with Jeff Bezanson
Creating Julia - a devtools discussion with Jeff Bezanson (JuliaHub). Console Devtools Podcast: Episode 9 (Season 4).
In this episode, we speak with Jeff Bezanson, one of the co-creators of the Julia programming language and the CTO of JuliaHub. We start with the history of Julia and why it took a while to take off, the key principles behind the language, how it provides the speed of C with the ease of Python, and what it's been like running such a large open-source project. He sheds light on the original motivation for Julia, the process of creating it, and its involvement in AI.
- "Telescoping Languages by Ken Kennedy"
- “Julia: Dynamism and Performance Reconciled by Design”
- Donald Fischer
- Chris Rackauckas: "A Julia Library for Neural Differential Equations"
- RR: Record and Replay Framework
- Dell XPS laptop preloaded with Ubuntu
About Jeff Bezanson
Jeff Bezanson is one of the co-creators of the Julia programming language, along with Stefan Karpinski, Alan Edelman, and Viral B. Shah. He is also a co-founder of JuliaHub, a company that grew out of this project. He has a Ph.D. from MIT where he worked as a research scientist and he has authored a number of academic papers on the Julia language. The intention behind the creation of Julia was to establish a language that was both high-level and fast. His work on it has earned Jeff the J. H. Wilkinson Prize for Numerical Software.
David Mytton [00:00:04]: Welcome to another episode of the Console DevTools Podcast. I'm David Mytton, CEO of Console.dev, a free weekly email digest of the best tools and beta releases for experienced developers.
Jean Yang [00:00:15]: And I'm Jean Yang, CEO of Akita Software, the fastest and easiest way to understand your APIs.
David Mytton [00:00:22]: In this episode, Jean and I speak with Jeff Bezanson, one of the co-creators of the Julia programming language. We start with the history of Julia and why it took a while to take off, the key principles behind the language, how it provides the speed of C with the ease of Python, and what it's been like running such a large open-source project. We're keeping this to 30 minutes. So let's get started.
David Mytton [00:00:43]: We're here with Jeff Bezanson. Let's start with a brief background. Tell us a little bit about what you're currently doing and how you got here.
Jeff Bezanson [00:00:51]: Hi. Thank you, David. Great to be here. So I'm Jeff Bezanson. I'm one of the creators of the Julia programming language. Developing that is most of what I do. I'm also a Co-Founder of JuliaHub, which is a company that grew out of that project. That's pretty much the rest of what I do.
Jean Yang [00:01:09]: Cool. Well, Jeff, I'm really excited that you can be on this podcast with us. I'd love to start by just talking about the original motivation for working on Julia. I mean, I still remember when we were teaching fellows together, and you were doing side jobs and working on Julia on the side. So can you tell us about the state of the world before Julia, and what gave you the motivation to start working and keep working on it?
Jeff Bezanson [00:01:35]: I think back to the nineties and the early 2000s, which was kind of the heyday of Moore's law, right? I remember there was a big culture among programmers that it would be silly to worry about performance because the hardware will give you free performance. So everybody was kind of rushing to try to trade away as much performance as possible. That was sort of the en-vogue thing to do, and that's where we got these kinds of scripting languages that became really popular.
It was – and people might disagree with this. But the way I've always seen it is like people are almost trying to make them as slow as possible because that was sort of in fashion. If you weren't giving up performance, you didn't understand Moore's law.
David Mytton [00:02:14]: Any particular languages you're referring to there? Or do you want them to remain nameless?
Jeff Bezanson [00:02:18]: Oh, there's a whole host of them on everything from Tcl, Python, Perl. You name it. There's a whole bunch of them. The result of that was that people in certain programming communities ended up using certain languages, right? Because you tend to use the same language as the people you work with. So you get these network effects, and you ended up with these communities and bubbles of language users.
In the scientific realm, that certainly happened. People ended up using mostly MATLAB, R, and Python. It became kind of an article of faith in those worlds that they were sort of the fun-to-use productive languages that they enjoyed using and worked really well for them but were slow. You had to give up performance. That was just a law of the universe that they all learned. Then if you wanted performance, you had to use C or Fortran or something. This was just the way it was.
