AWS

How to automatically deploy your full-stack JavaScript app with AWS CodePipeline

Published Sep 15, 2023

Updated Nov 2, 2023

9 min read

How to host a full stack javascript app with AWS - 1 Part Series

This article was written over 18 months ago and may contain information that is out of date. Some content may be relevant but please refer to the relevant official documentation or available resources for the latest information.

How to automatically deploy your full-stack JavaScript app from an NX monorepo with AWS CodePipeline

In our previous blog post (How to host a full-stack JavaScript app with AWS CloudFront and Elastic Beanstalk) we set up a horizontally scalable deployment for our full-stack javascript app. In this article, we would like to show you how to set up AWS CodePipeline to automatically deploy changes to the application.

APP Structure

Our application is a simple front-end with an API back-end set up in an NX monorepo. The production built API code is hosted in Elastic Beanstalk, while the front-end is stored in S3 and hosted through CloudFront. Whenever we are ready to make a new release, we want to be able to deploy the new API and front-end versions to the existing distribution.

In this article, we will set up a CodePipeline to deploy changes to the main branch of our connected repository.

CodePipeline

CodeBuild and the buildspec file

First and foremost, we should set up the build job that will run the deploy logic. For this, we are going to need to use CodeBuild. Let's go into our repository and set up a build-and-deploy.buildspec.yml file. We put this file under the tools/aws/ folder.

version: 0.2

phases:
  install:
    runtime-versions:
      nodejs: 18
    on-failure: ABORT
    commands:
      - npm ci
  build:
    on-failure: ABORT
    commands:
      # Build the front-end and the back-end
      - npm run build:$ENVIRONMENT_TARGET
      # TODO: Push FE to S3
      # TODO: Push API to Elastic beanstalk

This buildspec file does not do much so far, we are going to extend it. In the installation phase, it will run npm ci to install the dependencies and in the build phase, we are going to run the build command using the ENVIRONMENT_TARGET variable. This is useful, because if you have more environments, like development and staging you can have different configurations and builds for those and still use the same buildspec file.

Let's go to the Codebuild page in our AWS console and create a build project. Add a descriptive name, such as your-appp-build-and-deploy. Please provide a meaningful description for your future self. For this example, we are going to restrict the number of concurrent builds to 1.

The next step is to set up the source for this job, so we can keep the buildspec file in the repository and make sure this job uses the steps declared in the yaml file. We use an access token that allows us to connect to GitHub. Here you can read more on setting up a GitHub connection with an access token. You can also connect with Oauth, or use an entirely different Git provider.

We set our provider to GitHub and provided the repository URL. We also set the Git clone depth to 1, because that makes checking out the repo faster.

In the Environment section, we recommend using an AWS CodeBuild managed image. We use the Ubuntu Standard runtime with the aws/codebuild/standard:7.0 version. This version uses Node 18. We want to always use the latest image version for this runtime and as the Environment type we are good with Linux EC2. We don't need elevated privileges, because we won't build docker images, but we do want to create a new service role.

In the Buildspec section select Use a buildspec file and give the path from your repository root as the Buildspec name. For our example, it is tools/aws/build-and-deploy.buildspec.yml. We leave the Batch configuration and the Artifacts sections as they are and in the Logs section we select how we want the logs to work. For this example, to reduce cost, we are going to use S3 logs and save the build logs in the aws-codebuild-build-logs bucket that we created for this purpose. We are finished, so let's create the build project.

CodePipeline setup

To set up automated deployment, we need to create a CodePipeline. Click on Create pipeline and give it a name. We also want a new service role to be created for this pipeline.

Next, we should set up the source stage. As the source provider, we need to use GitHub (version2) and set up a connection. You can read about how to do it here. After the connection is set up, select your repository and the branch you want to deploy from. We also want to start the pipeline if the source code changes. For the sake of simplicity, we want to have the Output artefact format as CodePipeline default.

At the Build stage, we select AWS CodeBuild as the build provider and let's select the build that we created above. Remember that we have the ENVIRONMENT_TARGET as a variable used in our build, so let's add it to this stage with the Plaintext value prod. This way the build will run the build:prod command from our package.json. As the Build type we want Single build.

