Learn Bash, Nightmare and Prisma

Prisma is a next-generation open-source ORM For Node.js and TypeScript that helps developers build faster and make fewer errors.

It supports the following databases: PostgreSQL, MySQL, MariaDB, SQL Server, SQLite, and more recently, MongoDB (Preview).

Prisma provides a complete toolkit:

- Prisma Client: Auto-generated and type-safe database client
- Prisma Migrate: Declarative data modeling and customizable migrations
- Prisma Studio: Modern UI to view and edit data


prisma


Next up is creating a Planetscale database for our application. Planetscale works a lot like GitHub in that it uses branches for development and production.

We will create a new database and then create a dev branch to separate the data we create in dev from what is in production.

When the database is created, we'll set the `DATABASE_URL` in our `.env` file for the main and dev branches.

The last thing we will need to do is push the prisma schema to planetscale so that it can create the necessary tables for our application to function. 

Explore the prisma schema and tables created to get more familiar with what the application is doing.

Set up Planetscale Main and Dev Branches and Create Tables through Prisma

Prisma is an open-source ORM. It consists of the following parts: 

- Prisma Client: Auto-generated and type-safe query builder
- Prisma Migrate: Migration system
- Prisma Studio: GUI to view and edit data in your database

Prisma Client can be used in any Node.js or TypeScript backend application (including serverless applications and microservices). This can be a REST API, a GraphQL API, a gRPC API, or anything else that needs a database.

In this course, you will learn how to initialize Prisma in a TypeScript Node project, use Prisma Client to create and find records, use Express to create data dynamically, and more! 



Build a Backend with Prisma in a TypeScript Node Project

Prisma Client allows us to limit the amount of data that is returned from queries. It also allows us to be specific about which data should be included in the returned dataset. In this lesson, we use the select and include operators to be specific about the shape of our returned data.

Limit the Amount of Returned Data with Prisma Client

Prisma Client has a specific API for relating data from one table to another when creating new records. In this lesson, we use the connect operator to establish a link between two tables as new records are created.

Use Prisma Client to Connect Related Tables when Creating New Data

When two tables are related to one another, we can use Prisma Client to query for data through those relations. In this lesson, we see how to use Prisma Client to tie together data from two separate tables and pull it out in a nested fashion.

Use Prisma Client to Query for Related Data

Prisma Schema Language is used to model our database tables and establish relations between them. Prisma offers autocompletion to register the relations. In this lesson, we create a new model for a second database table and see how to relate our two tables together.

Create a Relation Between Two Tables in the Prisma Schema

Prisma is API-agnostic meaning that it doesn't matter how we build our APIs. They can be REST APIs, GraphQL APIs, or anything else. In this lesson, we see how to use express to accept JSON data to be forwarded off to Prisma to create new records in the database.

Use Express to Create Data Dynamically

Prisma Client offers methods to create new database records. These methods accept a data object and return the result to be consumed by clients. In this lesson, we use Prisma Client's create method to create new records in the database.

Create a Database Record with Prisma Client

Prisma Client has a full API for selecting subsets of data in our tables. We can use the WHERE clause to match specific conditions for which we would like to return data. In this lesson, we look at how to use the WHERE and its operators to return a subset of data.

Use Prima Client to Select a Subset of Data

Prisma Client is a fully type-safe database access client that is automatically generated based on our Prisma Schema files. In this lesson, we use Prisma Client to access records in our database programmatically. We show how to return the data from an endpoint built with express.

Use Prisma Client to Find Many Records in a Table

Prisma Studio is a GUI for viewing and interacting with our databases. In this lesson, we view our database with Prisma Studio and use it to create our first record.

Use Prisma Studio to Create a Record

Prisma offers a powerful database modeling language called the Prisma Schema Language. It allows us to describe the shape of our tables, the columns they should have (and their types), and relations between tables. In this lesson, we get started with our first table in our Prisma Schema file.

Create a Model for a Table and Run Migrations

The Prisma CLI comes with commands to initialize Prisma in a project, run migrations, generate Prisma Client and more. In this lesson, we install the relevant dependencies and initialize Prisma in a TypeScript Node project.

Initialize Prisma in a TypeScript Node Project

Prisma offers an extension for VS Code that enables syntax highlighting, autocompletion, and more for Prisma Schema files. In this lesson, we learn how to install the Prisma VS Code extension.

Install the Prisma VS Code Extension

Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

docker

This lesson uses some advanced concepts and some bash scripting to build a better Dockerfile. By using the concepts showed here, you will be able to build more future-proof and production-ready containers. If you prefer to focus on the content about containers, feel free to ignore this lesson and come back to it later on if needed.

