Learn Canvas, Elasticsearch and Natural

A <canvas> element provides scripts with a resolution-dependent bitmap canvas, which can be used for rendering graphs, game graphics, or other visual images on the fly.

canvas

The `canvas` element exposes a 2d context which allows you to draw lines using the `moveTo`, `lineTo`, and `stroke` api. You can combine this with Mouse events to make a simple drawing app.

Draw with Your Mouse on an HTML Canvas

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

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

In this lesson, we’ll take a look at Natural’s phonetics feature. We’ll learn how to check whether two words sound alike, looking at both the SoundEx and Metaphone algorithms.

Check if words sound alike 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

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. 

Identify the most important words in a document using tf-idf in Natural

This lesson introduces WordNet, which is an important resource in natural language processing. With WordNet, we can look up a word’s definition, or find its synonyms.

Find a word’s definition using WordNet in Natural

By this point we've seen that classification can take a long time, and with more data, it would take even longer. Luckily, Natural provides support to save your classifiers. In this lesson, we will learn how to save a classifier and load it into a new project in order to classify new data.

Using machine learning classifiers in a new project

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

An important component of many natural language processing projects is being able to identify the grammar of a piece of text. We’ll learn how to do that with Natural’s parts of speech (POS) tagger.  

There are many tags, and it's worth looking them up online (search "POS tag symbols") to become familiar with them all. 

The setup of the tagger may seem a little strange, but it allows you to replace the lexicon or the rules with a different lexicon or rule set of your choice. 

Tag parts of speech using Natural

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 Language Processing in JavaScript with Natural

A part of Natural Language Processing (NLP) is processing text by “tokenizing” language strings. This means we can break up a string of text into parts by word, sentence, etc. In this lesson, we will use the `natural` library to tokenize a string. First, we will break the string into words using `WordTokenizer`, `WordPunctTokenizer`, and `TreebankWordTokenizer`. Then we will break the string into sentences using `RegexpTokenizer`.

Break up language strings into parts using Natural

In this lesson, we cover several different ways to animate graphics drawn to Canvas.

Animating with Canvas

In this lesson, listening for and handling mouse and keyboard events in Canvas is covered.

Listening for Mouse and Keyboard Events with HTML Canvas

In this lesson, you'll learn how to read and write individual pixel color values from a canvas.

Canvas and Pixels

Transformations can change the default canvas coordinate system and create complex drawings with much less code.

Canvas Transformations

Learn some of the different ways you can affect the graphical content that you are drawing to a canvas. We'll cover global alpha, global composite operations and shadows.

Canvas Effects

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

This TypeScript tutorial explains how to use generics, allowing for different types to be passed as arguments. It discusses the use of shorthand syntax in generics and illustrates with examples. This method is useful for library creation, providing type safety and control to the user.

```typescript
interface PhotoMetadata<T> {
  title: string;
  takenAt: Date;
  customData: T;
}

// Function that processes a photo with custom metadata
function processPhoto<T>(photo: PhotoMetadata<T>): {
  processedTitle: string;
  daysSinceCapture: number;
  customDataSummary: T;
} {
  const today = new Date();
  const daysSinceCapture = Math.floor(
    (today.getTime() - photo.takenAt.getTime()) / (1000 * 60 * 60 * 24)
  );

  return {
    processedTitle: photo.title.toUpperCase(),
    daysSinceCapture,
    customDataSummary: photo.customData
  };
}

// Example usage with different custom data shapes
type CameraSettings = {
  iso: number;
  aperture: string;
  shutterSpeed: string;
}

type LocationData = {
  latitude: number;
  longitude: number;
  locationName: string;
}

// Using with camera settings
const photoWithCamera: PhotoMetadata<CameraSettings> = {
  title: "Sunset at beach",
  takenAt: new Date('2024-01-15'),
  customData: {
    iso: 100,
    aperture: "f/2.8",
    shutterSpeed: "1/1000"
  }
};

// Using with location data
const photoWithLocation: PhotoMetadata<LocationData> = {
  title: "Mountain vista",
  takenAt: new Date('2024-02-01'),
  customData: {
    latitude: 45.5231,
    longitude: -122.6765,
    locationName: "Mount Hood"
  }
};

const processedCameraPhoto = processPhoto(photoWithCamera);
const processedLocationPhoto = processPhoto(photoWithLocation);

processedCameraPhoto.customDataSummary.iso
processedLocationPhoto.customDataSummary.longitude

```

Basic Explanation of Typescript Generics

A very basic explanation of generic function arguments

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.

Build a Word Game with Windsurf's Cascade AI Agent



Discover how to harness the power of Cursor IDE's Composer Agent feature to optimize your code. In this lesson, you'll witness an AI agent doing all the work to generate test suites, implement performance benchmarks, and create optimized function variations - all without writing a single line of code. You'll learn how to validate performance improvements through data-driven benchmarking, ensuring that optimizations are backed by concrete metrics rather than speculation. This hands-on demonstration showcases a groundbreaking approach to automated function development that combines the precision of test-driven development with the efficiency of AI-powered optimization.


Agent-Driven Development in Cursor: Testing, Benchmarking, and Optimizing Functions

This course is aimed for using `zod` effectively in projects based on TypeScript (both browser and node.js). The benefits you gain with this course is not just simply coding, but also understanding when/why/how should you perform runtime validation.

`zod` is the missing piece to TypeScript's capabilities, which can guarantee type-safety only in compile-time.

`zod` can be used whenever the compiler cannot guarantee what happens in runtime. And... it's a very frequent usecase, such as HTTP calls, websockets, etc.

In this course you get quickly up and running by:
1. using `zod` with primitives and objects
2. adapting `zod` quickly to existing projects thanks to automatic generators
3. changing the strategy frm type-first to schema-first
4. using error-throwing `parse` or non-error-throwing `safeParse` basing on your needs
5. applying `zod` to the most common usecase - validating API responses
6. handling errors the most convenient way (out of many!)

Take advantage of runtime validation with `zod`. Take advantage of the detailed lessons in this course 🔥

Effective Runtime Type Checks with Zod


Cursor can now automatically run commands in the terminal and walk step-by-step towards building out your entire project. You give it a goal, then it will suggest the commands you need to run and the files that you need to create and you can work with it to achieve what you're building. This lesson does a quick walkthough of using a Cursor Agent to build a project from scratch and guide it along the way. Cursor Agents definitely feel like a sneak peek at the future of programming!

Cursor Agents Initial Impressions - Allow an AI to Build Your Entire Project

Mock Service Worker is an API mocking library that allows you to write client-agnostic mocks and reuse them across any frameworks, tools, and environments.



Mocking WebSocket APIs with Mock Service Worker


While using anonymous functions might seem convenient, they can create difficulties when trying to identify performance bottlenecks during profiling. This lesson will demonstrate how named functions make it easier to pinpoint and address performance issues, ultimately leading to more efficient and debuggable code. Learning this practice will greatly improve your ability to locate and fix performance issues in complex projects.

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