We can skip the deployment stage because we are going to set up deployment in our build job. Review our build pipeline and create it. After it is created, it will run for the first time. At this time it will not deploy anything but it should run successfully.

Deployment prerequisites

To be able to deploy to S3 and Elastic Beanstalk, we need our CodeBuild job to be able to interact with those services. When we created the build, we created a service role for it. In this example, the service role is codebuild-aws-test-build-and-deploy-service-role. Let's go to the IAM page in the console and open the Roles page. Search for our codebuild role and let's add permissions to it. Click the Add permissions button and select Attach policies. We need two AWS-managed policies to be added to this service role. The AdministratorAccess-AWSElasticBeanstalk will allow us to deploy the API and the AmazonS3FullAccess will allow us to deploy the front-end. The CloudFrontFullAccess will allow us to invalidate the caches so CloudFront will send the new front-end files after the deployment is ready.

Deployment

Upload the front-end to S3

Uploading the front-end should be pretty straightforward. We use an AWS CodeBuild managed image in our pipeline, therefore, we have access to the aws command. Let's update our buildspec file with the following changes:

phases:
# ...
  build:
    on-failure: ABORT
    commands:
      # Build the front-end and the back-end
      - npm run build:$ENVIRONMENT_TARGET
      # Delete the current front-end and deploy the new version front-end
      - aws s3 sync dist/apps/frontend/ s3://$FRONT_END_BUCKET --delete
      # Invalidate cloudfront caches to immediately serve the new front-end files
      - aws cloudfront create-invalidation --distribution-id $CLOUDFRONT_DISTRIBUTION_ID --paths "/index.html"
      # TODO: Push API to Elastic beanstalk

First, we upload the fresh front-end build to the S3 bucket, and then we invalidate the caches for the index.html file, so CloudFront will immediately serve the changes. If you have more static files in your app, you might need to invalidate caches for those as well.

Before we push the above changes up, we need to update the environment variables in our CodePipeline. To do this open the pipeline and click on the Edit button. This will then enable us to edit the Build stage. Edit the build step by clicking on the edit button.

On this screen, we add the new environment variables. For this example, it is aws-hosting-prod as Plaintext for the FRONT_END_BUCKET and E3FV1Q1P98H4EZ as Plaintext for the CLOUDFRONT_DISTRIBUTION_ID

Now if we add changes to our index.html file, for example, change the button to <button id="hello">HELLO 2</button>, commit it and push it. It gets deployed.

Deploying the API to Elastic Beanstalk

We are going to need some environment variables passed down to the build pipeline to be able to deploy to different environments, like staging or prod. We gathered these below:

COMMIT_ID: #{SourceVariables.CommitId} - This will have the commit id from the checkout step. We include this, so we can always check what commit is deployed.
ELASTIC_BEANSTALK_APPLICATION_NAME: Test AWS App - This is the Elastic Beanstalk app which has your environment associated.
ELASTIC_BEANSTALK_ENVIRONMENT_NAME: TestAWSApp-prod - This is the Elastic Beanstalk environment you want to deploy to
API_VERSION_BUCKET: elasticbeanstalk-us-east-1-474671518642 - This is the S3 bucket that was created by Elastic Beanstalk

With the above variables, we can make some new variables during the build time, so we can make sure that every API version is unique and gets deployed. We set this up in the install phase.

# ...

phases:
  install:
    runtime-versions:
      nodejs: 18
    on-failure: ABORT
    commands:
      - APP_VERSION=`jq '.version' -j package.json`
      - API_VERSION=$APP_VERSION-build$CODEBUILD_BUILD_NUMBER
      - API_ZIP_KEY=$COMMIT_ID-api.zip
      - 'APP_VERSION_DESCRIPTION="$AP_VERSION: $COMMIT_ID"'
      - npm ci
# ...

The APP_VERSION variable is the version property from the package.json file. In a release process, the application's version is stored here. The API_VERSION variable will contain the APP_VERSION and as a suffix, we include the build number. We want to upload this API version by indicating the commit ID, so the API_ZIP_KEY will have this information. The APP_VERSION_DESCRIPTION will be the description of the deployed version in Elastic Beanstalk.

