Jerod Santo: That’s right. There’s actually ETFs where you can buy Bitcoin and ETH, I believe.

Adam Stacoviak: Yeah. This world of GPUs is hard to understand as an outsider coming in. Founder’s edition. OC. Tough gaming. And I know these are subbrands of certain brands… Then you’ve got MSI, and you’ve got - EVGA, I believe, is another prominent brand that you can get. Now, I think that might be availability maybe pushing that brand, because MSI or somebody else might not be available… I don’t know. From the outsider coming in, someone who’s never been a gamer - I’ve never been a gamer. I’ve never built a PC to game on… So building even a machine to utilize a dedicated GPU is foreign to me. I’ve never done it. So even selecting which GPU, or having this history… Thankfully, we’ve got people like you out there, and my other good friend - I don’t know his name; I can’t remember his name. Tech Notice. Great dude.

He’s always on it with like the latest… And he’s strictly creator PC guy. Like, he’s not gamer PC guy. Now, he will talk about how it may influence or not influence if you’re a gamer too, but he’s primarily giving advice, generally on the tip of technology… And he’s got lots of videos out there. So he’s covered most of everything, really, from RAM, to storage, to whatever. And it’s all from a creator PC or a creator lens, not a gamer lens. Not that it’s a bad thing, but a lot of people are trying to build their best possible rig for creating, because the Mac has limitations, or they really want to push the boundaries, or they need more than one GPU for whatever reason.

[01:40:03.00] For me, if I really had unlimited funds, I would love to build an AMD Ryzen Threadripper Pro machine, that has a workstation-level motherboard, tons of PCI lanes, and I would love to have multiple, if not four… I think once you get to five it’s kind of hard, especially on like power and cooling… But at least two, maybe four GPUs. Like, if this AI theory – I know you’re laughing… If this AI theory plays out, I feel like, wow, I can build this rig… It might be expensive, but long-term it might play out, because I think AI will become the centerpiece of home labbers here soon enough. If models become, as we’ve talked about before - if they remain or become more open source, or available on Ollama, and Ollama becomes a a first-class citizen when it comes to integrations for platforms… I think if it’s like I integrate with ChatGPT and Ollama - if that becomes a real thing… Well then, that kind of machine will pay us dividends over time, as AI becomes more and more advanced, and as we allow it to inject itself more and more into this private world we have, into our home lab world.

I probably wouldn’t build an AMD Threadripper Pro machine for my own personal creation, like creator level. I think the Intel Core Ultra, or the Intel 14900K would be just great in that world… But AMD has some compelling things about it, it seems. Threadripper… That’s a cool name. Right? Threadripper’s cool.

Gina Häußge: And what happens then is at some point I’m through all of this, and then I’m happy, and stuff, and then I do the regular release thing. So I just click on Release on the GitHub release, I have already filled in the change log on all of that… And what now happens is a whole workflow runs through GitHub Actions, which first of all runs the whole test suite against everything, the unit tests are done, the end-to-end tests are done… And if all of this is green, and stuff is actually even released on PyPI and such as well, it triggers the test rig again. Because what it will do now is it will automatically build an updated image with the new OctoPrint version, so a new OctoPi version, with the new OctoPrint version. All of that will happen in GitHub Actions.

[01:38:26.17] And then when this image is built, then the flash host in my network here at home on my desk will be triggered to download this image, fire it against the Pi, flash it, run the end-to-end tests against it, and if that is green, I get a little an email in my inbox that says “Hey, this image tested green. Do you want to release it?” And if I then click “Yes”, then it will be released to the wild basically.

Gina Häußge: And what happens then is at some point I’m through all of this, and then I’m happy, and stuff, and then I do the regular release thing. So I just click on Release on the GitHub release, I have already filled in the change log on all of that… And what now happens is a whole workflow runs through GitHub Actions, which first of all runs the whole test suite against everything, the unit tests are done, the end-to-end tests are done… And if all of this is green, and stuff is actually even released on PyPI and such as well, it triggers the test rig again. Because what it will do now is it will automatically build an updated image with the new OctoPrint version, so a new OctoPi version, with the new OctoPrint version. All of that will happen in GitHub Actions. And then when this image is built, then the flesh host in my network here at home on my desk will be triggered to download this image, fire it against the Pi, flash it, run the end-to-end tests against it, and if that is green, I get a little an email in my inbox that says “Hey, this image tested green. Do you want to release it?” And if I then click “Yes”, then it will be released to the wild basically.

