Resources

Introduction

Last 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.



Logic & Data

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 Data

There 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 Syntax

Lambdas 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!

Views: 160

Happy 10th year, JCertif!

Notes

Welcome to Codetown!

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.

When you create a profile for yourself you get a personal page automatically. That's where you can be creative and do your own thing. People who want to get to know you will click on your name or picture and…
Continue

Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.

Looking for Jobs or Staff?

Check out the Codetown Jobs group.

 

Enjoy the site? Support Codetown with your donation.



InfoQ Reading List

C++26: Reflection, Memory Safety, Contracts, and a New Async Model

The C++26 standard draft is now complete, reports Herb Sutter, long-time C++ expert and former chair of the ISO C++ standards committee. The finalized draft introduces reflection, enhances memory safety without requiring code rewrites, adds contracts with preconditions and postconditions alongside a new assertion statement, and establishes a unified framework for concurrency and parallelism.

By Sergio De Simone

Meta Reports 4x Higher Bug Detection with Just-in-Time Testing

Meta introduces Just-in-Time (JiT) testing, a dynamic approach that generates tests during code review instead of relying on static test suites. The system improves bug detection by ~4x in AI-assisted development using LLMs, mutation testing, and intent-aware workflows like Dodgy Diff. It reflects a shift toward change-aware, AI-driven software testing in agentic development environments

By Leela Kumili

CNCF Warns Kubernetes Alone Is Not Enough to Secure LLM Workloads

A new blog from the Cloud Native Computing Foundation highlights a critical gap in how organizations are deploying large language models (LLMs) on Kubernetes: while Kubernetes excels at orchestrating and isolating workloads, it does not inherently understand or control the behavior of AI systems, creating a fundamentally different and more complex threat model.

By Craig Risi

Anthropic Introduces Agent-Based Code Review for Claude Code

Anthropic has introduced a new Code Review feature for Claude Code, adding an agent-based pull request review system that analyzes code changes using multiple AI reviewers.

By Daniel Dominguez

Presentation: Speed at Scale: Optimizing the Largest CX Platform Out There

Matheus Albuquerque shares strategies for optimizing a massive CX platform, moving from React 15 and Webpack 1 to modern standards. He discusses using AST-based codemods for large-scale migrations, implementing differential serving with module/nomodule, and leveraging Preact to shrink footprints. He explains how to balance cutting-edge performance with strict legacy browser constraints.

By Matheus Albuquerque

© 2026   Created by Michael Levin.   Powered by

Badges  |  Report an Issue  |  Terms of Service