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It is hard to imagine a time not long ago where AI has not been front and centre of our everyday news, let alone in the software engineering world? The advent of LLMs coupled with the existing compute power catapulted the use of AI in our everyday lives and in particular so in the life of a software engineer. This article breaks down some of the use cases of AI in software engineering and suggests a path to investigate the key question: Did we actually become more productive?

As our cities grow, heavy construction and expansion are putting a significant strain on the environment. Currently, urban areas contribute around 70% of global carbon emissions and consume over 60% of the world’s resources. This means real estate has a big opportunity — and responsibility — to lead the way toward a sustainable future. But how? A vital tool in this effort is Software as a Service (SaaS).

Product-led growth hinges on delivering exceptional user experiences. If you build a great product with valuable features, users are bound to come and stick to your services. But companies often neglect the role of APIs. Modern software relies heavily on APIs for integrations, automation, and data exchange. Not to mention, for API or API-first products, APIs drive the most crucial pieces of success goals. A poorly performing API creates friction, frustrates users, and impedes growth.

Digital transformation is no longer optional—it’s essential for staying competitive, driving value, and meeting evolving customer expectations. As Brian Solis puts it, “Digital transformation is to change the way you compete, how you create value, and how you can use digital tools and technology to be relevant as the world continues to evolve.” Organizations worldwide are taking this to heart, with spending on digital transformation expected to hit $3.9 trillion by 2027.

It’s not enough to just test our code's happy path (in other words, the error-free path we hope our users will take). To be really confident our code can’t be abused, either accidentally or on purpose, we must actively attack it to try and find ways of breaking it. If we don’t do this, someone else will, and they probably won’t be as friendly.

When integrating Snowflake, you have four main options to choose from, each suited to specific needs.

Python’s subprocess module stands as one of the most powerful tools in a developer’s toolkit. The module lets you spawn new processes, connect to their input/output/error pipes, and get return codes. Think of the subprocess module as your bridge between Python scripts and system commands, replacing older modules like os.system and os.spawn and providing a better, unified way to handle external processes.

While Elixir provides built-in libraries (like HTTPoison) for making HTTP requests, the Tesla library has gained popularity due to its simplicity, ease of use, and extensibility. Tesla offers a clean and composable way to define requests, handle responses, and customize client behavior. Even so, whenever you're working with external APIs, errors are inevitable. Network issues, server downtime, rate limiting, and unexpected responses can all lead to errors in your application.

In the alphabet of mobile app development, it’s always been our honor to be so close to BrowserStack, so when the opportunity arose to officially partner up to improve your mobile DevOps experience: it was a no-brainer. We’re partnering to offer our customers access to best-in-class mobile-first CI/CD and automated mobile device testing, including an incredible free product offer.