Learn 11ty, Cloudflare 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

n_epochs

Aside: Stop Sequence

Data for fine-tuning

An Overview of the AI

The Model and Object

Aside: How ChatGPT Models Work for Chatbots

KnowItAll Intro

Displaying the Image and Finishing off the UX

Aside: createImage

Refactor to Use Dependency II - The Dependency

Refactor to Use Dependency I - .env Variable

Tools

Models

The First AI Fetch Request

Getting an OpenAI API Key

The Boilerplate

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

Gemini 2.0 has a context window of a million tokens, and this means that you can upload hundreds of files and allow it to inspect and analyze massive refactors across entire code bases. 

This lesson demonstrates concatenating an entire code base into a single file and then uploading it to AI Studio. Once within AI Studio, we ask it to find the types of tasks that it can evaluate for us. Then we select one of the tasks and ask it to generate instructions for that task. Once those instructions are complete, we then turn them over to Windsurf's Cascade agent, and we allow Windsurf's Cascade to do all of the work for us as we sit back, relax, and let the AI do our jobs.

Use Gemini 2.0 to Create Agent Instructions to Refactor an Entire Codebase

Learn how to display custom components in a [Tanstack Table cell](https://tanstack.com/table/latest/docs/guide/cells), including how to render capitalized text, apply conditional styling with class helper functions, and format dates using the `Intl` API for readable display. 

`flexRender` allows you to utilize the `cell` callback method in your column definitions. Tanstack table will call this function to render the custom component you defined.

Render Custom JSX in a Tanstack Table cell

Use tanstack table flexRender method to render custom components in a table component. Capitalize letters, apply conditional styling, and render dates with Intl

I'm extremely excited for the potential of AI watching your screen while you work. I decided to take Google's AI Studio for a spin, which just introduced a feature called "Stream Realtime", which allows AI Studio to observe your screen and listen to your microphone while you work.

This video is my initial attempt to try and coach AI Studio through the process of building a site from a reference site, essentially trying to build egghead.io from a Next.js starter template. And we'll explore the various attempts we make and what works and what doesn't.

Let AI Watch Your Screen: Build Next.js Pages with AIStudio's Stream Realtime


[Shadcn](https://ui.shadcn.com/docs) is a popular component library that allows you to install components into your codebase.

Learn how to install the [Table component](https://ui.shadcn.com/docs/components/table) and use that presentational component with [Tanstack table](https://tanstack.com/table/latest).


Display Tabular Data in React with Tanstack Table

Build a dynamic table component in React using shadcn component library and tanstack react table.

TypeScript's satisfies operator finally clicked for me when I realized it's almost always used for specific implementations of configuration objects. So this lesson walks you through the naive approaches I've taken before and how to solve them using the satisfies operator. After this, satisfies will be another tool in your belt in TypeScript. To clean up all the TypeScript code you have around configuration.

TypeScript's "satisfies" for Configuration

Windsurf and Cursor are both able to take an image and then use an agent to build out what it sees in that image. This lesson is going to compare Windsurf and Cursor by uploading the same image to both and giving them the same prompt. Comparing not only the results, but also the steps that they take to get there. We'll also try uploading additional images throughout the process to see what they do as far as revising along the way and improving the site as they go along. 

Windsurf Cascade vs. Cursor Composer Agent: Build a Site from an Image

In this lesson, you'll discover how to style View Transitions using CSS pseudo-selectors to create custom animation effects. We'll transform the default cross-fade into a smooth slide transition by targeting the ::view-transition-old and ::view-transition-new pseudo-elements. You'll learn how these powerful selectors give you precise control over your transition animations, allowing you to create more engaging and personalized user experiences with just a few lines of CSS.

Customize View Transitions with CSS Using view-transition-old and view-transition-new

In this lesson, you'll learn how to implement smooth state transitions in your web applications using the browser's built-in View Transitions API. We'll create a simple example that demonstrates how to animate between different text states with just a few lines of code - no external libraries required. This powerful native API makes it easy to add professional-looking transitions to your user interface, enhancing the user experience with minimal effort.

Create Smooth UI Transitions with the Native View Transitions API

I've been using Cursor for a very long time and their "Agent" feature in Composer is fairly new, so I'm still getting used to it. But since Windsurf recently got onto the stage with their Cascade agent, they've slowly been gaining the mind share on the UX for coding agents. I decided to put them to the test by putting Windsurf and Cursor on the exact same screen side by side trying to accomplish the same task and to see which workflow I would prefer. I've been a diehard Cursor user for over a year now, so it's going to take a lot for me to switch to a different IDE. But I just have to say, even from this simple demo, Windsurf is looking pretty nice.

This lesson is just going to walk through creating a CLI using both tools to print out inspirational quotes and to explore how they approach the step-by-step of installing packages and running commands and setting up all the files we need to get this running.

Windsurf Cascade vs. Cursor Composer Agent Side-by-Side Comparison

I've been using Cursor as my primary editor for over a year now, but recently I've heard wonderful things about Windsurf AI Agent features. I decided to record myself building out something I've always wanted to make, but never made the time for.

In this lesson, I demonstrate how to leverage Windsurf's powerful agent process to create a fun word-guessing game inspired by Heads Up. Watch as I build an interactive application featuring category selection and word navigation, complete with data hosting on GitHub Gists and deployment to GitHub Pages - all in under 30 minutes (before editing the lesson down to 12 minutes)!

What makes this project particularly exciting is how Windsurf eliminates the typical friction points of development. While this game was always on my project wishlist, the overhead of setup, configuration, and deployment made it hard to justify the time investment. Windsurf's agent process handles all these pain points seamlessly, from managing URLs to orchestrating deployment.

This lesson perfectly showcases how AI agents can help developers bring their backlog of project ideas to life, transforming what would typically be time-consuming tasks into efficient, enjoyable development experiences.

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