Finally, we are going to update the buildspec file with the actual Elastic Beanstalk deployment steps.

phases:
# ...
  build:
    on-failure: ABORT
    commands:
      # ...

      # ZIP the API
      - zip -r -j dist/apps/api.zip dist/apps/api
      # Upload the API bundle to S3
      - aws s3 cp dist/apps/api.zip s3://$API_VERSION_BUCKET/$ENVIRONMENT_TARGET/$API_ZIP_KEY
      # Create new API version in Elastic Beanstalk
      - aws elasticbeanstalk create-application-version --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --version-label "$API_VERSION" --description "$APP_VERSION_DESCRIPTION" --source-bundle "S3Bucket=$API_VERSION_BUCKET,S3Key=$ENVIRONMENT_TARGET/$API_ZIP_KEY"
      # Deploy new API version
      - aws elasticbeanstalk update-environment --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --version-label "$API_VERSION" --environment-name "$ELASTIC_BEANSTALK_ENVIRONMENT_NAME"
      # Wait until the Elastic Beanstalk environment is stable
      - aws elasticbeanstalk wait environment-updated --application-name "$ELASTIC_BEANSTALK_APPLICATION_NAME" --environment-name "$ELASTIC_BEANSTALK_ENVIRONMENT_NAME"

Let's make a change in the API, for example, the message sent back by the /api/hello endpoint and push up the changes.

Now every time a change is merged to the main branch, it gets pushed to our production deployment. Using these guides, you can set up multiple environments, and you can configure separate CodePipeline instances to deploy from different branches. I hope this guide proved to be helpful to you.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Balázs Tápai
He is a Software Engineer with a passion for automated testing. He loves Angular on the Front-End and NestJS on the Back-End. He also uses Cypress to reduce developer anxiety before demo meetings.
@TapaiBalazs @TapaiBalazs

Incremental Hydration in Angular

Incremental Hydration in Angular Some time ago, I wrote a post about SSR finally becoming a first-class citizen in Angular. It turns out that the Angular team really treats SSR as a priority, and they have been working tirelessly to make SSR even better. As the previous blog post mentioned, full-page hydration was launched in Angular 16 and made stable in Angular 17, providing a great way to improve your Core Web Vitals. Another feature aimed to help you improve your INP and other Core Web Vitals was introduced in Angular 17: deferrable views. Using the @defer blocks allows you to reduce the initial bundle size and defer the loading of heavy components based on certain triggers, such as the section entering the viewport. Then, in September 2024, the smart folks at Angular figured out that they could build upon those two features, allowing you to mark parts of your application to be server-rendered dehydrated and then hydrate them incrementally when needed - hence incremental hydration. I’m sure you know what hydration is. In short, the server sends fully formed HTML to the client, ensuring that the user sees meaningful content as quickly as possible and once JavaScript is loaded on the client side, the framework will reconcile the rendered DOM with component logic, event handlers, and state - effectively hydrating the server-rendered content. But what exactly does "dehydrated" mean, you might ask? Here's what will happen when you mark a part of your application to be incrementally hydrated: 1. Server-Side Rendering (SSR): The content marked for incremental hydration is rendered on the server. 2. Skipped During Client-Side Bootstrapping: The dehydrated content is not initially hydrated or bootstrapped on the client, reducing initial load time. 3. Dehydrated State: The code for the dehydrated components is excluded from the initial client-side bundle, optimizing performance. 4. Hydration Triggers: The application listens for specified hydration conditions (e.g., on interaction, on viewport), defined with a hydrate trigger in the @defer block. 5. On-Demand Hydration: Once the hydration conditions are met, Angular downloads the necessary code and hydrates the components, allowing them to become interactive without layout shifts. How to Use Incremental Hydration Thanks to Mark Thompson, who recently hosted a feature showcase on incremental hydration, we can show some code. The first step is to enable incremental hydration in your Angular application's appConfig using the provideClientHydration provider function: ` Then, you can mark the components you want to be incrementally hydrated using the @defer block with a hydrate trigger: ` And that's it! You now have a component that will be server-rendered dehydrated and hydrated incrementally when it becomes visible to the user. But what if you want to hydrate the component on interaction or some other trigger? Or maybe you don't want to hydrate the component at all? The same triggers already supported in @defer blocks are available for hydration: - idle: Hydrate once the browser reaches an idle state. - viewport: Hydrate once the component enters the viewport. - interaction: Hydrate once the user interacts with the component through click or keydown triggers. - hover: Hydrate once the user hovers over the component. - immediate: Hydrate immediately when the component is rendered. - timer: Hydrate after a specified time delay. - when: Hydrate when a provided conditional expression is met. And on top of that, there's a new trigger available for hydration: - never: When used, the component will remain static and not hydrated. The never trigger is handy when you want to exclude a component from hydration altogether, making it a completely static part of the page. Personally, I'm very excited about this feature and can't wait to try it out. How about you?...

