Systems | Development | Analytics | API | Testing

Have you ever observed an application that was completely functional, and suddenly it starts lagging at peak usage? Most teams are concerned with functionality first and performance under load second. Open source load testing tools expose your application’s bottlenecks before your users notice them. In this blog, we explain how these tools work, compare their features, and discuss best practices.

The EU AI Act is here, and for many enterprises, it represents a massive coordination challenge. As the world’s first comprehensive AI law, it mandates strict governance on transparency, risk management, and data quality. For platform engineers and architects, the immediate question is operational: How do we comply with these new regulations without forcing every developer to rewrite their applications?

Testing is a very important and necessary step in the SDLC, but most teams ignore it or don’t care much about it, while some teams spend most of their time on testing instead of building features. AI is really changing the way we write code, but most people use it mainly for writing test cases, and we still end up doing it manually. So in this blog, let’s see what AI testing is, how AI helps in testing our software, what AI tools are available, and which tools help with which part of testing.

If you’ve worked in QA long enough, you’ve seen how much testing has changed inside Jira. What started as a mix of spreadsheets, manual checklists, and endless review cycles has grown into fully integrated Test Management workflows. But even with automation, some challenges never went away. Writing test cases from requirements still takes time.

Note: This article is the third instalment in a four-part series. Check out the first two posts here:- Why we ditched frontier AI agents and built our own- Building Bitrise’s context-aware, browser-integrated AI copilot that keeps engineers in control.

The integration of next-generation AI workloads — from large language models (LLMs) to speech-to-text and text-to-speech — demands a powerful, secure, and scalable infrastructure. This is especially true for building advanced AI voice agents, which rely on seamless, natural, and highly efficient user interaction.

Wealth management has always been about personal touch. Relationship managers provide a white-glove service to elite clientele - guiding investments, financial plans, and more. However, they’re under growing pressure to serve more clients and drive bank revenue, without diluting that personal connection and service quality. This dual mandate is placing relationship managers in a catch-22 situation. If they serve more clients their ability to provide personalized services diminishes, and vice versa.

BigQuery AI, brings together BigQuery’s built-in ML capabilities, generative AI functions, vector search, intelligent agents, and agent tools.