Kotlin Native Stranger Threads Ep 1

Kotlin Native Stranger Threads Ep 1

Episode 1 — Worker

My original post about Kotlin Native (KN) concurrency was written a while ago, with a much earlier version of Native and Multiplatform. Now that Kotlin Multiplatform is ready for production development, it’s time to revisit how Native concurrency works and how to use it in your application development.

Concurrency and state in KN is significantly different compared to what you’re likely used to. Languages like Java, Swift, Objective-C, and C++ give the developer tools to ensure proper concurrent state access, but using them properly is up to the developer. Writing concurrent code in these languages can be difficult and error prone. KN, by contrast, introduces constraints that allow the runtime to verify that concurrent access is safe, while also providing for reasonable flexibility. It is trying to find a balance between safety and access. What that means is changing, and even within Jetbrains there appear to be conflicting visions. What is clear, however, is that Jetbrains is committed to Saner Concurrency, and to building a platform for the future.

In this series we’ll cover the rules and structures of KN’s concurrency and state model, and how they apply in the context of application development.

Just FYI, if you see emoji in the doc, that’s generally a footnote with unnecessary info 😛.

Episode 1 — Workers ⏮

Kotlin Native (KN) concurrency is kind of a big topic. For developers familiar with Java and Swift/ObjC concurrency, there are several new concepts to learn, which presents a problem out of the gate. Where to start?

In general, I like to be able to play with the code right away, so we’ll start with a core KN concurrency mechanism: Worker. We’ll encounter some concepts before we’ve had a chance to explain them, but we’ll sort that out later on in the series.

The code samples in this post can be found here. You’ll need to have a MacOS machine to run them. Adding other platforms should be pretty simple, if anybody wants to give it a shot.

Most of the examples are implemented as unit tests 🔍. You can run them by typing:

./gradlew build


KN supports concurrency out of the box using a structure called “Worker”. A Worker is a job queue on which you can schedule jobs to run on a different thread.

The Worker related tests can be found here.

Creating a worker is relatively straightforward.

import kotlin.native.concurrent.Worker
class TestWorker {    
  val worker = Worker.start()

Each Worker instance gets a thread 📄. You can schedule jobs to be run on the worker’s thread.

worker.execute(TransferMode.SAFE, {"Hello"}) {
    //Do something on Worker thread

There are a few things to take note of in that call. Here’s the function definition for execute:

fun <T1, T2> execute(
        mode: TransferMode,
        producer: () -> T1,
        job: (T1) -> T2): Future<T2>

We’ll discuss TransferMode in part 2. In summary, there are two options: SAFE and UNSAFE. Just assume it’s always TransferMode.SAFE.

The producer parameter is a lambda that returns the input to the background job (generic type T1). That’s how you pass data to your background task.

It’s critically important to understand that whatever gets returned from the producer lambda is intended to be passed to another thread, and as a result, must follow KN state and concurrency rules. That means it either needs to be frozen, or needs to be fully detachable. In theory, being detachable is simple, but in practice it can be tricky. We’ll talk about that in a bit.

The job parameter is the work you intend to do on the background thread. It will take the result of the producer (T1) as a parameter and return a result (T2) that will be available from the Future.

Well discuss this more later on, but it’s a super important topic and can bear some repetition. It is very easy to accidentally capture outside state in the job lambda. This is not allowed and the compiler will complain. You’ll need to be extra careful to avoid doing that.

Execute’s return is ‘Future<T2>’. Your calling thread can block and wait for this value, but in an interactive application we’ll need a way back to the calling context that doesn’t interrupt the ui.


The producer’s job is very simple. Isolate a parameter value to hand off to the background job. You’ll see the producer lambda both here and when we need to detach an object from the object graph. It’s a little confusing at first, but understanding what’s happening with the producer will help clear up KN’s broader concurrency concepts.

Take note of the fact that the producer is a lambda and not just a value. It doesn’t look like this.

worker.execute(TransferMode.SAFE, "Hello"){
    //Do something

That is (presumably) to make isolating and detaching the object reference easier.

The producer is run in whatever thread you’re calling it from.The result of that lambda is then checked to make sure it can be safely given to the worker’s thread. However, to be clear, all of that activity happens in your current thread. We only engage the worker’s thread when we get to the background job.

