In the last post we set up our deployment, fully automated on merge to our main branch. In this post we will be building our UI (Frontend) applications. See the full overview of posts here.

At the end of this post we will have:

• A Next.js Frontend app with support for localization, using Tailwind CSS.
• A Leptos Rust/WASM Frontend app with support for localization, using Tailwind CSS.
• Automatic deployment of our Apps AWS using CDK, statically hosted using S3 + CloudFront.

We are essentially hedging our bets by building both a JavaScript-based Frontend, which is the safe bet, and a Rust/WASM-based Frontend, which is the future bet. We will be using the same GraphQL API for both, so we can easily switch between them.

There is quite a lot to cover. My recommendation is to clone down the Part 3 branch in the GitHub repository and use this post as an explanation of what is set up.

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A lot of web development is transforming JSON one way or another. In TypeScript/JavaScript, this is straightforward, since JSON is built into the language. But can we also achieve good ergonomics in Haskell and Rust?

Dear reader, I am glad you asked! 🙌

The comparisons we will see are not meant to show if one approach is better than another. Instead, it is intended to be a reference to become familiar with common patterns across multiple languages. Throughout this post, we will utilize several tools and libraries.

The core of working with JSON in Haskell and Rust is covered by:

• Aeson: a Haskell JSON serialization/deserialization library¹.
• Serde: a Rust JSON serialization/deserialization library.

The ergonomics is then improved in Haskell by grabbing one of the following options²:

• Lens: a heavy-weight library to transform and work with records (and much more!)³.
• Record Dot Syntax: an upcoming language extension in Haskell, which recently got accepted by the GHC steering Committee⁴.

We’ll go through typical use-cases seen in TypeScript/JavaScript codebases, and see how we can achieve the same in Haskell and Rust.

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