Will LLMs Help or Hurt New Programming Languages? (blog.flix.dev)

A recent exploration into the impact of large language models (LLMs) on the adoption of new programming languages poses the question: can LLMs help or hinder such languages? The Flix programming language, which focuses on effect-oriented programming, serves as a case study, particularly analyzing the capabilities of the Claude Code model (Opus 4.5) to write a Tic-Tac-Toe game in Flix. Despite its extensive training on popular languages such as Python and JavaScript, the scarcity of available Flix source code presents unique challenges for the LLM, testing its ability to understand and apply Flix's distinct syntax and semantics. The significance of this inquiry lies in its implications for the future of programming languages. It showcases that while LLMs can assist with syntactically similar languages, their effectiveness diminishes when faced with new languages that introduce novel constructs. The experiment highlights the LLM's struggle with Flix's unique features, such as the effect system and specific syntax rules, as well as its ability to adapt and learn from documentation. Ultimately, this evaluation suggests that while LLMs like Claude could facilitate initial engagement with new languages, their limitations in comprehending innovative programming paradigms signal the continued importance of human expertise in language design and development.