Haven’t left the calling thread yet

How do we determine that some state can be safely given to another thread? We have to respect KN’s two basic rules:

    1. Live state belongs to one thread
  1. Frozen state can be shared

Part two is all about the two rules, but in summary:

    1. Live state is the state you’re used to writing
  1. Frozen is, basically, super-immutable. You create frozen state by calling ‘freeze’ on it

Note: We’ll start using data classes rather than String. Strings, as well as other basic value types, are frozen automatically by the runtime.

Here’s a basic example:

data class JobArg(val a: String)
fun simpleProducer() {
  worker.execute(TransferMode.SAFE, { JobArg("Hi") }) {

We create an instance of JobArg inside the producer. There are no external references (nobody has a reference to that instance of JobArg), so the runtime can safely detach and pass the state to the job lambda to be run in another thread.

This, by contrast, fails.

fun frameReferenceFails() {
  val valArg = JobArg("Hi")
  assertFails {
    worker.execute(TransferMode.SAFE, { valArg }) {

When we call execute, valArg is being referenced locally, so the attempt to detach will fail.

This looks like a way to hide the reference, but also fails:

class ArgHolder(var arg:JobArg?){
  fun getAndClear():JobArg{
    val temp = arg!!
    arg = null
    return temp
fun stillVisible() {
  val holder = ArgHolder(JobArg("Hi"))
  assertFails {
    worker.execute(TransferMode.SAFE, { holder.getAndClear() }) {

Why? Well, this gets a bit into the weeds of how KN’s memory model works. Native doesn’t use a garbage collector 🚮. It uses reference counting. Each allocated object has a count of how many other entities have a reference to it. When that count goes to zero, that memory is freed.

iOS developers will have an easier time with this concept, as this is how Swift and ObjC work 🍎.

References to objects obviously include hard field references, but also include local frame references. That’s what’s wrong with the block above. The JobArg appears in the local frame context, however briefly, which still has a reference to it when the producer attempts to detach it.

Outside context has a local reference

This, however, will work:

fun makeInstance() = ArgHolder(JobArg("Hi"))
fun canDetach() {
  val holder = makeInstance()
  worker.execute(TransferMode.SAFE, { holder.getAndClear() }) {

The local ref is cleared in ‘makeInstance’. So again, if you’re wondering why the producer is a lambda, it’s to make avoiding local references easier. Look at simpleProducer again:

fun simpleProducer() {
  worker.execute(TransferMode.SAFE, { JobArg("Hi") }) {

Much simpler.


Passing live data is difficult syntactically. In fact, we don’t have multithreaded coroutines yet because JB still needs to reconcile the two systems 😟. I gave you some pretty weird examples out of the gate on purpose. KN makes passing mutable state between threads difficult, and in general that’s a good thing, because it’s risky. When I need to pass something into a worker I’ll almost always freeze it.

fun frozenFtw() {
  val valArg = JobArg("Hi").freeze()
  worker.execute(TransferMode.SAFE, { valArg }) {

Because frozen data can be shared between threads, the producer can return valArg. This is obviously a simple example, but as you get into Native development, you’ll generally find freezing data to be simpler, and in general, data that you’re passing around should be immutable anyway.

I should mention that you can bypass all of this and simply pass data unsafe with TransferMode.UNSAFE, and it’ll probably work most of the time. Don’t do it, though. It’s called UNSAFE for a reason, so if you can’t clearly explain why you would use it, you never should. We’ll discuss this in detail in part 2.

We spent a lot of time on the producer, but again, the producer introduces a lot of core, and potentially confusing topics. If you can fully grasp what’s going on with that you’ll have covered a lot of ground.

Background Job

What happens with the background lambda, compared to what was happening with the producer, is much simpler. The lambda takes a single parameter, which is the result of the producer (which, btw, can be empty). If the background job returns a value, it’ll be available from the Future.

fun backgroundStuff() {
  val future = worker.execute(TransferMode.SAFE, { 1_000_000 }) {
    var count = 0
    for(i in 1..it){
      //Do some long stuff
  assertEquals(1_000_000, future.result)

Here we’re going to loop and count. We pass the number of loops in the producer.

Just FYI, be careful with threads and unit tests 🍌. The ‘future.result’ forces the thread to wait for the background lambda to finish.

Until now, everything happened in the original calling context. The background job finally gets us into the second thread.

Since job is in a different thread, you can’t reference just any state. Only the lambda param of type T1, originally from our friend producer, and global state known to be frozen or thread local. In other words, only state that the KN runtime can verify is safe to access.