Mar 14, 2025

3 mins

AngularJavaScript

Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

Introducing the express-typeorm-postgres Starter Kit

Here at This Dot, we've been working with ExpressJS APIs for a while, and we've created a starter.dev kit for ExpressJS that you can use to scaffold your next backend project. The starter kit uses many well-known npm packages, such as TypeORM or BullMQ and integrates with databases such as PostgreSQL and Redis. Kit contents The express-typeorm-postgres starter kit provides you with infrastructure for development, and integrations with these infrastructures. It comes with a working Redis instance for caching and a second Redis instance for queues. It also starts up a Postgres instance for you, which you can seed with TypeORM. The infrastructure runs on docker using docker-compose. The generated project comes with prettier and eslint set-up, so you only need to spend time on configuration if you want to tweak or change the existing rules. Unit testing is set up using Jest, and there are some example tests provided with the example controllers. How to initialise API development usually requires more infrastructure than front-end development. Before you start, please make sure you have docker and docker-compose installed on your machine. To initialize a project with the express-typeorm-postgres kit, run the following: 1. Run npx @this-dot/create-starter to run the scaffolding tool 2. Select the Express.js, TypeORM, and PostgreSQL kit from the CLI library options 3. Name your project 4. cd into your project directory, and install dependencies using the tool of your choice (npm, yarn or pnpm) 5. copy the contents of the .env.example file into a .env file With this setup, you have a working starter kit that you can modify to your needs. TypeORM and Database When we started developing the kit, we decided to use PostgreSQL as the database, because it is a powerful, open-source object-relational database system that is widely used for storing and manipulating data. It has a strong reputation for reliability, performance, and feature richness, making it a great choice for a wide range of applications. It can also handle high levels of concurrency and large amounts of data, and supports complex queries and data types. Postgres is also highly extensible because it allows developers to add custom functions and data types to the database. It has a large and active community of developers and users who contribute to its ongoing development and support. The kit uses TypeORM to connect to the database instance. We chose TypeORM because it makes it easy to manage database connections and perform common database operations, such as querying, inserting, updating and deleting data. It supports TypeScript and a wide range of databases, such as PostgreSQL, MySQL, SQLite and MongoDB, therefore if you want to be able to switch between databases, it makes it easier. TypeORM also includes features such as database migrations, which help manage changes to database schema over time, and an entity model that allows you to define your database schema using classes and decorators. Overall, TypeORM is a useful tool for improving the efficiency and reliability of database-related code, and it can be a valuable addition to any TypeScript or JavaScript project that needs to interact with a database. To seed an initial set of data into your database, run the following commands: 1. npm run infrastructure:start - this starts up the database instance 2. npm run db:seed - this leverages TypeORM to seed the database. The seed command runs the src/db/run-seeders.ts file, where you can introduce your seeders for your own needs. The kit uses TypeORM-extension for seeding. Please refer to the src/db/seeding/technology-seeder.ts file for an example. Caching Storing response data in caches allows subsequent requests for the same data to be served more quickly. This can improve the performance and user experience of an API by reducing the amount of time it takes to retrieve data from the server. It can reduce the load on the database or mitigate rate limiting on third-party APIs called from your back-end. It also improves the reliability of an application by providing a fallback mechanism in case the database or the server is unavailable or slow to respond. There is a Redis instance set up in the kit to be used for caching data. Under the hood, we use the cachified library to store cached data in Redis. The kit has a useCache method exported from src/cache/cache.ts, which requires a key and a callback function to be called to fetch the data. ` When you need to invalidate cache entries, you can use the clearCacheEntry method by supplying a key string to it. It will remove the cached data from Redis, and the next request that fetches from the database will cache the new values. ` Under the src/modules/technology folder, you can see a complete example of a basic CRUD REST endpoint with caching enabled. Feel free to use those handlers as examples for your development needs. Queue A message queue allows different parts of an application, or different applications, to communicate with each other asynchronously by sending and receiving messages. This can be useful in a variety of situations, such as when one part of the application needs to perform a task that could take a long time, or when different parts of the application need to be decoupled from each other for flexibility and scalability. We chose BullMQ because it is a fast, reliable, and feature-rich message queue system. It is built on top of the popular Redis in-memory data store, which makes it very performant and scalable. It has support for parallel processing, rate limiting, retries, and a variety of other features that make it well-suited for a wide range of use cases. BullMQ has a straightforward API and good documentation. The kit has a second Redis instance set up to be used with BullMQ, and there is a queue set up out of the box, so resource-intensive tasks can be offloaded to a background process. The src/queue/ folder contains all the configuration and setup steps for the queue. Both the queue and its worker is set up in the queue.ts file. The job-processor.ts file contains the function that will process the data. To run int in a separate thread, we must pass the path to this file into the worker: ` When to use this kit This kit is most optimal when you: - want to build back-end services that can be consumed by other applications and services using ExpressJS - need a flexible and scalable way to build server-side applications - need to deal with CPU-intense operations on the server and you need a messaging queue - need to build an API with relational data - would like to just jump right into API development with ExpressJS using TypeORM and Postgres Conclusion The express-typeorm-postgres starter kit can help you kickstart your development by providing you with a working preset. It has testing configured, and it comes with a complete infrastructure orchestrated by docker-compose....

