The Pragmatic Engineer - The programming language after Kotlin – with the creator of Kotlin

Episode Date: February 12, 2026

Brought to You By:• Statsig — ⁠ The unified platform for flags, analytics, experiments, and more.• Sonar – The makers of SonarQube, the industry standard for automated code review• WorkOS – Everything you need to make your app enterprise ready.—Andrey Breslav is the creator of Kotlin and the founder of CodeSpeak, a new programming language that aims to reduce boilerplate by replacing trivial code with concise, plain-English descriptions. He led Kotlin’s design at JetBrains through its early releases, shaping both the language and its compiler as Kotlin grew into a core part of the Android ecosystem.In this episode, we talk about what it takes to design and evolve a programming language in production. We discuss the influences behind Kotlin, the tradeoffs that shaped it, and why interoperability with Java became so central to its success. Andrey also explains why he is building CodeSpeak as a response to growing code complexity in an era of LLM agents, and why he believes keeping humans in control of the software development lifecycle will matter even more as AI becomes more capable.—Timestamps(00:00) Intro(01:02) Why Kotlin was created(06:26) Dynamic vs. static languages(09:27) Andrey joins the Kotlin project(14:26) Designing a new language (19:40) Frontend vs. Backend in language design(21:05) Why is it named Kotlin?(24:37) Kotlin vs. Java tradeoffs(28:32) Null safety (31:24) Kotlin’s influences (39:12) Smartcasts (40:42) Features Kotlin left out(44:54) Bidirectional Java interoperability(55:01) The Kotlin timeline (58:00) Kotlin’s development process(1:07:20) From Java to Android developers(1:12:12) How Android became Kotlin-first (1:18:20) CodeSpeak: a language for LLMs(1:24:07) LLMs and new languages(1:28:20) How software engineering is changing with AI(1:36:12) Developer tools of the future (1:39:00) Andrey’s advice for junior engineers and students (1:42:32) Rapid fire round—The Pragmatic Engineer deepdives relevant for this episode:• Cross-platform mobile development• How Swift was built – with Chris Lattner, the creator of the language• Building Reddit’s iOS and Android app• Notion: going native on iOS and Android• Is there a drop in native iOS and Android hiring at startups?—Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [e-mail]. Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

Why would anyone create a new programming language today if AI can already write most of your code? Andrew Bressla has an interesting answer. Andrea is the creator of Kotlin, a language that runs on billions of Android devices and is one of the fastest growing languages in the world. Today we cover how Andrea designed Kotlin by deliberately borrowing ideas from Scala, C-sharp and Groovy, and why he considers leaving out the turnernery operator one of his biggest regrets. Why making Kotlin interoperates seamlessly with Java was a gigantic undertaking and what it took to get it done. How Kotlin adoption went through the roof after Google announced making it the official language for Android, in a move that even took Android and the Kotlin team by surprise.

Andrew's new project, Codespeak, a new programming language built on English, designed for an era where AI writes most of the code. If you're interested in the future programming languages from someone who built one of the most loved languages of today, then this episode is for you. This episode is presented by Statsig, the Unified Platform for Flags, Analytics Experiments, and more. Check out the show notes to learn more about them and our other season sponsor. sensors, sonar, and workOS. Andrei, welcome to the podcast.

Hello, thank you for having me. It is not often that I meet someone who designed such an influential language across mobile, across backend. So let's start with how did it all start? Okay, so that was a little messy because I went to school back in St. Petersburg, studied computer science. and I didn't really know exactly what kind of programmer I wanted to become. I knew I wanted to be a programmer.

And then, you know, at some point, while I was still in the university, I started teaching programming in school and, you know, it was a big, passionate hobby of mine. And then at some point I got a job with Borland and worked in some developer tools. You know, that was awesome. Like Borland was a very big name. And, you know, they went under pretty soon after I joined. and I hope it's not because of me. Yeah, but I worked on the,

it was at the tail end of the UML era, so we were doing some developer tools in the UML space. That was very interesting. I learned a lot, but then Borgland went under, and I went back to teaching full-time, and then I started PhD school, and, you know, all that was kind of not really planned out. And in my PhD, I was working on domain-specific languages,

and generally I was interested in languages. something I was curious about and specifically typed languages were interesting. I was always curious about how these things worked. But never really serious. When I started looking into DSLs, it was slightly more serious, although my PhD was a mess and I never defended because of that. But at some point, you know, someone reached out. He was actually a person who was in charge of the Orland's office in St. Petersburg,

and by that time he was already at Judbrains. and he reached out to me while I was in Tartu, in Estonia. I was there for a year on it because I'm visiting PhD student. It was a lovely time. So he reached out and invited me to, when I next visit St. Petersburg, to visit the JetBrains office there and talk about something about languages. What I thought was that there was about this project called MPS metaprogramming system that JetBrains had. I knew about it.

It's about DSLs. I worked on DSLs. You know, it was generally something like plausible that they would be interested in talking about something like that. But it turned out I was completely wrong. And what they wanted was to start a new programming link. And I was completely unprepared for that. Like, I, you know, I've never thought about doing something like this.

And my first reaction was, you don't do new language. Like, you don't need it. and the basic pitch was that the Java ecosystem needs a new language Java's outdated so on so forth we can talk a little more about this it was 2010 I think yeah 2010

yeah and I was like but there are other languages like everybody's doing fine why do you need to do that and then this conversation was actually a very insightful one because the guys of Jebranes they simply explained to me

how the things actually were and you know it was a big problem by that time so Java didn't really evolve and hadn't been for a long time what was the reason behind it can you take us back for those of us who are not in the ins and outs yeah so the last major version of Java by 2010 was Java 5 that was released in 2004 oh six-year-old language yeah and since then there were updates there was Java 6 that made no changes to the language at all. And then there was Java 7 that made minor changes. In parallel, there were things that there were happening in other languages,

especially C-sharp, was progressing very well. And by 2010, C-sharp had all the nice things. There already were lambdas, like how our functions and all that nice stuff. There were getters and setters and many other things that made the language much nicer. and Java was felt like it was standing still. And there was a project to work on Lambda for Java, but that was in the works and had been in the works for a long time and only came out in 2014.

So that was the situation. And, you know, the ecosystem didn't stand still in the sense that other people were building languages. And there was Scala, there was Groovy. And of course, people of Jabreins knew both Scala and Groovy. they built tools for them. It's traditional to build your tools in the language you're building the tools for,

so the Scala plugin was built in Scala, and there was a lot of Groovy use of Jebranes as well. So they knew what the issues were with the language, and both languages are very interesting and very good in their own ways. But they saw an opportunity in the market, because basically Groovy was too dynamic and too far from, you know, hardcore mainstream, large-scale production. Because dynamic languages are not for that, basically.

What are dynamic languages for? What are their strengths and best use cases? So the tradeoff, I guess, if you look at a statically type language like Java and Kotlin and Scala, for example, versus dynamic languages like Python and Ruby and JavaScript and Groovy, in dynamic languages, it's very easy to start and build something working very quickly because basically the language is not in your way as much. There's this saying that nothing limits the imagination of a programmer like a compiler. And, you know, this may be changing nowadays a little bit,

and this is in the part of what I'm working on now. But back in the day, it was completely true. You know, the whole art of making a good language was to restrict the user in a good way. Yeah, but in any case, the situation with dynamic languages is that they are much more user-friendly in the beginning. But then when the project scales, you have trouble making large refactoring. You have trouble making sure that everything works together. You need to do a lot more testing and rely on other things like that, as opposed to static languages where you have precise refactoring tools and other things that can make sure that at least a certain class of problems just doesn't.

happen. And, you know, this is why, at least in our mind back then, it was absolutely clear that if we're building a language for large projects, big teams, so on and forth, it has to be a static line. Static one, yes. Yeah. So with Groovy, that was a big issue of performance as well, because Groovee was building a dynamic language on top of a very static runtime, so there was quite a bit of tension there. So that was on the Groovy side and the Scala side. Scala is a one wonderful static language and incredibly powerful and with tons and tons of good ideas. But it had its own problems. It relied very heavily on implicits, for example, and I have a history of debugging one line of Scala for an hour to try and figure out what it does. Just because, you know,

it was pretty complicated. And also, the compiler was very slow and the warrior issues of stability. And many, many things were just not accessible enough for a lot of engineers. So from the experience, of using Scala jebrain, my colleagues, basically understood that it's not what's going to change the industry. Although Scala got a lot of adoption, and again, like, Martin Anderski is a great language designer, you know. And I think one of the biggest use cases was all Twitter. A lot of it was built on Scala and they scale to, however, you know, massive scale, etc.

And I think LinkedIn as well. Yeah, so in any case, these were, you know, it's always very, very, nice when other languages kind of pioneer things and then you can build on top of their successes and failure. And we were in that position basically. So the argument that people at JetBrains were making was basically that there is a wind of opportunity. People need this language. We JetBrains are the company who can actually put out a language and make it successful because we have access to the users, we have their trust. We can make good tools.