As mentioned previously, it’s pretty easy to capture other state in the lambda of your background task. The compiler attempts to prevent this, but only when you’re calling the worker method directly. We’ll dive deeper into that when we talk about actually implementing concurrency in your applications.

In simple examples, capturing extra state won’t be much of a problem. Where this quickly becomes problematic is capturing state when you call background tasks from your application objects. I found this difficult at first, but you get used to it. Frameworks help, and especially when multithreaded coroutines become available, running tasks in the background will be simpler 😴.


The ‘execute’ method returns a Future instance, which can be used to check the status of the background process, as well as get the value returned. The value can be Unit, which means you’ll simply verify that the process completed.

If it’s OK to block the calling thread, the simplest way to get your result is to call the result property on the Future instance. That’s what we’re doing in the test examples.

Alternatively you can poll status on the Future, or set up a result Worker to call back to. However, if you’re intending to use Worker in the context of a mobile application, going “back to the main thread” is somewhat more complex. We’ll discuss that later.


We don’t worry about it too much in the context of our test samples, but you should shut down Workers when you’re done with them. This is only necessary if you’re going to keep the process running but abandon the Worker. It your Worker instances are meant to live along with your process, you can leave them hanging around (they get shut down with the process).

fun requestTermination(){
  val w = Worker.start()

requestTermination returns a Future. If you need to wait for termination, check the result.

You Probably Won’t Use Worker

In the same way you probably don’t create a Thread instance or an ExecutorService very often in Java, libraries will probably keep you away from creating Worker instances directly. Unless KN state rules radically change, however, you won’t get away from those. You will, however, be seeing Worker a lot for the next few posts at least.

Up Next

Worker introduces us to the basics of running concurrent code on Native. Part 2 will go deeper into the why of KN state rules, freezing, detaching, and some more detail about what’s happening under the hood.

😛 But super interesting info!!!

OK. It’s not exactly Episode 1. The earlier post, from about 8 months ago, was supposed to be the start of the series, but things were changing really fast and I got more involved in library development. Yada yada, we’ll call that the pilot and this is the start ot the series.

🔍 The test code is configured with a common source set and a native source set. To get native code tests to run on the command line, the simplest way to do that is to build a macos target. The build process automatically builds and runs a command line executable. JVM is currently disabled because we’re not talking about the JVM 🙂

📄 The docs are pretty clear that you shouldn’t rely on that in the future as it may change, but for the foreseeable future, 1 Worker gets one thread.

🚮 That’s mostly true. There is a garbage collector in the runtime, but I’m pretty sure that’s there to deal with reference cycles. Memory is primarily managed by reference counting.

🍎 There are some important differences to note. KN can deal with reference cycles, so “weak” references aren’t a concern. Also, to be clear, KN objects are ref counted, and it’s conceptually similar to ARC, but it’s a separate system. While running on iOS, KN doesn’t use ARC for it’s ref counts.

😟 A fair number of people have expressed their hope that JB abandons the “Saner Concurrency” effort. The comment in that coroutines issue implies they might, or at least relax the rules somewhat. While I understand this stuff can be confusing, the ultimate goal is to produce a better platform. I would very much like some improved debug info from immutability related exceptions, and some improved library support, but once you get your head around this stuff it’s not that bad.

🍌 Calling for the future result forces the main thread to wait. That’s why this test works correctly. This can all get very tricky when trying to interact with the main thread, etc. There are frameworks and examples in more mature ecosystems to help out, but KN and multiplatform are in early days. Just an FYI.

😴 I’ve been asked if there’s any reason to learn this crazy threading stuff if the coroutines API will largely hide the details. Although we don’t know yet what changes, if any, will happen to the KN concurrency and state model to accomodate coroutines, unless Jetbrains radically changes their plan and abandons everything, you’ll definitely need to understand this stuff.

Stately, a Kotlin Multiplatform Library

Stately, a Kotlin Multiplatform Library

This started as a monster single post, now split in 2. Part 1, Saner Concurrency, is about what Kotlin is doing with concurrency.

During my talk at KotlinConf, I promised a part 2 of Stranger Threads to better explain threading and state in Kotlin/Native. I built a library instead.

Update!!! Video and Slides from my Droidcon UK talk!

What is Stately

Stately is a collection of structures and utilities designed to facilitate Kotlin/Native and multiplatform concurrency. As of today, it is a set of expect/actual definitions that most apps will wind up needing, and a set of frozen, sharable collection classes that allow you to maintain mutable collections across threads.

