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Drawing from multiple Scala LLM workshops we conducted this past year, I will share insights to significantly enhance your AI experience.
Large language models and agentic systems are currently very popular, with many advocating for their use. However, they sometimes fail to deliver the expected 'magical' results. Does this mean they are not useful? Absolutely not. With the right strategy and tools, you can leverage them effectively in Scala using existing solutions like the Metals Language Server. While this may not be a '10x developer' experience, even a 50% productivity boost is a significant win.
Drawing from multiple Scala LLM workshops we conducted this past year, I will share insights to significantly enhance your AI experience. I'll cover LLM fundamentals, but my primary focus will be on the Model Context Protocol and the agentic system tooling, particularly our solutions within the Scala ecosystem. These tools allow agents to interact with your machine and the external world in a controlled way, providing LLMs with crucial feedback to refine their output. My goal is for the audience to leave with a strong foundational understanding of how to use AI tools and where to seek further improvements.
Do you like it when compiler generates the boring code for you? Fast, mundane, boring-but-error-prone code? Do you need to implement such a code generator yourself? Have you found out that Shapeless/Mirrors bend your brain a bit too much?
In this talk, I’ll guide you through the crossroads where Scala intersects with AI, some applications aimed at boosting developer productivity, others focused on integrating your code with LLMs.
Writing client-facing APIs involves mundane tasks, whether it be REST, GraphQL, or gRPC. In this talk, I will pick two repetitive tasks during API development and demonstrate how we can utilize Scala to automate the most boring parts.