And it was another issue with Scala, for example. It was very difficult to build tools for Scala back then. Now Scala 3 is more tooling friendly. But back then, it was a nightmare. Like, I said that, you know, if you have a static language, you can't have precise refacturings if the language is not too complex. And, you know, some languages are particularly challenging. So Scala back then and C++ were incredibly challenging to make precise tools for.

So, and that was that was, that was, the basic pitch. And I quickly understood that, yeah, they were right. And this was something that was worth a shot in the sense that it was not completely hopeless, not completely dead in the water. I had no idea if we could pull it off. It was then when we actually sketched like some initial features on the whiteboard, just because JetBrains is generally run by engineers. Hold that thought from Android on how JetBrains is genuinely run by engineers. This is because I happen to know another company also owned by engineers. Sonar, our season sponsor.

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cold quality, join us at the Sonar Summit. To see the agenda and register for the event on March 3rd, head to sonar source.com slash pragmatic slash sonar summit. so everybody I talked with were deeply in the in the weeds with IDs and everything in new programming languages very well and we had a very technical discussion so I don't remember exactly

all of the features we're talking about but the current syntax for extensions in Kotlin was already there and I don't remember why exactly we focused on extensions but it was there so you know from day one we're basically building on top of ideas from other languages like extensions obviously came from C-sharp.

Yeah, so it was a very exciting conversation. But I didn't make a decision then because I was in Tartu and I needed to finish there. And it took me a few months to finish. And then I came to St. Petersburg for one month because after that I had an internship scheduled with Microsoft Research in Redmond. So I was going to Seattle to stay there for like three and a half months. And I was like, okay, guys, so I have this month. work in the office and we can try to sketch things, but then I'll go into Microsoft and then I

will decide whether I commit or not. Which in hindsight, I mean, well, I made the right decision in the end. I had a great time for this month or so. I worked with the guys in the office. It was mostly Max Schaffir, we were working with. And it was incredible. Like, we had such great discussions. And I actually saw Max this morning. And it was like, it was great time. So then I went to Seattle, did something completely different. There are Microsoft researchers, saw really great researchers. Working there actually was exposed to, like, the top-notch level of academia for the first time was very insightful.

But after that, I kind of realized what the question was, whether I want to sort of try to pursue an academic career, which, you know, I didn't feel like I was really built for that and was not sure whether I can be like a good researcher in my own or I'll have to follow in somebody else's footsteps versus like do a crazy thing and build my own language here. And I'm like, okay, I'm doing the crazy thing. So for those of us engineers, which will be the majority, who have not built a language from scratch,

how do you start with it? Like, you know, we know, speaking for myself, I know how to write code, I know how to open editor, I know how to write Hello World and a more complex app and even more complex one. How does a language start? In our case, we basically talked a lot for a few months. So it's, I think not everyone is like that,

but I think the best when I'm talking to people. And this was the ideal environment because we were basically discussing things with the Macs constantly. for many months. And there were a few presentations internal that I made at JetBrains and some of the slides survived so I can see, including my spelling mistakes in those slides, my English wasn't as good then. And you can see some of the evolution through those slides and I think there's a recording of one of those presentations. So we were basically doing wide board design for some time. And the great thing about doing this at JetBrains was that there were a lot

of people with opinions about not so much how to make a language, but what problems do programmers face and what they like and don't like in other languages. So I had tons of tons of input from other people and very good people. So that helped. And I really, I don't think I realized how special that environment was back then. Like, I was 26, to be clear. And I had no idea how things were done in general. But somehow these people just trusted me.

I'm not sure it was very rational in their part. It worked out, but I'm not sure I would recommend anyone to do this. And so in the first few months, I understand that you kind of whiteboarded and you kind of rolled down how you want this language to evolve.

You kind of, you know, like, wrote out like, we're going to have these features. Or how can we imagine? I guess the easiest way to explain this would be like this. So it basically went off what the pains were with Java. And there were quite a few, and there was a lot of experience of using Java

across the community and inside Jed brains. And we kept making lists of things we wanted to fix, and I came up with some ideas and some other people suggested other ideas about how things can be fixed and what is an actual problem and what we don't care about and so on and so forth. And for some time, I was just, you know,

pieces of the puzzle basically laid out in a table without fitting together, and then at some point we started fitting them together. And I was just doing a lot of that in my head, which is not the best way, but this is how I knew how to do it. There were also some crazy ideas that we thought were important back then. For example, I wanted to implement multiple inheritance, fully fleshed multiple inheritance, which was a dumb idea. Multiple inherits, meaning that a class can inherit from like several classes. And you have to take care of like, complex.

conflict resolution and all sorts of edge cases. Right. Yeah. So the actual challenge is not so much conflict resolution in terms of methods, but initialization of state. Constructors are really hard. And I was actually someone outside of Jibran's who explained to me that was a very bad idea and I'm very grateful to them.

Yeah. So, you know, there were crazy ideas as well. And some of them just pull off over time as we were discussing or prototyping. and I think I started writing code maybe six months in or something like that maybe a little earlier than that and I started with a parser and it was actually it was a very

also a very unique way to start a language because the idea was to start not with a compiler but with an ID plugin I have it in the editor first which is you know an ID plugin shares a lot with the front end of the compiler so it's not absolutely crazy

but I was just relying a lot on the infrastructure that was available in Intelligent ideas of all the parsing infrastructure and it was awesome. Like, parsing infrastructure and intelligence idea is better than anything else in the world because it's the heart of the idea. It has to be incredibly fast and very robust and so forth.

But then later, someone who knew the infrastructure a lot better than I do had to factor that bit out to make the Colin compiler autonomous. And it was the Dimirov who did that. And he's an awesome engineer. He's probably one of the best people to refactor a large code base and then take this one bit out of something that was already 10 plus years old back then.

So we started with this ID plugin. I think Max wrote the scaffold and I actually plugged in the parser and everything. And that was an interesting story because it was very interactive. So I could show off the language as if it existed because it had some tooling. But I couldn't compile anything in the very beginning. And that was actually a very good way to experiment with the syntax. But then soon after I started working on a full-fledged front-end and on some translation and Dimitri and Alex Skatchman were working on the back end.

Everybody was part-time. When you say you work on front-end, they work on back-end. In a language context, what does that mean? It's slightly different in different languages. But basically, the front-end is what deals with the syntax and with the checking and understanding what the program means. and the backend is what translates to the executable code. In our case, the front end is like reading the text and parsing and doing types and all that,

and the backend generates Java bytecode. And Kotline has multiple backends for different target languages like we have Java backend. We have a native backend for our, like iOS and other native platforms and JavaScript backend, WASM backend. At that time, nobody was full time working at this project. even I was part-time, a PhD student, part-time, call on developer. And it was like in the very early days.

And then at some point I gave up my PhD and focused 100%, which was also like, isn't it a weird decision to start a new language part-time? Yeah. Looking back, I was young and stupid. Yeah, there's a saying that we didn't do it because it was easy. We did it because we thought it was easy. Absolutely that.

I didn't realize how hard the problem was. I also had an unreasonable amount of hubris. I just thought I knew how to do everything. I didn't, but it worked out in the year. So when the language started, what did you call it internally? There's always internal code names, right? Right, yeah. So I don't think there was a discussion of this first name at all.

It was generally understood that the language will be named Jet, and it was logical. So we had all the code base was using the name Jet. or we had a jet parser and, you know, jet editor or whatever, jet highlighter, something like that. And then someone realized that the name was trademarked by someone else. And it was actually people we know, they are in the Novosibirskan, Russia, doing something, it's not a language, but it's a compiler. And we couldn't use it. And this is when we started looking for another name.

It was very painful. Like looking for names, guys, this is so bad. It's one of the worst things, because you never know what name will work, unless you want to do like an extensive study. And then all the good names are taken, of course. And then some of the names that are not taken are not taken because they're not really Googlable. And, you know, some people are just very brave,

people who named their language go. This is why people now call it Go-Lang, because otherwise you can't identify it. It's a verb in English, a very common way. Yeah, so we had weird options, and in one of my old presentations, I found a list of early names, and we had Robusta there as a flavor of coffee, and we had up, for example, or G, or something else like that. And those weren't great. By that time, other languages were popping up, and one of the alternative languages was called Ceylon,

And the logic was that Java was the island of coffee, and Ceylon was an island of tea, and Dmitri Jemerov basically looked out of the window and said, okay, we have an island here in St. Petersburg, in the Gulf of Finland, there's a big island called Kotlin. And it's a good name in the sense that it's very Googlable. Nobody uses it for anything. It's very recognizable.

It's not super smooth for many languages, but it's kind of okay. nobody was in love with that name and we were kind of hesitant and you know Kot means a bad thing in German and also there was like some negative connotation in Mandarin I was told or something like that

you know it's always some language has some nasty association with any word and we basically were super hesitant so when we announced and we had this deadline so we were basically putting this off when we announced we were still not sure so we called it we decided

would be a code name. We call it Project Kotlin to have a wiggle room to later replace the name, but it stuck. The first thing we did, we put out a basically a confluence page with a description of a language. It was just a bunch of wiki pages, and there was no compiler available, I think. And there, you know, the word Kotlin appeared many, many times. And I was like, my God, this thing doesn't, like, I can't do search and replace and then change the name everywhere. So the workaround that I came up with was create an empty page called Kotlin. And so it has a name. And then everywhere else you mention it as a page.