Why does it exist?

Kotlin/Native, and hopefully soon, all of Kotlin, will be implementing Saner Concurrency. Native, today, has runtime rules, and notably a lack of “standard” concurrency primitives, that help ensure runtime concurrency sanity. The 2 basic rules are:

  1. All mutable state is in one thread. It can be transferred, but is “owned” by one thread at a time
  2. All shared state is immutable (frozen)

These are good rules, because they will make concurrent code safer.

However, there are times where being able to access shared, mutable state is pretty useful. That may change as we adapt architectural thinking, but at least for today, there have been a few tricky situations to arise without that available. Global service objects and caches, for example.

Kotlin/Native has a set of Atomic classes that allow you to mutate state inside of an immutable (frozen) object. The Stately collections are constructed with atomics.

For the most part, I’d expect Stately to be used sparingly, if at all, but it’s one of those things that you’ll really miss if you need it and don’t have it.

What you shouldn’t use it for

Kotlin/Native has rules that help encourage safer concurrency. Changing frozen state is, on some level, enabling manually managed concurrency. There are practical reasons to have these collections available, but if you’re using them a lot, it might be better to try for some architectural changes.

If you’re new to Native and running into mutability exceptions, you might be tempted to make everything atomic. This is equivalent to marking everything synchronized in Java.

It’s important to understand how Native’s threading and state work, and why you need a concurrent collection. But, you know, no judgements.

Basic Principles

The collections mostly act like their mutable counterparts. You designate generic types, and get/set/remove data entries. One key thing to note.

Anything you put into the collection will get frozen.

That is very important to understand. The collection itself is “mutable” in the sense that you can add and remove values, but the collection and values it holds are all frozen.

For data objects, this is generally OK, but for callbacks, this may be somewhat mind bending to understand. That’s not a Stately problem so much as a Kotlin/Native problem.

I’ve talked to several people who struggle with this reality. All I can say is it seems weird at first, but is not as big of a deal as you think. You just really need to be aware of what that means.

For example, in the Droidcon app, we’re using LiveData to build a reactive architecture. To avoid freezing everything in the Presenter/UI layer, we simply keep all of our callbacks thread local to the UI thread, which means they don’t need to be frozen. The lower level callbacks all exist in Sqldelight, and are frozen, but they exist just to push data back to the main thread and don’t capture UI-layer state.

Summary, it’s different, but not that bad. The details would turn this blog post back into a monster, so I’ll just push out my promised “Stranger Threads” deadline out by a couple weeks.

A lot of how we think about architecture will change as coroutines mature on Native, and as the community has some time to think about the implications. I suspect there will be less use for shared collections as that happens, but for today, they’re pretty useful.

Available Collections


Similar to Java’s CopyOnWriteArrayList. In fact, the JVM implementation is Java’s CopyOnWriteArrayList. Useful in cases with infrequent changes and critical read stability. Registering callback listeners, for example. If you’re changing the list often, or it’s large, each edit requires a full copy. Something to keep in mind.


This is a mutable list that will have reasonable performance. All edits lock, just fyi, but individual edits themselves are pretty small, so locks are quick. You can hold onto a node reference that will allow you to swap or remove values without traversing the list, which is important in some cases.

There are 2 basic flavors. One has unstable but performant iterations, the other I’d call CopyOnIterateList. It’s similar to CopyOnWriteList, except the copy happens when you call iterate. This may prove more generally useful than COWAL, as it should handle frequent changes better, but if you’re editing often and iterating often, remember you’re dealing with an aggressively locking data structure.

The other flavor will let you iterate without copying or locks, but edits to the list will be reflected while you’re iterating. The edits are atomic, so you won’t get errors, but you’ll also wind up with potentially fuzzy iterations. If that isn’t an issue, this should be a better choice.

I am aware that there are lockless implementations of linked list, but I didn’t attempt one. Going for simple.


Implements a pretty basic version of our favorite structure HashMap. Performance should have similar characteristics to what we’re used to from Java🌶, although obviously locking will produce different absolute numbers. In short, same big O.

I probably don’t need to go into where a hash map would be useful, but in the service object context, it’s probably caches, which leads to…


Shared version of a least recently used cache. If you’re not familiar, there’s a cap on the number of values, and the “oldest” get bounced first. “Oldest” being defined as the least recently accessed.


