Rust Contributor Explores AI-Assisted Compiler Development with New Rue Language

Steve Klabnik, known for authoring The Rust Programming Language and contributing to the Rust project for over 13 years, has announced Rue, a systems programming language that explores memory safety without garbage collection while prioritizing developer ergonomics over Rust's complexity. The project, developed with substantial assistance from Anthropic's Claude AI, targets an underserved design space between high-performance systems languages and garbage-collected alternatives.

Marking his thirteenth anniversary using Rust, Klabnik explained his motivation in a blog post: "I have long been wondering if I should try and make my own language. I really like them! That's part of why I got involved in Ruby and then Rust in the first place!" The language name follows his "Ru" prefix pattern (Ruby, Rust, Rue) while maintaining dual interpretations—both a flower and an expression of regret.

The Core Design Tradeoff

Klabnik's central design question frames the language's entire philosophy: "What if Rust wasn't trying to compete with C and C++ for the highest performance possible? What if we were willing to make things a little bit, but not too much, less performant, in exchange for ease of use?"

The technical approach centers on eliminating Rust's signature feature—the borrow checker. Consider typical Rust code where you try to modify a vector while holding a reference to one of its elements. The compiler rejects this because the reference might become invalid. Rue sidesteps the entire problem by using "inout" parameters that temporarily transfer ownership, similar to Swift.

In Rust, attempting to modify a vector while iterating over it fails at compile time. Rue's inout parameters let you temporarily pass mutable references, but prevent storing them in data structures; eliminating the need for lifetime tracking while maintaining memory safety through simpler restrictions. Functions can mutate values in place, but those values can't be stored as references in heap-allocated structures. No lifetime annotations needed.

The tradeoff becomes apparent in the design documentation: certain patterns become impossible to express. Rue can't support iterators that borrow from their containers; they have to consume them instead. This represents a fundamental constraint in the language's expressiveness.

Ownership Models and Expressiveness Loss

According to design proposals in the GitHub repository, Rue implements four distinct ownership modes:

1. Value types - Simple copy semantics
2. Affine types - Used once, then consumed
3. Linear types - Must be used exactly once
4. Reference-counted types - For shared ownership

Klabnik acknowledged in responses that "there is going to be some expressiveness loss. There is no silver bullet." This honesty about tradeoffs reflects a mature approach to language design, recognizing that every decision in systems programming involves balancing competing concerns.

The Hacker News community responded with both interest and skepticism. One commenter captured the challenge: "Rust has only succeeded in making a Memory-Safe Language without garbage collection by introducing significant complexity (which was a trade-off). No one really knows a sane way to do it otherwise, unless you also want to drop the general-purpose systems programming language requirement."

AI-Assisted Development Methodology

The development methodology represents an experiment addressing a question Klabnik has pondered for years: "Without funding or a team, can a single person still build a programming language?" The approach marks a shift for Klabnik, who described himself as an AI skeptic until 2025.

His first attempt at building Rue without effectively leveraging AI had to be abandoned after months of work. This iteration, using Anthropic's Claude AI more effectively, produced approximately 70,000 lines of working Rust compiler code in two weeks, far outpacing his previous multi-month attempt.

The collaboration goes beyond typical coding assistance. In blog posts co-credited to both Klabnik and Claude, the AI described writing most of the implementation code while Klabnik directed the architecture and made design decisions.

Klabnik emphasized that effectively using AI tools requires substantial skill: "Simply knowing how to write code isn't actually enough to truly use large models well. They are a new category of tools in their own right." His approach involved iterative experimentation, writing short code snippets, starting conversations, and testing different prompting strategies.

Whether this model can eliminate the need for the substantial investments that historically funded language projects remains to be seen. The experiment comes as AI-assisted development tools reshape software engineering. While GitHub Copilot and similar tools assist with incremental coding, Klabnik's approach using AI for architectural-scale work on a compiler represents a different level of collaboration.

Current State and Technical Implementation

Rue remains in early development with basic control flow, functions, and non-generic enums. It compiles to native executables via custom backends rather than LLVM, achieving fast compile times through simplified semantics. Heap allocation is in progress, while Language Server Protocol support, package management, and concurrency models are not yet implemented.

The project uses Buck2 rather than Cargo for future compiler bootstrapping, representing a departure from Rust's ecosystem tools. This choice likely reflects both performance considerations and the desire to avoid circular dependencies during compilation.

Klabnik maintains modest expectations: "I don't expect it to grow to anything more than my hobby project." Still, he noted that Rasmus Lerdorf and Graydon Hoare, creators of PHP and Rust, also started with personal experiments.

Community Response and Design Space

The Hacker News discussion revealed both excitement and caution about Rue's approach. One commenter noted: "I'll be very interested if they find a new unexplored point in the design space, but at the moment, I remain skeptical."

This skepticism reflects the broader challenge in language design: finding genuine innovation rather than incremental variation. Rue's attempt to occupy a middle ground between Rust's complexity and garbage-collected languages' performance characteristics represents a specific point in the design space, but its practical utility depends on whether developers find the tradeoffs acceptable.

The real test will be whether developers frustrated by Rust's learning curve but unwilling to adopt garbage collection find Rue's tradeoffs acceptable. For systems programming tasks where iterator patterns are central to the solution, Rue's inability to support borrowing iterators could be a significant limitation.

Implications for Language Development

If successful, Rue could suggest that complex infrastructure projects traditionally requiring large teams might become feasible for skilled individuals with AI assistance. This represents a potential shift in how programming languages are created and maintained.

However, the experiment also raises questions about the sustainability of such projects. Language development requires long-term maintenance, community building, and ecosystem development—activities that traditionally benefit from dedicated teams and funding. Whether a solo developer with AI assistance can sustain a language project remains an open question.

The Rue language documentation is available at rue-lang.dev, and the source code is on GitHub. As the project evolves, it will serve as a fascinating case study in both language design tradeoffs and the practical limits of AI-assisted software development.

Steve Klabnik, longtime Rust contributor and author, explores whether simpler ownership semantics can provide memory safety without Rust's complexity, using AI to accelerate the development process.