Learn Ember, Microsoft 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

Just about every tool in our accessibility auditing toolkit can help us identify any issues with sufficient color contrast ratio on a web page.  The react-axe library and axe browser extension both report findings on all elements with insufficient contrast ratios.  The tota11y browser extension will annotate any elements with their contrast ratios and color those that are insufficient in red.  

Additionally, we need to use our experience tools to see what some of our users out there may we seeing.  We need to test with high contrast browser extensions as well as the display settings in Windows and Mac.  If our users with visual impairments are using these tools we need to see what our application looks like using them because we may find that under a certain mode some things may suddenly be harder to read or virtually invisible.

## Resources
* https://www.scottohara.me/blog/2019/02/12/high-contrast-aria-and-images.html

Test for Insufficient Color Contrast Accessibility Issues

We should not use color alone as the only visual means of conveying information, indicating an action, prompting a response, or distinguishing a visual element.  Users may not be able to see/understand the differences in color and require additional indicators.

Along with color, use additional indicators such as:
* typography
* shapes
* grids
* spaces
* and/or allocate more weight to important elements

## References:
* http://www.particletree.com/features/interfaces-and-color-blindness/
* https://www.smashingmagazine.com/2014/10/color-contrast-tips-and-tools-for-accessibility/

Use More than Color Alone to Convey Information in a Web Page

Express is a minimal and flexible Node.js web application framework that provides a robust set of features for web and mobile applications.

express

To replicate your production application its helpful when developing a local NodeJS Express application is running under SSL. 

There are some web technologies which do not play nicely with standard http and its likely to become more common e.g. Web Push Notifications.

This lesson will show you how to create a local SSL certificate for localhost and then convert it into a format that will work with Express.  It will also show you how to make sure that Chrome will trust it.

This lesson is for Windows as it uses a powershell command, as well as the Microsoft Management Console (MMC)
 

Create Local SSL Certificates for an Express App on Windows

Microsoft Windows is a group of several graphical operating system families, all of which are developed, marketed, and sold by Microsoft. Each family caters to a certain sector of the computing industry.

microsoft

For a production Windows server it makes sense to run NodeJS applications as a Windows service.  This way they can be automatically restarted if the server is rebooted.

It also makes it easier to use monitoring tools like datadog to detect if there are issues with the service which is critical for production systems.

Install a Node.js Application as a Windows Service

A JavaScript framework for creating ambitious web applications.

ember

In this lesson we will create an `updateContent` method that sets the value of our `content` property to the current epoch timestamp. We need to call `toString()` on the `Date.now()` method in order to make TypeScript happy.

```
updateContent() {
  this.content = Date.now().toString()
}
```

We create a constructor that takes the options parameter, call `super(options)` and call into our `this.updateContent()` method.

```
constructor(options) {
  super(options)
  this.updateContent()
}
```

When we refresh our page we can see that the value of `content` is set to the epoch timestamp. 

To make the content really dynamic we use `setInterval()` to call into `updateContent` each second: `setInterval(() => this.updateContent(), 1000)`.

When we save our file we see that our content does not get updated anymore and when we check the console we see that we get an error telling us that: The 'content' property on the my-component component was changed after it had been rendered. The error also informs us that we should Use the @tracked decorator to mark this as a tracked property.

We add the @tracked decorator to our property, we import tracked from @glimmer/component and we refresh the page. We verify that `content` gets updated each second.

We change our `updateContent` to add our the timestamp to the array. When we use `this.items.push(this.content)` to achieve this we see that the list does not get updated. This is because the `@tracked` decorator only tracks the re-assignment of properties.

The way to get this working is by re-assigning our `items` array inside `updateContent`. We assign a new array, de-structure the current items array and add the new content.

```
this.items = [...this.items, this.content]
```



Use dynamic data in a GlimmerJS component

In this lesson we will add a variable called `content` to our component class, initialize it as a string and display the variable in our class using the double curly braces syntax, which is the handlebars expression for expressions, `{ {content }}`. 