This is pretty new. As Kotlin 1.3 matures, we should have a more stable deployment, and may add some other features. I’ll try to add some issues for “help-wanted” in case anybody wants to contribute.

Also, as of today (Friday 10/26) the JS code has an implementation but tests need to be wired in. That means don’t use the JS until that happens.

The only supported Native implementation is for mac and iOS. Other Native targets should work, except we’ll need to find a lock implementation. Pthread_mutex is fine, except you need to destroy it explicitly, and K/N has no destructor. That means a ‘close’ method on the collection, which I’d rather avoid. Right now there’s a spin lock, but not sure if that’s a great idea.

The collection implementations could be described as minimal. My main goal was to start replacing some of the custom C++ code we’d put into earlier K/N implementations, which generally existed because of a need for shared state. If there’s a real need for something else, open an issue to discuss.

🌶 Similar to Java 7 and below. That is, if your bucket size doesn’t increase, as your entry size increases, or you have a bad hash, you start to approach N, because the bucket list means a lot of scanning. In Java 8, after the list gets to size 8(ish), it’ll store those values as a tree, so worst case gets capped. Our hash map resizes like Java’s, so you should only find yourself in “worst case” if your hash is bad, or if you have horrible settings on your map. You probably don’t need to know this, but Java 8’s optimization is kind of cool, and I didn’t bother trying to implement it 🙂

Saner Concurrency (and the cost of change)

Saner Concurrency (and the cost of change)

I’ve been talking about Kotlin/Native for a little while now, and one of the big things I tell people is that threading will be very different than what they’re used to. It’s just something that you need to learn and understand.

Kotlinconf was a couple weeks ago. Progress on Multiplatform was definitely a big topic. A more general discussion surfaced around how Jetbrains and the Kotlin team are thinking about concurrency. Andrey said in his Keynote that future Kotlin will focus on Saner Concurrency. I think we should unpack this a bit.

It is generally accepted that managing concurrency by using mutexes, synchronized blocks, etc (AKA. “manually” or “status quo”), is difficult and error prone. Concurrency in Java, with Java’s concurrency primitives, is difficult.

Some languages attempt to improve upon this situation using various approaches. Another point from the keynote I thought was great is that being unique is not important. Assimilating the best ideas of other languages is important (2:40).

Saner Concurrency

Kotlin/Native has baked into it some ideas about concurrency that make managing it safer. Andrey alluded to the idea that Kotlin, across variants, will start attempting to tackle “Saner Concurrency”. This is good to watch (37:00) to get a sense of where Kotlin is going, which makes me hopeful that not only will the various incarnations of Kotlin share the same concurrency concepts, but that those concepts will start to emerge as first class parts of the language at compile time rather than strictly runtime (Rust-like?).

By “various incarnations” I mean Kotlin-JVM will hopefully start getting some of the “new stuff” Kotlin/Native is getting🚧.

One of the consequences of change is that things will be different. We’ll need to learn new ideas, build different libraries, and adopt different ways of coding. That inevitably means resistance and time.

I wound up on the closing panel of the conference (thanks Hadi!), and one of the questions I gave an answer to addresses this issue. It wasn’t actually a question so much as expressing concern that the different threading model will slow down adoption (at 18:00)

I jumped in because, frankly, I had the same concerns earlier on. A few months ago I drafted up a blog post talking about how such a departure will impact adoption and may ultimately derail the effort. Thankfully, like most of my blog posts, I never finished it, and I don’t think that way anymore.

Considering it was on stage, at the end of a conference, I think I expressed my thoughts OK, but let me expand a bit here.

At the beginning of my talk, I told everybody about how I got there. I’d spent a couple years working on an architectural framework based on J2objc called Doppl. In short, compatible Android structures, lots of familiar libraries, and a gradle tool. Replicating testable mobile architecture across Android and iOS, in a familiar and practical manner.

How practical? The day after Google announced room, I got Room working on iOS, from the java source jars, in about 90 minutes.

Here’s the thing, though. Nobody cared. I bet more people read this post in the first week than read that post after a year and a half.

Obviously these aren’t direct parallels. The Android community wants Kotlin and iOS wants Swift, so pitching a Java/Objc thing isn’t fun (trust me). Community interest is critical, no matter how practical your pitch may be.

However, I think it’s also important to have a vision. Kotlin wants to adopt better, safer concurrency practices. They’re making the language available on many platforms, including Webassembly, and making “tough love” choices for a better future platform.