I got introduced to that world of thinking in college and I thought it was really surprising because I knew that high-level languages could be fast. I knew there were good Lisp implementations, you had the ML family languages, there were really good high-level languages that had really, really good compilers and could be fast. And nobody seemed to be using them, which I just thought was amazing.
I made it this mission to “Can we get all these people to realize that high-level languages can be fast, and they should be using a high-level language that's fast?” So this is my attempt to do that. I wasn't really able to do it until I kind of learned a lot of stuff and met the right people to figure out what the design should really be and how to position it, which took quite a few years to get into that position where I could actually do that.
David Mytton [00:03:58]: Right. That makes sense. So I suppose several years. What was it like going through that journey? How did you keep yourself motivated? Then I suppose what was it that you learned from those people that made things change for you?
Jeff Bezanson [00:04:12]: I learned a lot more about how R and MATLAB worked. So I was mostly a computer science person. I didn't do that much with applied math kind of stuff. So I didn't spend as much time on MATLAB, R, Fortran languages as most of the applied math people who would have been my user audience. So I happen to learn a lot more about those. For example, at my job at Interactive Supercomputing, which was a kind of short-lived startup out of MIT that did a parallel computing platform, and I learned a lot about MATLAB and what those kinds of programmers were looking for and wanted. It's a group that is very, very focused on notation. If a*b doesn't do matrix multiply, they won't use your language, one simple example.
Jean Yang [00:05:01]: Something I find very inspiring about the Julia story, especially in the age when people talk about this ChatGPT app became an instant multi-million user super hit overnight, literally, is how long it took you to really get Julia right, you and the team. So I was hoping you could walk us through the timeline of what was it like when you started. I think that a lot of the years really were necessary to get the right idea, have the world be ready for it. So I'd love to just walk through that whole story.
Jeff Bezanson [00:05:37]: That's a really good point because now, I'm actually worrying that it took so long that we no longer maybe need programming languages or system software of any kind. The ChatGPT thing, it almost makes me think the concept of reuse that has been sort of one of the guiding stars of computer science for decades might just be over now. I think it could go that far. So I'm kind of worried about is there any future in system software at all.
Jean Yang [00:06:06]: Well, I think if there's any language well-poised for ChatGPT, it seems like Julia, with its reuse and high-level scripting nature, would be at the forefront of being ChatGPT. So it's a feature.
Jeff Bezanson [00:06:20]: Well, that’s certainly an optimistic spin.
Jean Yang [00:06:22]: I love your retellings of the story of like, “Well, first, we did this, and we did this, and this is when Julia finally took off.”
Jeff Bezanson [00:06:29]: I was working at Interactive Supercomputing. Well, actually, even before that, I had a summer job as a research programmer starting at the end of high school. I had seen what the work was like when you had to take in a bunch of data and look at images and make lots of plots and write programs to process them. People, actually, in that group mostly used C, and it was just incredibly inefficient. Yet I saw that it's, at that time, that was quite a while ago, the whole Python and NumPy toolchain was not nearly where it is today. So this was quite a long time ago. There wasn't really anything out there that could have handled it a lot better.
That's one of the things that got me thinking that, boy, there's this entire programming that is so inconvenient to do. All the other programming I had done fits neatly in the available tools of the C toolchain, essentially, because that was – for doing systems and most application stuff, it worked fine. For this stuff, it was just clearly the wrong level of abstraction. So that's one of the things that got me thinking about needing some totally different kind of platform.
Then many years later, I ended up in Interactive Supercomputing. I mean, I applied for that job because I said, “Aha, this is a place I can go that I can maybe solve this problem. These are people who are working on this, so I should go there,” which is why I was interested in it. But, of course, we're working on MATLAB and Python, and R in that shop.
Python is a pretty good language. But I really don't care for R and MATLAB very much. So I was sort of constantly ranting at everyone there about how it's too bad that we have to shoehorn our parallel computing platform into MATLAB and R because it's the – if we just had a good language, it would be a lot easier. I would just sort of rant to people about this.
