LOADING
Protobuf is commonly associated with code generation. However, in large projects with tens of thousands of message definitions, this approach can lead to an overwhelming amount of generated code. In this talk, I’ll share my journey in search of a different approach to this problem.
Protobuf is commonly associated with code generation. However, in large projects with tens of thousands of message definitions, this approach can lead to an overwhelming amount of generated code. Combined with additional boilerplate or macros for converting between generated classes and domain entities, it can significantly impact both compile times and developer productivity.
Is another path possible? In this talk, I’ll share my journey in search of a different approach to this problem—how it led me to an alternative path, the challenges I faced, and the key discoveries I made along the way.
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.
When writing software, we currently seem to have to choose between an imperative style - easy to read and write, hard to reason about - and a monadic style - hard to read and write, easy to reason about.This talk is about being greedy and getting the best of both worlds, because we deserve it.
Drawing from multiple Scala LLM workshops we conducted this past year, I will share insights to significantly enhance your AI experience.