I think building something practical but somewhat compromised will make it easier to learn, and definitely easier to develop the library ecosystem necessary to support what currently exists, but it’s also uninspiring. It does not encourage change.

In short, yes, I think “giving up” on change would make adoption quicker for an individual iOS developer, but I don’t think it would be appealing to the iOS developer community. Change and new ideas, although they will take more work, are appealing and inspiring, and will ultimately be the best bet at gaining adoption.

When somebody says, “Kotlin is just Swift for Android”, this is meaningful pushback. It’s not simply “another language”.

In any case, it doesn’t matter. They’re doing it ✈️

This post started as one post, but turned into a monster. Part 2 will be released after my Droidcon UK talk Friday where we announce a new Kotlin Multiplatform state management library…

🚧 I didn’t want to derail up top, but one major issue for right now is that JVM and Native do some runtime things fairly differently. That is likely to create confusion early on, especially with teams that have engineers fully on one platform or the other. It would be great if we could run JVM with the same runtime checking as Native, even if just in a debug configuration.

Future of Shared Code = Kotlin Multiplatform

Future of Shared Code = Kotlin Multiplatform

If you’ve been following our development of Doppl for the last couple years, we’ve built a Gradle toolset for J2objc, an architectural structure with an Android compatible API, and published a number of popular libraries for that ecosystem. In other words, a full toolset extension of J2objc which allows sharing logic and architecture from Android to iOS.

J2objc is a mature, stable technology. If you have an iPhone, you’re more likely to have some J2objc in there than all the other non-HTML frameworks put together. J2objc makes sense not because it lets Java developers publish iOS code. It makes sense because it allows a platform native language produce platform native libraries, with optional code sharing, without any significant barrier or translation layer.

We use this framework with current clients, and would absolutely take new projects with it if we have the bandwidth to do so.

However, we’re officially out of the Doppl business.

The future of shared code and architecture, as far as we’re concerned, is Kotlin Multiplatform.

Some of the reasons we like it are the same as why we like J2objc. Native language, optional sharing, platform compatibility. As of the last few months, all R&D and open source development has been focused on KMP. As of KotlinConf this year (or shortly after) the tooling and libraries will be ready for production work. It is time to burn the ships.

To be better open source citizens, and to avoid internal distractions, we’ll be unpublishing Doppl and related libraries. If you’re one of the handful of orgs who use the library, please reach out and we’ll make sure you can access the repos.

I want to be clear that this doesn’t mean I think J2objc is bad tech. I still think it’s amazing technology. If you wanted to maximize your shared mobile architecture today, it would be significantly more productive than other options.

However, the community excitement around Kotlin Multiplatform is intense.

Over the next several months there will be a number of library announcements. It’s also not just for Android/iOS logic, but back end and front end architecture, including native mobile, web with JS, and webassembly. There is a lot of work to be done, but there’s a much wider scope of problems to solve.

We are, of course, looking for products and orgs interested in getting started. Also, I’m starting the search for our first Multiplatform specific engineer (besides me). Reach out.

Kotlin Multiplatform in the App Store!

Kotlin Multiplatform in the App Store!


I don’t know if this is the first Kotlin Multiplatform app in the iOS store, but it’s our first Kotlin Multiplatform app in the iOS store!

Technically we updated the existing Droidcon NYC/SF app, so the approval process was easier than a new app would be, but approved it was!

To read more about the app check out the other blog post

I most recently gave a talk about this on Thursday (8/15/2018) at Android Summit. I did my first experiment with recording my own talk, which went OK I think…

I’ll be doing a few more talks through the remainder of 2018. I’m most excited about KotlinConf, but will be doing a few other events as well.

Future Plans

We still need to release the Android app. Sometime in the next couple days.

The underlying libraries are being updated to Kotlin 1.3 eap as well as the latest Kotlin/Native builds. That should also be early this week.

That will probably be it till after Droidcon NYC. Speaking of, today is the last day for standard price tickets (use code ‘kotlinmp’ for a discount)!

App Size

Just a quick note about app size. We’ll write more about this later, but the summary is Kotlin/Native does really well with regards to download and install size vs other frameworks. The download size is under 10m. Our last version with J2objc was just under 30m. It had more images in it, but still. By comparison, we did the flutter sample a few months back, which came out to 20m. React Native kind of beat everybody, but anyway. Will need to make some bare-bones apps and do some comparisons.

Reach Out

As always, Touchlab is looking for orgs that want to get started with this tech. Reach out.