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Rewriting Bun in Rust

For builders of AI workflows, it shows that AI coding agents can now safely execute major rewrites that were previously too risky, opening up new strategies for improving performance and reliability in production systems.

Simon Willison··3 min readopinion
opinionRewriting Bun in Rust
simonwillison.net

What happened

Simon Willison reports that Jarred Sumner, the creator of the JavaScript runtime Bun, has rewritten the entire project from Zig to Rust using AI coding agents. The rewrite was driven by persistent memory-management bugs—use-after-free, double-free, and leaks—that stemmed from mixing garbage-collected and manually-managed memory, an uncommon combination that no language handles well. Sumner credits Zig for getting Bun this far but notes that moving to safe Rust eliminates those bug classes. Willison emphasizes that this is a landmark example of AI agents changing the software engineering calculus: the traditional wisdom against full rewrites (as Joel Spolsky argued in 2000) no longer applies when frontier models can autonomously manage such a complex migration. Sumner's blog post details the agentic workflow, including dynamic task decomposition, trial runs, and adversarial review. For developers building AI workflows, this demonstrates that AI coding agents can now take on massive, previously infeasible refactoring projects, offering a new path to improve reliability and performance without years of manual effort.

Key takeaways

  • Bun's creator Jarred Sumner rewrote the runtime from Zig to Rust using AI coding agents.
  • Motivation was persistent memory bugs from mixing GC and manual memory management.
  • Sumner praises Zig but states safe Rust eliminates use-after-free and double-free errors.
  • AI agents enabled dynamic workflows, trial runs, and adversarial review during the rewrite.
  • Willison notes this challenges the classic 'never rewrite' advice, as AI makes large-scale rewrites feasible.

Why it matters

For builders of AI workflows, it shows that AI coding agents can now safely execute major rewrites that were previously too risky, opening up new strategies for improving performance and reliability in production systems.

This is an original editorial digest by AI Workflow Center. Full reporting at the source:

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