We’re joined today by Anand Naidu, our resident development expert, to discuss the recent Gleam 1.14.0 release. With a deep proficiency in both frontend and backend languages, Anand is uniquely positioned to break down the latest updates to this statically typed language that targets both the Erlang VM and JavaScript runtimes.
In our conversation, we’ll explore how Gleam is strengthening its type-safe bridge to existing JavaScript and Erlang ecosystems. We will also delve into significant compiler optimizations that are making Gleam code faster and more efficient, from advanced pattern matching on binary data to smarter equality testing. Finally, we’ll touch on new syntactic enhancements designed to improve developer ergonomics and code maintainability, painting a picture of a language that is maturing with a clear focus on both performance and practicality.
Gleam 1.14.0 introduced the @external annotation, allowing programmers to specify an Erlang or TypeScript type definition instead of falling back to “any.” Could you provide a step-by-step example of how this improves type safety and developer workflow when integrating with an existing JavaScript library?
Absolutely. This is a game-changer for interoperability. Before, when you referenced a type from an external JavaScript or Erlang library, Gleam had no real insight into its structure. It had to fall back to the vague “any” type, which essentially turned off the type checker for that interaction. Imagine integrating a JavaScript charting library. You’d be passing data objects to it, hoping you got the structure right, with no help from the compiler. Now, with the @external annotation, you can declare the precise TypeScript definition right in your Gleam code. The compiler suddenly understands the exact shape of the data the library expects. This means you get immediate feedback, catching errors at compile time instead of seeing a blank chart and a cryptic console error at runtime. It transforms the workflow from one of guesswork and defensive coding into a confident, type-safe process.
With inference-based pruning now optimized for int segments in binary pattern matching, what kind of performance metrics have you seen? Can you describe a specific scenario where this optimization makes a tangible difference in detecting redundant patterns and improving code efficiency?
This optimization is particularly impactful in areas like network protocol parsing or any form of binary data manipulation. Consider a scenario where you’re matching on a binary stream where the first few bytes represent an integer message type. You might have a case statement that handles different message types. If you accidentally included two identical patterns—say, two arms that both match on an int segment with the value 5—the old compiler might not have caught it. Now, this extended optimization for int segments makes the compiler smarter. It can detect and flag these redundant, unreachable patterns, which cleans up the code and prevents subtle logic bugs. More importantly, it prunes these impossible branches during compilation, resulting in smaller and faster code because the runtime doesn’t have to perform unnecessary checks. It’s a direct improvement to both performance and code quality.
The compiler now normalizes different number formats for pattern matching analysis. Besides enabling further optimizations, what were the key technical challenges in implementing this canonical representation, and what other benefits might this normalization unlock for the compiler in the future?
The primary challenge was ensuring the normalization process was completely robust and lossless across all possible formats—decimal, hexadecimal, octal, and even scientific notation for floats. You have to create a single internal representation that the entire compiler can trust, regardless of how the developer wrote the number in the source code. This means the pattern matching engine can now see that 16, 0x10, and 0o20 are identical without any extra work. This unification was the key that unlocked the ability to apply more powerful optimizations, like the inference-based pruning we just discussed. Looking ahead, this canonical representation is a foundational improvement. It could be leveraged for more advanced compile-time evaluations, better constant folding, or even more sophisticated static analysis, as it gives the compiler a much clearer and more consistent understanding of numeric values throughout a program.
Performance for equality testing on field-less custom types was improved when compiling to JavaScript. Could you elaborate on the technical changes that made this possible and explain how this specific optimization reflects Gleam’s overall philosophy for its target runtimes?
This is a fantastic example of Gleam’s pragmatic approach. Field-less custom types are essentially enums, like type Status { Ok, Error }. When compiling to JavaScript, a naive approach might represent these as objects, making an equality check like Ok == Ok a surprisingly heavy operation. The technical change here was to compile these variants down to highly optimized primitive values, like integers or strings, behind the scenes. This turns a potentially complex object comparison into a simple, lightning-fast 0 === 0 check in the generated JavaScript. This reflects Gleam’s core philosophy perfectly: it’s not enough to just work on a target runtime. Gleam aims to produce idiomatic, high-performance code that respects the optimization strategies of that specific environment, whether it’s the Erlang VM or a JavaScript engine in a browser.
The record update syntax is now usable in constant definitions, allowing constants to be built from other constants. Please walk us through a practical use case for this feature and explain how it helps developers write more maintainable and expressive code.
This is a wonderful quality-of-life improvement for developers. Imagine you have a constant record that defines a default configuration for your application, with maybe a dozen fields. Now, you need a specific configuration for your test environment that is identical except for one or two fields, like a debug_mode flag. Before this change, you would have to copy the entire default configuration and change those few values, leading to code duplication. If you later updated a value in the default config, you’d have to remember to update it in the test config too. Now, you can simply define the test constant based on the default, like const test_config = { ..default_config, debug_mode: True }. This is far more expressive and maintainable. It eliminates duplication and ensures that derived constants automatically inherit changes from their base, which significantly reduces the chance of bugs creeping into your configurations.
What is your forecast for Gleam?
My forecast for Gleam is incredibly positive. Based on updates like these, it’s clear the language is maturing with a dual focus on rigorous type safety and pragmatic, real-world performance. It’s not just an academic exercise; it’s being built to solve problems effectively on two of the most important runtimes in modern software: the Erlang VM and JavaScript. I believe Gleam will continue to attract developers who crave the safety of a language like Rust or Elm but need the battle-tested concurrency of the BEAM or the universal reach of JavaScript. It is carving out a powerful niche for building robust, maintainable, and performant systems that can bridge these two critical ecosystems.
