Learn Figma, Particle and Python

OpenGraph images are the images you see when you paste a link into a social platform and it "unfurls" into an image, title, and description. This happens on Twitter, Discord, Slack, and many other platforms. This collection goes over everything from designing OpenGraph images in Figma, to implementing them in CodeSandbox, to returning headless browser screenshots from Netlify Functions, and finally using Cloudinary as a write-through cache. 

You will come away from this collection with the ability to ship an API that you can use on any of your sites, and also on-demand, that can generate images for not only OpenGraph unfurls, but also for Instagram, GitHub, and more. Using headless browsers with playwright to generate our images means we get full access to all the responsive power of CSS and all the logical power of JS to handle layout, importing assets like pngs, choosing different fonts, and more.

The image below is generated via the project you'll build and deploy in this course

![An example opengraph image](https://relaxed-payne-d1bfbe.netlify.com/opengraph?title=Building%20an%20OpenGraph%20image%20generation%20API%20with%20Cloudinary%20Netlify%20Functions%20and%20React&tags=figma,netlify%20functions,playwright&author=chrisbiscardi)

React is one of the web’s most popular frameworks for building JavaScript applications. 

If you know what you’re doing, React can drastically simplify how you build, use, and maintain code.

Whether you’re a React newbie or you’re ready for advanced techniques, you can level-up with egghead. Explore our in-depth courses, lessons, and community resources to build more powerful applications and crack open your career.

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Building an OpenGraph image generation API with Cloudinary, Netlify Functions, and React

Figma is a collaborative prototyping tool that allows you to responsively design your applications, with resolution presets for all of the most popular devices. In this course, we will explore the fundamentals of Figma’s toolset that you can use for various design functions. 

We will start from scratch, then walk through scenarios you may encounter when designing a website or application. We will cover building reusable designs — known as “components” in Figma — all the way up to prototyping your designs to show screen transitions and animations between state. 

By the end of the course, you will understand the basics of Figma so you can use it to your advantage when building your next app or website.

Design, prototype, and gather feedback all in one place with Figma.

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The Beginner's Guide to Figma

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

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Fully Connected Neural Networks with Keras

Figma is a powerful design tool built with web technologies. Using the same technologies you can extend it with plugins.
You can use Typescript and/or javascript, you can load your favorite UI frameworks like Vue.js or React.js and build powerful plugins.

Using a plugin you can:

* Load Real Data from your server and visualize it 
* Change elements in your design programmatically 
* Convert your design to JSX or HTML, the sky is the limit

Learning these skills will help you make your design workflow faster by making Figma doing exactly what you want





JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

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Create a Figma Plugin Using Typescript

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

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Introductory Machine Learning Algorithms in Python with scikit-learn

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

Up and Running with AWS Amplify Functions

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Sometimes you get strings instead of objects and you have to parse them out. Here's an example where the raw data I got from a Twitch chatbot message wasn't useful to me until I parsed it into a dictionary.

Turning a key/value string into a dictionary in Python

In this video, you will learn how to bootstrap a Basic Figma plugin using the Figma app

Bootstrap a Figma Plugin

In this video, you will learn how to send and receive data from Figma to use it in your plugin

Communicate between the plugin and Figma 

In this video, you will learn how, in your plugin, to change the text value of a selected text node in Figma 

Change the text value of a select node

Now you have your plugin built and ready it's time to publish it.

In this video, you will learn to publish your Figma plugin to the public

Publish a Figma Plugin

Figma is a web-based collaborative interface design tool that we can use to prototype the look and feel of our OpenGraph cards. We dive into a few of the basic features using [Ryan Warner's](https://twitter.com/ryanwarnercodes) community [social branding templates](https://twitter.com/ryanwarnercodes/status/1239578329340170240), such as text placement, using the eyedropper and color tools, as well as pulling in gradients as images to fit our working area.

* [Social Branding Template](https://www.figma.com/community/file/820337605519256142)
* [Gradient Magic](https://www.gradientmagic.com/)

Designing OpenGraph unfurls in Figma

In this lesson, you'll learn how to deploy a serverless function using the Python runtime, connect it to an API endpoint, and interact with it from a React application.

Creating a full stack serverless app with Python, React hooks, and AWS Amplify Functions

Using the `open` function, and the `as` and `with` keywords, we'll open up and read from a file. At the end of this lesson, you will be able to loop through the lines of text in a file, process the text in Python, and then print them to the terminal.

Read from a File in Python

Figma Auto Layout gives our designs superpowers. By adding auto-layout to our button component, we can change the text within the button while automatically maintaining consistent horizontal and vertical padding, just like we would in code.

Build an Adaptive Button with Figma Auto Layout

Keyboard shortcuts speed up our workflow, and make us aware of some features we didn't know existed. Figma has an awesome in app menu for exploring the keyboard shortcuts available to us.

Figma Keyboard Shortcuts

Learn how to create a tuple in Python. A tuple is an immutable data type that contains several items similar to a list. Items in a tuple can be of any data type.

Create a Tuple in Python

Learn to create a one item tuple. When creating a one item tuple, a comma has to be included after the item. Otherwise, your tuple will not return tuple as its data type. 

Create a One Item Tuple in Python

Tuples are immutable so there is no way to add or replace items in tuples. There is a way to get around this by adding two tuples into one. 

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