Ever written code that reads almost like English? Kotlin domain-specific language creation transforms how we express complex operations. As developers, we constantly search for ways to make our code more readable and maintainable.
Kotlin, developed by JetBrains, offers powerful language features that make building custom language structures surprisingly accessible. Whether you’re working on Android UI DSL development, creating type-safe builders, or implementing method chaining in Kotlin DSLs, the language provides exceptional tools for domain modeling.
This guide explores how to build DSLs in Kotlin using techniques like lambda with receiver, extension functions, and operator overloading. We’ll walk through practical examples inspired by successful implementations in the JVM ecosystem, from Gradle Kotlin DSL examples to Spring Framework extensions.
By the end, you’ll understand:
- Core Kotlin features that enable natural DSL syntax
- Steps to build your first simple DSL
- Advanced techniques used in production-ready code
- Best practices from the Kotlin community
Let’s build expressive, type-safe DSLs that make your code sing.
Core Kotlin Features for Building DSLs
Kotlin offers powerful language features that make it ideal for building domain-specific languages. Let’s dive in.
Extension Functions and Properties
Extension functions transform how we interact with existing classes in Kotlin. They enable natural syntax by allowing developers to “add” new functions to any class without modifying its source code.
fun String.emphasize() = "**$this**"
// Usage
val text = "Important note"
println(text.emphasize()) // Outputs: **Important note**
This capability is crucial for DSL development as it lets you create property-like behaviors that feel native to the language. When developing custom language abstractions, extension functions serve as the foundation for fluent APIs in Kotlin.
JetBrains, the creator of Kotlin, designed these features specifically to support readable code structures. Extension properties work similarly, adding computed values that appear as regular properties:
val String.wordCount: Int
get() = split(" ").size
// Usage
"This has four words".wordCount // Returns: 4
Practical examples of extensions in DSLs include Ktor’s routing definition, where HTTP methods are implemented as extensions:
routing {
get("/api/users") {
call.respond(userService.getAllUsers())
}
post("/api/users") {
// Process new user
}
}
The entire KotlinX libraries for DSLs ecosystem leverages extensions extensively.
Higher-Order Functions and Lambda Expressions
Higher-order functions accept functions as parameters or return them. This is key for passing behavior as arguments in a DSL.
Lambda with receiver is perhaps the most important Kotlin feature for DSL creation. It provides context-specific scopes that make your code more concise and readable:
fun buildString(action: StringBuilder.() -> Unit): String {
val sb = StringBuilder()
sb.action()
return sb.toString()
}
// Usage
val result = buildString {
append("Hello, ")
append("World!")
}
In this example, the lambda has access to all StringBuilder methods directly within its scope. This pattern is fundamental to type-safe builders in Kotlin.
The Kotlin Standard Library uses this approach extensively. Function literals with receiver create expressive, context-aware code blocks where the meaning of the code changes based on its context.
Operators and Operator Overloading
Kotlin allows custom operators through operator overloading, creating intuitive syntax for your DSL. Martin Fowler, known for his work on DSLs, often emphasizes the importance of readable expressions in domain-specific languages.
data class Vector(val x: Int, val y: Int) {
operator fun plus(other: Vector) = Vector(x + other.x, y + other.y)
}
// Usage
val v1 = Vector(1, 2)
val v2 = Vector(3, 4)
val sum = v1 + v2 // Vector(4, 6)
Infix functions enable more natural language-like constructs:
infix fun Int.times(action: () -> Unit) {
repeat(this) { action() }
}
// Usage
3 times { println("Hello") }
When implementing method chaining in Kotlin DSLs, follow best practices for operator definition to avoid confusion. Remember: operators should behave predictably and match their mathematical or logical expectations.
Type-Safe Builders
Type-safe builders form the structure and components of many Kotlin DSLs. The builder pattern in Kotlin uses lambdas with receivers to create hierarchical structures with strong typing.
class HTML {
fun head(init: Head.() -> Unit) = Head().apply(init)
fun body(init: Body.() -> Unit) = Body().apply(init)
}
class Head {
fun title(text: String) = "title"
}
class Body {
fun div(init: Div.() -> Unit) = Div().apply(init)
}
class Div {
fun p(text: String) = "paragraph"
}
fun html(init: HTML.() -> Unit): HTML = HTML().apply(init)
// Usage
html {
head {
title("DSL Example")
}
body {
div {
p("Hello, World!")
