Operations | Monitoring | ITSM | DevOps | Cloud

Splitting and parallelizing Android UI tests with Espresso and CircleCI

For Android developers, test automation on CI/CD platforms such as CircleCI has become an indispensable part of the development workflow. But merely implementing automated testing is no longer enough to remain competitive and continue to develop at speed. Developers must also work to continuously monitor, maintain, and improve their test automation. As an application grows in complexity, the scale of development grows, as does the number of automated tests.

Achieving AI development at scale ft. Luis Ceze of OctoAI

In this episode, Rob is joined by Luis Ceze, CEO of OctoAI and a distinguished professor of computer science at the University of Washington. Together, they unpack the surge of interest in AI, attributing it to the convergence of factors like the unprecedented availability of data thanks to the internet boom and the accessibility of powerful computing resources.

What is iteration?

In Agile development, where development is repeated in short periods, the key unit of the development cycle is called an iteration. Iterations, consisting of Design, Development, Testing, and Improvement are usually set for 1 to 4 weeks, and they are characterized by completing a full cycle of system development. After completing one cycle and releasing it, known as Iteration 1, the process is repeated with Iteration 2, Iteration 3, and so on.the.

Build and test LLM applications with AIConfig and CircleCI

The power of LLMs to solve real-world problems is undeniable, but unfortunately, in some cases, only theoretical. What’s stopping us from getting the most out of OpenAI’s text completion capabilities in production apps? One common problem is the inability to confidently guard against bad outputs in production the way we’re used to doing with non-AI test suites. Let’s go one step deeper. There is no equivalent of code coverage for an LLM.

Integrating AI and DevOps for Software Development Teams

For a long time, the domains of Machine Learning and AI on one side, and software development on the other side, were separate kingdoms. Sometimes, they touched, and something magical would happen. But more often, things didn’t really work out. They faced challenges stemming from a lack of mutual understanding, shared language, and compatible tools. With the meteoric rise and increased accessibility of powerful generative AI and LLMs, the need for collaboration to achieve real-world engineering and customer value has never been more vital.