There was this one guy there, Verald, who wouldn’t listen to me about it, and he said, “You sound a lot like this guy, Stefan. He says the same things that you say. You guys should talk to each other.” So he put us in touch and we started this furious email exchange where we were sending our ideas back and forth about how it should be. “It should work like this. If everything was just like this, then these problems wouldn't exist.” There was just thousands of emails or something over a couple of years where we started talking about this and hashing it out and starting to design a language.
Jean Yang [00:08:37]: Yes. I remember Jeff was the one who taught me about how bad MATLAB was. I just accepted it, and I just thought that it was supposed to be slow if you're doing such computationally-intensive things. But I love that story.
Jeff Bezanson [00:08:51]: My favorite story about that is I've heard people talk about their MATLAB programs and it’s a – how long it takes to run as a feature because they talk about, “Our simulation is so sophisticated, it has to run for two days to produce a result." It never occurred to me that that would be a good thing to someone. So it's just as kind of surprising the things you find out.
But then, oh, of course, I should add the next step that was crucial is that when that company was acquired, I had to look for something to do next. Viral suggested I talk to Alan Edelman, who I could pitch on this whole project. I showed him what we were talking about and what we wanted to do. He thought it seemed like a pretty crazy and hopeless idea so he decided to support it.
Jean Yang [00:09:33]: Yeah, I was very happy when I showed up to grad school one year and Jeff was there, and he was there working on Julia.
David Mytton [00:09:43]: What are the core principles behind the language, for someone who hasn't seen it perhaps or has only used Python or I suppose R as well? If you're in an academic circle, what are the principles behind it?
Jeff Bezanson [00:09:53]: The principle is, in these kinds of technical computing platforms, there's a lot of kind of polymorphism and objects and types that have these really kind of complex behaviors that you don't get in some other domains. If you think about something like units, for instance, units are very much like numbers in many ways. But they're not closed under multiplication, for example. Or you have matrices where you've got multiplication is now not commutative. The size of the matrices goes into whether they're compatible. There are also lots of different kinds of structured matrices. There's a whole zoo of matrix structures like bi-diagonal, tri-diagonal.
So you just have such a large diversity of objects and combinations that are possible. It leads to these kinds of libraries that have lots of very complex behaviors built into them. Yet the compiler wants to know about it because the compiler would like to be able to reason about all of these things, usually, for performance, I mean, because some people, of course, would want to check the program. But we knew that performance is what sells, so we were really only interested in compiling for performance at first when we looked at these kinds of complicated library behaviors.
The other thing about it is that not everyone wants the same thing. So in different domains, people use different definitions of things. There's one Julia package called Infinite Arrays, where it has – it gives you array types where some of the dimensions can be infinity. To a lot of people, the size of an array dimension better be an integer, and infinity is not an integer. So that can potentially break a lot of things.
Depending on who you talk to, that may or may not make sense for that to exist, right? So there's this great diversity of kinds of objects you might want. You can't just build it into one standard library and then say, “There, it’s good.” Not even to the level of just the type signatures, right? You can't even say the size of an array dimension must be an int, which is something we'd all be used to. Of course, that's a reasonable constraint to have, right? Well, no. There's just this great diversity of possible behaviors. So, therefore, they can't be built into the language because you can't just set them once and for all. They all have to be written in libraries. The guiding principle was that you could just write all of these behaviors as libraries, and then let the compiler figure it out.
There was a research program that did something kind of like this for compiling MATLAB programs. It was called Telescoping Languages by Ken Kennedy, I think. It was a research project many years ago. I heard about that, and that kind of inspired some of this thinking where the idea was that they would take libraries of MATLAB code, and annotate them with types in various ways, and analyze them thoroughly using program analysis techniques, and sort of turn those libraries into a compiler where you could then compile other programs against those libraries. We do a similar kind of specialization where the idea is to just analyze the libraries thoroughly to be able to compile code against things that have these kind of potentially wacky behaviors.
Jean Yang [00:12:46]: Cool. So, Jeff, you have this tagline, “The speed of C with the ease of use of Python.” I love to dig in on is there a core way to summarize what Julia does differently from other languages that enables this? Or is it really just a series of good, smart decisions and language design and implementation?