}
}
}
This pattern is extensively used in Gradle Kotlin DSL examples and Compose Multiplatform for UI construction. Type inference in Kotlin DSLs makes these builders even more powerful, as the compiler can often determine types without explicit declarations.
Creating Your First Simple DSL
Let’s apply these concepts to build a practical DSL.
Designing Your DSL’s Structure
Start by identifying the domain problem your DSL will solve. Is it for UI layouts? Database queries? Configuration? The domain dictates the vocabulary and structure of your language.
Planning the syntax and user experience is crucial. Sketch how you want your DSL to be used before implementing it. Good DSLs feel natural to their domain. Consider this example for a hypothetical task scheduling DSL:
schedule {
daily {
at("8:00") {
runTask("Check emails")
}
at("12:00") {
runTask("Lunch break reminder")
}
}
weekly(MONDAY) {
at("9:00") {
runTask("Team meeting")
}
}
}
Setting boundaries and constraints early helps manage scope. Think about what your DSL should and shouldn’t do. Android UI DSL development, for instance, focuses specifically on view construction and not business logic.
Building a Basic Configuration DSL
Let’s implement a step-by-step configuration DSL for a simple server application using Kotlin language embedding techniques:
class ServerConfig {
var host: String = "localhost"
var port: Int = 8080
fun security(init: SecurityConfig.() -> Unit) {
val security = SecurityConfig().apply(init)
// Apply security settings
}
}
class SecurityConfig {
var enabled: Boolean = false
var sslEnabled: Boolean = false
var tokenValidity: Int = 3600 // seconds
}
fun server(init: ServerConfig.() -> Unit): ServerConfig {
return ServerConfig().apply(init)
}
// Usage
val config = server {
host = "example.com"
port = 9000
security {
enabled = true
sslEnabled = true
tokenValidity = 7200
}
}
This example demonstrates creating nested configurations with a clear hierarchy. The IntelliJ IDEA DSL support provides excellent code completion for such structures.
Adding validation and default values improves reliability:
class ServerConfig {
var host: String = "localhost"
var port: Int = 8080
set(value) {
require(value in 1024..65535) { "Port must be between 1024 and 65535" }
field = value
}
// Other properties and methods
}
Business logic abstraction is a key benefit here. The DSL hides implementation details while exposing a clean interface.
Testing Your DSL
Writing effective unit tests is essential for DSL development. Start with basic functionality:
class ServerConfigTest {
@Test
fun `basic configuration works correctly`() {
val config = server {
host = "test.com"
port = 8080
}
assertEquals("test.com", config.host)
assertEquals(8080, config.port)
}
}
Ensure correct behavior by testing nested configurations and complex interactions. The Spring Boot Kotlin DSL and KotlinX Serialization provide excellent examples of comprehensive test suites.
Testing edge cases and error handling validates robustness:
@Test
fun `invalid port throws exception`() {
assertThrows<IllegalArgumentException> {
server {
port = 100000 // Invalid port
}
}
}
Using a testing framework in Kotlin like JUnit or Kotest makes verification straightforward. Stack Overflow contains many examples of DSL testing patterns you can adapt.
By focusing on these core elements and following domain-specific abstractions principles, you can create expressive, type-safe DSLs that improve code readability and maintainability. The Kotlin compiler extensions and functional programming capabilities make DSLs a natural fit for complex domain modeling.
Advanced DSL Techniques
Building simple DSLs is just the beginning. Let’s explore advanced techniques that take Kotlin DSLs to the next level.
Receiver Scope Control
When creating complex DSLs with nested scopes, ambiguity can become an issue. Kotlin provides tools to manage this.
The @DslMarker annotation solves scope confusion in nested blocks by preventing outer receiver access from inner lambdas. It’s a game-changer for DSL design:
@DslMarker
annotation class HtmlDsl
@HtmlDsl
class Table {
fun tr(init: Tr.() -> Unit) {}
}
@HtmlDsl
class Tr {
fun td(init: Td.() -> Unit) {}
}
@HtmlDsl
class Td {
fun text(value: String) {}
}
With this annotation, calling tr{} from inside td{} would cause a compilation error. This enforces better code organization and prevents mistakes.