Adam Stacoviak: I thought it was like in Silicon Valley, Jerod, there’s a two and a half second song that Guilfoyle plays every time Bitcoin is not profitable enough to continue to mine.

[01:00:55.06]

“That’s the song “You Suffer” by Napalm Death.”

“Oh, yeah? That’s a whole song. It’s like a second.”

“It’s an alert, whenever the price of Bitcoin dips below a certain value; it’s no longer efficient to mine. When it comes back up, it is. So I need to know when it breaks that threshold, so that I can remotely toggle my rig at home.”

“Okay… Any idea how often that might happen?”

“Bitcoin is very volatile, so…”

“This is so loud…”

“A lot?”

“Good. Alright. Well, maybe turn it down, or something.”

Kris Moore: So I guess I’ll talk about the business side a little bit. So iXsystems, of course, we’re the makers of TrueNAS, we do all the primary development, all the testing, all that good stuff on the software side. And then on the enterprise side of the business, we offer TrueNAS as an appliance. And that competes with more of your traditional vendors out there, if you can think of who they are out in the wild. But we’re the kind of young [unintelligible 00:56:01.02] guys who do the really cool stuff, and offer a lot of neat functionality that’s just all inclusive, it’s there; we’re not nickel and diming you for license fees, and all that good stuff. But we take that software, and we marry it to different hardware appliance platforms. The key thing is, we offer both single controller variants, so if you’re not in a high-availability need situation, you can do that. Or we have the HA platforms, which everything’s fully redundant. So one chassis, two discrete controllers in there, they all have redundant access to the storage, and we can do failovers between them. So if you do have catastrophic system failure or something, it can failover to the other controller, and you’re back in business within seconds, because it’s all accessing the same ZFS pool, and it makes upgrades super-simple, because you can failover between them. It’s quite nice. So we do that on the enterprise side, and that’s very popular. Our customers really love that.

And then we do things a little nicer, like the proactive support as well, which means we’re getting alerts and notices if a drive starts to behave flaky, or if we detect some other error conditioner on the box… We can reach out to you and let you know “Hey, we’ve detected something on your rig here. We’re gonna send you a new drive. Or we need to schedule a call to go dive into this deeper and see what’s going on.” So really popular on that side.

One thing I will speak about a little bit is I don’t think a lot of people realize how much work goes into doing an enterprise product like that. So we come out of the home lab space, right? We’re used to going on Amazon, or Newegg, and buying our parts and putting together our system, and kind of do it yourself… And that works to a degree of works. But to go to the real next level of enterprise-level functionality and stability is huge. Now we’re talking - we spend a lot of time working with vendors, firmwares, trying to make everything as reliable as possible to get all the nines on uptime we can, on every single platform we sell and support. That’s not something you get if you homelab it. A lot of times for homelab it’s fine, it’s good enough, but for an enterprise that says “Man, I’m running critical infrastructure off this. I cannot accept any kind of downtime.” Like, there’s a lot of extra work that goes into TrueNAS to polish it to that level, to make sure it’s just rock-solid stable for the most critical of environments.

[58:17] And the beautiful thing is the open source community benefits a lot from that too, because a lot of those fixes end up in the open source side, of course… And then a lot of it is on firmware and vendors and all that good stuff, to make sure that everything’s compatible, and it’s just hunky-dory, and hotplug, always works… And yes, enclosure management always works, and you get the nice visuals, and you can tell the remote hands in the data center which drive to pull by just looking at a graph and saying ‘Okay, it’s the second down, third to the right, go pull that one that’s flashing”, that kind of thing. So a lot of work goes into that. But that’s –

Adam Stacoviak: I would be like Guilfoyle when he tested out the VR rig from the dude in Silicon Valley. And now I’d be like “This thing is rad now”, because it has the compression algorithm that makes it better. But I don’t think I would really enjoy it, honestly. I think it’d be like a toy. I’m not that attracted to it. And I don’t like to be a naysayer about technology, because I’m like, that is such innovation. I recognize everything that it promises, but it’s just not for me.

Cory Doctorow: That is the – like, we are in the year of our Lord 2023, where we have distraction rectangles in our pockets that spy on us from asshole to appetite, and we do not have a federal privacy law. It’s been a quarter of a century, and we still don’t have one. So here’s Kahn, and she’s like “Spying on people this way is clearly unfair. And it’s often deceptive. I’m just gonna make a federal privacy regulation. Not a law.” So that is like government officials doing good for you. It’s pretty great.

