Agentic AIs are showing promise for tedious work. But it’s hard to explain exactly how you want it done—and getting it wrong could create big problems. This episode of Compiler investigates how Agentic AIs could carry out their tasks and how some agents are taking their baby steps in the wide world. The team also considers the difficulties humans have expressing what we want computers to do for us, and how that could create unintended consequences.

Does your health insurance chatbot need to tell jokes? No. Does it need to be accurate? Absolutely. That's hard when biases get in the way. The introduction of bias into a model can be unintentional, but it can have significant consequences for those relying on its guidance. The Compiler team examines the ways bias can creep in, and what steps can be taken to address it.

AI is notorious for making stuff up. But it doesn’t always tell you when it does. That’s a problem for users who may not realize hallucinations are possible. This episode of Compiler investigates the persistent problem of AI Hallucination. Why does AI lie? Do these AI models know they’re hallucinating? What can we do to minimize hallucinations—or at least get better at seeing them?

With the massive flow of AI-generated content onto the internet, it was only a matter of time until all of those bits of data found their way back into AI models. But what do you get when generative AI models start getting their answers from that content? The Compiler team digs into AI feedback loops, and the unique challenges they present for technologists...and everyone else.

"Copyright infringement is a huge issue for AI training and use. Can LLMs give you copyrighted content? What data can you use to train and tune your own model? In this episode of Compiler, we explore who owns what when AI models learn from protected content—and why it matters."

There’s no one AI model to rule them all. Each project has its own requirements. Where do you get started building your own model? Compiler continues its conversations with big dreamers about their big projects—and how they’re piecing together the building blocks of their AI models.

There is a lot of excitement around AI models, but can it meet the expectations set by blockbuster movies? What’s the current inflection point between what’s feasible and what’s not? The Compiler team talks to both big dreamers and heavy adopters wading into the space, hearing their thoughts on how AI can help scale daunting work, fill in the gaps, and make the fantastic into reality.

Everyone’s talking about AI. Everyone says it’s the future. To find out where we’re going, we should know how we got here—and exactly what we’re working with. We hear a short history of AI development before diving into how it’s already changed the ways we learn and code.

Online multiplayer games are a staple of modern entertainment. But internet connectivity is an unpredictable factor in the experience. That’s why netcode exists. But what is it? And how does it really affect players?

From healthcare to banking, there’s a lot of industries that require the utmost protection for sensitive data. But for those sectors, innovation is also essential. That’s why disconnected environments—or air-gap environments—can be seen across the IT landscape. But how do they work? How do technologists ensure the data is accurate and usable, and how can these environments foster innovation?

Red Hat Enterprise Linux has been around for a while. For many technologists, be they professional or hobbyist, it jump started their interest in open source. But how did the operating system, and the culture behind it, strike a chord with so many people?

We all know what industry conferences are for. You see the talks, you do the networking, you get the swag. Anything else? Well, yes, actually. In our second episode on tech conferences, we cover how to make the most of attending them. How can you prepare? What do you do in the moment? And how do you use that experience after you return to the office?

Last year, we discussed the impact of data centers on the global power grid. We know open source technology can help us optimize our power consumption. But people are looking at sustainable energy usage beyond the server. How can technologists think about the balance between sustainability, business operations, and their customers’ needs?

Career paths are full of unexpected challenges. In the last installment of our career series, we hear how a little empathy and authenticity helped two budding IT professionals stay the course and find their calling.

Everyone wants to work for a good boss. That’s not always possible. Sometimes, a bad boss is easy to spot. Sometimes, a boss who’s been great will say something devastating. They’ll make you want to move on. We hear stories about managers who stalled their employees’ careers—and how those workers found a way to thrive in spite of those bad bosses.

We all start somewhere. For these two IT professionals, the ground floor is where they found their way into the tech industry. When you don’t know how to navigate a career in technology, it pays to be flexible. Because as Luis Martin and Victor Santiago would learn, where someone begins isn’t necessarily where they end up.

We could all use some help making decisions. The advice we get isn’t always the most helpful. Sometimes, it’s easy to spot the bad advice. But what do you do when you can’t tell? Two technologists share times of indecision where they had to figure out whether the guidance they asked for was really in their best interests.

Big mistakes. Colossal setbacks. Bad days. We’ve all been there. But how do we find a way forward? The Compiler team shares two stories of unexpected challenges and happy endings.

People react differently when they hear the word “risk”. While there’s the possibility of reward, it takes a lot to step out of the comfort zone. The Compiler team asks if there’s ever such a thing as a safe bet, and hears from experienced risk-takers on the best time to take a leap of faith.

They say wisdom comes from experience. Starting out in the tech industry likely means you’re short on experience and looking for advice. One area where people could use a leg up? Learning the warning signs of a problematic project. We hear stories from people who’ve figured out the hard way how to spot potential red flags, and what can happen when they’re missed.