Jeff Bezanson [00:13:08]: It's based on design decisions. There was a paper from Northeastern where we tried to write some of this up called “Dynamism and Performance Reconciled by Design”. What we did was that people have been trying to speed up dynamic languages of various kinds for a long time. That's been one of the long-running research threads in computer sciences, starting with a language like Smalltalk, for instance. How do you make it run fast?
There's a whole zoo of both dynamic and static techniques. There are some really cool stuff people have invented to take these languages that you can't necessarily statically analyze using standard compiler techniques, and yet, nevertheless, generate fast code from them. It’s a fun game to play is how do we compile these languages that are not cooperative? So that makes it a challenge, which makes it a good research problem.
But to me, it's kind of annoying because why do you always have to fight the language design? So instead, I approached it from the opposite direction and said, “All right. What are all the techniques that are known and available for doing this? Then how would you design a language to make those techniques work well, right?” Because those techniques aren't perfect, right? They work pretty well in practice.
But you often run up against things where, okay, not every program can be optimized. You might get unlucky, and you don't hit the fast path. So we said, “Okay, we're going to be using these techniques. What can we do to make them work as well as possible?” We kind of cheated because we could just design it from the beginning so that we'd be able to optimize programs better.
Jean Yang [00:14:37]: Yeah, I love that.
David Mytton [00:14:38]: Do you have any specific examples that could help illustrate the kinds of techniques you've used?
Jeff Bezanson [00:14:43]: There are certain things you want to avoid. So definitely, you want to enable local reasoning. You want to minimize the impact of possible action at a distance kinds of things. For example, if I have a variable on one line, maybe I've figured out its type, and then I call a completely unknown function on the next line, right? Because these kinds of analyses are going to be approximate. You're not guaranteed to ever get any information about something, right?
You might hit a bad case where there's totally unknown function call. We don't know what happens. Then on the next line after that, do I still know anything about the variable that I set before that? Is it possible, for instance, that that totally unknown function might have reached into my local scope and changed that variable, for example? Because if so, now, I have to throw out that information. So that would be bad. You want to avoid those kinds of effects so that you can have a partially analyzed program and still be able to kind of keep the information you have as much as possible.
David Mytton [00:15:43]: Okay. Yes, that makes sense.
Jean Yang [00:15:45]: That makes sense.
David Mytton [00:15:46]: How does this lead into Julia's use for parallel and distributed computing and the optimizations there and why Julia is a particularly good language for that kind of workload?
Jeff Bezanson [00:15:57]: The parallel and distributed cases are pretty different. I mean, you can kind of do distributed in any language because you can just start up processes on lots of machines, and you send messages, and that's that. So it's sort of equally impossible in every language, right?
ean Yang [00:16:13]: And, unfortunately, equally too easy.
Jeff Bezanson [00:16:16]: Yeah, yeah. There are some things we could do. We knew we wanted to do distributed computing very early on. So we've always had a pretty good serialization library. So we know we at least could send any object around. But any language can do that, really. I don't know if there's anything that distributed-specific.
But in terms of the parallel, I think it does help that the language is kind of function-oriented, I would say. It's not purely functional, but it's very function-oriented. So people tend to write purely functional code anyway, a lot of the stuff. We were adding threads several years ago in the beginning, even before everything was sort of thread-safe. Stuff, by luck, sort of ended up working because people tend not to randomly mutate things and stuff like that, just because the design of the language lends itself to it.
Jean Yang [00:17:01]: Cool. Jeff, something I want to dig into is the formation of JuliaHub because it seems like Julia has gone through many stages. There was the pre-academia stage. There was a period of time when you were in grad school, and Julia was an MIT project. Then you formed a company, and then you took VC funding for the company. What was the transition from forming a company to taking funding like? I'd love to just hear more about that.
Jeff Bezanson [00:17:29]: Things really picked up quite a bit very soon after we released it in 2012. So pretty much since that point, we've all been really, really busy developing the language and helping people use the language. To me, it sometimes just feels like since that first blog post by Stefan where we released it, it’s just been that all day, every day, on some level.