Jan 13, 2023

5 mins

NodeJSExpress.jsTypeScriptPostgreSQL

Increasing development velocity with Cursor

If you’re a developer, you’ve probably heard of Cursor by now and have either tried it out or are just curious to learn more about it. Cursor is a fork of VSCode with a ton of powerful AI/LLM-powered features added on. For around $20/month, I think it’s the best value in the AI coding space. Tech giants like Shopify and smaller companies like This Dot Labs have purchased Cursor subscriptions for their developers with the goal of increased productivity. I have been using Cursor heavily for a few months now and am excited to share how it’s impacted me personally. In this post, we will cover some of the basic features, use cases, and I’ll share some tips and tricks I’ve learned along the way. If you love coding and building like me, I hope this post will help you unleash some of the superpowers Cursor’s AI coding features make possible. Let’s jump right in! Cursor 101 The core tools of the Cursor tool belt are Autocomplete, Ask, and Agent. Feature: Autocomplete The first thing that got me hooked was Autocomplete. It just worked so much better than the tools I had used previously, like GitHub Copilot. It was quicker and smarter, and I could immediately notice the amount of keystrokes that it was saving me. This feature is great because it doesn’t really require any work or skilled prompting from the user. There are a couple of tricks for getting a little bit more out of it that I will share later, but for now, just enjoy the ride! Feature: Ask If you’ve interacted with AI/LLMs before, like ChatGPT - this is what the Ask feature is. It’s just a chat feature you can easily provide context to from your code base and choose which Model to chat with. This feature is best suited for just asking more general questions that you might have queried Google or Stack Overflow for in the past. It’s also good for planning how to implement a feature you’re working on. After chatting or planning, you can switch directly to Agent mode to pick up and take action on something you were cooking up in Ask mode. Here’s an example of planning a simple tic-tac-toe game implementation using the Ask feature: Feature: Agent Agent mode lets the AI model take the wheel and write code, make edits, or take other similar actions on your code base. The goal is that you can write prompts and give instructions, and the Agent can generate the code and build features or even entire applications for you. With great power comes great responsibility. Agents are a feature where the more you put into them, the more you get out. The more skilled you become in using them by providing better prompts and including the right context, you will continue to get better results. The AI doesn’t always get it right, but the fact that the models and the users are both getting better is exciting. Throughout this post, I will share the best use cases, tips, and tricks I have found using Cursor Agent. Here’s an example using the Agent to execute the implementation details of the tic-tac-toe game we planned using Ask: Core Concept: Context After understanding the features and the basics of prompting, context is the most important thing for getting the best results out of Cursor. In Cursor and in general, whenever you’re prompting a chat or an agent, you want to make sure that it has all the relevant information that it needs to provide an answer or result. Cursor, by default, always has some context of your code. It indexes your code base and usually keeps the open buffer in the context window at the very least. At the top left of the Ask or Agent panel, there is an @ button, and next to that are badges for all the current items that have been explicitly added to the context for the current session. The @ button has a dropdown that allows you to add files, folders, web links, past chats, git commits, and more to the context. Before you prompt, always make sure you add the relevant content it needs as context so that it has everything it needs to provide the best response. Settings and Rules Cursor has its own settings page, which you can access through Cursor → Settings → Cursor Settings. This is where you log in to your account, manage various features, and enable or disable models. In the General section, there is an option for Privacy Mode. This is one setting in particular I recommend enabling. Aside from that, just explore around and see what’s available. Models The model you use is just as