Rewriting Bun in Rust

Original Article ↗ Hacker News Discussion ↗

Bun, a JavaScript runtime originally written in Zig, has been mechanically ported to Rust using Anthropic’s Claude-based tooling, reportedly in 11 days and at a token cost of about $165,000. Commenters debate whether the move reflects genuine technical needs—memory safety, stability, smaller binaries, and better tooling—or primarily serves as marketing for Anthropic’s new Fable model, especially given the heavy use of `unsafe` Rust and a rocky, community-alienating rollout. The thread also uses this case to probe broader questions about Zig’s prospects, Rust’s suitability as an AI-era systems language, and how agentic LLM coding may reshape software engineering work and rewrite economics.

Context of the Rewrite & Process

  • Bun was ported from Zig to Rust largely via Anthropic’s Fable model and Claude Code, over ~11 days, using ~50+ automated workflows and extensive test suites.
  • The rewrite is presented as mostly mechanical, function‑by‑function, with structure and data models preserved, then iteratively refined via adversarial AI reviews and human oversight.
  • The Rust version has already been running in production for Claude Code since mid‑June without obvious catastrophic failures, which some see as strong validation.

Cost and Economics

  • Token spend was ~5.9B uncached input, 72B cached input, 690M output tokens, estimated at ~$165k at API pricing.
  • Many argue this is cheaper and much faster than a small team spending a year; others counter that human teams (especially outside high‑cost regions) or weaker/cheaper models might match this at lower total cost.
  • Some note that even if this is expensive now, similar rewrites will likely get much cheaper.

Code Quality, Safety, and Maintainability

  • Reported benefits: fixed memory leaks, fewer crashes, ~20% binary size reduction (with linker optimizations), ~5% performance gains.
  • Supporters say Rust’s guarantees, compiler errors, and Miri/memory tooling give stronger safety than Zig plus style guides.
  • Critics highlight large unsafe usage (~13k instances early on), initial UB found by Miri, and argue that many unsafe blocks and weak SAFETY comments suggest misunderstood Rust invariants.
  • There is disagreement over whether AI‑generated code at this scale is truly “maintainable” or just appears stable under tests.

Impact on Zig and Language Debates

  • Some see this as bad optics for Zig: a “naive” port away from it seemingly improved stability and size.
  • Others stress Bun’s Zig code was written against an evolving pre‑1.0 language and that similar gains were possible by tightening Zig code and tooling.
  • Long subthreads debate Rust vs Zig vs C/C++ vs GC languages on safety, ergonomics, compile times, and suitability as LLM targets.

AI Tooling, “Vibe Coding”, and Future of Work

  • Many view this as a showcase of LLM‑assisted large‑scale translation when backed by a strong, language‑independent test suite.
  • Some call the process “vibe coding” (LLM‑driven without exhaustive human review); others argue translation with tests is distinct from unconstrained, speculative AI development.
  • There is active anxiety about what such capabilities mean for software jobs, especially mid‑tier roles; others expect Jevons‑style effects (more software produced, not fewer engineers).

Project Governance & Community Concerns

  • Several commenters criticize how the rewrite was communicated and merged:
    • Early assurances that the Rust branch might be thrown away vs rapid merge later.
    • No LTS or security‑fix plan for the Zig line, effectively forcing upgrades.
    • Perception that Bun is steered more by Anthropic’s marketing and internal needs than by its external community.
  • Supporters respond that pre‑merge code is supposed to be rough, regressions were documented and fixed, and results for users appear positive so far.