Javascript

Local-First Application Development is Back with Dev Agrawal

Published May 9, 2024

Updated May 10, 2024

2 min read

In a world where connectivity is often taken for granted, the concept of "local first" applications is gaining traction. In this episode of the Modern Web Podcast, Dev Agrawal sheds light on this innovative approach, which prioritizes user devices as the primary data source. By enabling offline access and user control, local-first apps are revolutionizing the way we interact with technology.

Traditionally, applications have relied on server-centric models, where data is stored and managed on remote servers. However, local-first architecture flips this paradigm by placing user devices at the forefront. By doing so, these apps empower users with offline access and control over their data.

One of the key advantages of local-first architecture is its ability to create personalized experiences. By leveraging the data stored on user devices, apps can tailor their functionality to individual preferences and needs. Moreover, local-first apps excel at synchronization, ensuring that data remains consistent across multiple devices. This seamless synchronization allows users to switch between devices effortlessly, without worrying about data loss or inconsistencies.

Data ownership is a critical aspect of local-first applications. With the user's device holding the definitive data version, individuals have greater control over their information. This is particularly important in collaborative environments, where multiple users need to access and modify shared data. To address this, local-first apps employ Conflict-free Replicated Data Types (CRDTs), which ensure that conflicts are resolved automatically and data integrity is maintained. By prioritizing data ownership and collaboration, local-first architecture fosters a more inclusive and efficient work environment.

While local-first architecture offers numerous benefits, it is important to strike a balance between local and server data management. Certain data, such as large media files or complex computations, may still be better suited for server storage. Additionally, server-centric models can provide valuable backup and recovery options. As web development paradigms evolve, finding the right balance between local and server data management will be crucial in creating integrated user experiences and adaptable frameworks.

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Introduction to Zod for Data Validation

As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. However, I find that this can lead to confusing situations when trying to import one or the other—my personal preference is to name the Zod schema with Schema at the end to make it clear which is which. Conclusion Zod is an excellent tool for easily and confidently asserting that the data you're working with is exactly the sort of data you were expecting. It gives us the ability to assert at runtime that we've got what we wanted, and allows us to then craft strategies to handle what happens if that data is wrong. Combined with the ability to infer TypeScript types from Zod schemas, it lets us write and run more reliable code with greater confidence....

