Learn Axios, Hapi and Openai

OpenAI's technologies offer a range of AI-powered tools and capabilities, making it easier for developers to build intelligent, responsive, and user-friendly applications.

openai

We can teach an AI model to better understand the information we want to generate responses from. To do that, we can use training data in a particular format, JSONL.

Once we have our data generated and uploaded to OpenAI, we can now use it to fine tune or customize a model to provide better results.

We'll use the training data we generated and uploaded in previous lessons to create a new fine-tuned model based off of the OpenAI davinci model.


- [https://platform.openai.com/docs/api-reference/fine-tunes/create](https://platform.openai.com/docs/api-reference/fine-tunes/create)

Fine Tuning OpenAI Models with Training Data Files

Once we have our training data, we need to a way to deliver it to OpenAI.

We can do that by uploading our training data file to OpenAI directly where we can then reference that with our fine tuning.


- [https://platform.openai.com/docs/api-reference/files/upload](https://platform.openai.com/docs/api-reference/files/upload)


Uploading Training Data Files to OpenAI for Fine Tuning AI Models

Using the models that OpenAI provides will get you really far, but their knowledge base can be somewhat limited for particular information or if its past a certain date that the model doesn't support.

To improve the experience, we can train models to have a better understanding of the data we want to work with, whether that's documentation for a new developer tool or guiding the model to respond closer to how how we expect.

The problem—is you need a lot of data in a specific format to do this and its not very practical to write it all our by hand. So we can instead use GPT, where by providing our data, we can ask it to generate JSONL training data for us and format it in a way that we can use to fine tune our model.


- [https://platform.openai.com/docs/guides/fine-tuning/prepare-training-data](https://platform.openai.com/docs/guides/fine-tuning/prepare-training-data)

Generating & Preparing JSONL Training Data with GPT for Fine Tuning an OpenAI Model

Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications. Node.js uses an event-driven, non-blocking I/O model that makes it lightweight and efficient, perfect for data-intensive real-time applications that run across distributed devices.

node

With a reliable data API, we can start using our generative output in creative ways, including feeding that directly into a new generation like creating an image based off of a blog post.

We'll look at taking a GPT-generated blog post and feeding it into DALL-E along with some stylistic prompt engineering to generate an image for our post.

Use Chat Completions for Image Generation Prompts with DALL-E

An important part in creating a data API whether based on generative systems or not is being able to reliably generate responses in the format you need. Variable data structures and irrelevant information could break your application or frustrate the users of your API, resulting in downtime or lost customers.

Using the messaging API, we can train GPT to become more aware of our needs by using System roles to instruct GPT on behavior and User to Assistant examples to give GPT precedent to base its next response on.

[https://platform.openai.com/docs/guides/chat/introduction](https://platform.openai.com/docs/guides/chat/introduction)

Training GPT Using Roles & Message Examples for Better Chat Completions

Getting a long string as a response makes a lot of sense if you're building an app or tool that appears conversational or if you just want to display a little bit of information as a response, but if you want to do something programmatic with the information, such as using different datapoints from the information in different parts of the UI, you'll have a hard time parsing out that information from a single request.

To get around this, we can simply ask GPT to return our data formatted as JSON! Further, we can ask it in a particular shape. You'll learn how to format a chat completion prompt to request JSON data and gain confidence in that data by defining the shape in the request.

Requesting JSON Chat Completions from OpenAI for Programmatic Use


Requests that we typically make in an application come all at the same time. We make the request, we get a response. But using a streaming mechanism, we can get our information as it comes resulting in a quicker initial response, as well as set ourselves up for different user experiences including conversational-like messaging.

Using Edge Functions in Next.js, we can stream our response back to the application and create an experience similar to the official ChatGPT interface. You'll learn how to set up a new Edge function, make a request directly to the OpenAI API, and listen for the stream events in Next.js with [eventsource-parser](https://www.npmjs.com/package/eventsource-parser).

- [https://vercel.com/docs/concepts/functions/edge-functions/edge-functions-api](https://vercel.com/docs/concepts/functions/edge-functions/edge-functions-api)

Stream OpenAI Chat Completions to a Next.js App Using Vercel Edge Functions

Get Started with AI-Driven App Development Using the OpenAI Node.js SDK

ChatGPT and similar conversational AI tools have quickly made impressions in the tech industry and beyond by giving the people using those tools a more natural and a potentially more helpful means to get answers to the questions they have. Bringing GPT into applications can unlock new ways to solve problems and quickly help customers to solve theirs.