And when you rename a page, it gets renamed everywhere. So this is why there was an empty page called Kotlin in that documentation. But yeah, the name stuck. And it turns out to be not a bad name. So when it started, what were the main differences with Kotlin compared to Java? Because Java was what was the big one? How did you explain to developers, you know, who initially started on board or wanted to give it a go? Yeah, I guess there were a few major selling points, and then there were other things on top of that.

When we started, like in the very beginning, we didn't have null safety in mind. Nall safety came a little later. After one of the internal presentations, it was Max Rfeer, who invited Roman Yelazhar, who later was the project lead for Kotlin. and Roman came and listened to the presentation, gave some feedback, and said, like, guys, if you want to do something really big for enterprise developers, figure out null safety. And we did.

And it took a while. So in the very beginning, it was the general idea of, like, what makes Java feel so outdated. And there were a bunch of things. Lambda's were very big. The general, like the general feeling from Java back, then was it was very verbose. It was called the ceremony language, you know, a lot of people were grumpy about too many keywords like public static void main is something everybody was really grumpy about. But also, you know, there were getters and setters for every property. There

were, you know, constructors and overloads and all that stuff that looks like boilerplate because it is. Yeah. And it's super annoying to type out. Yeah. And, you know, the problem with boilerplate is, on the one hand, it's annoying to type out. But tools can generate it for you and fold it and so on and so forth. But the bigger problem is always readability. So reading is more important. Reading code is more important than writing code. We do a lot more of that.

And with boilerplate, it's terrible because if some tiny thing is different in the middle of completely standard boilerplate code, you'll miss it. You become blind to it and you can debug for days, not seeing them. So, you know, that was the point of sort of modernizing Java, making Java programs be more about what they do and less about the ceremony of making the compiler happy, basically. And, you know, type inference was also a big thing because Java was repeating types a lot

and many other things like that were like semicolons, you know, the modern languages of the time already got rid of semi-colons. And so in Kotlin, you also got rid of it? Yeah, yeah. So we got rid, basically, in terms of syntax, we got rid of semi-colons and duplicated types, and that was a lot of noise across the code. What does it mean that Java had duplicated types? Yeah, so in that version of Java, when you declare, say, a local variable, you say it's a list of string called strings equals new area list of string.

Oh, yes. I remember this one. Yes, yes. You need to type it out twice, and if you get one of them wrong, compile error, etc. Right. So, and at the best, you could omit the second mention of string by using a diamond operator, but that only came later, you know, basically it was very robust, especially if you typed a long. Like if it's just a list of string, it's sort of not so bad, but if it's a map from something to some to a list of string, for example, that's already really long and you don't want to read that. So, and a bunch of things like that were really annoying to a lot of people, especially compared to C-sharp or Scala. So we did all of that. And then, you know, on top of that, there were other value-ed features, and null safety was a big thing that would spend multiple years, actually, on implementing.

And I think it's one of the main differentiating factors now for Kotlin, alongside with extensions and other things. but null safety is one of the core features. And can we just spell out why null safety is so big? I mean, I just today I came across a bug on the, I couldn't send a package because in JavaScript and the Dutch post website, there's a null issue happening in production.

But, you know, like before Kotlin and a lot of languages, why is it such a big problem? It is. Yeah, so dealing with null references is a big hassle in most languages and I think it was Tony Hoer who called it a billion dollar mistake at some point because like introducing, I think it was about introducing

null pointers to C or something. So basically when we look at all the runtime errors that we have in Java code, I think null pointer exceptions will be at the top. You know, the type system of the language is supposed to protect you from those unexpected errors. So there are errors you're designed for,

and maybe errors that are not even your fault, like a file system error or something like that. But there are also errors that should be prevented by the compiler. So for example, class cast exception or missing method error, for example, are things that the compiler is trying to protect you for. It's trying to make sure that this never happens in your program if, unless you will switch off the check by making an enforced cast or something.

And with nulls, it's not a thing in Java. Like, anything can be null, and if it's null, it will just fail. Yeah, it throws an exception and program dies. It's a very common thing. So a lot of people are kind of used to it, and there are, like, different ways of being disciplined about it and so on and forth. But basically, this is a plague across any copy. You know, there are different approaches to this. And in Kotlin, we took the approach of a enforcing it in the type system, but all

also making it free at runtime. What does that mean that you made it free? So one very common way of dealing with nulls is to use something like an option type, where you have a box, which might be empty or might have an object in it. And that box is not free. Like you have to allocate it,

you have to carry it around everywhere. And, you know, this easily creates a lot of objects in the old generation for the garbage collector, so it can be challenging. And what we did was just have a direct reference. At runtime, our nullable or not null reference is the same as Java's reference. All we do is compile time checking and some runtime checking when we cross the boundary. But that's a lot cheaper than allocating objects.

Although, you know, the runtime is getting better and they kind of can optimize some of those objects away. But still, like, it's an overhead. What are features that you took in from Kotlin that were inspired by other languages, that you admired. A lot of them. I have an entire talk about this. It's called Shoulders of Giants. And we really learned from lots and lots of languages.

And it was always the point. Andre just mentioned how Kotlin was built on top of the shoulders of giants, taking good ideas that existed, not reinventing them. This was one of the reasons Kotlin succeeded as much as it did. But jumping forward from 2010 to 26, one thing that is totally different today is the speed of things. AI is allowing nimble teams to build faster than ever before. Companies that used to take years to move into the enterprise are doing it in months.

This speed creates a new problem. Enterprise requirements, authentication, security, access controls show up almost immediately. This is where WorkOS, our season sponsor, comes in. WorkOS is the infrastructure layer that helps AI companies handle that complexity without slowing down. SSO for enterprise buyers, MCP off for agendic workflows, even protection against free trial abuse with radar. teams like Open AI Cursor, Perplexity, and Versailles rely on Workerwas to power identity and security as they scale. If you're building AI software and want to move fast and meet enterprise expectations, check out WorkerWos.com. With this, let's get back to Andre and how Kotlin was standing on the shoulders of giants.

So the slogan for Kotlin was pragmatic language for industry. And the pragmatic bit, which I mean is a nice sort of nice rhyme with your podcast, the pragmatic bit was kind of coming from the experience with Scala being called an academic language and a lot of people having trouble getting their heads around a lot of the very smart tricks in the design. And so our idea was like we're not doing academic research here. We're not trying to invent

anything. Like if we don't get to invent anything, it's a good thing, not a bad thing. And I think from the engineering perspective it's generally a good idea to do this. Usually you end up making something new. But most of what you're doing shouldn't be very new because you want familiarity,

you want people to easily grasp what you're doing, and this has to be familiar from other languages. And also, if you're taking, you know, building on top of the ideas of other languages, you have the benefit of them having tried it, and you can look at their designs and their community's reactions and all that and the implications all over the place, and that gives you a huge benefit.

So we did a lot of that. And I think the language that influenced Codlin the most is, of course, Java, because, you know, the entire runtime of Kotlin is the JVM, and we depend on that. But apart from that, Scala had a huge influence, and we used so many ideas from Scala, from, you know, primary constructors and data classes and valves and vars and all these things, and two, some interesting tricks about how generics work, for example, you know, variance, declaration say theorians is a great idea of Martians

and it's a huge pity that it didn't make it into Java design. It was flipped at the very end of the design process to what Java has now and it's definitely the the Martyr's idea was much better. We had to sort of on the boundary, modeling Java boundary, we had to fix the problem of Java having it

different and figured that out. There were like many, many ideas we took from Scala and that was very helpful and we usually we transformed those ideas a little bit to adapt to our setting and to build on the knowledge of how it actually works in practice

and we left some things out and simplified some things, for example, Scala had traits and traits are a very powerful construct where it's like an interface and you can have method implementations in traits but also in Scala

traits, you could have fields as well, properties that what you couldn't have was constructor arguments. Like, you have always have a default constructor and can initialize all your fields and it's not as bad as multiple inheritance and so plus plus. But it's still a little complicated when it comes to in what order you're calling the constructors that we decided we don't want to deal with that. It's a complex algorithm. It's hard to explain.

let's just get rid of the state and interfaces and only have method bodies. I think it was a good compromise, especially given that Java ended up in the same place. It was easier to integrate. Yeah, so Scala was a big influence. C-sharp was a very big influence. Extensions, of course, and we learned quite a lot from how C-sharp compilers do things. There, there was also one particular trick that makes C-Sharp syntax a lot nicer,

nicer than Java's and nicer than Scalas that will learn from C-sharp. It was actually my colleague who worked on the C-sharp ID who told me about this, which is basically a super-pragmatic thing they do in C-sharp. When you call generic functions,

you use angle brackets inside an expression. But the thing is that there is no such thing as angle brackets. There is less and greater. Yeah. Right? And the parser can easily get confused and think that this expression, since we're not in type context, it's an expression context,

this expression is a comparison. It's not an inequality, right? It's not a call. And this is mathematically unresolvable. It's an ambiguous grammar. Yeah, you can do anything about it. And the way other languages handle this is Java, for example, when you're passing type arguments to a call, it has to be after a dot.