Now, on these lawsuits, when the antitrust changed in the Carter administration, and then mostly in Reagan - Carter pulled out one or two Jenga blocks, and Reagan was just like pulling them by the fistful. And when that happened, all the precedent went against the action those administrations were taking. And they had to create a new edifice of law in order to support these actions. They had to lose a lot in order to reverse and overturn all the precedents that had existed to that point.

[00:56:19.09] It’s not like Brown v. Board of Ed or something, where one day segregation is legal and the next day it’s not, right? It was a bunch of wins here, wins there, big swings and little swings, that built up this presidential edifice that eventually became the edifice that Kahn is now chipping away at. It is brittle, it’s ossified, it runs counter to the public interest, people are getting angrier about it… It’s not just tech antitrust that Kahn is fighting, and it’s not just tech antitrust we need to care about. Pharma antitrust is pretty goddamn important. The fact that Wall Street landlords are colluding to rig rents across the country - that’s pretty important. Like, all of this stuff is really important. And Kahn is now chipping away at this stuff from every point of fracture, in the hopes of bringing the whole cliff face down.

And when you start taking swings that no one has taken for two generations, for more than 40 years, you’re gonna wiffle a bunch, right? Like, you’re gonna hit a lot of fouls. I hope those sports metaphors are good. I don’t exactly know how many baskets you need to win a baseball game…

Breakmaster Cylinder: Yeah. I redid the guitar solo for that guy who’s like strapped to the big rig… He’s got like a skull mask and he’s shooting fire of it… I made that sound more like you’re walking into a guitar center, and someone who doesn’t know how to play is just kind of noodling… You know, one of the warboys is listening to Gary Numan as you pass by, as the camera passes by… “Here in my car… I feel safest of all…”

Daniel Thompson: But until then, we have to conserve electricity, we have to protect the planet, and every little thing we can do is important. And like I was saying, as Tauri grows more and more relevant and more widely used beyond the Fig.io’s and Spacedrives out there, it actually concretely positively impacts the planet.

Now, you could argue that the most performant app, the most secure app is the one you don’t build. But I think that’s a red herring. I think that people are going to continue to build apps, and we just want to make sure that they’re sussed out with the right tools.

[58:08] What does Tauri also do better than Node.js? Well, we can integrate very easily with third languages. There’s actually a dynamic library example that lets you rig Tauri from C++. Like I was saying before, people are building Elixir bindings, and Python wrappers, and PHP engine backends… And that ability for your preferred piece of backend and frontend to come together with the Tauri components working as glue is the really compelling part of it. Because I’m not gonna lie, I wrote a lot of JavaScript in my life. The thing about JavaScript is that – and it’s happened to me… I’d be writing something and I wasn’t sure which context I was in. Am I like on the frontend? Am I on the server? Wait a second, how does this work again? That isomorphism really – I lost my place sometimes… And I think that the fact that you can now use Rust, Elixir, Zig, C++, means that there’s a lot more entrypoints into the system… And combined with the fact that the work we’ve done on the bundler and updater is going to become more broadly available to others in the ecosystems, then I would argue that the Tauri project itself is grown beyond itself already. I think that it’s grown to see itself as a way of supporting the much larger ecosystem.

Amal Hussein: Yeah. Which is – now I want to rig this game, to rig you back, you know? So that’s my…

Zach Lloyd: Yeah, so a really simple thing; here’s what we use it for at Warp. An example would be we have a command that we run in G Cloud, that whitelists an IP address so that from that IP address you can access our staging rig if you need to. And so developers have to run that when they go work from a coffee shop, or like switch locations, wherever their IP changes. It’s a thing that people wouldn’t know how to do. You can put it in a script, but what Warp Drive is - it lets you create one of these templated workflows. You create it for yourself first and foremost; people will make these “Hey here are my common or uncommon Git things that I do, my uncommon Docker things that I do.” They’ll save those… You can parameterize them, meaning you can be like “These are the arguments that go into them as flag values.” You can give those parameters actual documentation, and then as the final step, what you can do is you can share it with your team. And then everyone on your team, whenever they’re like “Oh, crap. I need to whitelist my staging thing”, rather than slacking, or going to Notion, or pulling up a Google Doc, or searching through their scripts or whatever, they as part of basically our command search, so as part of Ctrl+R, you could just be like staging whitelists, and all of a sudden, you have that templated command. So it’s a much easier way of sharing knowledge, and sharing commands in the terminal. So that’s the first thing.

