Systems | Development | Analytics | API | Testing

When it comes to making HTTP requests in Node.js, developers are spoiled for choice: from the built-in and browser-familiar Fetch API to third-party libraries like Axios, Got, Superagent, Ky, and others. Navigating this diverse landscape can be a little daunting. This comprehensive guide aims to simplify your decision-making process by comparing the features and nuances of popular options, so you can select the ideal solution for your next project.

Rails partials have been around for years, but they can be clunky since they're just ERB snippets without a backing object structure. Recently, libraries like ViewComponent and Phlex have tried to improve the view layer by adding more semantic structure to the templates. These are great libraries and I personally reach for ViewComponent on almost every project I work on. That said, I still feel the humble Rails partial still works great for many use cases.

Azure Blob Storage is an object storage service that is very similar to AWS S3. ActiveStorage from Rails has built-in support for both ActiveStorage and S3 for file storage, making it easy to integrate and even swap out providers. The Honeybadger Blog has already explored using S3 for file storage in Rails, and in this article, we'll explore using Azure to allow users to upload files in a Rails application. You can find the final code here on Github.

Local preview environments are transforming how developers test and validate code changes before merging them into the main codebase. Acting as temporary cloud environments, they provide a production-like setting where new features and bug fixes can be tested in isolation, catching issues early and streamlining the development code review process. These environments are crucial for enhancing development velocity, especially in CI/CD workflows used by DevOps engineers and QA teams.

Organizations are harnessing the power of AI to revolutionize products and services across industries. But AI-powered solutions have been getting more sophisticated. We need to redesign and amend our approach to understanding how customers experience these solutions. Unlike traditional products, AI solutions are dynamic, continuously learning and adapting. Traditional metrics may fall short of capturing the nuances of how users interact with AI.

The DevOps landscape continues to evolve rapidly. As more organizations embrace DevOps to stay competitive, the landscape is shifting to include more diverse players and specialized offerings. But what's next for the space? Gartner's latest Magic Quadrant for DevOps Platforms report highlights the latest developments and standout players in the space, including Bitrise's growing impact on mobile DevOps.

It sounds counterintuitive—using a technology that has trust issues to create more trustworthy data. But smart engineers can put generative AI to work to improve the quality of their data, allowing them to build more accurate and trustworthy AI-powered applications.