So you say collections dot, angle brackets, form. function name. Really awkward. Which is kind of weird. Yeah. And the way Scala deals with that, they use square brackets for types. And then arrays can't use square brackets, so they use round brackets. Which is unfamiliar.

Like, it's not the end of the world. Scala is doing fine, but still. And C-sharp uses angle brackets because there's a hack in the parser that basically disambiguates ad hoc. and we did the same or something very similar and it just works and this index is very familiar and very intuitive

and we're very happy about that when you read it as a person I never get confused like this is not a smaller sign like I know it's a genesis yeah okay most of the time it's not a practical problem yeah and it's then there is a way

to disambiguate if you like so C Sharp was a big influence Groovy was a big influence as well Jebrin's used Groovy for build scripts and there were incredibly useful patterns in the groovy syntax that they called builders, which is not about building programs, but building objects.

And this is what inspired something fairly novel that we did in Kotlin, which was types builders, where we had the same syntactic flexibility, or almost the same syntactic flexibility as groovy, but it was all typed, and we could make sure that all the arguments matched and so on and forth. So all that side basically was, inspired by how groovy people did this and reworked into a typed setting.

And this is why we have, for example, extension function types, and this is why we have dangling lambdas and other things that are actually very nice and dacted constructs. So, yeah, many, many things came from different languages. A less known language called Gosu, I think it was what inspired us to do smartcasts. What are smart casts? Oh, yeah. So I think SmartCosts are one of the nicest things a compiler can do to a developer,

because it's a very common situation when you say, if X is string, so you do an instance of check, basically, then do something with X. The annoying thing is that in a lot of languages, you have to cast X to string again. Like you've done the check. If you know it's a string, but then you need to write it out again.

Yeah, so you've just done the check, but you have to say string again to make the compiler happy. So smartcasts basically get rid of that. So that cast gets figured out automatically. So if that's a string and then inside the bracket, you can now use it because it's a string. Yeah, you can use it as a string,

and isn't it an easy thing, right? So nice. Yeah, it's a very nice thing. Yeah, it's a pretty complicated algorithm. Because, you know, variables can change values and the check that you've just made can go stale. And, you know, there's a bunch of algorithmic trickery around this. And you can't do a smart guy on any expression.

It has to be a certain type of expression that can be stable enough and so on and so forth. But, you know, it's a very nice thing. And you can get rid of so much noise in the code because, like, all the code in the world is riddled with this instance of cast, instance of cast. So we wanted to get rid of that. And it worked. And it was fun to implement.

were things that you looked at other languages, you considered, maybe we should bring it in, but you, after debate, you're like, no, let's just leave this out. Like, not all of them, obviously, but some of the big ones that kind of came close. We had a design for pattern match in Kotlin that was inspired by functional languages like Scala and Haskell and others. But at some point, it was early on when I was still working on the parser, I just realized that this is a huge feature. So when I was sketching it out on a piece of paper, it looked like

a very useful thing, you know, and just another feature in the language. But then when I started working on the parts, I realized it's an entire language in size. Like, you have to create a parallel universe in syntax for pattern matching. I was like, okay, this

will be a lot of work. Let's postpone it. And then later on, when we were doing review for 1.0 or maybe a little earlier than that, I just realized that smartcasts plus we have something called destructuring. Together, they give us like 80% of all the good things pattern matching can do to normal developers. And then there is another group of developers that can be very vocal, which are mostly compiler developers. And people super into functional programming.

And they have a point, but that point is only relevant to them and there are not very many, that we decided to not have pattern matching back then. And, you know, maybe there comes a day that pattern matching gets added to Kotlin. And pattern matching is, is it in the case? Yeah, it's... So you can have like a lot nicer case statements, a lot more expressive ones, right?

Yeah, so generally, so Conlin has this compromise where you have our version of Switch case, which is called When, and you can have smart costs there. So you can say, like when my expression is a string and then use it as a string, or it is a pair and then you can use it as a pair.

So that kind of gives you a lot of the niceties of pattern matching, but some things you can't express like that. And, you know, that was, I think it was a good compromise because it's a really big feature. It's hard to design well. That would be a lot of work on the tooling side. So, you know, but maybe it gets on the roadmap one day. I'm not sure.

Java is trying to get towards. pattern matching. So we'll see. Maybe they kind of make it more mainstream. Why did you, I'm an infamous turner operator, which is when you write out something, the question mark, and the dot? And it confuses new developers every single time, if you've not seen it before. Yeah. Was it for readable reasons? This is the saddest story, I think, in the design of Kotlin. I didn't realize how much people liked it. And yeah, so the reason was, so Kotlin used this principle from functional languages that everything we can make an expression is an expression.

So if is not a statement and Kotlin is an expression. And the ternary operator is the sort of a patch on the design on C and other C-like languages that makes an if expression, basically. And the logic was, okay, we have if as an expression already, can we just get rid of this extra syntax construct, especially given that it's using very precious characters. Like there is a question mark and a colon, and we might find some other use for that.

So we decided to not have it. We used question marks for nullable things and columns for types and so forth. But it turned out that if, as an expression, it's pretty verbose, people don't like it. And like, I resisted for some time, and then by the time I agreed, it was too late because you can't retrofit the turner operator

and the current syntax in Kotlin because it just doesn't agree with how other operators are done. So you're actually sad about it not being there a little bit? Yeah, I think in retrospect, it was a mistake because, you know, pragmatically, it's more use than harm to have it, but we just can't retrofit it. What are some other interesting features that you like about the language that you added that?

We could just explain for those who are not familiar. Okay, so the good ones, there's quite a lot of them. So one feature that is not a traditional kind of language feature is a Java interoperability. That's probably the single thing we spend the most time on. And I always say that, you know, if someone offers you a job to create a system that interoperates transparently with another huge system you don't control, ask for a lot of money. It's a very tricky deal to figure this out. Interoperable means that from Kotlin,

you can invoke Java, and from Java, you can invoke Kotlin, and I mean, you do a bunch of work there, but it just works in the end as a developer. You don't need to think about it. Yeah, so the idea is, whenever you have a Java library, somewhere in the world,

you can always use it from Kotlin. And it was a big selling point. Because, you know, if you start as just a language in a vacuum, and you don't have any libraries, that's not a good start. In this direction, definitely, it was an absolute requirement for Kotlin.

But also we had the requirement to go the other direction in an existing project. You could just rewrite parts of your code from Java to Kotlin, and everything keeps work. And some libraries actually did that. And many projects started using Kotlin bit by bit. You know, all other people started with just writing tests. but then, you know, you start adding things in codlins, new things, for example.

And all the Java code around that has to transparently use the Kotlin code. So we put a lot of efforts into that. And that was fun. Can you explain to us as engineers? Like, you know, it sounds like it was a frigging big project. What is the work, right? Because from the outside, again, I'm just being your average developer. We're like, all right, I'm invoking, okay, I'm invoking a Java class.

and things I can think of like, well, maybe, you know, Kotlin or Java doesn't support things in a certain way or maybe, but I mean, is it really that hard? What is hard? Tell me, tell me, I'm dying to know. So one thing to note here is that we don't control the Java compiler. So we somehow need to make it work so that you, in your Java code, you make a call into something that only exists in the Kotlin source. and the Java compiler somehow agrees to call it to begin with.

It's not a Java file, it doesn't know it exists. So the way it actually works is when we build a mixed project, what we do is we first compile all the Kotlin code, and that can depend on the Java sources in the project. So we have a Java front end baked into the Kotlin compiler, so we can resolve everything in the Java code. And then we produce class files, so binaries, for the JVM, that the Java compiler can read.

So when Java compiles, it takes Kotlin sources as binaries. And this is how it works. So, you know, we would have to implement a Java compiler otherwise. Fortunately, Java has separate compilation. So this works. So this trick means that, you know, whenever you have in your tooling, like in your ID, for example, when you navigate from Java sources to Kotlin sources,

has to be a special trick. So someone needs to go and teach the Java world to know about Kotlin well. Of course, the ID doesn't do the compilation to navigate. But in the compilation time, we don't control the compiler. So we did our own ID. So we could do something about the Java tooling, but we couldn't do anything about the Java compiler.

So that's trick number one. And then, you know, when it comes to incremental compilation, it becomes even funnier because Java incremental compilation is a complex algorithm on its own. And now we are incrementally compil. telling two languages at once, and that's fun. And, you know, incremental compilation algorithms are generally a very messy, very complicated heuristic

that you have. So, you know, there are tons of corner cases. So that's like one example. But then, you know, you start making interesting new things in Kotlin. You need to expose them to Java. You need to make sure that whatever fancy thing you have, Java can actually interoperate with that. And one example there would be Kotlin.

We figured out how to make Java collections nicer in Kotlin without rewriting the collections using the same library. So Java collections are what's called invariant because they're all readwrite. So if you have a list, it always has a set method. And that's a little bit of a problem because whenever you have a list of object, you cannot assign a list of string to that.