We can create ChatGPT-like conversational messages with the OpenAI SDK in a Node.js project. You'll learn how to use the [createChatCompletion](https://platform.openai.com/docs/api-reference/chat/create) method and configure the gpt-3.5-turbo model to generate a response based on a message prompt.

[https://platform.openai.com/docs/api-reference/chat/create](https://platform.openai.com/docs/api-reference/chat/create)


Generate Chat Completions with AI in Node.js Using the OpenAI SDK & GPT

The more descriptive an image prompt, the better the results will be. That includes being able to take user input to help articulate what the image should look like.

Dynamic prompts in Next.js serverless functions can help us generate images based off of application input with the OpenAI SDK. You'll learn how to configure an API Endpoint to parse a body of a POST request to grab a dynamic prompt value and use it to configure the prompt for the createImage method.

[https://nextjs.org/docs/api-routes/introduction](https://nextjs.org/docs/api-routes/introduction)

Create Dynamic Image Generation Prompts for the OpenAI SDK in Next.js API Endpoints

Image generation provides us a way to bring life to natural language through a visual medium. Through a simple or detailed description, we can generate a completely unique image that tells the story we want whether that's for dynamically creating blog images or exploring new forms of art.

We can use AI to generate images with DALL-E and the OpenAI SDK in Node.js. In this lesson you'll learn how to use the [createImage](https://platform.openai.com/docs/api-reference/images/create) method generate an image using a descriptive prompt, the number of images you want to generate, and the size of the image. 

[https://platform.openai.com/docs/api-reference/images/create](https://platform.openai.com/docs/api-reference/images/create)


Generate Images with AI in Node.js Using the OpenAI SDK & DALL-E

OpenAI is an AI research lab that has developed a number of tools for researchers and developers including natural language processing, robotics, and reinforcement learning. By leveraging the OpenAI APIs, we can build faster and smarter, while bringing new and interesting capabilities to help enhance existing solutions and solve new challenges.

Let's get started building with AI using the [OpenAI SDK](https://platform.openai.com/) by installing and configuring the SDK in a Node.js project. Here you'll learn how to install the [openai npm package](https://www.npmjs.com/package/openai) into an existing project, import the openai dependency, and configure the SDK with your OpenAI API Key to make authenticated requests to the OpenAI API.

[https://platform.openai.com/overview](https://platform.openai.com/overview)
https://platform.openai.com/overview

Install and Configure the OpenAI SDK in a Node.js Project

Vue (pronounced /vjuː/, like view) is a progressive framework for building user interfaces. Unlike other monolithic frameworks, Vue is designed from the ground up to be incrementally adoptable. The core library is focused on the view layer only, and is very easy to pick up and integrate with other libraries or existing projects. On the other hand, Vue is also perfectly capable of powering sophisticated Single-Page Applications when used in combination with modern tooling and supporting libraries.

Let's see how to use the `vue-axios` NPM package to make a request to an API, and make use of the data returned.

Make a request to an API with Axios in Vue

A rich framework for building applications and services hapi enables developers to focus on writing reusable application logic instead of spending time building infrastructure.

hapi

hapi makes handling POST and PUT payloads easy by buffering and parsing them automatically without requiring additional modules or middleware. This video will demonstrate how to handle JSON and form based request payloads via hapi's route configuration.

hapi.js - POST and PUT request payloads

hapi automatically responds with JSON for any error passed to a route's `reply()` method. But what if your application needs errors rendered in HTML? This lesson shows how to implement friendly HTML error messages using hapi extension events.

hapi.js - Friendly error pages with extension events

hapi supports request validation out of the box using the joi module. Request path parameters, payloads, and querystring parameters can be validated with joi's simple, yet powerful, notation. This lesson covers the basics of validating requests using joi.

hapi.js - Request Validation with Joi

hapi has built-in support for parsing cookies from a request headers, and writing cookies to a response, making state management easy and straight-forward. It even has built in support for cookie encryption and auto detects when a cookie contains JSON, parsing or stringifying automatically.

hapi.js - Managing State with Cookies

Instead of using middlware, hapi provides a number of points during the lifecycle of a request that you can hook-in to provide additional functionality, called "extension events". This lesson will give you an introduction to using extension events, and show how they can be used to manipulate the request or response in-flight.

hapi.js - Extending the request with lifecycle events

hapi is a battle-tested, full-featured, framework for building web applications and services with Node.js. With integrated support for essentials like authentication, caching and validation, and a powerful plugin system, hapi is ideal for projects and teams of any size.

