Codetown ::: a software developer's community

ResourcesLast week, we went over higher order functions in Kotlin. We learned how higher order functions can accept functions as parameters and are also able to return functions. This week, we will take a look at lambdas. Lambdas are another type of function and they are very popular in the functional programming world.
Computer programs are made up of two parts: logic and data. Usually, logic is described in functions and data is passed to those functions. The functions do things with the data, and return a result. When we write a function we would typically create a named function. As we saw last week, this is a typical named function:
fun hello(name: String): String {
return "Hello, $name"
}
Then you can call this function:
fun main() {
println(hello("Matt"))
}
Which gives us the result:
Hello, Matt
Functions as DataThere is a concept in the functional programming world where functions are treated as data. Lambdas (functions as data) can do the same thing as named functions, but with lambdas, the content of a given function can be passed directly into other functions. A lambda can also be assigned to a variable as though it were just a value.
Lambda SyntaxLambdas are similar to named functions but lambdas do not have a name and the lambda syntax looks a little different. Whereas a function in Kotlin would look like this:
fun hello() {
return "Hello World"
}
The lambda expression would look like this:
{ "Hello World" }
Here is an example with a parameter:
fun(name: String) {
return "Hello, ${name}"
}
The lambda version:
{ name: String -> "Hello, $name" }
You can call the lambda by passing the parameter to it in parentheses after the last curly brace:
{ name: String -> "Hello, $name" }("Matt")
It’s also possible to assign a lambda to a variable:
val hello = { name: String -> "Hello, $name" }
You can then call the variable the lambda has been assigned to, just as if it was a named function:
hello("Matt")
Lambdas provide us with a convenient way to pass logic into other functions without having to define that logic in a named function. This is very useful when processing lists or arrays of data. We’ll take a look at processing lists with lambdas in the next post!
Tags:
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
Check out the Codetown Jobs group.

DoorDash has launched a multimodal machine learning system that aligns product images, text, and user queries in a shared embedding space. Trained on 32 million labeled query-product pairs using contrastive learning, the system improves semantic search, product ranking, and advertising relevance. Embeddings also support other machine learning tasks across the marketplace.
By Leela Kumili
Stefan Dirnstorfer discusses the shift from DOM-based testing to visual UI agents. He explains why LLMs often fail at precision tasks - like spotting one-pixel shifts or broken road networks - and shares how advanced image registration and "Chain-of-Thought" vision processing are essential for reliable QA. Learn why combining generative AI with classical algorithms is the future of automation.
By Stefan Dirnstorfer
Celebrating its 23rd year, Devnexus 2026 was held from March 4-6, 2026 at the Georgia World Congress Center in Atlanta, Georgia. The event featured speakers from the Java community who delivered workshops and talks under tracks such as: AI Generative; AI in Practice; Core Java; Java Frameworks; and Security and Developer Tools.
By Michael RedlichAndres Almiray, a serial open-source contributor and the creator of JReleaser, discusses the project's state, noting that the tool is usable across any ecosystem, not just Java. He also touches on the Common House Foundation's mission.
By Andres Almiray
This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
By Amit Kumar Padhy
© 2026 Created by Michael Levin.
Powered by