And that's a little annoying because, you know, you want to be able to represent a list of anything, and you need to play with question marks, wildcards, and stuff like that. It would be very nice if we had a read-only list interface that doesn't have a set method, and then there is no problem in assigning a list of SAP classes to a list of superclasses. But this interface doesn't exist at runtime. Right, right, we can't just invent it. Or can we?

So we actually can. No. And so in the column compiler, we have. have this layer of trickery specifically for Java collections where Kotlin always sees Java collections. Like if they come from the Java well, they are read-right, mutable collections, we call them, but... Mutable, right? Yeah. Yeah. So the Java collections are always mutable. Yeah. Or platform mutable, I'll talk about that layer. But when you do it in Kotlin, you can actually distinguish between read-only mutable collections and it's all very

nice on the Codlin side, but then when Java sees the Codlin collections, they are normal again. Like, when we expose them through binaries, the Java weld always sees them as normal collections. They're mutable for Java, and it's all right. Okay. I'm starting to see why you said, like, you need a lot of money for this, because this is just one of many things, but this itself sounds like, I don't know how you solved that. Yeah, so just to add a little bit of detail to this. So the nice thing about those three-dollar collections is that you can pass a list of string for a list of object, right?

Wouldn't it be nice if a Kotlin method that takes a list of any could accept a list of string in Java, but aren't we erasing all the Kotlin nice stuff? We are, but we know that this list is actually what's called covariant so we can expose it to Java as a list of question mark extends

and not just list of object. So, you know, it becomes covariant for the Java weld as well, and that's like one hack that makes it a little more transparent, and there's a bunch of them. So, you know, so that's another thing that we had to play with. But the biggest thing is, of course, Nolable Types. And actually, we handle Nolable Types and these things with collections kind of similarly, which makes the whole typing layer of the interrupt quite interesting.

But basically, so Java doesn't know anything about nulls, right? Well, it knows about nulls, but not about nullable types. Yeah, so it does not exist. Yeah, Java doesn't know about nulls at compile time. Yes. So in terms of types, it's just not represented. So technically, every Java type is a nullable type. And this is where we started.

We said, okay, so Kotlin types can be not null, and it's very convenient. And when you have a not-null type, you can just call a method on it normally. Right? but if something is nullable, you can't just do reference it. You have to first check for null and then use it. Right, or there is a safe call operator question mark. Dot will just propagate knowledge on the left hand side. So we started with saying, okay, all Java types are nullable,

which is a conservative, like very mathematical way of treating. Yeah, this is correct, right? Yeah, you're not going to be wrong with that. Yeah, and we implemented that, and we started using it inside JetBrains, and the feedback was horrible. Like your code is plagued with those null checks and you know that there shouldn't be there because you can't express anything on the Java side

the right way and there were like we had some annotations for the Java side. It was also brittle and not always worked because there can be long chains and stuff and some libraries just don't have the annotations. And we struggled with that for a long time. And basically we realized that this assumption that everything in Java has to be treated as nullible

doesn't work. This was a turning point where we sat down and reimagined the whole thing and we worked with a great type theory type practice, I would say, guy from, I think it was back then he was in Cornell. Ross State. So Ross helped me figure out the sort of mathematical side of how you can represent those types that come from Java

and should be, like we should be aware of that they are from Java and can possibly be nullable. but we shouldn't treat them as nullible because it was very inconvenient. And Ross put together a very nice sort of calculus about those, and when we started implementing it, all the nice things are gone. The actual, yeah, the mathematical beauty is completely gone from all that. And I think we took the general idea of sort of splitting a type in two, and everything else is just very messy, industrial kind of thing.

That's not sound, but it works well. Okay. And interpolably sounds like it was a journey, but a necessary one. How long did it take? Can you give me just as a sense of like how many people working on it, how much? Because I think in traditional project we can get a sense, but I have no idea with a language. How does this work? And how long did you think it would take versus how much it took?

Yeah, so let's start with that. So every time I was asked when we were going to release Kotlin, I would say one year from now. Yep. And, you know, this is, this is not a plan. I had no idea. I had no idea. I also had the illusion that the initial version I was building was a prototype and we would write everything.

And I'm sure a lot of people out there have been there. I think that prototype has been written more or less completely now. But it took six years, something like that. Yeah. So maybe longer, actually. Yeah. So I had no idea and I always said like, okay, a year from now, we all's far enough, we'll probably be done by then.

In practice, we started in 2010, yeah, autumn of 2010, basically. And we released in 2016, February, 2016. So, you know, it was a long time, five-ish years. And that, you know, in part was just because I didn't know how to manage projects. and my initial team, the people who worked full-time on the project, I looked up on GitHub to verify that. Everybody who, almost everybody, who joined JetBrains to work on Kotland,

was a fresh graduate. Because I used to teach, and I had some good students, and I knew how to work with students, and so basically everybody on the team was a student, apart from a few veterans from JetBrains who were helping, not all of them even full-time. So we started getting experienced engineers on the team

a bit later. And, you know, to be fair, a lot of those people, you know, people who are following Kotlin know those names. People who are core contributors who built out, like, absolutely foundational parts of Kotlin,

joined as fresh graduates. And they became great engineers. But I think I've, I overdid it a little bit. So it's great to have, you know, younger people have no fear. And that's wonderful. But, you know, the balance was not right.

And how big was it seem initially and then towards the release? So we started, we started out basically with four people part time. And yeah, we went like that for maybe a year or something. So the initial prototype was built like that. And then people started joining in. By the time we were released, I think it was. around 25 people or something. And the team grew quite a bit.

So by the time I left in 2020, it was about 100 people in team, 70 of them engineers. So it became a pretty big undertaking. Can you tell us about the development process inside language? I think a lot of us are used to building, you know, like services, backend services or products or mobile apps, etc.

They typically have a release process. How does this work inside a language? Like what is your release process and what is the, I guess, best practices like, do you even do code reviews or, you know, like, how can we imagine? Because, again, it feels such a rare project. There are people building languages, but not many of them. Yeah, so one peculiar thing about building languages is what's called bootstrapping when you write your compiler in your language. Oh, nice.

Which means that, you know, to compile your code, you need a previous version of your compiler. and you better agree with your colleagues which version it is. It can be really tricky, especially when you do things about the binary format. And there is quite a lot of bootstrapping magic going on. And I don't think you can reproduce the Kotlin builds from scratch. Because if you just take a snapshot of the Kotlin repo, you can only build that with the Kotlin compiler. And I don't think we kept all the bootstrapped versions. So it might not be really possible without a lot of manual intervention

to rebuild all the sources from the very beginning and reproduce all the versions. Because sometimes, you know, we had to commit a hack into a branch and use that branch as a bootstrap compiler for the next build and then throw the branch away. So that was like a one-off compiler used to facilitate some change in the binary format or syntax or something. So that's a separate kind of fun.

But generally, I mean, many, many practices are very similar. Like, had code reviews pretty early on. It's my personal quirk, again, that I like to talk to people. So in code reviews, I often just sat together with someone and either they reviewed my code or I reviewed theirs. But this is, you know, I can't argue that it's much better or worse. It's just how I prefer it because I like talking to people. So code reviews, yes.

And of course, we had an issue tracker like everybody else. was always open, so everybody can submit bugs to the cotton bug tracker, which was very helpful. It's hard to manage because there will be like with usage, there will be a lot of bugs and a lot of feature requests and all kinds of stuff. But it's worth it. You have a communication channel. Release cadence is a very difficult thing to figure out for such projects. Because one big consideration you have for languages is backwards compatibility.

in part this is what delayed 1.0 because we wanted to be reasonably sure we can maintain compatibility as soon as we call it 1.0 in part because it was the expectation especially Java is incredibly stable and very good with that until Java 9 came about and also Scala had a lot of trouble because they were breaking compatibility a lot

and the community was struggling really So we really didn't want to repeat that. But, you know, there turns out you can even break compatibility Python 2 to Python 3 and survive. So, you know. Barely. Barely survive. They're doing very well. Now they're doing well, yes.

Yeah. So we were really serious about that. But basically what it means is you start doing interesting things like deprecation cycles. And so we actually invented an entire tool set for compatibility. compatibility management. So before 1.0, we tried to help people migrate. So we had those milestone builds. Embarrassingly, we had 13 of those.

And, you know, when we broke the language in major ways, we tried to provide tools for automatic migration. That's nice of you. Which was, I don't think it was a standard practice in industry back then. Now people are doing it more. So I'm like very happy to have sort of popularized this idea. And then when we were prepared, for 1.0, we did a major review of everything and took a year to sort of view all the design

and what we're doing was basically trying to anticipate what changes we might want to make or what new features will require and to basically prohibit things that might block that. So we try to make sure that the changes that we were planning were guarded well by compiler errors to make sure that users don't accidentally write anything that looks like a new feature. And that was fun. Like we had design meetings, I think every day at some point. Basically working at that like, okay, let's outlaw this, let's prohibit that. And we prohibited a lot of stuff correctly and some stuff incorrectly.

But, you know, generally worked out. So this compatibility thing was a big deal. there's also a lot of stuff that we didn't anticipate. So we had to figure out ways to manage this. And there is something in Kotlin in the Kotlin compiler called Message from the Future, which is basically when in a newer version of a compiler, you introduce something that the old compiler doesn't understand. You have different options.