Now, the way that we’ve built Warp Drive is that it won’t – this will not be the only type of thing that you can share. For example, as a next thing that’s coming - and this is public on our website, so I’m fine saying it’s coming - we’re building a notebook data type that lives in the terminal. This is something where if you add an entire documentation plus commands workflow - and that could be like “Here’s how you do cherry picks into a release”, or “Here’s what you do if there’s high 500 errors on a server, if you want to debug it from the command line”, that type of thing can also be created for an individual and share it to the team.

So we’re trying to bring these pieces of terminal knowledge that either live in tools like Notion, or confluence, or GitHub readmes directly into the tool, and integrate them deeply, so that they’re there, they’re easy to find, and they’re easy to use. So that’s the vision of it.

Jerod Santo: Yeah, I read that one as well, and I was kind of sad… I managed to find it. Here’s a quick quote from that one. He’s comparing himself with his co-worker, who seems to have no problem just doing this. And he says “It came overnight for me. I had no choice. My boss had no choice. I am now able to create, rig and animate a character that’s spit out of Midjourney in two to three days. Before, it took us several weeks in 3D.” He says “The difference is I care, he does not.” He’s referring to his boss. “For my boss it’s just a huge time/money saver.”

So huge productivity boost… You can’t argue it, right? But - and this person doesn’t even want to argue that they should be doing this, but it’s just the joy… The joy has been sucked out.

Matt E. Patterson: Sure. Let me start with a kind of anecdote and an analogy. A couple years ago I was having beers with a friend of mine who’s a really excellent Rails developer, as well as a really excellent Ember developer… And he had been spending most of his career recently working in both, with Rails as his API and Ember as his frontend. And we were just talking about this sort of topic, the future of what the web is gonna look like… And I posited - because I spent a couple of years in the video game industry - that if you look at game companies that are doubling down now on this notion of a sort of thin terminal streaming future… And say whatever you will about Cyberpunk’s issues, the fact that it actually runs reasonably well on Stadia is nothing short of astounding. The game industry would much rather push towards that than asking everyone to buy an $800 monstrous gaming rig in a tiny box that you put in your living room.

So I posited with my friend, “Well, wouldn’t web apps ultimately go the same route as broadband penetration increases across the country and the world?” Wouldn’t the right approach be to ultimately get to where if I’m a business that’s serving you a website, I’m paying for all the cost of serving you that site. I’m not without your consent jamming megs and megs of JavaScript onto your device and asking your device to give up 60% of its computing power in order to look at my website.

So at what point do we say “Hey, you know what - there’s a way that we can give users on this sort of thin terminal approach just as rich of an experience, but everything is constantly just being pushed to them, streamed to them (if you wanna use the video game approach) from the server side.” So he thought, “Yeah, that’s an interesting idea” and we kind of dropped it at that.

[16:07] Fast-forward a few years and I discovered Stimulus Reflex, and of course, that kind of was born out of Chris McCord’s LiveView over the Phoenix framework, and I thought “Gosh, this is kind of that.” So to your actual question, the general approach, and whether you’re really using WebSockets or Basecamp’s new Hotwire library can even fall back to just using Ajax to essentially accomplish the same thing… But the idea is that rather than have a server that’s presenting an API of raw data - your list of users from the database, or your list of widgets - instead, just like in the old days of a server-rendered app where you would push a whole HTML page to the frontend on every click, every action, every page change, instead what we’re gonna do is we’re gonna send you the full page to start, and then any action that you take, just like if you’re in an Ember or an Angular app, we’re gonna have a seamless interface, because what we’re actually doing under the hood is sending that request down a WebSocket and telling the backend, “Okay, this is what the user wanted to change.” But unlike the HTML request/response cycle, all we’re really doing is telling the backend to do this. So it changes the database, it changes state, whatever.

