Systems | Development | Analytics | API | Testing

Many teams outsource web application testing to ship faster and free developers from test maintenance — without the cost and complexity of hiring in-house QA engineers. The right web application testing service can deliver on that promise. But most don’t. Instead, they introduce new issues into the testing process: Before outsourcing, ask how their testing service tackles these seven issues. In this guide, we show how our web application testing service, Rainforest QA, solves these problems.

Artificial Intelligence is transforming the world, but for those managing AI infrastructure, it can feel like they’re being consumed by complexity. AI solutions often promise automation, efficiency, and intelligent decision-making, but behind the curtain, DevOps teams and IT professionals are wrestling with an ever-growing, complex web of software challenges.

Managing multiple apps with different store accounts just got easier. Bitrise Release Management helps developers distribute apps efficiently, whether delivering internal builds or publishing to the App Store or Google Play. It’s built to support teams at scale, with teams relying on it to release hundreds of apps across different platforms and accounts.

AI coding tools are no longer a novelty. From startups to enterprises, developers are using them to accelerate development, auto-generate tests, and build products faster than ever. Some engineering teams claim they’ve become up to 10x more productive with the right AI coding tool in their workflow. To get a real-world understanding of how these tools are being used, we hosted a community discussion with over 70 tech leaders—CEOs, CTOs, and engineering managers.

Conventional testing methodologies falter where CI/CD performance testing triumphs. This revolutionary approach weaves performance validation intrinsically into your development lifecycle, transforming performance from an afterthought into a perpetual cornerstone throughout development. By vigilantly scrutinizing performance with every code alteration, you’ll preemptively neutralize issues before they cascade to users, ensuring flawless performance consistency across every release.

Continuous Integration and Continuous Deployment (CI/CD) performance testing involves automatically testing your application’s performance as part of your development pipeline. By integrating performance tests into your CI/CD workflow, you can catch performance issues early, before they reach production. This approach ensures that every code change is verified not just for functionality, but also for performance impact.

Rails 8 shipped with a bold premise: “No PaaS Required.” As cloud platforms have grown more costly, Ruby on Rails has shifted to reducing external infrastructure dependencies so that developers can deploy and run applications with fewer ever service dependencies. Traditionally, deploying a Rails app to the internet has meant provisioning a database server (e.g., PostgreSQL) and additional services like Redis for caching, background jobs, or WebSockets.

Today, organizations need to know what data they have, what it means, when it changes, and how it delivers value faster than ever before. Modern businesses have a problem, though – a continuously growing mountain of data, made up of many different operational processes and performance metrics, which users must proactively monitor to ensure business goals are met, and insights are deciphered. Embedded dashboards are one way for your users to keep track of key information while using your application.

In today’s data-driven world, organizations face the challenge of data processing and integrating vast amounts of information from diverse sources. Open source ETL (Extract, Transform, Load) frameworks have emerged as powerful tools to streamline data workflows, offering cost-effective, scalable, and customizable solutions. This blog delves into the benefits, features, and top ETL solutions in the open source ETL landscape.

In the prior blog, we previewed some of the reasons that we settled on using Anthropic’s Computer Using Agent (CUA) over other alternatives and promised to provide more information as to what and why, with facts and figures. If you haven’t read that blog, check it out here. In this blog, I hope we can bring to light some of the “why” behind our decisions, and what makes our agentic AI so powerful.