Mario is a senior principal software engineer at Red Hat working as Drools project lead. He has a huge experience as Java developer having been involved in (and often leading) many enterprise level projects in several industries ranging from media companies to the financial sector. Among his interests there are also functional programming and Domain Specific Languages. By leveraging these 2 passions he created the open source library lambdaj with the purposes of providing an internal Java DSL for manipulating collections and allowing a bit of functional programming in Java. He is also a Java Champion, the JUG Milano coordinator a frequent speaker and the co-author of "Modern Java in Action" published by Manning.
How many times have you implemented a clever performance improvement, and maybe put it in production, because it seemed the right thing™ to do, without even measuring the actual consequences of your change? And even if you are measuring, are you using the right tools and interpreting the results correctly? During this deep dive session we will use some examples, taken from real-world situations, to demonstrate how to develop meaningful benchmarks, avoiding the most common, but also often subtle, possible pitfalls and how to correctly interpret their results and taking actions to improve them. In particular we will illustrate how to use JMH for these purposes, explaining why it is the only reliable tool to be used when benchmarking Java applications, and showing what can go horribly wrong if you decide to measure the actual performance of a Java program without it. At the end of this session you will be able to create your own JMH based benchmarks and more important to effectively use their results in order to improve the overall performance of your software.
Ever wondered just how far you can push things with LLMs? We will explore the limits of the possible by building some outrageous applications and navigating the fine line between pure genius and epic fail. Using LangChain4j and Quarkus, we will create agents that can perform very complex tasks but also wreak havoc on your bank account and database. We will let LLMs run their own code, evaluate each other, pit them against one another (welcome uncensored models), and have them perform prompt injection on themselves. Join us for a fun and crazy ride full of oohs and ouches!
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