Currently, we use `sed` to overwrite the hard-coded path to the API. This works well when there is a single value to replace. Now if one of your team members replaced the value of localhost:3000, your build will break.

We'll use `jq` to fix this issue. Jq is a command-line tool that makes it easy to change values in a JSON file. The base image that we used for building the application does not contain this tool, but that is not an issue. To install `jq`, you will need to download the [binary](https://github.com/stedolan/jq/releases/download/jq-${JQ_VERSION}/jq-linux64.), we'll need that to execute `jq` commands in our Dockerfile. This will allow us to update specific properties i

Modify a JSON Configuration File with jq

In this lesson, we will learn how to model data relationships with Prisma Schema. 

We will be using a social network as our example, create data models such as users, posts, likes, comments, and model the relations between them. After this lesson, you should be able to model common relations like *one-to-one* (1:1), *one-to-many* (1:n), *many-to-many* (n:m) with Prisma.

At the time of recording this lesson, *Prisma Migrate* is in an experimental state, so the extra flag `--experimental` is needed to perform the `save` and `up` commands.


Work with Models and Relations with Prisma Schema

Apart from creating entries, it's also very intuitive to read, update, or delete data from the database with Prisma Client.

In this lesson, we will explore the [Prisma Client API](https://www.prisma.io/docs/reference/tools-and-interfaces/prisma-client/crud) for `findMany`, `findOne`, `updateMany`, `deleteMany` and so on.

Notice that since all these methods are tailored to the schema that we defined earlier, VS Code is aware of what is valid and is able to prompt us with helpful autocomplete options, making it much easier to explore the Prisma Client API.

Perform basic database queries (CRUD) with Prisma Client

In this lesson, we will write a code snippet which creates a new entry in the database. We will import and use the Prisma Client that we previously generated by running the command `npx prisma generate` to perform this query. 

Thanks to type-safety, we can now directly catch mismatched data field types while we are coding, drastically reducing the chance of having bugs in the code.

Tip - When in doubt, you can always use the key combination `ctrl-space` to invoke the autocomplete feature of VS Code. This is also a huge benefit made possible by Prisma's type-safety, and is super handy for exploring the Prisma Client API.

Create an entry in the database with Prisma Client

Extracting meaningful data from strings is a crucial skill when working with bash, and bash gives us powerful tools to do so: `head`, `tail`, `cut`, `sed`, and `awk`. But these tools can be hard to understand without spending time to learn them, so many developers fall into a rut of blindly copying and pasting commands. No more! With this mini-course, you'll understand the basic ins and outs of these commands in under 15 minutes. 


Bash is a Unix shell and command language written by Brian Fox for the GNU Project as a free software replacement for the Bourne shell. Bash is a command processor that typically runs in a text window, where the user types commands that cause actions. Bash can also read and execute commands from a file, called a shell script.

bash

Manipulating strings in bash

In this lesson, we'll use the power of awk to select the columns and rows of data that match our condition. We'll pass a `,` (comma) to the `-F` flag (the F stands for field separator), to tell awk to split on commas. From there, the world is our oyster and we can tell awk to only return rows that match whatever parameters we pass.

Extract Columns of Data from a CSV with awk

`awk` is a specialized tool and programming language for querying and transforming text.   In this lesson, we’ll scratch the surface of the possibilities it provides by leveraging it in one of its most common use cases: extracting data from columns of text. Awk makes it easy to select and print just the columns and rows that we want from commands, like `ps`, that return a table of data.

Extract Data From Columns of Text with awk in Bash

 We map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the table. This allows us to add more columns to our pokemon table.

Fetch and display row details with Promise.all in Tanstack Table

map over the initial set of results then use `Promise.all` to efficiently fetch the details of each pokemon rendered in the tanstack table

Create a custom hook to fetch remote data by passing a promise to the React 19 `use` hook. Then render this data in Tanstack Table by memoizing the results and passing them to `useReactTable`.

Render remote data in a Tanstack Table with React 19's use hook and a promise

Create a custom hook using React 19’s use to fetch data with a promise. Render it in a Tanstack Table by memoizing and using useReactTable.

You know when you're deep in the code trenches and need someone to challenge your assumptions? That's exactly what this is about.

Instead of waiting for someone to poke holes in your master plan, you can paste that strategy straight into AI Studio and let it pressure test your ideas. We're talking about using this tool to validate everything from your feature proposals to your ambitious refactoring strategies.

The real magic happens when you hit that microphone button. Now you're in a real-time verbal sparring match. It's like rubber duck debugging but the duck actually pushes back and makes you defend every architectural decision you've made.