Controlling visibility of functions and properties is another key aspect. Using Kotlin’s visibility modifiers, you can expose only what’s necessary for DSL users while hiding implementation details:
class QueryBuilder {
private val conditions = mutableListOf<String>()
fun where(condition: String) {
conditions.add(condition)
}
internal fun build(): String {
return if (conditions.isEmpty()) "" else "WHERE ${conditions.joinToString(" AND ")}"
}
}
Abstract syntax tree manipulation often happens behind the scenes in powerful DSLs. JetBrains leveraged these techniques when developing the Gradle Kotlin DSL.
Context-Aware DSLs
Adapting behavior based on context makes DSLs more intuitive. The Kotlin language specification includes features that support context-dependent code:
class FormDsl {
fun input(type: String = "text", init: InputContext.() -> Unit = {}) {
val context = when (type) {
"text" -> TextInputContext()
"number" -> NumberInputContext()
else -> InputContext()
}.apply(init)
// Process the context
}
}
open class InputContext {
var name: String = ""
var required: Boolean = false
}
class TextInputContext : InputContext() {
var maxLength: Int? = null
var pattern: String? = null
}
class NumberInputContext : InputContext() {
var min: Int? = null
var max: Int? = null
}
// Usage
form {
input("text") {
name = "username"
required = true
maxLength = 50 // Only available in text context
}
input("number") {
name = "age"
min = 18 // Only available in number context
}
}
Dynamic property resolution can be achieved through Kotlin’s delegated properties. This technique powers frameworks like Exposed SQL library:
class PropertyDelegate {
operator fun getValue(thisRef: Any?, property: KProperty<*>): String {
return "Value for ${property.name}"
}
}
class Example {
val dynamic by PropertyDelegate()
}
Context receivers in Kotlin (an experimental feature) allow for even more powerful contextual programming, enabling DSLs to access multiple receiver contexts simultaneously.
Error Handling and Validation
Proper error handling distinguishes professional DSLs from simple ones. You can choose between compile-time vs. runtime validation depending on your needs.
Compile-time validation through the type system is preferable when possible:
// This won't compile if the wrong type is provided
inline fun <reified T : HttpMethod> route(path: String, noinline handler: () -> Unit) {
// Implementation
}
// Usage
route<GET>("/users") { /* handler */ }
Creating helpful error messages improves user experience:
fun validatePort(port: Int) {
require(port in 1024..65535) {
"Port number $port is invalid. Must be between 1024 and 65535."
}
}
The Arrow Kt functional DSLs library exemplifies defensive programming in DSLs by using robust error handling patterns. Their approach prevents runtime surprises through careful design.
Performance Considerations
DSLs should be efficient. Start by minimizing object creation:
// Less efficient, creates many temporary objects
fun inefficientDsl(init: Builder.() -> Unit): Result {
val builder = Builder()
builder.init()
return builder.build()
}
// More efficient with inline
inline fun efficientDsl(init: Builder.() -> Unit): Result {
val builder = Builder()
builder.init()
return builder.build()
}
Efficient parsing strategies matter especially in large DSLs. Consider using memoization or lazy evaluation:
class LazyDsl {
private val computations = mutableListOf<() -> Unit>()
fun compute(block: () -> Unit) {
computations.add(block)
}
fun execute() {
computations.forEach { it() }
}
}
Benchmarking and optimization techniques should be applied to identify bottlenecks. The Kotlin/Native and Kotlin/JS compilation processes have different performance characteristics, so multiplatform DSLs need careful tuning for each target.
Real-World DSL Examples
Let’s explore practical applications of Kotlin DSLs with examples across different domains.
Building a UI DSL
Android Studio developers increasingly use Kotlin for UI construction. Creating layouts and components with DSLs offers clear advantages over XML:
ui {
verticalLayout {
padding = 16.dp
textView {
text = "Hello, World!"
textSize = 18.sp
textColor = Color.BLACK
}
button {
text = "Click me"
onClick {
// Handle click
}
}
}
}
Handling events and interactions becomes more natural with lambdas:
button {
text = "Submit"
onClick { view ->
validateForm()
submitData()
}
}
Google Developers have embraced this approach with Jetpack Compose, a declarative UI toolkit using Kotlin DSLs. Making it extensible is crucial for real-world applications:
// Custom component
fun CustomScope.roundedButton(text: String, onClick: () -> Unit) {
button {
this.text = text
this.onClick = onClick
cornerRadius = 8.dp
elevation = 2.dp
}
}
// Usage
ui {
roundedButton("Login") {
performLogin()
}
}
The Compose Multiplatform framework extends these concepts beyond Android to desktop and web applications.
