Learn Figma, Natural and Particle

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.

react

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

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 this course we’ll work through Natural’s API for natural language processing in JavaScript. We’ll look at how to process text: learning how to break up language strings, find the word roots, work with inflectors, find sequences of words, and tag parts of speech. We’ll learn how to find important stats about a body of text: how to compare strings, how to classify text with machine learning, how to use the tf-idf tool to find relevant words. We’ll look at some of the extra tools Natural gives us, including the dictionary/thesaurus of WordNet, a phonetics comparer that lets us see if two words sound the same, and a spellcheck feature. We’ll also look at tries and digraphs, two data structures that help us better analyze bodies of text.

Natural is a general natural language facility for nodejs. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported.

natural

Natural Language Processing in JavaScript with Natural

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

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

React Native lets you build mobile apps using only JavaScript. It uses the same design as React, letting you compose a rich mobile UI from declarative components.

react-native

How to create an app icon from scratch, generate the required sizes, and add to your iOS app.

Image editing - [Figma](https://www.figma.com/) is a free online design tool used in the video. You can use any design tool you like, such as Photoshop, Sketch etc, as long as the output is a 1024x1024 .png image.

Asset generator - [Iconizer](https://raphaelhanneken.com/iconizer/) is a free mac app for generating the correct sized assets from your icon.

Design and Add a Custom App Icon with Figma to Your React Native iOS App 

We will learn about “stemming,” the process of finding the root of words, often in order to group words by a common base root. We will look at the Porter and Lancaster Stemmers, briefly touch on Natural’s support for Russian and Spanish stemmers, and introduce the function to stem and tokenize at the same time.

Find the roots of words using stemming in Natural

Inflectors are the modifiers of a word that indicate grammatical categories. While Natural’s coverage of inflectors is not comprehensive, we will show how Natural can pluralize/singularize nouns and count numbers. 

Pluralizing nouns and counting numbers with inflectors in Natural

N-grams are sequences of words, where the 'n' stands for the number of words in the sequence. In this lesson, we will see how to find bigrams (2-grams), trigrams (3-grams), and any other length n-gram in a body of text. 

Find sequences of words (n-grams) using Natural

We will learn how to compare how similar two strings are to each other, examining three algorithms: Jaro-Winkler, Levenshtein, and Dice’s Coefficient.  

You should note that none of these algorithms are inherently better than the others. Instead, it's important to choose the one that best fits your text data. 

Compare similarity of strings through string distance in Natural

In this lesson, we will learn how to train a Naive Bayes classifier or a Logistic Regression classifier - basic machine learning algorithms - in order to classify text into categories.

Classify text into categories with machine learning in Natural

In this lesson, we will learn how to train a Naive Bayes classifier and a Logistic Regression classifier - basic machine learning algorithms - on JSON text data, and classify it into categories.

While this dataset is still considered a small dataset -- only a couple hundred points of data -- we'll start to get better results.

The general rule is that Logistic Regression will work better than Naive Bayes, but only if there is enough data. Since this is still a pretty small dataset, Naive Bayes works better here. Generally, Logistic Regression takes longer to train as well. 

This uses data from Ana Cachopo: http://ana.cachopo.org/datasets-for-single-label-text-categorization

Classify JSON text data with machine learning in Natural

Tries are a data structure that provide an efficient way to search for the existence of a word or phrase in a body of text, or to search by prefix. 

Search more efficiently with tries using Natural

In this lesson, we’ll see how to use Natural’s probabilistic spell-checker, which uses the trie data structure. 

Include spell-check in text projects using Natural

Tf-idf, or term frequency-inverse document frequency, is a statistic that indicates how important a word is to the entire document. This lesson will explain term frequency and inverse document frequency, and show how we can use tf-idf to identify the most relevant words in a body of text. 

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