We did get consulting requests within a couple of years. So we started doing consulting, helping people adopt the language in various environments to help them get the most out of it. That's how it started, of course. Then this investor, Donald Fischer, showed up. He showed up at Alan Edelman's office one day. It turned out he was an investor who specialized in open-source languages, and he had been watching. He somehow saw all of this that was going on, and he showed up to talk to us. We were kind of confused about what if we don't really have a company? What’s – he said, “Oh, no, no. There's something here. Trust me.” So he was quite early to it. It’s quite impressive. Yeah, I had no idea at what an early stage some of these investors smell what's happening and jump in. So it was pretty amazing.
David Mytton [00:18:40]: He just turned up at your office unannounced?
Jeff Bezanson [00:18:43]: Probably wasn’t unannounced. I mean, that's – the way I remember it is he just sort of walked in, but I'm sure it was –
Jean Yang [00:18:51]: Somebody got announced to. It wasn't you.
David Mytton [00:18:56]: What about the open source side of things? What have you learned running a project that's used by so many people?
Jeff Bezanson [00:19:03]: It is a fun thing to do. I mean, I do like it working with all these people all over the place, many of whom I don't even know who they are. It's kind of fun. But it's getting pretty busy. It gets pretty chaotic. Many days, I am not really sure how we're able to keep it working as well as it does. There is just a lot of activity. I can't really keep up with it anymore.
For me, personally, I'm going through kind of a difficult transition. Whereas for a surprising amount of time, I could kind of keep my head above water and sort of know everything that was going on, now, it’s just really not possible. So I'm still learning how to cope with that. Lots of changes coming in. Sometimes, I open up the source file and, “Oh, what happened here since I last looked at this?” That makes it difficult. Hopefully, ChatGPT is faster at understanding large code bases than I am, which I suspect it is. So maybe that'll save the day.
David Mytton [00:19:57]: So become like your assistant, your product manager who will feed everything back to you in a summarized form.
Jean Yang [00:20:03]: Yes. It sounds like a hard problem. Good one to have.
Jeff Bezanson [00:20:05]: That would be the hope.
Jean Yang [00:20:07]: So, Jeff, have there been tensions between the desire to keep the open-source community happy and the commercial needs of Julia?
Jeff Bezanson [00:20:17]: Yeah, we’ve never really thought so because the open-source community is the base of the pyramid for everything we do. It’s the base of the pyramid of my career at this point. So that's always been something that is going to remain as it is. Of course, the project and the core language itself and the community around it, keeping that working the way it does now is at a very high priority. Everyone involved can have a voice in that, and that's still going. We'd never give that up for any commercial reason.
So then it's on us to find a way to fund the whole thing under those principles, which we think is fully possible. It's not so easy, but we think it's completely possible by just having a very clear separation between the things that are for particular markets that they would be willing to pay for versus just a language.
David Mytton [00:21:03]: Is that the large-scale data processing and machine learning-type applications that you advertised on the JuliaHub website? Is that where you feel like the commercialization opportunity is?
Jeff Bezanson [00:21:14]: Yes. It's a little more targeted than that. It's focused in particular areas of modeling and simulation, specifically. So pharma simulations or physical systems, and now, hopefully, soon circuits.
David Mytton [00:21:26]: Interesting. What's the infrastructure behind the scenes for that? Have you built a custom platform? What does that look like?
Jeff Bezanson [00:21:32]: Our kind of library-oriented design that I was talking about, that really was successful. It worked much better than we expected because there were a lot of people out there who had what you call the domain expertise to write, say, differential equation solvers. But it would be very difficult to do it in C++.
Especially, not only is the language harder to use, but if you – once you want to add very advanced capabilities, lots of really fancy features, it just becomes kind of untenable to use a low-level language. So a lot of people like that were able to pick up Julia in lots of areas like image processing, various things. Were able to pick up Julia, write the libraries they needed that had the features they needed, then the performance was basically just more or less automatic. So already good performance and you have more capabilities than you ever had before.
One of the more illustrious of those efforts was Chris Rackauckas’ differential equation libraries, which I think now we have the best in class libraries for that stuff. There's just really incredibly productive toolchain for writing down equation systems in a very high-level mathematical notation, and that will just go all the way to some of the fastest solvers out there with just very little effort.
Jean Yang [00:22:46]: Cool. Jeff, we've talked about this at various points along the way, but I'd love to hear about some of the lessons you've learned from working with a long-running open-source project. How have you evolved with your community, and what are some of the things you've learned from working with your community?