Creating a Testing DSL
Testing frameworks in Kotlin often use DSLs to make test cases readable:
describe("Calculator") {
val calculator = Calculator()
it("adds two numbers correctly") {
val result = calculator.add(2, 3)
expect(result).toBe(5)
}
it("multiplies two numbers correctly") {
val result = calculator.multiply(2, 3)
expect(result).toBe(6)
}
}
Simplifying assertions with custom DSL functions improves test readability:
fun expect(actual: Int) = ExpectInt(actual)
class ExpectInt(private val actual: Int) {
fun toBe(expected: Int) {
if (actual != expected) {
throw AssertionError("Expected $expected but got $actual")
}
}
fun toBeGreaterThan(expected: Int) {
if (actual <= expected) {
throw AssertionError("Expected > $expected but got $actual")
}
}
}
Supporting different testing scenarios becomes easier with context-specific DSL extensions:
fun asyncTest(block: suspend () -> Unit) {
runBlocking {
block()
}
}
// Usage
asyncTest {
val result = await(api.fetchData())
expect(result.status).toBe(200)
}
Kotlin coroutines DSL provides powerful primitives for asynchronous testing.
Database Query DSL
Building type-safe SQL-like syntax is one of the most practical DSL applications:
val users = table("users")
val name = column("name", users)
val age = column("age", users)
val query = select {
users.all()
}.where {
(name eq "John") and (age gt 18)
}.orderBy {
age.desc()
}
Managing connections and transactions becomes clearer with DSL scope functions:
transaction {
val user = User.new {
name = "Alice"
email = "alice@example.com"
}
user.profile = Profile.new {
bio = "Software developer"
}
}
The Exposed library, part of the JVM ecosystem, demonstrates these concepts elegantly. Query optimization considerations can be built into the DSL:
// The DSL can decide to use an index based on the query
val query = select {
users.all()
}.where {
name eq "John" // This might use an index
}
JSON/XML Processing DSL
Creating data structure builders with DSLs simplifies working with serialization formats:
val json = json {
"name" to "John"
"age" to 30
"address" to json {
"street" to "Main St"
"city" to "Boston"
}
"hobbies" to array {
+"Reading"
+"Hiking"
}
}
KotlinX Serialization provides powerful DSLs for parsing and serialization:
val data = json.decodeFromString<Person>(jsonString)
val jsonString = json.encodeToString(person)
Schema validation implementation becomes more intuitive with DSL builders:
val schema = jsonSchema {
string("name") {
minLength = 2
required = true
}
number("age") {
minimum = 0
}
array("tags") {
items = string {
minLength = 1
}
}
}
The GitHub community has developed several excellent JSON/XML processing libraries using Kotlin’s DSL capabilities.
These real-world examples demonstrate why Kotlin has become a popular choice for DSL creation. Its type-safe builder pattern, extension functions, and functional programming features combine to create expressive, maintainable domain-specific languages that improve developer productivity and code quality.
Understanding these advanced techniques and studying existing implementations will help you create your own DSLs that solve domain problems elegantly. The Kotlin YouTube channel and Kotlin Slack community offer additional resources for learning from experts in the field.
Best Practices for DSL Design
Creating effective DSLs requires thoughtful design. Let’s explore the key practices that lead to successful domain-specific languages in Kotlin.
Consistency and Readability
Establishing naming conventions makes your DSL intuitive. Names should reflect domain concepts clearly.
// Less intuitive naming
fun makeNewItem(i: String) {}
fun deleteExistingItem(i: String) {}
// More consistent naming
fun createItem(id: String) {}
fun removeItem(id: String) {}
Use similar patterns for similar operations. If one function returns the receiver with apply, all related functions should do the same. JetBrains emphasizes this principle in their Kotlin documentation.
Creating intuitive syntax patterns means aligning with how domain experts think:
// Database DSL that mirrors SQL syntax
val users = query {
select("name", "email")
from("users")
where("age" greaterThan 18)
orderBy("name")
}
Documentation strategies should complement your code. KDoc documentation provides context that IDE tooltips can display:
/**
* Creates a route that responds to GET requests.
