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In part one of this series, we explored how Angular Signals shift the reactive model away from the RxJS-centric approach we’ve relied on for years. We walked through the core API signal(), computed(), and effect() primitives, and demonstrated how they simplify state management by removing the need for subscriptions, teardown logic, and deeply nested observables. We also introduced a minimalist CartService that held cart items in a private signal and exposed a computed total price.

Python has long relied on pip as its standard package manager, but a blazing-fast alternative is now changing the landscape. uv is a Rust-based package manager that aims to transform Python dependency management with unmatched performance and simplicity. In this guide, you will learn everything you need to know about uv and how to smoothly transition from the traditional pip ecosystem. Let's get started!

When writing Elixir, most developers quickly get familiar with IO.inspect as a quick way to see what's happening inside their code. But what many overlook is that IO.inspect is far more powerful than just a method that prints a variable to the console. In fact, with the right options and placement, IO.inspect can become a precise, highly targeted debugging tool, one that doesn't interrupt your program flow and works seamlessly with Elixir's functional pipelines.

In the world of Angular, reactivity has long been synonymous with RxJS. For years, developers have used observables, operators, and async pipes to build dynamic, reactive applications. But while powerful, RxJS brings a steep learning curve and often requires more mental juggling than necessary. Angular Signals change that. They introduce a new reactivity model that is intuitive, precise, and designed for clarity.

If you work with Elixir, chances are you've used structs plenty of times and are likely aware of Ecto schema. However, you may not have explored structs in depth or used Ecto schemas beyond the database context. In this post, we'll explore the ins and outs of structs and Ecto schemas.

React 19 introduces a suite of features that empower developers to build seamless, performant user interfaces. Among these, async transitions stand out as a game-changer for handling asynchronous operations without freezing the UI. This is particularly impactful when building forms where users expect instant feedback, smooth interactions, and no jarring loading states.

It's important and useful to schedule background tasks for your Python application. Tasks allow your app to perform time-based or long-running operations without blocking the main thread or slowing down the user-facing functionality of your app. Background tasks can be used for anything from running recurring jobs like data cleanup or reporting, to sending asynchronous emails or other notifications.

When it comes to data-driven applications, developers and data engineers are always trying to balance factors such as scalability, speed, flexibility, latency, and availability. In other words, databases and infrastructure are the foundations for well-structured applications: just like bricks are for houses. This article explores Redis' data store features and includes use cases. We'll learn how to use Redis in Python with a step-by-step tutorial. Let's get started!

In part one of this series, we looked at how to extend the ActiveStorage ingest process with custom analyzers. In this post, we will reverse the procedure and explore how to utilize ActiveStorage previewers to display data.

In this tutorial, we will build a Flask app with Docker, create a background worker with Celery, and use RabbitMQ as the message broker between the two services. This (relatively) simple example will be used to demonstrate how Docker makes it easier to run multiple services and share configuration details between developers. In the last section, we will also build and optimize our app for deployment to Render, so we can avoid some of the common gotchas it is easy to fall victim to along the way!