Nov 15, 2024

7 mins

JavaScriptZod

Exploring Angular Forms: A New Alternative with Signals

Exploring Angular Forms: A New Alternative with Signals In the world of Angular, forms are essential for user interaction, whether you're crafting a simple login page or a more complex user profile interface. Angular traditionally offers two primary approaches: template-driven forms and reactive forms. In my previous series on Angular Reactive Forms, I explored how to harness reactive forms' power to manage complex logic, create dynamic forms, and build custom form controls. A new tool for managing reactivity - signals - has been introduced in version 16 of Angular and has been the focus of Angular maintainers ever since, becoming stable with version 17. Signals allow you to handle state changes declaratively, offering an exciting alternative that combines the simplicity of template-driven forms with the robust reactivity of reactive forms. This article will examine how signals can add reactivity to both simple and complex forms in Angular. Recap: Angular Forms Approaches Before diving into the topic of enhancing template-driven forms with signals, let’s quickly recap Angular's traditional forms approaches: 1. Template-Driven Forms: Defined directly in the HTML template using directives like ngModel, these forms are easy to set up and are ideal for simple forms. However, they may not provide the fine-grained control required for more complex scenarios. Here's a minimal example of a template-driven form: ` ` 2. Reactive Forms: Managed programmatically in the component class using Angular's FormGroup, FormControl, and FormArray classes; reactive forms offer granular control over form state and validation. This approach is well-suited for complex forms, as my previous articles on Angular Reactive Forms discussed. And here's a minimal example of a reactive form: ` ` Introducing Signals as a New Way to Handle Form Reactivity With the release of Angular 16, signals have emerged as a new way to manage reactivity. Signals provide a declarative approach to state management, making your code more predictable and easier to understand. When applied to forms, signals can enhance the simplicity of template-driven forms while offering the reactivity and control typically associated with reactive forms. Let’s explore how signals can be used in both simple and complex form scenarios. Example 1: A Simple Template-Driven Form with Signals Consider a basic login form. Typically, this would be implemented using template-driven forms like this: ` ` This approach works well for simple forms, but by introducing signals, we can keep the simplicity while adding reactive capabilities: ` ` In this example, the form fields are defined as signals, allowing for reactive updates whenever the form state changes. The formValue signal provides a computed value that reflects the current state of the form. This approach offers a more declarative way to manage form state and reactivity, combining the simplicity of template-driven forms with the power of signals. You may be tempted to define the form directly as an object inside a signal. While such an approach may seem more concise, typing into the individual fields does not dispatch reactivity updates, which is usually a deal breaker. Here’s an example StackBlitz with a component suffering from such an issue: Therefore, if you'd like to react to changes in the form fields, it's better to define each field as a separate signal. By defining each form field as a separate signal, you ensure that changes to individual fields trigger reactivity updates correctly. Example 2: A Complex Form with Signals You may see little benefit in using signals for simple forms like the login form above, but they truly shine when handling more complex forms. Let's explore a more intricate scenario - a user profile form that includes fields like firstName, lastName, email, phoneNumbers, and address. The phoneNumbers field is dynamic, allowing users to add or remove phone numbers as needed. Here's how this form might be defined using signals: ` > Notice that the phoneNumbers field is defined as a signal of an array of signals. This structure allows us to track changes to individual phone numbers and update the form state reactively. The addPhoneNumber and removePhoneNumber methods update the phoneNumbers signal array, triggering reactivity updates in the form. ` > In the template, we use the phoneNumbers signal array to dynamically render the phone number input fields. The addPhoneNumber and removePhoneNumber methods allow users to reactively add or remove phone numbers, updating the form state. Notice the usage of the track function, which is necessary to ensure that the ngFor directive tracks changes to the phoneNumbers array correctly. Here's a StackBlitz demo of the complex form example for you to play around with: Validating Forms with Signals Validation is critical to any form, ensuring that user input meets the required criteria before submission. With signals, validation can be handled in a reactive and declarative manner. In the complex form example above, we've implemented a computed signal called formValid, which checks whether all fields meet specific validation criteria. The validation logic can easily be customized to accommodate different rules, such as checking for valid email formats or ensuring that all required fields are filled out. Using signals for validation allows you to create more maintainable and testable code, as the validation rules are clearly defined and react automatically to changes in form fields. It can even be abstracted into a separate utility to make it reusable across different forms. In the complex form example, the formValid signal ensures that all required fields are filled and validates the email and phone numbers format. This approach to validation is a bit simple and needs to be better connected to the actual form fields. While it will work for many use cases, in some cases, you might want to wait until explicit "signal forms" support is added to Angular. Tim Deschryver started implementing some abstractions around signal forms, including validation and wrote an article about it. Let's see if something like this will be added to Angular in the future. Why Use Signals in Angular Forms? The adoption of signals in Angular provides a powerful new way to manage form state and reactivity. Signals offer a flexible, declarative approach that can simplify complex form handling by combining the strengths of template-driven forms and reactive forms. Here are some key benefits of using signals in Angular forms: 1. Declarative State Management: Signals allow you to define form fields and computed values declaratively, making your code more predictable and easier to understand. 2. Reactivity: Signals provide reactive updates to form fields, ensuring that changes to the form state trigger reactivity updates automatically. 3. Granular Control: Signals allow you to define form fields at a granular level, enabling fine-grained control over form state and validation. 4. Dynamic Forms: Signals can be used to create dynamic forms with fields that can be added or removed dynamically, providing a flexible way to handle complex form scenarios. 5. Simplicity: Signals can offer a simpler, more concise way to manage form states than traditional reactive forms, making building and maintaining complex forms easier. Conclusion In my previous articles, we explored the powerful features of Angular reactive forms, from dynamic form construction to custom form controls. With the introduction of signals, Angular developers have a new tool that merges the simplicity of template-driven forms with the reactivity of reactive forms. While many use cases warrant Reactive Forms, signals provide a fresh, powerful alternative for managing form state in Angular applications requiring a more straightforward, declarative approach. As Angular continues to evolve, experimenting with these new features will help you build more maintainable, performant applications. Happy coding!...