And one option in a lot of languages go for is the new kind of binaries is completely unreadable for the old compiler. So the version is higher. I don't read it. That's it. I bail. But it's a little hard for people then to manage their versions because new libraries, new versions of libraries come with the new compiler expectations and you have to migrate your entire project to do that. It's a little annoying. And if what you're adding is like one method that basically invalidates the whole library for an old compiler, that's not great. So what we're doing, a newer compiler, can write something into the binary that tells the old compiler,

okay, this method is what you can't understand, but everything else is fine. Wow, that's smart. Yeah, so we call this a message from the future and it can provide some details. So there's that, and there's also the discipline of experimental features, which is incredibly helpful, and I am very happy to see other languages doing it now,

and even Java does experimental features now, which is wonderful. Andrea just talked about experimental features in programming languages, and how that used to be rare back in the 2010s. What this reminded me is that running experiments in production used to also be rare. Not because teams did not want to do it, but because doing it meant building a lot of internal tooling around it. Assignment, rollouts, measurements, dashboard, debugging, the whole thing.

For a long time, only a handful of companies really pulled this off at scale, companies like meta and Uber. Which brings me to Statsic. Static is our presenting partner for the season. Static gives engineering teams the tooling for experimentation and feature fly. that used to require years of internal work to build. Here's what it looks in practice. You ship a change behind a feature gate and roll it out gradually, say to 1% or 10% of users at first.

You watch what happens. Not just did it crash, but what did it do to the metrics you care about? Conversion, retention, error rates, latency. If something looks off, you turn it off quickly. If it's trending the right way, you keep rolling it forward. And the key is that the measurement is part of the workflow. You're not switching between three different tools in trying to match up segments and dashboard, after the fact. Feature flags, experiments, and analytics are in one place, using the same

underlying user assignments and data. This is why teams and companies like Notion, Brex, and Atlastion use Statsig. Statsic has a generous free tier to get started, and pro-pricing for teams starts at $150 per month. To learn more and get a 30-day enterprise trial, go to Statsig.com slash pragmatic. And with this, let's get back to Andrei and experimental features in Kotlin. So we did quite a lot of work, you know, when you were doing some experimental. This is something that's supposed to break. And you want to emphasize this to make sure that the user is aware that, you know, this is something we are not promising to keep compatible. This is something we are going to break. And, you know, we used to put the word experimental

and package names for people to understand that this is going to be renamed. And, you know, warnings when you use language features and we require like compiler keys to enable language features and stuff like that. It kind of helps. So we did quite a lot of that. So all this is an extra layer. Unlike a SaaS system, for example, a compiler leaves behind, but not behind, but creates a lot of artifacts that pin down its history in the world.

There are a source out there and there are binaries out there, and you're guaranteed to encounter them. Every time anyone hopes that, no, this is an obscure case. Nobody will ever hit that. with enough users, you hit every freaking case. And this is so surprising. And I discovered this fairly early on, I think before 1.0, when we had a few thousand users,

I realized that if something's possible, some person out there will actually do it. Now, you got 1.0 out. Can you tell me how Kotlin grew in popularity? When you released it, what was your target audience? and then how did Android happen? Okay, so that's complicated story. Let's try to not get off track

because this is like, has a lot of side-track to it. So when we started Kotlin, we were not really very aware of Android. And I mean, we'd be knew that there was a thing called Android. Kind of ironic. Yeah. From now, message from the future.

Right. Yeah, so basically in 2010, we were focused on the majority of Java developers that was all about the server site. Yep. Back in clear. Yeah. So the most money IntelliJ was making was on string users and you know, everybody knew that this was what the Java platform was about by then. So we were targeting server side developers basically and also desktop

developers because JetBrains had the probably the last desktop application written Java or at least in swing. So that was the target. It was initially not even a plan to do Android. And Codlin got some usage for the server site and it's still there and it's growing there, not as fast as on Android, but still has quite some representation on the server side. But then a few years in, some person on the internet asked us, like whether Cotlin works in Android. And I was like, I heard Android uses Java, the Kotlin should work. We never tried, go and try.

And I think it was either the same user or a different user, came back and said like the tool chain crashes. And it wasn't even the Kotlin toolchain. It was the Android toolchain that crashed. And, you know, we looked into it and it turns out that it's some, some tool in the Android tool chain that's written C that just fails with a core dump, and it's not very clear what's going on.

And we later figured it out, and it turned out that, you know, the Android developers, and the people who built the Android platform, they actually read the spec of the JVM. Unlike the people who implemented the hotspot VM, because the hotspot VM, I suspect, came before the spec. So it was the reference implementation,

but it was actually specified. after it was built. So the Hotspot VM was super lenient to weird things. Like, there would be, like, if we put a flag on a class file that was not allowed for classes, hotspot wouldn't care.

And we ran everything on Hotspot. And so we thought everything was fine. But then the Android side, those were the people who actually read the spec. And they actually implemented it. Yeah, they would complain about everything. And this is why we used Android. tool chain as a testing environment, basically,

because, you know, this is how we could get rid of stupid things in our bytecode, and they helped us a lot with validating everything. But, you know, there were some goutches there, and some legacy stuff nobody cares about in the mainstream Java, just, you know, were faithfully implemented on the Android platform. That was fun. So, you know, and at some point, pretty early. on, I think, I had this

realization that Android was a growing platform which to me then, I don't think I had much of understanding of, you know, dynamics of markets then, but to me it meant that there will be a lot of new applications.

And it's much easier to start completely anew with a new language. So I made sure at some point that we worked well on Android. It was already after the lawsuit. So, you know, the big context to all this was that when Oracle acquired Sun Microsystems, they sued Google for billions of dollars for using Java.

And I think that is settled. It was settled in some way, yeah. And then everyone could go on their own way. Right. But it took years and years to settle. So back then, it was very much a thing. And, you know, so that dispute was somewhere in the background. But, yeah, so basically we.

We saw that a lot of people on Android really liked Kotlin. They loved it. Yeah. As soon as it was stable, pretty much. I mean, I think for all the things that you mentioned, right, like it was just so much nicer than Java. Easier to write, easier to read, lots of nice features. So you use Android as a way to actually, you know,

make sure that Kotlin compiled correctly. And then why did it take off on Android? Yeah, so the situation in Android was pretty interesting because unlike Java server side, that is kind of under control of the teams that develop on it. In the case of Android, there are devices in the pockets of people, right? And when you have billions of those devices, and those devices don't always update the virtual machine.

So people in Android were basically stuck with old Java. And even when Java started progressing, and for example, Java 8 came out in 2014, it was very difficult to roll out this new version of Java across the entire Android ecosystem because it required updates to the virtual machine. And there were workarounds and Retro Lambda really helped and so on and so forth.

But, you know, there was still a lot of people stuck with really old Java. So Java wasn't, you know, on par with Kotlin or C-sharp in 2014, but it still was much better, like, and solve the major problem. But it was not available to the Android people. So there was a lot more frustration with Java in the Android community. And also, there was Swift on iOS.

Oh, yeah. Where, you know, it was a real example of a big ecosystem transitioning from a really dated language to something really nice. Yep. And I think compounding these two things were like the major factors.

And also, I mean, we made sure that Kotlin worked well on Android. Also, very fortunately, at some point, Google switched the developer tooling from the Eclipse platform to the IntelliJ platform when IntelliJ was open sourced back in, I don't remember, 2014, 2013, I think, or something like that. So, you know, it was, we had a nice plugin because everything worked on the Intelogy platform and the same plugin work for Android and many other things were just very smooth.

Well, very smooth. There were a lot of bucks, but reasonably smooth. So it felt like a very good match and a lot of people appreciated that. And we really wanted to somehow draw the attention of the team of Google to

maybe talk about it or something and it just didn't happen. So we released in 2016 and there was, you know, we had some communication with Google in general, but there was no interest in that side. They're like, okay, I guess we'll just keep going as we do. And some people were already building Android applications. And well, some people were building production applications in Kotlin before we released 1.0. And, you know, kudos to the brave people because they gave us invaluable feedback.

But you guys are too brave. Yeah. So, you know, it just grew organically. And when we started in the beginning, I set this internal goal to myself that if we get to 100,000 users, it's a success. Like, I've done well enough if it gets to 100,000. And of course, it's hard to tell how many users a language has, but, you know, you can kind of estimate that. And I think we were on track to get to 100,000 users during 2016. because it was growing, it was in the tens of thousands,

you know, looked good. But then some people from Google reached out and said they wanted to chat. And it turned out they wanted to chat about announcing official support for Kotlin at Google I.O. 2017 that would be in like three months

from the time of that conversation. They were like, yeah, sure, let's do it. What do we need to do? and it turned out we had to figure out quite a few things, but we managed. And I think it was the heroic effort on the side of the Google team. They did amazing things, impossible things there. And I have good friends among them now.

And it was like, it was really, really close. Like we could have missed that deadline, but we figured it out. And, yeah, on our side also we had to make many things work and figure out how we, you know, now we interoperate with the Android Studio better and then, you know, how do we set up processes and everything. But there was like a big legal thing around it.