And then it on its own terms, because you have an open, full duplex socket connection, can then just tell your client “Hey, I need you to change this chunk of the DOM.” The silly example is you create a button and a little counter, and when you click that button, it just tells the DOM to replace that number with another number. But the point there is that the backend knows what number it just told the DOM to change to. It’s not a “the frontend has a data store, the backend has a data store, and gosh, we hope that they’re in sync”, which is where a lot of the complexity comes in. It’s not that it’s an impossible problem to keep those in sync, but the complexity of what happens when the backend just flakes out on what the user just told it to do from the frontend… So now you’ve already updated the frontend to make it look really slick and fast, but now the backend didn’t actually do the thing, so then the frontend has to have some code to then figure out how to roll back the visual change it just made…

Braden Hancock: Yeah, I completely agree with you; I think we see a lot of people make that shift, and it’s perhaps not too surprising, just given how rapidly the field is growing; the people have to come from somewhere. But for me, I think part of it is just how much faster the science is in computer science; the fact that you can iterate so much more quickly. An experiment can be run in seconds, or minutes, certainly set up and run in a day, often, for certain experiments… Compared to when you’ve got a mechanical rig and there are parts, and – you know, one bad transistor and the whole thing is kind of suspect… There’s just so many more failure modes and such longer timeframes that I kept coming back to – I want to be able to answer questions quickly, and I can do that so much faster in CS.

Daniel Whitenack: Yeah, sure. So I’m actually going to - while we’re talking, I’m gonna pull up a couple links where I started my research… Originally, when I was looking at this I was like “Do I really want to roll my own workstation, and get all the parts and do all that? Or just get a really cool workstation, that is still gonna be really cool and sit by my desk, but isn’t gonna require all of that work?”

[19:52] So I started looking at places like Lambda Labs, or System76. They have these pre-built deep learning workstations with 4-8 GPUs, different configurations… And I really liked the look of those and what you could do with them, but when I started calculating out the costs, it was gonna be pushing $10,000 to $12,000, which - rightfully so; they need to make profit on those, and there is a significant amount of work in terms of fabrication of what they’re putting together, and the design in that, and making sure it’s maintainable, and all that… But I was looking at definitely a lower price tag than that, in terms of what I could accommodate…

So I’ve found a couple of blog posts that appealed to me right out of the bat. The first one of these was from a guy named Jeff Chen, who is out in California… I believe at the time he wrote the blog post he was at university, doing research. It’s from 2019, and the title is “How to build the perfect deep learning computer AND save thousands of dollars”, which is obviously very appealing to me.

The next set of blog posts was from a guy named Curtis Northcutt. He has a few different builds, but one of them he talks about the best 4-GPU deep learning rig only costs $7,000, not $11,000. So if you remember, I was looking at those pre-built ones around $11,000. I didn’t think I needed four GPUs in terms of the initial workloads I was gonna run, so I thought maybe I could also bring it a little bit under that price point… So my thought was I could follow something like these blog posts talked about, and get a workstation up and running for much less than what we would be spending over the summer in cloud costs if we were to run our workloads there.

But also, I wanted to get it expandable for the future, I guess… So start out with maybe two GPUs, but also have it expandable to maybe four GPUs, because I knew our workloads might expand, or more team members might come on the team eventually, and that sort of thing as well.

Hilary Mason: There’s a lot to talk about there. But I think the principles of it are really to clearly identify the problem you’re trying to solve, and not brush to – like, try to get to the question; don’t rush to the answer. And then figure out the people who you’re answering this question for, and figure out what’s actually useful to them. And this, honestly, is a mix of both quantitative analysis and qualitative analysis, and really trying to have empathy for the people who you’re trying to build for, and understanding where this fits into their lives, which are always interesting and chaotic… And then it comes back to what we were actually talking about before, where you start to build the simple things that can start to potentially address the problem to understand if it’s even worth investing in the best things.

So it really is a process that sort of merges the practice of product management and product design with the practice - if you’re building what I would call a data product, which is something that really depends on some sort of data science or machine learning capability to build the core feature of the product, so something like a weather prediction… Google Maps is always my favorite data product; the navigation stuff. These products could not exist without the underlying representation of the data… So you’re really trying to figure out what decisions are people making, in what context, how do I get that information to them in that context, so they make a better decision; how accurate, how good does it have to be for them to first get benefit from it, but then second, actually use it? If your weather predictor is 1% more accurate than another one, that’s not a compelling reason for me to use it, if the other one is more convenient… So really thinking about all of that.

