How AI Shapes Rue: A New Programming Language by a Rust Veteran
A new programming language called Rue is being developed by Steve Klabnik, a long-time Rust community contributor and co-author of The Rust Programming Language. What makes Rue unusual isn’t only its goals as a systems language, but the way it’s being built: Klabnik is openly using Anthropic’s Claude as a copilot to explore design ideas, prototype compiler pieces, and iterate faster than a traditional solo effort. The result is a rare public look at what “AI-assisted language design” actually looks like when the work is real, messy, and full of tradeoffs.
- Rue is an experimental systems language being built in the open by Steve Klabnik, with Claude used as a copilot for rapid iteration.
- The project is explicitly framed as an experiment in compiler-building and in how effective a copilot can be for large, multi-stage engineering work.
- The most important lesson is not “AI can build a language,” but how to keep control: strong reviews, tests, constraints, and clear boundaries for what a copilot may decide.
Collaboration Between Human and AI in Rue’s Design
The healthiest way to understand Rue’s process is to treat Claude as a fast collaborator, not an authority. On the project’s GitHub page, Klabnik describes Rue as “just for fun” and says he’s building it for two main reasons: to play around with a compiler and to see how good Claude is at building compilers. That framing matters: it makes the collaboration explicit and keeps expectations grounded in experimentation rather than hype.
In practice, the human role doesn’t shrink. It changes. Instead of writing every line, the human becomes the editor-in-chief: deciding what the language should be, what constraints must not be violated, what corner cases matter, and what quality bar is acceptable before code is merged. That is the part that does not automate well—because it’s tied to judgment and taste, not just syntax.
- Claude is useful for: drafting implementations, suggesting parsing strategies, generating example programs, and producing quick alternatives.
- The human is responsible for: semantics, safety invariants, performance constraints, and deciding what “correct” means.
- The workflow succeeds when: decisions are reviewed, tested, and recorded so the language doesn’t drift into contradictions.
Iterative Development Enabled by AI
Language design is an iterative grind: syntax choices, parsing rules, type checking, error messages, runtime decisions, standard library shape, tooling, and packaging. AI copilots can compress the “first draft” phase of each iteration, which is exactly where solo builders usually slow down. That speed is valuable because compilers are multi-stage pipelines: a delay in one stage often blocks all later stages.
The key is to keep iteration structured rather than chaotic. When a copilot can produce plausible code quickly, it’s easy to accidentally run a project by momentum. The better pattern is a loop like this:
- State the goal clearly: “Add a parser rule for X” or “Define how Y should type-check.”
- Generate multiple options: ask for two or three approaches so you don’t anchor on the first output.
- Run tests immediately: unit tests, example programs, and regression checks after each change.
- Record the decision: keep short notes explaining why the design is the way it is.
This kind of loop highlights the real productivity advantage: not “free engineering,” but faster cycles of propose → implement → test → refine.
Advantages of AI-Assisted Language Design
Rue illustrates several benefits that show up when a copilot is used on a deep technical project rather than on small scripts:
- Faster prototyping: you can explore design space quickly, especially early in a project when many choices are still reversible.
- Less “blank page” time: compiler work often stalls on boilerplate; copilots can draft scaffolding so you can focus on semantics.
- Better exploration of alternatives: asking for multiple ways to implement a feature can reveal tradeoffs sooner.
- Improved documentation velocity: examples, explanations, and “why” notes can be drafted quickly and then corrected by the author.
In other words: AI doesn’t remove the need for expertise. It increases the amount of output that expertise can shape per day—if review discipline is strong.
Considerations and Challenges
The hardest part of AI-assisted language design is not typing. It’s trust. A copilot can confidently produce plausible-looking code that contains subtle flaws: incorrect edge cases, unsound assumptions, or behavior that violates the language’s intended invariants. In compilers, small mistakes can become huge problems because they propagate silently into generated programs.
- Semantic drift: features get added without a coherent model of how they interact.
- Error-handling gaps: “happy path” code works, but failures produce confusing diagnostics.
- Overconfidence in examples: a few examples pass, but the feature fails under real programs.
- Security blind spots: unsafe parsing assumptions or unchecked inputs can create vulnerabilities.
- Maintenance debt: fast code generation can create uneven style and hard-to-reason modules.
There is also an authorship challenge. When a copilot contributes heavily, the most important clarity is practical: the human still owns the design, the review process, and the responsibility for what merges. If that responsibility becomes ambiguous, quality usually collapses.
Broader Implications for Technology Development
Rue is bigger than a new language experiment. It’s a preview of a development style that is likely to spread: one expert using a copilot to move faster through deep technical work. If this pattern becomes common, it changes what “solo” and “small team” can attempt. It also changes what organizations should measure. The important KPI isn’t “lines of code.” It’s whether the system stays comprehensible and testable as output accelerates.
The most durable takeaway is that AI-assisted building works best when engineering fundamentals get stricter, not looser: clearer specs, more tests, stronger constraints, and better review habits. Without that, acceleration tends to amplify mistakes.
FAQ: Tap a question to expand.
▶ What role does Claude play in designing Rue?
Claude acts as a copilot: drafting implementations, suggesting approaches, and producing examples. The human author remains responsible for design decisions, correctness, and what ultimately gets merged.
▶ How does the development process work between the designer and AI?
It follows an iterative cycle: propose a feature, generate one or more candidate implementations, test quickly, and refine based on results. The key is keeping the loop structured with tests and clear decisions.
▶ What are the biggest risks when using AI in language design?
The biggest risks are semantic inconsistencies, subtle correctness bugs, weak error handling, and maintenance debt from uneven code quality. These risks are reduced by strict review and strong automated testing.
Conclusion
Rue is an experiment in both language design and development process. It shows how an experienced language designer can use a copilot to explore ideas faster, prototype compiler stages quickly, and iterate without a full team—while still needing strong human judgment to keep the project coherent. The future hinted by Rue is not “AI replaces language design.” It is “AI makes the design loop faster,” and forces developers to get serious about reviews, tests, and the discipline that keeps complex systems trustworthy.
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