Jeff Bezanson [00:23:04]: Well, testing is certainly really important. I can say that. I mean, everybody knows this, but it always feels like you're learning it over and over again. Anything that is not tested is going to break. There are a lot of things that don't seem like they could be easily tested, but you nevertheless have to find a way to test it, or it is going to break. So that's just really, really important. That's, I think, what sort of keeps us going, at this point.
Jean Yang [00:23:33]: Have the people who showed up been different in profile over the years, or is it just a larger number of the same kind of people?
Jeff Bezanson [00:23:40]: The profile has changed a little bit. Of course, it's still the typical Julia developer is still probably in applied math or sciences in some way. But there's certainly been a greater diversity of people from the initial small number of early adopters, I think. It's a little bit hard to characterize because, actually, in many cases, as I said, I don't even know who the people are. Some people just use GitHub usernames. Yes, sometimes, I don't even know who they are. So it's a little bit hard to have informal demographic data, I guess. It's hard to say anecdotally.
We do have a developer survey that we send out every year. The results of that will kind of tell you maybe. That's probably the best information we have. And there has been a slight increase in the diversity of kinds of people.
Jean Yang [00:24:26]: Jeff, I'm going to go off-script with this question but now that Applied Math is everywhere with ChatGPT and AI everywhere, is Julia getting into the AI game?
Jeff Bezanson [00:24:37]: We're kind of in the AI game. There are certainly frameworks for doing AI machine learning in Julia. In fact, one place where we've been a little bit ahead of the curve is in this SciML toolchain as it's called, where the program is to integrate the traditional kinds of scientific knowledge — it's based on equations and deterministic rules — with machine learning so that you can get the best of both worlds.
Because if you have an agent that's trying to learn to do something in a physical environment, say, you can try to have it learn everything but why not just also tell it what we know about physics? Then that will give it a huge leg up in which it turns out that does indeed work. If you let an agent like that just already know the laws of physics, it improves much, much more rapidly than having to reconstruct absolutely everything.
There's very various things you can do as well like using machine learning techniques to develop fast surrogates for components of simulations. It's a really fancy way to optimize a function effectively by just learning a neural net approximation of it that runs incredibly fast. So there's a whole SciML ecosystem, and Julia has lots of tools for doing that. So I think if you want to combine those kinds of traditional mathematical models with machine learning techniques, that's actually the best package of stuff out there. But as far as I know, people are not using that to build, say, ChatGPT.
David Mytton [00:25:57]: You kind of alluded to it at the beginning of the episode about how you think programming, in general, is going to be changed by the likes of ChatGPT. Are you optimistic or pessimistic? Because it wasn't entirely clear from your answer of which view you take.
Jeff Bezanson [00:26:12]: Everyone always asks the question is, are you optimistic or pessimistic? But I think it really has to be broken down to the technical question and kind of the moral question, right? Do I think the technology works and will be effective? Then, yes. In that sense, I'm optimistic. I do think it works. People are going to be using these coding assistance at the very least, plus things that maybe we can't even imagine yet.
I'm very curious how it's going to work out for maintenance programming because I mostly do maintenance programming. I wish my job was to sit down and write a program that does X. That would be great, but I don't really get to do that. I do maintenance programming. I'll ask you, have you seen any exploration of using ChatGPT for maintenance programming kind of tasks and not just “write a function that does X” kind of tasks?
Jean Yang [00:27:00]: So my sense is that Copilot should be able to help with some of this.
Jeff Bezanson [00:27:00]: Maybe. I haven't tried it, unfortunately.
Jean Yang [00:27:07]: I would say from first principles, a lot of the people I worked with in program synthesis when I was in research, maintenance programming was one of the use cases because the goal of program synthesis is to drive down boilerplate. So when you have boilerplate, when you have to visit a pattern something a lot, when you have to do maintenance programming, I feel like those are the two big cases or when you have to string together lots of APIs. Those are the three big situations where you want to do automatic programming.
In the research area, I feel like Copilot is adjacent too. There should be a lot of AI for maintenance programming. But as for how effective it is, I'm not sure. I've never personally tried to use Copilot for maintenance programming.