Here's the workflow:
1. Drop your master plan into the system instructions
2. Tell the AI to challenge every assumption
3. Hit that mic and defend your choices like you mean it
4. Watch your plan either emerge stronger or get rightfully dismantled

This approach is perfect when you're:
- Stuck in your own head with a complex problem
- Need to validate if your approach holds water
- Want to catch edge cases before they catch you
- Looking for opportunities you might have missed because you're too close to the code

It's like having a senior dev on call 24/7, ready to challenge your ideas and push you to think deeper about your solutions.

Now go forth and get your ideas challenged. Your projects will thank you for it.


An Uncomfortable and Challenging Conversation with AI Studio

In this lesson, you'll learn how to effectively brainstorm your project ideas before working with AI agents. We'll cover:

1. The importance of having a clear plan before starting work with AI agents
2. Using traditional brainstorming techniques to gather your thoughts
3. Leveraging video recording to capture your brainstorming session
4. How to review and analyze websites for inspiration
5. Documenting likes, dislikes, and potential features for your project
6. Using AI tools (like AI Studio) to organize and process your recorded brainstorming session

The lesson emphasizes a modern approach to brainstorming that combines traditional techniques with video recording, making it easier to later process your ideas through AI tools. This method helps ensure that no ideas are lost and that your thoughts can be effectively organized by AI for future reference.

How to Brainstorm Your Project Features to Prepare for AI Agents

Many people share .cursorrules files, but trying to craft your own from scratch is a huge pain. Instead, this lesson shows you how to grab ALL community-created .cursorrules files to build a custom rule set for your projects. We'll use Gitingest and Google AI Studio to gather these examples and generate an awesome starting point customized to your specific project.

Generate Custom .cursorrules for Your Project Based on Community Examples

The GitHub CLI allows you to search issues and filter on different criteria like amount of comments. This allows you to search for real-world situations and pains/solutions people have using different technology. 

With the GitHub CLI in hand we'll drop 100s of issues as context in Googles AI Studio and have Gemini determine the 10 most frequently asked questions. This will give us a sense of how people use a library and where people get hung up.

Search 100s of Issues with GitHub CLI as Context for AI Studio

I often find myself wondering how to use certain APIs in certain situations, and I have a couple of keywords that I try and search up on Google. 

What you can do in this situation is turn to GitHub and see how other people have actually used this code on public projects. The GitHub CLI enables us to run searches like this, and we can take the results and work on them programmatically.

Inside of Cursor with their Composer AI agent, we can search for the files we want, tell the agent to download them, and inspect them for better understanding of how those things work together.

Search All of GitHub's Code and Pass the Results to AI Agents in Cursor

Implement virtual scrolling to handle large datasets in an HTML table. Start with a basic table that renders 10,000 rows of data. The browser can struggle with this amount of DOM nodes to render at one time, especially on low end devices. 

We implement tanstack react virtual to render only the necessary amount of rows the user is viewing. The implementation includes proper column sizing, accessibility considerations, and convenient scroll controls, resulting in a smooth and performant table experience regardless of the dataset size.

Create a row virtualized HTML table with Tanstack Virtual

Learn how to implement virtual scrolling in React tables using TanStack Table and Tanstack Virtual with the useWindowVirtualizer React hook.

The most difficult tasks will require the strongest AI models. And those models simply aren't available as AI agents inside of your tooling. So the best workflows are to drag and drop the files you need to be changed and analyzed inside of a reasoning model such as OpenAI's O1 Pro, then letting it create a giant plan for your dumber AI agents to work on. The key here is that instead of just copying and pasting the entire plan into Cursors Composer or your AI agent, it's to create a file inside of your project and ask your agent to tackle the plan one step at a time. This allows you ample opportunity between each step to test and verify the micro steps along the way. Otherwise, as I've experienced many times, a session can go way off track and you burn a lot of time and burn a lot of AI requests Cursors Composer and it starts churning through a plan Cursors Composer and making lots of unwanted changes. Cursors Composer and it's to guide an AI agent to hold its hand along the way, especially through the most complex tasks. Cursors Composer and the most complex tasks Cursors Composer and the most complex tasks require a step-by-step plan.

Planning AI Agent Sessions with Reasoning Models

AI agents will inevitably make mistakes—it's just a matter of when. A key tool in your toolbox, once they do make mistakes after working on a problem for a while, is to use **git diff** to capture all of the changes they've made. 

Once you have those changes in a text file, you have a portable document that you can test against other AI models. This allows you to move the problem into smarter reasoning models with smaller context windows, helping you isolate and focus on the issue at hand.

**git diff** has been invaluable when working with AI agents because they'll often make many good changes but also slip in one tiny error. Finding that one small mistake is **git diff**’s superpower.

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