This course will introduce hapi, some of it’s plugins, and guide you through many of hapi’s core features.

Introduction to Node Servers with Hapi.js

View engines, or template engines, allow you to maintain a clean separation between your presentation layer and the rest of your application. This video will demonstrate how to use the vision plugin with hapi to enable template support.

hapi.js - View engines 

hapi does not support serving static files out of the box. Instead it relies on a module called Inert. This lesson will cover serving static files using Inert's custom handlers.

hapi.js - Serving static files

Creating powerful and context-aware tools for Cursor IDE becomes straightforward when leveraging information directly from your current project. In this lesson, you'll learn how to build a custom MCP (model context protocol) tool that intelligently requests essential details—such as the current project path and conversation summaries—from your Cursor project.

Workflow demonstrated in this lesson:

- Define MCP tool parameters to request specific project details.
- Pass project path and conversation summaries into your MCP tool.
- Automatically write or update files within your current project using these parameters.

Key benefits:

- Automate tasks with precision based on your active project's context.
- Simplify interactions by creating AI-driven CLI commands tailored to your needs.
- Open endless possibilities for integrating external APIs and advanced automation.

Through this practical demonstration, you'll gain a versatile technique for enhancing your development workflow in Cursor IDE.

[https://github.com/johnlindquist/mcp-cursor-tool-starter](https://github.com/johnlindquist/mcp-cursor-tool-starter)

John Lindquist

Build MCP Tools Using Project Context for Custom Cursor IDE Automation

Setting up custom MCP (Machine Command Protocol) servers in Cursor can involve tedious steps, such as configuring node paths or referencing additional commands. This lesson shows how to streamline this process by bundling your MCP server directly into a standalone executable.

Workflow demonstrated in this lesson:

- Use a simple MCP server project starter.
- Run a single bundling command to create an executable.
- Right-click the generated executable, copy its full path, and add that directly to your Cursor settings.

Key benefits:

- Eliminates the need for complex node path configurations.
- Simplifies the setup and integration of custom MCP servers into Cursor.
- Makes the server executable instantly accessible within your Cursor environment.

By following this streamlined approach, you can quickly and easily integrate custom MCP servers into Cursor, making your development workflow cleaner and more efficient.

[https://github.com/johnlindquist/mcp-cursor-tool-starter]()

Easy MCP Server Setup in Cursor IDE with Bundled Executables

TanStack Router is the foundation for TanStack Start, which is gaining significant attention due to its innovative approach to application development.

This course focuses on getting effective TanStack Router, a modern and scalable routing solution for React applications. You will learn how to:
- set up and scaffold new applications,
- use file-based routing
- create nested routes
- manage dynamic route parameters and more.

The course covers advanced features like route loaders, caching strategies, and lifecycle hooks, which are crucial for enhancing application performance and user experience.

By understanding TanStack Router, you can build more efficient, type-safe, and maintainable applications, leveraging its integration with data fetching libraries like TanStack Query.

This knowledge is essential for developers looking to improve their routing skills and stay ahead in the field.

Tomasz Ducin

Up and Running with Tanstack Router and React

Learn TanStack Router for React: set up apps, use file-based routing, manage routes, and boost app performance.


In this lesson, we explore how TanStack Router integrates with the [native browser history API](https://developer.mozilla.org/en-US/docs/Web/API/History) to manage navigation.

As we navigate through different routes, the URL changes are stored in the browser's history, allowing users to use the forward and backward buttons to traverse their navigation history.

This functionality also **enables permalinks**, meaning users can copy and paste URLs to access specific routes directly.

Additionally, we learn how to programmatically interact with the history object to move forward or backward through the navigation stack, enhancing the user experience by providing full control over navigation actions.

This compatibility with the native history API ensures that TanStack Router applications behave similarly to traditional websites, offering a seamless and intuitive user experience.