After this we create a new class variable called `items`, initialize it as an array with strings. To display this list in our template we add a `<ul></ul>` and inside this unordered list we use the handlebars `each` helper to display the list items.

And inside our each statement we will print a `<li>` tag and render `{{ item}}` inside it.

```
<ul>
  {{#each items key="@index" as |item| }}
    <li>{{item}}</li> 
  {{/each}}
</ul>
```



Use template variables in a GlimmerJS component

In this lesson we will use the ember cli to generate a new component. Running `ember g glimmer-component my-component` generates the template and component class. We change the content of our component and add it to the main template in order to show it on the page.

Create a new GlimmerJS component

[GlimmerJS](https://glimmerjs.com/) is a UI component library built by the Ember.js team. It is fast and lightweight and uses a combination of TypeScript and Handlebars to build the components.   

In this lesson we will use the Ember CLI to initialize a new GlimmerJS project.

We will start by installing Ember CLI globally by running `npm install -g ember-cli/ember-cli`.

Next we will use the `ember` command to create our new project by running `ember new egghead-glimmer -b @glimmer/blueprint` . 

When our project is created we `cd` into the project directory and start the development server. We will view the page in the browser and change the base template.

Initialize a project using Glimmer.js

<p>Today Joel catches up with Iheanyi Ekechukwu. Iheanyi is a Product Engineer currently working at DigitalOcean. He previously worked at IBM on Watson. They also talk about Iheanyi's education, stack, and side projects</p><p>Iheanyi started out majoring in Computer Engineering, but switched to Computer Science after he figured out hardware just wasn't for him. He now lives and works in Brooklyn and spends most of his time coding (though he always brings his design skills to the table).</p><p>Iheanyi's design comes from a dual degree program at Notre Dame, the college where he graduated. He noticed a lot of subpar interfaces coming from pure programmers, and he was frustrated with that, so he took his school's opportunity to learn design and apply it to his work. Even if he isn't a designer, he uses his skills daily to communicate with designers and make whatever he works on that much better.</p><p>Iheanyi started using Ember back during college. He was frustrated by his school's class search interface, and he set out to improve it. Ember and Rails were like a match made in heaven for him, Ember having been authored by ex-Rails core team member, Yehuda Katz.</p><p>Joel discusses with Iheanyi what he's currently using in his work. They get into GO, and how Iheanyi has enjoyed working with a statically typed language when doing back-end work. Not having to worry about full test coverage when refactoring has been great!</p><p>Lastly, they talk about the various side projects Iheanyi has going. Such as Interface Lovers, a blog where top designers are interviewed and share their work music playlists. Also, Seeker, a job-board app that allows you to connect your strip account to it and have companies submit jobs.</p><hr /><h3>Transcript</h3><p><a href="https://egghead.simplecast.com/episodes/47aebd17/transcript">"Iheanyi Ekechukwu on education, programming, and managing side projects" Transcript</a></p><h3>Resources</h3><ul><li><a href="https://seeker.company">seeker</a></li><li><a href="https://interfacelovers.com/">Interface Lovers</a></li><li><a href="http://creative.black/">Creative Black</a></li><li><a href="https://www.ibmchefwatson.com/">Chef Watson</a></li></ul><h3>Iheanyi Ekechukwu</h3><ul><li><a href="https://iheanyi.com/">Website</a></li><li><a href="http://www.twoblacknerds.com/">Podcast</a></li><li><a href="https://twitter.com/kwuchu">Twitter</a></li><li><a href="https://github.com/iheanyi">Github</a></li><li><a href="https://egghead.io/instructors/iheanyi-ekechukwu">egghead.io</a></li></ul><h3>Joel Hooks:</h3><ul><li><a href="https://twitter.com/jhooks">Twitter</a></li><li><a href="https://joelhooks.com/">Website</a></li></ul>

Today Joel catches up with Iheanyi Ekechukwu. Iheanyi is a Product Engineer currently working at DigitalOcean. He previously worked at IBM on Watson. They also talk about Iheanyi's education, stack, and side projects

Iheanyi Ekechukwu on education, programming, and managing side projects

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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