*
* @param path The URL path to match
* @param handler The function that handles the request
*/
fun get(path: String, handler: RouteHandler.() -> Unit) {
// Implementation
}
Readable code structures in Kotlin DSLs improve maintainability. The Kotlin language specification includes features specifically designed to support clear, expressive code.
Usability and Learning Curve
Making DSLs discoverable helps new users. IntelliJ IDEA DSL support includes code completion suggestions that guide users through available options:
html {
// After typing "h", IDE suggests head(), header(), etc.
head {
// Inside head, IDE suggests title(), meta(), etc.
}
}
Consider progressive complexity for new users. Start with a simple core that addresses common cases:
// Basic usage
route("/users") {
get { /* simple handler */ }
}
// Advanced usage for experienced users
route("/items/:id") {
authenticate {
rateLimit(100, TimeUnit.HOUR) {
get { /* complex handler */ }
}
}
}
The Kotlin Foundation supports this approach in its design guidelines. Providing simple defaults while allowing customization makes your DSL approachable yet powerful.
Extensibility and Maintenance
Designing for future changes is crucial for long-lived DSLs. Keep your core API stable while allowing extensions:
// Core API
interface RouteBuilder {
fun get(path: String, handler: () -> Unit)
fun post(path: String, handler: () -> Unit)
// Other basic methods
}
// Extension point
interface AuthRouteBuilder : RouteBuilder {
fun authenticated(handler: () -> Unit)
}
Versioning strategies help manage evolution. Consider using distinct builder classes for major versions:
// V1 API
fun configV1(init: ConfigBuilderV1.() -> Unit): Config
// V2 API with new features
fun configV2(init: ConfigBuilderV2.() -> Unit): Config
Andrey Breslav, Kotlin’s original lead designer, often discussed the importance of backward compatibility. Your DSL should respect this principle to maintain user trust.
Backward compatibility considerations include providing migration paths:
@Deprecated("Use newMethod instead", ReplaceWith("newMethod(param)"))
fun oldMethod(param: String) = newMethod(param)
fun newMethod(param: String) {
// New implementation
}
Type system utilization helps enforce compatibility across versions. The Arrow Kt library demonstrates excellent practices in this area.
Integration with Existing Code
Working with Java and other JVM languages requires careful design. Kotlin DSLs should be usable from Java when possible:
// Kotlin DSL
fun server(init: ServerBuilder.() -> Unit): Server {
val builder = ServerBuilder()
builder.init()
return builder.build()
}
// Java-friendly alternative
fun createServer(builder: Consumer<ServerBuilder>): Server {
val serverBuilder = ServerBuilder()
builder.accept(serverBuilder)
return serverBuilder.build()
}
Bridging between DSL and traditional APIs provides flexibility:
// Traditional API
class UserService {
fun createUser(name: String, email: String): User { /* ... */ }
fun findUser(id: String): User? { /* ... */ }
}
// DSL wrapper
fun userDsl(service: UserService, init: UserDslContext.() -> Unit) {
val context = UserDslContext(service)
context.init()
}
class UserDslContext(private val service: UserService) {
fun create(init: UserBuilder.() -> Unit): User {
val builder = UserBuilder()
builder.init()
return service.createUser(builder.name, builder.email)
}
fun find(id: String): User? = service.findUser(id)
}
Gradual adoption approaches make integration smoother. You can start with simple extension functions and evolve toward a full DSL:
// Step 1: Simple extensions
fun HttpClient.get(url: String) = /* ... */
fun HttpClient.post(url: String, body: String) = /* ... */
// Step 2: Basic DSL
fun HttpClient.request(init: RequestBuilder.() -> Unit) = /* ... */
// Step 3: Complete DSL
fun http(init: HttpDsl.() -> Unit) = /* ... */
The Spring Framework’s Kotlin extensions demonstrate this pattern effectively, starting with simple extensions to the Java API and gradually adding more DSL features.
FAQ on How To Build DSLs In Kotlin
What makes Kotlin suitable for creating DSLs?
Kotlin excels at DSL creation through its support for extension functions, lambdas with receivers, and operator overloading. These features enable readable code structures that feel natural to domain experts. The type system allows compile-time safety while maintaining fluent APIs in Kotlin. JetBrains designed these capabilities specifically for expressive, maintainable code.
How do lambdas with receivers work in Kotlin DSLs?
