Learn Puppeteer, Python and Realm

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.

python

To take the user input in Python you use a built-in method input(). The data type of the user input is string. Later based on the use case the data type of the user input can be changed to a different data type based on the requirement.

 Example:

1. 
```
>> a = input("Enter your birthday date ")
>> 31
>> a
>> '31' 
```

2. 
```
>> b = input("Enter List of numbers: ")
>> [1, 2, 3, 4]
>> b
>>  '[1, 2, 3, 4]'
```

NOTE: While creating the example Python interpreter is used which is why the program waits for the user to input. And, once the user enters the input then prompts the user to enter the next line of code.


Use built-in method input() to take User Input in Python

Use the equality operator to compare the values of the objects. Identity operator is used to verifying that objects are equal but are not referring to the same object in memory. 

Use cases where identity operator is used are:
- to test if the object is None
- to test if the object is of a particular type or not

There may be use cases where the output returned by both the operators is the same. But, in your program, you have used the identity operator instead of the equality operator. 

To avoid such mistakes it is important to understand the difference between identity and equality operator.


Difference between Equality Operator and Identity Operator in Python

The identity operator is used for checking whether the object is of the mentioned type.

Example:

```
>> a = "Hello World!"
>> type(a) is str
>> True
```
The result returned in the above case is true because the type of object is str. 

Identity operator is also used to compare the object with None. 

Example:

```
>> a = None
>> a is None
>> print(a)
>> True
```

Internally, the identity operator compares the memory address of the objects. Built-in `id()` method returns a unique number i.e address of the object in memory. If the address of both the objects in memory is the same identity operator returns the result as True.

Example:

```
>> a = 10
>> b = 10
>> a is b
>> True
>> id(a)
>> 916744
>> id(b)
>> 916744
```

The above code snippet tells us that value of both the objects is the same and variables `a` and `b` are referring to the same object in memory. 



Identity Operator in Python

Variable names in Python can be of any length. Variables names can have alphabets [A-Z][a-z], digits [0-9] and underscores. A variable name starting with a digit is an invalid variable name. 

Variable Names in Python

Built-in id() method determines the memory address of the object in Python. Knowing this concept will help you in understanding what happens under the hood when you use identity operator (is and not is). 

Use Built-in id() Method to Determine the Memory Address of the Object

While solving real-life use cases through programming and also while testing your code you will come across scenarios where you have to compare the value of different objects. And, then to solve the problem you can use the equality operator.

Use Equality Operator to Compare the Value of Objects 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. 

Understand Immutability with Python Tuples

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.

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.

javascript

Up and Running with AWS Amplify Functions

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

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

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

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

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

Puppeteer is a Node library which provides a high-level API to control headless Chrome or Chromium over the DevTools Protocol. It can also be configured to use full (non-headless) Chrome or Chromium.

puppeteer

When taking screenshots, it's important that the browser is big enough to show all of the content in the DOM that we want to screenshot. We can do this by setting the viewport.

Setting the viewport on a Playwright headless browser

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

Once we have built a multi-class classification network, we'll use it to make predictions on new data that wasn't used during training.  We'll start by calling the `predict` method, which returns the probability that each input data row belongs to each one of the possible classes.  Then, we'll use the `predict_classes` method to only output the class prediction as an integer, which might be easier to use in a production system.

Make Predictions on New Data with a Multi Category Classification Network

Instead of training the network every single time it is run, we’ll want to save the Keras model so that we can use it in the future.  Keras makes it easy to save both the model weights and topology to a single file, so we’ll do that here.

Save a Trained Keras Model Weights and Topology to a File

We will view a summary of the different networks as we create them by using the `summary` method on the model.  This will help us better understand the structure of the model.

Understand the Structure of a Keras Model by Viewing the Model Summary

We will load a saved model from a file, and then use it to make predictions on new data.  We won’t need to know anything about the model to do this - we can do it all in a new file by loading a saved model.

