 Courses from Andrew Van Slaars

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.



Mocking WebSocket APIs with Mock Service Worker


When attempting to load the same module twice in JavaScript you'll hit a cache and code won't re-run. In scenarios where you actually do want to have state in your modules, you'll have to use a cache-busting technique by passing a query parameter to a dynamic import. This lesson walks through how to add a query parameter, how to add the appropriate types for loading dynamic modules in TypeScript, and when these techniques are valuable.

Import the Same JavaScript Module Multiple Times with Cache Busting



The include keyword in GitHub Actions empowers you to add custom variables to your matrix strategy, providing flexibility beyond standard configurations. In this video, we'll explore how include allows you to define specific combinations of variables, enhancing control over your workflow runs. You'll see how this feature enables you to tailor jobs for unique environments or scenarios not covered by the default matrix options. By leveraging include, you can efficiently manage complex build processes with greater precision. This technique helps you streamline your CI/CD pipeline, making it more adaptable to your project's evolving needs.

Use GitHub Actions Matrix Include for Complex Job Configurations

While running `zod` schema validation agains domain-rich objects, the error message saying that _a string is not a number_ might not be really useful.

Especially, when you need to provide a meaningful feeback to the user, e.g. after they have submitted a form with lots of data.

`zod` allows to customize messages on a very granular level. We can also extract repeatable customization to `zod` error maps.

Finally, in order to process an error message further (e.g. display it to the user), we need to handle the `zod` validation errors effectively. Especially, when a big object validation has failed and we could receive a handful of errors.

That's what we learn in this lesson. 🔥

Customize zod Error Messages and Handle zod Errors Effectively

customize zod validation messages, customize zod schema property messages, reuse error maps across different schemas, navigate through zod error objects

You might want to run `zod` schema checks in different styles.

Sometimes you prefer to conditionally throw the error - that's what `parse` does. But another time you'd prefer to receive a `boolean` property that determines whether the validation was successful or not - and perform further actions accordingly.

Additionally, the error-throwing API of `parse` works extremely well with [TypeScript assertion functions](https://www.typescriptlang.org/docs/handbook/release-notes/typescript-3-7.html#assertion-functions). Their combination is just... perfect! 👌

That's what we learn in this lesson.

Use parse and safeParse Runtime Type-checks with zod

use zod parse function to conditionally throw ZodError, use zod safeParse to get a boolean value and run typescript type guard over success property

In this lesson we learn that TypeScript cannot guarantee correct shapes of responses. That's due to TS performing checks in compile-time, while the response will be availbale in runtime - when TS doesn't exist anymore.

In some situations, such as HTTP requests, runtime type checks such as `zod` are essential for providing quick feedback around data-related errors.

Understand where are the possible mistakes to make while using TypeScript with HTTP calls. Use `fetch` and `axios` along with `zod` schema checks.

Also, note that similar mistakes or mistakes could happen while using web storage (e.g. `localStorage`)

Validate HTTP API Response with zod, fetch and axios

use zod as an HTTP response validation tool along with fetch and axios in order to make sure that the response has expected data structure

In this lesson we learn that the `zod` schemas become the single source of truth.

We shall not update TypeScript types manually, since they are generated automatically from the schema.

Same with validation functions.

Whenever the schema changes, all the rest will update accordingly. It's an important mind shift.

Treat zod Schemas as Single Source of Truth

Zod Schemas are the single source of truth for both typescript types, and interfaces - and runtime validation functions. Schemas should be updated first.

`zod` is powerful, but writing the schemas manually for existing JSON objects, especially big ones, can be time-consuming.

But that's what `quicktype` is for! You can get 80% work done instantly! 🥳

Depending on your need - whether you want to automate the process, or do it just one time, you can:
- use an online app
- use an IDE extension
- run a CLI script
- generate a script to be executed within CI/CD environment/tooling

And this is what we learn in this lesson! 🔥🔥🔥

Get started with zod quickly!

Generate zod Schemas Automatically using Quicktype App, Extensions and Scripts

Use quicktype app, extension or scripts to automatically generate zod schemas from existing json files. Save time and get started with zod quickly!

`zod` is a powerful library allowing to run runtime type-checks.

It's complementary to TypeScript, which does type checks in compile time.

In this lesson we create few schemas that represent primitive type values, along with `zod` utilities which make these more precise. We also create object schemas and nest one inside another.

All schemas are being validated against example values. We analyze how to interpret error messages in case of failures.

Perform Runtime Type-checks on Primitives and Objects with zod

Create zod schemas to validate primitive type and object type values in runtime. Analyze the errors to see how a value doesn't correspond to schema expectations


Using Cursor Chat, you can easily reference an entire directory of files and ask it to generate anything based on those files. This opens up the possibility of using your current code as a reference guide to create new code that follows existing patterns. With this approach, the result can be a polished prompt you can use with any AI to generate consistent and reliable output.

Focus less on crafting the perfect prompt upfront - what matters most is testing and iterating based on the AI's responses. The more results you see from the prompt, the more you can reinforce the good results and prune out the bad results until your prompt is polished enough that you can rely on the prompt to give you consistent quality results.


Andy Van Slaars

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