One of the gaps I see broadly in our practice right now is that we often have product designers and product managers who are in the position to make the best decisions about the use of data science machine learning in their products, but they don’t have necessarily the background, the knowledge, the access to that talent or tools. They’re certainly not gonna build their own deep learning rig in their house… And then you have on the other hand your data scientists who often are not connected to the customers, or to the ultimate people who will benefit from the work they’re doing. So I think in our field of practice there’s actually a lot for us to figure out, not just from a “I’m a startup and I wanna build something new” point of view, but really from your day-to-day practice as somebody in this field… Like, how do you think about doing this work in the organizational structure you’re in? I think it’s kind of a mess right now, and that’s a big opportunity for us.

Keith Lynn: Yeah, so at Lockheed Martin we’re looking now at autonomous vehicles for use in humanitarian aid and disaster relief, and there’s a lot of different scenarios, especially in that area, where you have difficult to reach environments, or you have destruction of local infrastructure and it’s really hard to put a human in there, and it’s very time-sensitive; that’s a great near-term application where autonomous vehicles can do a lot of good.

You’re already seeing things in the news about them being used to deliver blood, or medical supplies to hard-to-reach places - that’s another immediate application. And then as we really start to grow into things like space exploration, going back to the Moon, or putting a human on Mars, you get in these situations where you have communications latency, given the huge distance, and you need to make decisions local, so you need an autonomous vehicle that can process information and make its own decisions… So I think as we get into the new space age, you’ll see AI playing a huge role there.

And then I think you mentioned undersea as well… This is an area – it’s actually already in practice. Some autonomous vehicles for development at Lockheed Martin - we’ve already used them for things like undersea oil rig inspection after some of the hurricanes in the Gulf… Just yet another domain where it’s hard for a human to go safely, and it’s also hard for a human to analyze the situation very well, because it’s not their traditional element. These types of things are just right for AI.

Mireille B. Reece, Psy.D: Yeah, exactly. In my line of work I will see the people where this sense of attachment and connection and feedback loop didn’t go so well. I always say it’s sort of like they jury rig things. They learned how to best function in their lives, as well as they could, but we know this whenever we jury rig something and don’t actually fix it the way it was supposed to be - what happens? It breaks down.

Chris Coyier: Well, I was a little scared to run that one… No, but I was like, “God, what is it? It’s millions and millions, right?” I’m embarrassed that I don’t know. It’s like a glamour step, but it doesn’t kind of matter… We passed seven million in April 2016, and growth has been a lot crazy since then. I bet it’s over 50 million pens, I bet… And they’re not spam either, because we’re really aggressive with our spam slaughtering across our database. It’s not like they’re all 8-bit gaming rig kind of pens; a lot of times they’re just tiny little demos that people make… In fact, that’s I’m sure the vast bulk of it, but… It’s a lot of pens out there, and we index all of them, and they’re at a public URL, they’re MIT-licensed as well, so there’s 50 million easily-searchable, quickly-searchable MIT-licensed things out there for you to go grab and use if you want.

Rich Harris: At its heart, Svelte is just a compiler that takes an HTML file in, or rather the contents of your HTML file, and spits out a JavaScript module at the other end, and some CSS, if your component has styles. That’s quite low-level; you wouldn’t typically use the compiler directly, you would typically use it via an integration for Webpack, or Rollup, or Parcel, or whatever your preferred build setup is, Svelte has a plugin that will work for it.

The way that you use it is inside your application source directory, in the same way that you write JavaScript modules, utils.js, you would write these HTML components at .html. Some people prefer to do at .svelte, it’s really up to you. Then you just import from that file, import App from './App.html' and then that is a standalone component that has the Svelte API, const app = new App(), same way that you would use a lot of other constructors. It’s just a regular JavaScript class at that point.

If people wanna get started with Svelte more easily, then having to rig up a whole build setup. We do have some really easy to use starter templates, and if you go to the website, svelte.technology, you can go into our REPL, start playing around with that, and just click the Download button and you will have a project fully set up and ready to go, with all of the optimizations and all of the bells and whistles.

Brian Ketelsen: Speaking of the barbecue rig, I may have to get new hardware for mine, because it stayed out in a rainstorm the other day, and it’s not looking pretty. It’s a good thing I just got that new Raspberry Pi 3 B+, or whatever the latest Pi is… I got a new one, so I might have to replace it. Stupid Florida rain…

Tim Mecklem: The thing that people use to loop is their rig; it’s just the tiny computer and the battery and everything, but they call them “the rigs.” Amanda has a few rigs, and they’re all of different sizes and varying technologies and things, but yeah, the loopers use rigs.