Lambdas with receivers change the scope within code blocks, giving direct access to methods and properties of the receiver object without qualification. This creates context-specific scopes where the meaning of code adapts to its location. The technique forms the foundation of type-safe builders in Kotlin and enables the nested structure common in DSLs.
What is the @DslMarker annotation used for?
The @DslMarker annotation prevents scope confusion in nested blocks by restricting implicit access to outer receivers from inner lambdas. This prevents accidental calls to methods from parent contexts, making DSLs more predictable. It’s essential for larger DSLs with multiple nested levels, as recommended in the Kotlin language specification.
How can I implement builder patterns in Kotlin DSLs?
Use lambdas with receivers to create fluent builders:
fun html(init: HTML.() -> Unit): HTML = HTML().apply(init)
class HTML {
fun head(init: Head.() -> Unit) = Head().apply(init)
fun body(init: Body.() -> Unit) = Body().apply(init)
}
This pattern is common in Gradle Kotlin DSL examples and Android UI DSL development.
What’s the difference between internal and external DSLs?
Internal DSLs use the host language (Kotlin) and its features to create domain-specific syntax, maintaining all compiler benefits. External DSLs are separate languages with custom parsers. Kotlin excels at internal DSLs through function literals with receiver and custom language rules, providing type safety while avoiding the complexity of parser development.
How do I handle validation in Kotlin DSLs?
Implement validation at build time using Kotlin’s properties with custom setters:
var port: Int = 8080
set(value) {
require(value in 1024..65535) { "Port must be between 1024-65535" }
field = value
}
For business logic abstraction, consider validation at both property level and final build step using exceptions or Result types.
Can Kotlin DSLs be used from Java code?
Yes, though with limitations. Create Java-friendly facades that use consumers instead of lambdas with receivers:
// Java-friendly API
fun createServer(consumer: Consumer<ServerBuilder>): Server
JetBrains provides guidance on designing APIs compatible across the JVM ecosystem for gradual adoption approaches in mixed codebases.
How do DSLs differ from regular fluent APIs?
DSLs focus on modeling specific domains with syntax resembling the domain’s natural language. Fluent APIs chain methods but typically maintain traditional programming syntax. Kotlin DSLs use scope functions and higher-order functions to create context-aware code blocks with richer structure than simple method chaining in Kotlin DSLs.
What performance considerations should I keep in mind?
Use inline functions for lambda-heavy DSLs to reduce overhead. Be cautious with excessive object creation in builder patterns. Measure performance through benchmarking and optimization techniques. Consider compile-time processing where possible. Kotlin/Native and Kotlin/JS may have different performance characteristics requiring custom optimization for each target.
How do I test Kotlin DSLs effectively?
Use unit tests that verify both syntax correctness and expected behavior:
@Test
fun `configuration DSL produces correct settings`() {
val config = server {
port = 8080
host = "localhost"
}
assertEquals(8080, config.port)
}
Testing frameworks in Kotlin provide DSL-friendly assertion styles through extension functions.
Conclusion
Learning how to build DSLs in Kotlin transforms how you approach software architecture patterns. The language’s exceptional support for custom language development empowers you to create intuitive interfaces that domain experts can understand. Through type-safe builders, function literals with receiver, and expression functions, Kotlin makes domain modeling remarkably accessible.
Key takeaways from our exploration:
- Kotlin’s lambda with receiver syntax creates context-specific scopes that feel natural
- Extension functions and infix notation enable readable expressions without sacrificing type safety
- The @DslMarker annotation prevents scope confusion in nested blocks
- Internal DSL implementation provides compile-time validation while maintaining flexibility
- Tools like IntelliJ IDEA DSL support streamline the development experience
The JVM ecosystem continues to embrace Kotlin DSLs for everything from Gradle builds to Android UI development. As Andrey Breslav and the Kotlin Foundation advance the language, we’ll see even more powerful abstractions emerge. Whether you’re building database queries, UI layouts, or configuration systems, the techniques we’ve explored will serve you well.
Remember: great DSLs evolve from solving real problems. Start simple, test thoroughly, and refine based on usage. Your efforts will yield code that’s not just functional, but truly expressive.
With over a decade in the tech field, Bogdan blends technical expertise with insights on business innovation in technology.
A regular contributor to TMS Outsource's blog, where you'll find sharp analyses on software development, tech business strategies, and global tech dynamics.
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