important as your prompt and the context that you provide. Models are the underlying AI/LLM used to process your input. The most well-known is GPT-4o, the default model for ChatGPT. There are a lot of different models available, and Cursor provides access to most of them out of the box. Model pricing A lot of the most common models, like GPT-4o or Sonnet 3.5/3.7, are included in your Cursor subscription. Some models like o1 and Sonnet 3.7 MAX are considered premium models, and you will be billed for usage for these. Be sure to pay attention to which models you are using so you don’t get any surprise bills. Choosing a Model Some models are better suited for certain tasks than others. You can configure which models are enabled in the Cursor Settings. If you are planning out a big feature or trying to solve some complex logic issue, you may want to use one of the thinking models, like o1, o3-mini, or Deep Seek R1. For most coding tasks and as a good default, I recommend using Sonnet 3.5 or 3.7. The great thing about Cursor is that you have the options available right in your editor. The most important piece of advice that I can give in this post is to keep trying things out and experimenting. Try out different models for different tasks, get a feel for it, and find what works for you. Use cases Agents and LLM models are still far from perfect. That being said, there are already a lot of tasks they are very good at. The more effective you are with these tools, the more you will be able to get done in a shorter amount of time. Generating test cases Have some code that you would like unit tested? Cursor is very good at generating test cases and assertions for your code. The fewer barriers there are to testing a piece of code, the better the result you will get. So, try your best to write code that is easily testable! If testing the code requires some mocks or other pieces to work, do your best to provide it the context and instructions it needs before writing the tests. Always review the test cases! There could be errors or test cases that don’t make sense. Most of the time, it will get you pretty close to where you want to be. Here’s an example of using the Agent mode to install packages for testing and generate unit tests for the tic-tac-toe game logic: Generating documentation This is another thing we know AI models are good at - summarizing large chunks of information. Make sure it has the context of whatever you want to document. This one, in particular, is really great because historically, keeping documentation up to date is a rare and challenging practice. Here’s an example of using the Agent mode to generate documentation for the tic-tac-toe game: Code review There are a lot of up-and-coming tools outside of Cursor that can handle this. For example, GitHub now has Copilot integrated in pull requests for code reviews. It’s never a bad idea to have whatever change set you’re looking to commit reviewed and inspected before pushing it up to the remote, though. You can provide your unstaged changes or even specific commits as context to a Cursor Ask or Agent prompt. Getting up to speed in a new code base Being able to query a codebase with the power of LLM’s is truly fantastic. It can be a great help to get up to speed in a large new codebase quickly. Some example prompts: > Please provide an overview of this project and how to get started developing with it > I need to make some changes to the way that notifications are grouped in the UI, please provide a detailed analysis and pseudo code outlining how the grouping algorithm works If you have a question about the code base, ask Cursor! Refactoring Refactoring code in a code base is a much quicker process in Cursor. You can execute refactors depending on their scope in a couple of distinct ways. For refactors that don’t span a lot of files or are less complex, you can probably get away with just using the autocomplete. For example, if you make a change to something in a file and there are several instances of the same pattern following, the autocomplete will quickly pick up on this and help you tab through the changes. If you switch to another file, this information will still be in context and can be continued most of the time. For larger refactors spanning several files, using the Agent feature will most likely be the quickest way to get it done. Add all the files you plan to make changes to the Agent tab’s context window. Provide specific instructions and/or a basic example of how to execute