Compiler

The software stack is like an onion. Or a sheet cake. Or lasagna. Or is it? It’s often described as having layers that sit on top of each other. The reality is much more complicated—and learning about it can help any tech career. The Great Stack Debate is the first episode in Compiler’s series on the software stack. We call it Stack/Unstuck. We explore each layer of the stack, what it’s like to work on them, and how they come together into a whole application.  

Mistakes are part of growth. If we’re lucky, we’re in an environment where they’re not punished harshly. That lets us fix the problem, learn to do better, and move on—but also to tell the story once the sting has passed.Last episode, we heard three stories of people blundering into trouble and coming out the other side a little bit wiser. This episode adds three more stories of mistakes being made—but the culprit isn’t always as clear. 

Oops. We all make mistakes. Most of the time, they’re small enough no one notices. But every now and then, we do something that makes us break into a cold sweat. The “Oops” becomes a curse, desperate pleas—or horrified silence as we process what just happened. In the moment, they’re panic-inducing. But once the dust settles, are those big mistakes that big of a deal?On this episode of Compiler, we hear three stories of people who wish they had an easy undo button. But making those mistakes taught them all something important—and changed how they do their jobs. Because those big mistakes end up being valuable lessons for the rest of their careers. 

Movies are culturally important. They transform language and communication. Motion pictures present fantasy worlds we can get lost in, helping us understand the world differently. Discussing data and movies can make the fantasy seem…a little less fantastic. It can feel sterile, mass produced, and devoid of imagination. But data is vital, both for those behind the camera and those sitting in theaters (or at home). This episode will cover some ways data science and machine learning can inform filmmaking, from conception to post-production.

Updates. They take time out of your day and your devices out of commission. That’s about the extent of it for an end user. But for organizations? Updating their systems is a big deal. And forgoing regular updates is a recipe for disaster. “If it ain’t broke, don’t fix it” isn’t a good option for enterprise IT. Your system may keep running but if you haven’t updated in awhile, you may be missing some stability and performance improvements—and probably some security patches too. Regularly updating is a good habit. How do we make it less annoying? 

We have a finite time on earth. But the data we generate? It can last much, much longer. We have limited control over what happens to our data during our lives. And while you might not care about anything when you’re gone, you and your loved ones might have an interest in how your information is used after you pass. So we wondered: When should data die? It’s a tricky question. In the digital age, individuals generate mountains of data over their lifetimes. But who has the right to decide whether that data remains, or when it is deleted? How should IT organizations handle their datasets given the complexities of privacy, legacy, and ownership that they need to consider?

Technologists are often asked to make decisions based on future industry advancements—or basically, things that haven’t happened yet. It’s already difficult to choose the right path for a project without the pressure to be clairvoyant. But everyone wants to feel like they are leading the pack on the next big thing. What do we need to know to make a good prediction for where technology is headed? Alternatively, what do we need to know to avoid the wrong choice? We speak to experts in the DevOps space about betting wrong on the future, how development projects go awry, and what teams can do to get things back on track.  

We live in what some call a “distraction economy.” There are countless messages, emails, tickets, bugs to fix, and meetings to attend. For those who have to build software, platforms, and services, as well as those who maintain them and keep them running, it can be difficult to decide what to focus on first. The immense pressure to be productive is challenging to balance with passion projects, personal responsibilities, or just with the need to rest. Our team spoke with tech-minded creators in the productivity space on how to achieve full focus, and how to make time for work, relaxation, and creativity. We would love to see you (virtually) at Red Hat Summit. Register for the virtual experience of Red Hat Summit at red.ht/summit2022.

It’s about time we asked a question about compilers. It’s been a scary proposition. Compilers have a reputation for density, complexity, and a fair bit of mysticism. But when we looked into them, we learned they’re really just like any other program. So we wondered: Who’s afraid of compilers? In this episode of Compiler, we start to break down the reputation by opening up the black box. What do compilers do? How do they work? And what can you gain by learning more about the inner workings of compilers? 

Traffic jams and congested streets are an annoyance many people have to endure. Turns out, the same technology keeping us constantly connected—the Internet of Things—can also improve how we move people and things from place to place. But what are the considerations for technologists? Connectivity can’t be the only piece of the puzzle. Smart road technology can make travel safer, easier, and more efficient. But how can it make travel enjoyable?