This is when the Collin Foundation was invented and we had to design the protocols for decision making the Collin Foundation and, you know, Google owned the trademark for Kotlin for one year because of legal things.

It was basically a guarantee from the Jetbrain side until the foundation gets set up. So you can look up the public record. Google was in possession of the Kotlin trademark for a year. But then the foundation was set up and transferred to the foundation. So, you know, it was fun.

It was pretty crazy time. But it was amazing to see how happy people were at Google I.O. when the announcement happened. And then you said most of skyrocketed. You probably blew past 100,000 pretty quickly. Yes. Yes. I think we went, yeah, we probably got into millions that year.

This is what was basically like the moment happening. And, you know, I knew many years before that, I knew that the easiest way for a language to succeed is to be part of a platform. And, you know, like C was part of Unix, basically, or C-sharp was part of Windows or JavaScript was part of the web platform. And I knew that Conlin had no platform. So it was supposed to be much tougher

time for Kotlin than for some other languages. But yeah, the platform came along somehow. Jumping forward to a lot more closer today, you have, you left Kotlin in 2020, later you left JetBrains. What are you doing right now? Yeah, so I'm also working on a language right now, but it's sort of a different kind of language because the times have changed. And, you know, you can look at it from a similar perspective. Like in Kotlin, we wanted to get rid of boilerplate. We wanted to make programs more to the point and less of a ceremony.

And I think this is where we, today we have a great opportunity to do the same thing at a different level. Because of AI, right? Because of AI. It's all because of AI. Yes. AI is great because many things that are obvious to humans are obvious to LLMs as well, which closes this gap between what the machine can understand and what a human can understand. end quite a lot, which means we might not need to write dumb code anymore.

That would be very nice. So on the one hand, the entire history of programming languages is going from lower to higher levels of abstraction. We started with machine codes and assembly was a step up, actually. Assembly language is a higher level language. And then machine code. Okay, yeah. Yeah.

And then C was a high level language back in the day. And then, of course, like managed languages like Java were a great step up and made programming a lot more accessible. And like teams could grow and you didn't have to be a super competent programmer to build working software. And then, you know, things like Kotlin built on top of that success and we raised level of instructions some more. But now we can do even better into that. So you can imagine like a normal program, some application code. A lot of the things in this code are obvious. To you and to me,

so if you ask me to write this code, you don't spell everything out. You explain what the program needs to do, and I can implement it. It will work the way you want. It depends on how detailed the specification is, but you can tell me a lot less than you would have to tell a compiler.

Yeah. Yeah. And so this is the point with code speak. We want to basically shrink the amount of information a programmer needs to tell the computer to make the program work. And from my current anecdotal experience,

you can shrink a lot of the code about 10x which means that a lot of projects out there can be a lot smaller. And it will be a lot easier for humans to deal with that and a lot easier to read and reading is the most important bit and it's easier to navigate and it becomes

the essence of software engineering when you are not like dealing with a stupid compiler you're not restricted by that anymore what you're expressing is what only you know about what needs to happen because everything else the machine knows as well so can you tell me a bit more on

what code speak is or what this language is is it designing an actual kind of formal language is simpler Is it using, of course, we know that AI and LMs and agents can do all the funky stuff. Where is this? What is this? Okay, yeah, so I'll try to explain this. So I think the best way of thinking about Codespeak is it's a programming language that based on English.

It's not a formal language or not an entirely formal language. But it's a programming language. It's a language that's supposed to be used by engineers. But it uses LLMs heavily. and this is like the way new languages will be. Because, you know, you can think about the ultimate language of today as a normal programming language that uses an LLM as a library. You know, there was a time where NPM was wonderful

because, you know, it's a huge repository of all kinds of JavaScript libraries. The No Packet Manager, the biggest package news in the world, right? Right. Yeah. have a huge library out there that you can all... But now you have an even better NPM, the LLM, that has seen all the code in the world, and if you're inventive enough,

you can fish this code out of the LLM. Yeah, you need to handle it to prompt. Right. And the trick is, like, it would be really nice to have a programming language that has the entire LLM as a library, or as a bag of libraries. Right? The trick is to take anything out of an LLM,

you have to use natural language. So the query language to this incredible database of all the knowledge is informal. And there is no way, at least, known today that you can make it formal. So inherently, this ultimate language of today has to be, at least in part, informal. And this is what we're working on.

So it's still in the air, like how formal can we make it? and, you know, it's not the goal to make it super restricted, but the goal is to leverage all the power and support the user, you know, we need to rule out stupid mistakes and things like that. We're still working on that. But the basic idea is if you, instead of spelling out every line of code and every bit of your algorithm, you can basically communicate intent the same way I can communicate it to you,

you will just get there much faster. So one question that I asked Chris Latner was, which I'm going to ask you as well, you're talking about designing a language for software engineers to build software more efficiently, maybe more concise and a new way, and it sounds super exciting. But going to the other side, we have LLMs. Do you think there is a need to design a new type of programming language for LLMs to use more efficiently? That's a very interesting question. And I had a few discussions about this.

My position is it's probably misguided because of a number of things. So one, to get an LLM to understand some language well, you need a huge training set. And with the new language, that training set is not there. You can try to synthesize it and so on and forth, but it's not going to be as good as other languages. Like, for example, right now, the newer languages are just harder for LLMs than the more established ones. Like, any LLM writes Python better than it writes Rust, or

even Kotlin. Even the LMs that write Java very well, won't write Kotlin as well, because it's not as present in the training set, because it's younger. And, you know, there are ways around it, and I think the later models added some more Kotlin

into the RL sets, and it's getting better, but still, like, it's pretty hard. And so that's challenge number one. Also challenge number two, I don't think they're necessarily have to is to exist a language that makes it better because LLMs are trained on human language. Their knowledge of programming languages

is part of that. Their power is in having been exposed to all the code in the world and its existing code. And inventing a new language for that, I don't know how promising that can be. You can do another thing, which is an interesting research project. You can sort of extract a language from an LLM because, you know, internally, it has some intermediate representations of what's going on and during inference,

and maybe you can sort of extract the optimal prompting language. It's not guaranteed to be intelligible to humans. And there are some experiments that show that, you know, you can create completely unintelligible prompts that give the same results as normal human prompts, but they will be shorter. maybe you can do something like this. I don't know if it will help a lot. But what we're doing in Codespeak as part of working in this language, we need to really nail down this query language capacity.

And what we're doing now is we're looking at existing code and we're trying to find the shortest English descriptions for this code that can generate equivalent implementations, Not necessarily character to character, but they have to work the same way. And that's an interesting exercise, because you need to figure out how to represent the ideas in the code in a way that A, you can generate the same kind of code, but the ideas you represented were a lot more compact. But also, this code you represent, it evolves over time, right? So you have a commit history on top of this version.

And so going forward in time, you need to be able to represent all. the changes in your Codespeak version and you know you need to make sure that when it's a small change in the original code the change in the spec is smaller. That's an interesting challenge. So in this way

we're sort of discovering Codespeak as a language at least parts of it and not really designing that bit of it. You know it's a very new world in the sense that you know nowadays if you work with AI everything is a machine learning problem and that means, you know, back in the day,

if you had a very smart algorithm on paper, you could just implement it and make sure it works. Nowadays, whatever algorithms you have in mind, you need the dataset. First of all, like if you don't know how to collect a dataset, don't even start. And yeah, this is what we're doing. So just taking a look at,

you are using these tools day and day out, I mean, you're building with them. how do you think programming as a whole? I'll say software engineering is being changed by AI. And how do you think the future is starting to look? Especially thinking about software engineer. You're a software engineer yourself. You've written so much code in your life.

And are you still writing code? Yeah, I'm writing some code. Yeah. Sorry, typing or prompting? I'm doing both. Sometimes I'm just typing. More often, I'm typing with cursor tab completion. I'm doing quite a lot of prompting as well.

And, you know, that's a combination of all this. But cursor's completion is really a step up from traditional IDs. And I think the Intelligy side has something similar now. So it's like a lot of coding, but in a very different kind of mindset and a different tool set. Yeah, so in terms of what's happening to programming, I think we are in the early days of the new era. So, you know, it's only last year that. figured out that coding agents are good. No.

Clawed code and cursor agent and so on and so forth. And I think this is a very early step. Right now, we are in this phase where a lot of people are in love with agents and it can be very useful and I use them every day. But I think there are inherent problems with the model with how you interact with a coding agent because it's a one-on-one champ. And as a human, I talk to the agent. in human language.

So I'm communicating my intent on a high level. And that intent gets translated into code. And it's the code that I commit to the repo. And it's the code that my teammates will see. So my chat history is lost. Big problem. Yeah. So it turns out I'm talking to a machine in human language.

But the way I communicate with my team is the machine language. That's kind of backwards. So, yeah. So what we're trying to do in the code speak is to elevate everything to the human language level. So this is where we start. We say, okay, we have this incredible tool. We can prompt agents to implement code for us.

And we are just picking it up. So I think a lot of teams haven't yet realized how difficult it is to review all the code. And I've talked to people who are like, maybe we can just not review this code. I'm like, yeah, I mean, you can for a couple of days. And then it just collapses. And I think another big theme of today is that we'll be doing a lot of testing.