Browse History in Tanstack Router

Browse history in TanStack React Router using the native history API for seamless navigation

In this lesson, we explore how to hook into TanStack Router's lifecycle hooks to execute custom logic during route changes.

The hooks we'll look into are: `onEnter`, `onStay`, and `onLeave`, which are triggered when entering, staying within, or leaving a route, respectively.

All hooks receive a detailed match parameter, providing information about the route's path, ID, and tons of other details.

This allows for the implementation of features like animations or logging when navigating between routes.

We also learn how to use these hooks to track and manage state changes as users interact with the application, making it easier to implement complex behaviors based on route transitions.

Hook into Route Lifecycle Hooks in Tanstack Router

Hook into TanStack React Router's lifecycle hooks for custom route logic and animations.

In this lesson, we learn how to handle not found errors in TanStack Router by using the `notFoundComponent` at both nested and global levels.

To trigger the display of a `notFoundComponent`, we must throw a specific error using the `notFound` function within a loader or other lifecycle method of a route. This approach allows us to provide custom error messages for specific routes or globally across the application.

We explore how to define a notFoundComponent within a specific route's configuration and how to set a `defaultNotFoundComponent` at the router global level, which applies to all routes unless overridden locally. This flexibility ensures that users receive meaningful feedback when encountering non-existent routes.

Provide NotFoundComponent at Nested Route or Global Level in Tanstack Router

Handle notFound errors in TanStack Router with notFoundComponent at nested or global levels.



In this lesson, we learn how to access dynamic route parameters using the `useParams` hook in TanStack Router.

When used with a specific `Route` object, `useParams` provides type-safe access to route parameters, ensuring that TypeScript knows the exact types of these parameters.

This approach eliminates the possibility of undefined/optional values, making it safer for accessing parameters like `employeeId`.

However, when using `useParams` globally without specifying a route, it requires an options object with `{ strict: false }`, which reduces type safety.

In this case, the hook can access parameters from any route in the application, but it does not guarantee their existence or type. This flexibility allows components to be reused across different routes, but it requires careful handling of potential errors.



Access Dynamic Route Parameters with useParams in Tanstack Router

Access dynamic route parameters with useParams in TanStack React Router for type-safe routing.

In this lesson, we learn how to reorganize file-based routes in TanStack Router to accommodate nested routing structures.

We explore how to create additional nested routes by converting files into directories, allowing for further nesting.

For example, to create a nested route like `/employees/$employeeId/edit`, we can create a **directory** (!) named `$employeeId` and place an `index.tsx` file inside it to define the route component for this nested route.

This approach provides flexibility in organizing files while maintaining the convention that file names and locations align with route paths. We also see how to adapt existing components and loaders to work with these reorganized routes, enabling features like edit forms or delete actions (or whatever) within nested routes.


Reorganize File-Based Routes in Tanstack Router

Reorganize file-based routes in TanStack Router for flexible nested routing.



In this lesson, we learn how to parametrize the loader for a nested route in TanStack Router.

We focus on a nested route like `/employees/$employeeId`, where the `$employeeId` is a **dynamic parameter**.

To fetch data for this route, we use a `loader` function that accesses the `$employeeId` parameter. This is achieved by using the params object available in the `loader` function, which contains the dynamic segments of the URL. We then use this parameter to call a function like `getEmployeeById`, which fetches specific employee details from an API.

We highlights that TanStack Router **automatically infers types** for these parameters, ensuring type safety for nested routes.

Parametrize Route Loader for Nested Route in Tanstack Router

Parametrize nested route loaders in TanStack Router using path parameters

In this lesson, we learn how to create nested routes and outlets in TanStack Router using file-based routing.

We start by creating a new `employees` directory and files to represent nested routes, such as `/employees/$employeeId`. The `$` symbol is used to denote dynamic parameters.

We then use the `<Link>` component from TanStack Router to create links to these nested routes, passing necessary parameters.

To render the nested content, we use the `<Outlet>` component (nested), which automatically renders child routes. This approach allows us to dynamically display stuff under a nested route.

All the time, the TanStack Router generator automatically updates the route tree based on file changes - our routing configuration remains consistent 🔥



Create Nested Route and Nested Outlets in Tanstack Router

Create nested routes and outlets in TanStack Router using file-based routing

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