the refactor. Let the Agent work, if it doesn’t get it exactly right initially, you can always give it corrections in a follow-up prompt. Generating new code/features This is the big promise of AI agents and the one with the most room for mixed results. My main recommendation here is to keep experimenting. Keep learning to prompt more effectively, compare results from different models, and pay attention to the results you get from each use case. I personally get the best results building new features in small, focused chunks of work. It can also be helpful to have a dialog with the Ask feature first to plan out the feature's details that the Agent can follow up on and implement. If there are existing patterns in your codebase for accomplishing certain things, provide this information in your prompts and make sure to add the relevant code to the context. For example, if you’re adding a new form to the web page and you have other similar forms that handle validation and making back-end calls in the same way, Cursor can base the code for the new feature on this. Example prompt: Generate a form for creating a new post, follow similar patterns from the create user profile form, and look to the post schema for the fields that should be included. Remember that you can always follow up with additional prompts if you aren’t quite happy with the results of the first.. If the results are close but need to be adjusted in some way, let the agent know in the next prompt. You may find that for some things, it just doesn’t do well yet. Mentally note these things and try to get to a place where you can intuit when to reach for the Agent feature or just write some of the code the old-fashioned way. Tips and tricks The more you use Cursor, the more you will find little ways to get more out of it. Here are some of the tips and patterns that I find particularly useful in my day-to-day work. Generating UI with screenshots You can attach images to your prompts that the models can understand using computer vision. To the left of the send button, there is a little button to attach an image from your computer. This functionality is incredibly useful for generating UI code, whether you are giving it an example UI as a reference for generating new UI in your application or providing a screenshot of existing UI in your application and prompting it to change details in reference to the image. Cursor Rules Cursor Rules allow you to add additional information that the LLM models might need to provide the best possible experience in your codebase. You can create global rules as well as project-specific ones. An example use case is if your project has some updated dependency with newer APIs than the one on which the LLM has been trained. I ran into this when adding Tailwind v4 to a project; the models are always generating code based on Tailwind v3 or earlier. Here’s how we can add a rules file to handle this use case: ` If you want to see some more examples, check out the awesome-cursorrules repository. Summary Learn to use Cursor and similar tools to enhance your development process. It may not give you actual superpowers, but it may feel like it. All the features and tools we’ve covered in this post come together to provide an amazing experience for developing all types of software and applications....

Apr 18, 2025

11 mins

AICursor

Let's innovate together!

We're ready to be your trusted technical partners in your digital innovation journey.

Whether it's modernization or custom software solutions, our team of experts can guide you through best practices and how to build scalable, performant software that lasts.

How to automatically deploy your full-stack JavaScript app with AWS CodePipeline

How to host a full stack javascript app with AWS - 1 Part Series

How to automatically deploy your full-stack JavaScript app from an NX monorepo with AWS CodePipeline

APP Structure

CodePipeline

CodeBuild and the buildspec file

CodePipeline setup

Deployment prerequisites

Deployment

Upload the front-end to S3

Deploying the API to Elastic Beanstalk

Balázs Tápai

You might also like

Incremental Hydration in Angular

Introduction to Zod for Data Validation

Introducing the express-typeorm-postgres Starter Kit

Increasing development velocity with Cursor

Let's innovate together!

You might also like

Incremental Hydration in Angular

Introduction to Zod for Data Validation

Introducing the express-typeorm-postgres Starter Kit

Increasing development velocity with Cursor