last one, I’m happiest when?
Thierry Bleau (38:26):
Hanging out with my wife and my dog.
Thierry, thank you so much for joining me and sharing with listeners the things that Playground’s working with, and talking a little bit more about the recent launch of Playgrounds API. If listeners want to learn more about you, follow some of the things you’re working on, what’s the best way for them to stay in touch?
Thierry Bleau (38:51):
The best way to stay in touch would be joining our discord or following me on Twitter.
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