Jeff Bezanson [00:27:49]: I'm curious what happens if you try to ask it to do a complicated refactoring, say.
Jean Yang [00:27:54]: Oh, interesting. Yes. In my mind, maintenance programming is sort of like you change one data type, and you got to thread that through your entire system. I feel like Copilot would be great for that. But a refactor would be harder.
David Mytton [00:28:07]: So at the moment, it’s just generating, it’s almost like advanced autocomplete when it's in your editor and that's pretty helpful for the boilerplate stuff. But I suppose when it can understand a bug report and propose a solution, I haven't seen anything that can do that yet, but I have seen things like “convert this code into a different language”, so from one language to another. That seems –
Jean Yang [00:28:28]: Yes, transpiling.
David Mytton [00:28:29]: Yes, the translation and stuff.
Jean Yang [00:28:31]: Any refactor, that's a transpile, I feel like, this AI stuff is good for. But anything that needs a core fundamental understanding of the underlying programming semantics, I feel like there would be some weird stuff going on.
Jeff Bezanson [00:28:44]: Yes, that's kind of true. Actually, the use case of converting code from one language to another is actually – was the first thing I was super excited about with this because this is actually the perfect technology, in my opinion, for doing that. Because when you are converting from one language to another, which we encounter a lot because we have, of course, when you're trying to sell a language, a lot of people need to port their code from C++ or Python or MATLAB to your language. So that's the first thing that comes up.
This is a really particular kind of task that's really well-suited to this because you actually – in a way, you want to kind of do it approximately because you don't want a compiler. If you tried to use like a C++ to Julia compiler, you would not get very nice code out.
Jean Yang [00:29:25]: Yeah, yeah. So, Jeff, see, this ChatGPT business can help.
Jeff Bezanson [00:29:30]: It probably would not work very well. You wouldn't like the results.
David Mytton [00:29:34]: Yes. Well, before we wrap up then, I have two lightning questions for you. So the first is what interesting DevTools or tools generally are you playing around with at the moment?
Jeff Bezanson [00:29:46]: My favorite toy by far is RR, the record and replay framework.
Jean Yang [00:29:50]: Oh, cool.
Jeff Bezanson [00:29:51]: That has been the biggest life-changer game-changer thing that I've encountered in quite a few years. You can just do things that you couldn't do otherwise. A lot of things are kind of timesaver kind of tools. Even, I think, most of the stuff in the IDE, it’s convenient. But if you look at how much time does it actually save you, it's like a few seconds here and there, which is maybe worthwhile because it adds up. But this is just like a tool that comes pretty close to just finding the bug for you in five minutes, instead of hours of frustration. It's a major step up.
David Mytton [00:30:22]: Okay, great. Then what is your current tech setup? What hardware and software do you use each day?
Jeff Bezanson [00:30:29]: My tech setup, I got to admit, is pretty old school at this point. I mean, I have a – I use a Dell XPS laptop that comes preloaded with Ubuntu. I've used those for a long time because it has – yeah, it comes with Ubuntu preloaded. So I like those, and it's a nice machine.
Jean Yang [00:30:44]: It’s gotten increasingly hard to install Ubuntu, given the BIOS setup, so these modern machines.
Jeff Bezanson [00:30:51]: I've been using Linux for, I don't know what, a very long time, more than 20 years. So the idea of a laptop preloaded with it is still sort of like novel in some part of my brain. Other than that, it's pretty old school. It's like Emacs, and terminal is most of it.
David Mytton [00:30:51]: Which version of Ubuntu are you on? Do you stay up to date?
Jeff Bezanson [00:31:07]: Yes, I stay pretty up-to-date. I've got 2204 right now.
David Mytton [00:31:11]: Yes, that's pretty recent. Excellent. Well, unfortunately, that's all we've got time for. Thanks for joining us.
Jeff Bezanson [00:31:17]: Yeah, certainly.
[00:31:20] DM: Thanks for listening to the Console DevTools Podcast. Please let us know what you think on Twitter. I'm @davidmytton and you can follow @consoledotdev. Don't forget to subscribe and rate us in your podcast player. If you're playing around with or building any interesting DevTools, please get in touch. Our email is in the show notes. See you next time.
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