Load and Use a Saved Keras Model

We can specify several options on a network optimizer, like the learning rate and decay, so we’ll investigate what effect those have on training time and accuracy.  Each data sets may respond differently, so it’s important to try different optimizer settings to find one that properly trades off training time vs accuracy for your data.

Change the Learning Rate of the Adam Optimizer on a Keras Network

We’ll break our training up into multiple steps, and use different learning rates at each step.  This will allow the model to train more quickly at the beginning by taking larger steps, but we will reduce the learning rate in later steps, in order to more finely tune the model as it approaches an optimal solution.  If we just used a high learning rate during the entire training process, then the network may never converge on a good solution, and if we use a low learning rate for the entire process, then the network would take far too long to train.  Varying the learning rate gives us the best of both worlds (high accuracy, with a fast training time).

Change the Optimizer Learning Rate During Keras Model Training



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.

Always Name Functions in JavaScript


When attempting to load the same module twice in JavaScript you'll hit a cache and code won't re-run. In scenarios where you actually do want to have state in your modules, you'll have to use a cache-busting technique by passing a query parameter to a dynamic import. This lesson walks through how to add a query parameter, how to add the appropriate types for loading dynamic modules in TypeScript, and when these techniques are valuable.

Import the Same JavaScript Module Multiple Times with Cache Busting



The include keyword in GitHub Actions empowers you to add custom variables to your matrix strategy, providing flexibility beyond standard configurations. In this video, we'll explore how include allows you to define specific combinations of variables, enhancing control over your workflow runs. You'll see how this feature enables you to tailor jobs for unique environments or scenarios not covered by the default matrix options. By leveraging include, you can efficiently manage complex build processes with greater precision. This technique helps you streamline your CI/CD pipeline, making it more adaptable to your project's evolving needs.

Use GitHub Actions Matrix Include for Complex Job Configurations

While running `zod` schema validation agains domain-rich objects, the error message saying that _a string is not a number_ might not be really useful.

Especially, when you need to provide a meaningful feeback to the user, e.g. after they have submitted a form with lots of data.

`zod` allows to customize messages on a very granular level. We can also extract repeatable customization to `zod` error maps.

Finally, in order to process an error message further (e.g. display it to the user), we need to handle the `zod` validation errors effectively. Especially, when a big object validation has failed and we could receive a handful of errors.

That's what we learn in this lesson. 🔥

Customize zod Error Messages and Handle zod Errors Effectively

customize zod validation messages, customize zod schema property messages, reuse error maps across different schemas, navigate through zod error objects

You might want to run `zod` schema checks in different styles.

Sometimes you prefer to conditionally throw the error - that's what `parse` does. But another time you'd prefer to receive a `boolean` property that determines whether the validation was successful or not - and perform further actions accordingly.

Additionally, the error-throwing API of `parse` works extremely well with [TypeScript assertion functions](https://www.typescriptlang.org/docs/handbook/release-notes/typescript-3-7.html#assertion-functions). Their combination is just... perfect! 👌

That's what we learn in this lesson.

Use parse and safeParse Runtime Type-checks with zod

use zod parse function to conditionally throw ZodError, use zod safeParse to get a boolean value and run typescript type guard over success property

In this lesson we learn that TypeScript cannot guarantee correct shapes of responses. That's due to TS performing checks in compile-time, while the response will be availbale in runtime - when TS doesn't exist anymore.

In some situations, such as HTTP requests, runtime type checks such as `zod` are essential for providing quick feedback around data-related errors.

Understand where are the possible mistakes to make while using TypeScript with HTTP calls. Use `fetch` and `axios` along with `zod` schema checks.

Also, note that similar mistakes or mistakes could happen while using web storage (e.g. `localStorage`)

Validate HTTP API Response with zod, fetch and axios

use zod as an HTTP response validation tool along with fetch and axios in order to make sure that the response has expected data structure

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