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An experimental project called ts-rust (also published as tsc-rs) has used an AI coding agent to port Microsoft’s Go-based TypeScript 7 compiler, type checker and language server to Rust. Maintainer Theo Browne reports spending about $24,047 in API charges over two weeks on the successful attempt, after earlier OpenAI-model efforts exceeded $420,000. The result is promising and reportedly fast, but it remains an early, machine-generated experiment—not a proven replacement for the production TypeScript toolchain.
What the $24,000 experiment actually produced
ts-rust is software distributed through GitHub and npm under the package name tsc-rs. Its goal is behavioral compatibility with Microsoft’s native TypeScript 7 implementation: the command-line compiler, type checker, language-server behavior and public API. The Rust project began from scratch for the successful run and reached a working version in roughly 10 hours, according to Browne’s account.
The headline cost is the maintainer’s reported API-priced token usage, not a product price or a fixed cost that another team should expect. Earlier attempts using OpenAI models consumed more than $420,000 and generated over 1.3 million lines of Rust before stalling at about 84% compatibility. Browne says the later Claude Opus 5.5 run cost approximately $24,047 over two weeks and produced the current working project.
“I wanted to see if LLMs could port the TypeScript compiler, checker and lsp to Rust. Turns out they can.” — Theo Browne, project README (2026)
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How complete is the port?
The current README reports 184,773 ported Go tests passing. It also reports matching diagnostics for named real-world projects including TanStack Query core and Hono, along with matching language-server and API oracle results. These figures are claims from the project’s own test suite and compatibility checks; they are not an independent certification.
The repository is pinned to a particular TypeScript development revision. A fair comparison therefore requires using the same upstream revision on both implementations. Results can change when TypeScript’s source changes, so compatibility with the pinned snapshot should not be treated as compatibility with every future TypeScript 7 build.
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What remains uncertain
- The maintainer says he has not manually read the generated code.
- The README documents known edge cases and labels the release experimental.
- Memory behavior, security properties and long-term maintenance have not been established by the supplied results.
- Other tools can be faster in some workloads while emitting different diagnostics, so speed alone does not establish interchangeability.
“I’ve never read a line of this code.” — Theo Browne, ts-rust maintainer, project README (2026)
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How fast is tsc-rs compared with TypeScript 7?
The project’s dated benchmark was run on October 13, 2026 UTC using Blacksmith 16-vCPU Ubuntu runners. The table below reproduces the README’s T3 Code timings. “Effect diagnostics” refers to the diagnostics configuration used for the Effect workload; the second column uses the same project without those diagnostics.
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| Command | With Effect diagnostics | Without Effect diagnostics |
|---|---|---|
tsc-rs --fast |
9.77 seconds | 6.91 seconds |
tsc-rs |
14.30 seconds | 7.95 seconds |
tsc 7 |
42.55 seconds | 19.45 seconds |
tsc 6 |
191.00 seconds | Not stated in the README table |
Across six open-source applications, the README reports version 0.6.0 of tsc-rs as 2.10 times faster than tsc 7 on geometric mean. Those are repository-reported measurements under the stated hardware, software revisions, flags and diagnostics configurations. They do not predict the result for every codebase, operating system or compiler option.
What the speed claims do—and do not—prove
The results indicate that this Rust implementation can substantially reduce type-checking time on the tested workloads. The difference between --fast, ordinary tsc-rs, and the two TypeScript versions also shows why a single headline multiplier is insufficient: checker settings and diagnostics can dominate elapsed time.
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A faster compiler is useful only if its diagnostics, module resolution, language-server responses and API behavior remain acceptable for a project. The README’s matching results on selected applications are encouraging, but they do not cover every language feature, editor integration, build system or platform. The project also notes that a tool such as Bun may be faster in some cases while producing different diagnostics, illustrating the compatibility-versus-speed trade-off.
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Could Rust make TypeScript easier to deploy in WebAssembly?
The Register has identified a possible systems advantage: Rust does not require a garbage collector, which could make WebAssembly deployment inside a V8-based agent environment easier than embedding a Go runtime. That is a proposed use case, not a demonstrated production deployment by this project. The current evidence concerns compiler compatibility and benchmarks, not a finished WebAssembly integration.
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Is the AI-written compiler safe for production use?
There is not enough evidence to call it production-safe. The strongest reasons for caution are the experimental release status, the pinned development revision, documented edge cases and the maintainer’s admission that he has not read the generated source. Passing tests and matching diagnostics can reveal functional regressions, but they do not replace code review, security review, fuzzing, dependency auditing or operational testing.
A sensible evaluation path
- Pin the exact TypeScript revision used by the project and build both implementations from reproducible sources.
- Run your own project’s full type-check, build and editor-integration tests, not only the repository’s ported tests.
- Compare diagnostics, exit codes, declaration output, module resolution and language-server responses on representative repositories.
- Measure wall time and memory on your actual CI hardware, with the same compiler flags and diagnostic settings.
- Keep the established TypeScript compiler as a fallback until regressions and maintenance ownership are understood.
How to judge tsc-rs against other implementations
Use the following comparison axes rather than relying on the 2.10× figure alone:
- Compatibility: behavior against the project’s pinned TypeScript revision and against the features your code uses.
- Diagnostic parity: whether messages, locations, codes and exit behavior match your existing toolchain.
- Language-server and API coverage: editor features and programmatic consumers can fail even when command-line compilation succeeds.
- Benchmark methodology: hardware, operating system, project set, compiler flags, diagnostics and warm-up conditions.
- Memory and platform behavior: the supplied benchmark reports timing, not a complete resource or operating-system matrix.
- Maintenance model: who will track upstream TypeScript changes and fix newly discovered edge cases.
- Human review: whether generated code has been read, audited and maintained by experienced Rust and compiler engineers.
What the two spending figures really demonstrate
The contrast between more than $420,000 and about $24,047 demonstrates that agentic code-generation cost can vary dramatically with model, workflow and starting strategy. It does not establish a universal price for porting a compiler. The first effort’s 1.3-million-line codebase and roughly 84% compatibility also show that producing more code is not the same as reaching behavioral parity. The later run’s working result in about 10 hours suggests that a fresh approach can outperform a prolonged partially compatible effort, but the ongoing cost of testing, review, fixes and upstream synchronization is not included in the token bill.
Bottom line for developers
tsc-rs is a notable demonstration that an AI coding agent can generate a large Rust port of a modern compiler, checker and language server and achieve strong project-reported test and benchmark results. It is best treated as an experimental drop-in candidate for controlled evaluation. Teams should verify diagnostics and editor behavior on their own repositories, match the pinned upstream revision, measure memory and CI performance, and retain a trusted fallback before considering wider adoption.
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