And you may not need to review the code if your tests are really good. You need to verify it, right? That's what you're saying is. And verifying might not mean reviewing. Right. Or it could not mean. Yeah, depending on the domain. Of course, of course.

You might get by without reviewing the code as much, but being sure somehow either reviewing the test or somehow, how else making sure that your tests are good. That's a trend, and we are putting a lot of effort to Codespeak into automated testing and making sure the tests actually check the right things and that they check all the code and all that stuff. It's very interesting computer science.

And also, it's now a question of, especially in the case of code speak, and I think for other agents as well, like, yeah, reviewing code can be too much, but can we present the tests we change? rated to the user in a way that actually verifies that we did what was to be done, it's tricky. Some tests will be just very long and tedious to read. And, you know, we're working on that. And that's where we are.

And I think we'll see a lot of development in terms of power of the models and we'll get some quote-unquote obvious things implemented in agents. For example, the agents are just starting to use, like, language servers. And basically all the stuff that we've always had for code is not very utilized. And, you know, if you compare like ID integrated agents like Cursor or Juni at JetBrains, you have a lot of like code navigation capability and, you know, databases of code is indexed and you can navigate it very quickly. You can find things very quickly.

When you run cloud code, for example, it might not have that and use Grimages. up and will be as successful, but take a lot longer and burn a lot more tokens. So, you know, I'm sure this year all these tools come to most agents and will have a lot more sophisticated scaffolding around the models. So that's one thing. But then, you know, my question is always what's going to happen in the end game or in further future? And there, it's very hard to predict. And we can assume that models will become much smarter. an important thing is that humans will not. So one thing I know about the future,

and it's hard to know the future, but this thing I do know about the future, humans will be as smarter as dumb as they are today. And if we have incredibly smart models, what we will be doing is constrained by how humans are. And this is one of the reasons why I'm working on Codespeak, because Codespeak is a tool for humans, not for models. And humans, I know, I can build a tool for,

for them. I guess an important footnote is that many people will say things like, you know, if we have smart enough models, they can review the code themselves and they can test the code themselves. But then my question would be like, who's making the decisions here? You know, if all the software engineering work is done by models, it means humans don't have any say in that. And this has a name. It's called technological singularity. When humans are not making decisions, It means we're not in charge. Yep. So this is not the future I'm building codes before.

Nobody should build any projects for that future. In that future, we're gone. Your projects don't matter. So my assumption when I'm talking about the future is that the technological singularity is not happening. And so the basic assumption is humans are in charge. And if humans are in charge, it's their job to communicate intent. So we have to say what kind of software we need to build. And when we're talking about like serious software, it's always complex.

There's no way there's some very simple thing that will make a difference. And when we talk about this complexity, this is what our jobs will be, like dealing, managing this complexity, figuring out what we actually need to do. And this is absolutely engineering. There is no way someone can tackle huge amounts of complexity without an engineer mindset. It can be called software engineering, can be called something else, but you will have to do it. You will have to navigate this complexity, organize this complexity, figure it out. And I'm not talking about the complexity of many, many layers of implementation.

Maybe not. Maybe that is what's called accidental complexity, something that happens, like, or arises from how we implement systems. But there is also essential complexity. How we wanted to behave is complex enough. that we need to figure it out. And this is why I believe there will be teams of engineers working on systems like today.

Maybe they will be a lot more powerful teams. Maybe fewer people can deliver a lot more software. Yes, but still teams of people working on organizing complexity, and this is what CodSpeak is for. Going back to where we are today with what the models can do today, what do you see with developer tools?

It feels a little bit of a wild, wild west, right now very much. So I mean, there's a lot of, you know, obviously with CloudCode, with Curse or with others, but what are areas that you think we will see, we will have to see new different, better tools to actually just catch up with, with how we can generate? And what parts feel the most messy and the most interesting? Especially because of Kotlin, you have, and the team has built so many tools for developers. Right. So I think, as I already mentioned, this year will be the year of making developer tools available to agents.

And there are some technical challenges, but you can figure it out. The people will be doing that. There's also a surprising advantage to using a good UI for your agent. It's very nice to have everything in your terminal in one sense, but then you can have a lot better user experience if it's a dedicated environment. And the terminal tools, especially cloud code, are amazing. and it's a complete breakthrough of what you can do in a terminal, but generally you can do better in a specialized environment.

So I think we'll see more of this integration into development environments or just new development environments built from the ground up to work with agents primarily. So that is an important thing. Since we are putting a lot more emphasis on review, there should be new tools for review, and I think we can do better than what we're doing now. in the many respects.

I don't expect many breakthroughs in testing this year because it's hard. I'm doing it right now. It's hard. It's not going to happen this year. But maybe some advances will arrive this year. But generally, I think the big lesson of the last couple of years is that all the things that were, quote, unquote, obviously needed.

And, you know, the idea of connecting agents to developer tools was absolutely the trivial thing to think of two years ago. But they take a long time to happen because it's hard. And, you know, nobody's in this industry is lazy. Like, everybody's working their asses. But it just takes time. You know, you need to figure out the basics before you can do advanced things. So, you know, all the straightforward ideas will get implemented at some point.

I think there's been this massive jump with AI, especially over the winter break, where the coding agents, the CLIs have become a lot more capable. And I know so many developers who are actually just prompting most of their code, if not all of it, it's just a massive, massive jump. I don't think we've seen anything this fast. I see a lot of engineers scared because it can shake you to the bone. You know, it took 10 years to get really good at coding. And the writing the code part feels that it's kind of going out, you know, the trash can. You yourself have been coded for a longer time.

what would your advice be for developers who are feeling like this, that they're feeling, you know, it is scary. I think we, and I talk with some folks, a lot of people message me as well. How are you thinking about this, specifically these last few months? It's really hard to give advice. There are a few ideas I can share. So one thing is there's a lot of hype and a lot of it gets to the management. And a lot of people make suboptimal decisions. but that will go away.

So, you know, there's, there's like more and more news about people not hiring junior developers, for example. This is dumb. It's stupid. This is dumb. This is not going to stay for long. I mean, it's hard to tell how long this can go on, but people will figure out that they need new people in the industry. And a lot of other things can be really stressful in the moment, but some of them will be rolled back. So that's one thing.

Another thing, it's absolutely worth it to invest your time into learning these tools and getting good at it. There's a lot of skepticism around in the developer community about how useful it actually is and, you know, I try it on my project and it's no good. There is quite a bit of skill to using these tools. Unfortunately, it's not super formalizable. At least so far, nobody figured out a really good. clear way of communicating how to do it well. But there are people who can do it much better than

others. They not always can't articulate why their prompts work better, but you know, you can learn it. You can get a lot better at it. And, you know, not necessarily believing everyone on Twitter, you know, some people claim crazy things, but you can be very productive with these things when you use them well and it's absolutely worth investing into that. And yeah, so as I mentioned before, In the future, it will still be engineers building complex systems. So keep that in mind. It's not like we all go to nothing. And for new grads, people coming out of university,

what would your advice be for them who are like determined like, all right, I actually want to be a standout engineer, maybe with these tools I can do it faster. What would you advise them to focus on either skills or experiences to get? I guess it's a matter of what your inclinations are. if you can just become incredibly productive and put out a lot of working code that is like really robust

and you can evolve it for a long time, get good at that. And like there is a lot to be done there. If you can or like to do like harder things, go into the most hardcore things you can and get good at that because it will be your rare expertise, it will be marketable.

Even if that, thing goes away, you will just become a lot smarter through that. So, you know, generally, like, if you have any inclination in looking under the hood and figuring out how things work, go as deep as you can. As a younger person, you have a lot of mental capacity for that. And this helps a lot. You become a very good expert in very wide fields just through, you know, drilling down on many things. That's closing. I just wanted to do some rapid questions. I just ask and you shoot what comes back. What is a favorite tool that you have? It can be digital. It doesn't have to be digital.

Well, I love my AirPods. They're incredibly convenient. They fit under my earmuffs. Well, another tool would be earmuffs. Earmuffs. Incredibly good. Yeah, I saw you wearing it. I'll take that one earmuff. And what's a book recommendation that you recommend and why? There is this classic that's been recommended across the tech community for many years.

It's called Zen and the Art of Motorcycle Maintenance. I heard that recommended. Yeah, it's a very good book. I mean, there is a part of it that's about technology and how to deal with the real systems and others, but it's also a very good novel. I really like it. Well, Andrew, thank you so much.

This was very interesting and, I think, inspiring as well. Thank you very much. It was great to check. It was great. Thank you. The thing that struck me with most from this conversation with Andre was his observation about how we work with AI coding agents. today. You talk to an agent in plain English. It generates code. You commit the code. But that conversation, your actual intent, it disappears. You communicate with the machine in human language, but work your teammates in code in machine language. Whether or not code speak becomes the answer,

what is sure that we're missing an intent layer? And someone is going to figure out how to preserve it. If you enjoy this episode, please do share with a colleague who's been thinking about where programming is headed. And if you're not subscribed yet, now's a good time. We have more conversations like this one coming. Thank you and see you in the next one.