Georgios works for Red Hat as a Senior Principal Software Engineer and is currently the most active contributor for Quarkus, where he works in all sorts of areas, including but not limited to LangChain4j, RESTEasy Reactive, Spring compatibility, Kubernetes support, testing, Kotlin and more.
He is also an enthusiastic promoter of Quarkus that never misses a chance to spread the Quarkus love!
Wouldn’t it be nice if while searching on the Internet for the latest performance results of our beloved frameworks, we could trust whatever is written and call it a day?
In this session we’ll examine a few different poorly written benchmarks, dissecting and dismantling them down to the last bit, while also demonstrating a psychological trick that is used to distract the user's focus from what’s actually important.
We will show you how to uncover the lies, damn lies behind articles and benchmark results, with skeptical pragmatism and methodologies borrowed from the performance engineering field and philosophy..
You should come away from this session with a better understanding of the tools to reveal the weak points of a benchmark or, conversely, to convince users of some new benchmarking lie 👿
Generative AI has taken the world by storm over the last year, and it seems like every executive leader out there is telling us “regular” Java application developers to “add AI” to our applications. Does that mean we need to drop everything we’ve built and become data scientists instead now?
Fortunately, we can actually infuse AI models built by actual AI experts into our applications in a fairly straightforward way, thanks to some new projects out there. We promise it’s not as complicated as you might think! Thanks to the ease of use and superb developer experience of Quarkus and the nice AI integration capabilities that the LangChain4j libraries offer, it becomes trivial to start working with AI and make your stakeholders happy 🙂
In this session, you’ll explore a variety of AI capabilities. We’ll start from the Quarkus DevUI where you can try out AI models even before writing any code. Then we’ll get our hands dirty with some code and exploring LangChain4j features such as prompting, chaining, and preserving state; agents and function-calling; enriching your AI model’s knowledge with your own documents using retrieval augmented generation (RAG); and discovering ways to run (and train) models locally using tools like Ollama and/or Podman AI Lab. In addition, we’ll take a look at observability and fault tolerance of the AI integration and compile the app to a native binary. Maybe we’ll even try some new features, such as generating images or audio!
As a profession, we are privileged to have a good chance of possessing disposable income at a young age. At the same time, a lot of us were brought up with a saving rather than an investing mindset.
This combination can lead to poor financial choices that are rooted in large measure, to lack of exposure to the world of investment.
The talk provides a gentle introduction to the world of index investing through ETFs, which have become quite approachable in recent years, while also being the most suggested investment vehicle for new investors.
Armed with the exposure provided by this talk, we hope to kick-start your journey into making your money work for you instead of just serving the interest of your bank.
Lately, it feels like everything is about AI. While exploring this rapidly evolving landscape we often forget what makes development fun (us included).
So, let's hit a pause on the AI buzz and get back to basics with Quarkus, the modern java framework that helps you work smarter with or without AI.
We’ll kick things off with a quick intro for newcomers, then dive into some good old-fashioned live coding. Along the way, we’ll explore spring compatibility, persistence, testing, messaging, and more.
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