Oct 4, 2024

5 mins

AngularJavaScript

Build Facial Recognition and Chatbot AIs using TypeScript with Jack Herrington

In this JS Drop, Danny Thompson is joined by YouTuber Jack Herrington to explore a unique TypeScript and AI project that lets you recognize TV show characters in real time. Just point your camera at a character, and instantly get their details or even chat with them as if they were real! Jack walks through the tech, explaining how client-side face recognition and server-side AI work together to make this possible using the Versal AI library. They discuss prompt engineering, building efficient APIs, and ensuring smooth, interactive AI responses. Jack also shares potential real-world applications, from entertainment to security. Chapters - 0:32 Project Overview – Jack explains the AI-powered character recognition project - 2:17 Setting Up the Project – Overview of how the application is structured and initial setup - 3:04 Client-Side AI – How face detection and character recognition work on the client side - 5:12 Switching to Server Side – Jack demonstrates server-side AI and setting up API endpoints - 8:20 Explaining AI Tooling – How tools and prompts are used to give context to the AI - 10:01 Detailed Prompt Structure – Breaking down the prompt and character context for AI responses - 12:40 Client-Server Interaction – Using the Versal AI library to manage streaming responses - 15:09 Handling Character Data – Training the AI on specific character images and details - 18:15 Practical Use Cases – Discussing potential real-world applications for the face recognition tool - 21:34 Challenges and Lessons Learned – Jack shares obstacles he faced and how he overcame them - 25:45 Building the API – Tips and considerations for creating reliable API endpoints - 28:40 Handling User Inputs – Testing unexpected questions and how the AI responds - 32:00 Using Advanced AI Models – Jack talks about choosing GPT-4 and issues with smaller models - 35:47 Introducing ProNextJS.dev – Jack discusses his new Next.js course, covering advanced topics - 37:20 Closing Thoughts – Danny and Jack wrap up with final thoughts and a link to the GitHub repo Follow Jack Herrington on Social Media Twitter: https://x.com/jherr Linkedin: https://www.linkedin.com/in/jherr/ YouTube: https://www.youtube.com/@jherr Sponsored by This Dot: thisdot.co...

Nov 11, 2024

2 mins

AIArtificial Intelligence

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

In this episode of Leadership Exchange, host Rob Ocel chats with Jerry Reghunadh, Senior Director of Enterprise Architecture at Data Nimbus, about leadership, AI adoption, and data-driven decision-making. Jerry shares his career journey, insights on leveraging tools like ChatGPT and Copilot, and strategies for building effective data pipelines. They explore how companies can avoid "analysis paralysis," adopt AI strategically, and evaluate new technologies to solve real-world problems. Tune in for practical advice on aligning innovation with business goals and staying competitive in a rapidly evolving tech landscape. Chapters 00:00 – Introduction 00:31 – Jerry’s Leadership Journey 02:01 – Discussing AI in Leadership 03:59 – Experimenting with AI Tools 06:24 – Overcoming Analysis Paralysis 09:18 – Importance of Early AI Adoption 13:12 – AI’s Impact on Efficiency 14:30 – Sponsor Message 16:26 – Setting Realistic Goals for AI 19:41 – Data Management and AI 23:09 – Is AI Just a Fad? 27:22 – Testing New Technologies 29:56 – Final Thoughts on AI 30:25 – Connect with Jerry 30:44 – Closing Remarks 31:20 – Outro Follow Jerry on Social Media Linkedin: https://www.linkedin.com/in/jerrymannel/ Sponsored by This Dot: thisdot.co...

Nov 18, 2024

1 min

AIEngineering Leadership

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Local-First Application Development is Back with Dev Agrawal

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Introduction to Zod for Data Validation

Exploring Angular Forms: A New Alternative with Signals

Build Facial Recognition and Chatbot AIs using TypeScript with Jack Herrington

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh

Let's innovate together!

You might also like

Introduction to Zod for Data Validation

Exploring Angular Forms: A New Alternative with Signals

Build Facial Recognition and Chatbot AIs using TypeScript with Jack Herrington

AI Leadership: Data-Driven Decision Making & Avoiding "Analysis Paralysis" with Jerry Reghunadh