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Rewriting Bun in Rust (bun.com)

773 points by afturner · 2 days ago · 526 comments on HN

Article summary

The author of Bun, a JavaScript and TypeScript transpiler, bundler, and package manager, has rewritten the project in Rust using Anthropic's Claude Fable model. The rewrite aimed to improve stability and prevent memory leaks, which were issues in the original Zig implementation. The author used a combination of mechanical porting, adversarial review, and testing to ensure the new Rust codebase was correct and maintainable. The rewrite took 11 days and resulted in a 20% reduction in binary size and a 5% improvement in performance.

Main themes

  • Rewriting code in Rust
  • LLM-assisted coding
  • Code maintainability
  • Language choice and tradeoffs
  • Stability and performance improvements

What commenters say

  • The rewrite of Bun in Rust using an LLM is a significant achievement, but its maintainability and long-term viability are uncertain.
  • The use of LLMs in coding can lead to 'vibe-coded' projects that may not be maintainable in the classical sense, but can still be successful and profitable.
  • The definition of 'maintainable' code is subjective and can vary depending on the team, project, and goals, with some considering LLM-generated code to be maintainable if it can be fixed and updated by the LLM.
  • The success of the Bun rewrite is not solely due to the LLM, but also the result of careful planning, testing, and review processes.
  • The choice of programming language, such as Rust or Zig, can have significant implications for project stability, performance, and maintainability.
  • The use of LLMs in coding raises questions about the role of human developers and the potential for 'throwing tokens' at problems rather than addressing underlying issues.
  • The rewrite of Bun in Rust has improved its stability and performance, but its impact on the Zig community and the broader programming language ecosystem is still to be seen.
  • The article and subsequent discussion highlight the need for clear definitions and metrics for evaluating code maintainability and the effectiveness of LLM-assisted coding practices.