From: aidotengineer
Rust is presented as the language of artificial general intelligence (AGI) [00:03:00], emphasizing an “AI first, human second” approach [00:13:00]. This perspective highlights Rust’s unique advantages for AI code generation over languages like Python and JavaScript.
Rust’s Characteristics
Rust, a programming language celebrating its 10-year anniversary [00:53:00], has consistently been the most beloved programming language in Stack Overflow’s developer survey for the past decade [01:09:00]. It boasts an 82% admiration rate, significantly higher than other languages on the list, including Python, JavaScript, SQL, HTML, TypeScript, and Go [01:50:00].
Human Learning Curve
Despite its high admiration, Rust’s desired usage is not as high as Python’s [02:06:00], though it is higher than Go’s [02:11:00]. This is largely attributed to Rust’s steep learning curve for humans [02:34:00]. Its powerful compiler forces developers to write correct and optimized code from the outset [02:48:00].
In contrast:
- Languages like C++, Python, and JavaScript allow for bug-prone or non-optimal code because they lack strong type systems or even compilers [03:01:00].
- Python and JavaScript are popular because they are easy to write and yield quicker results, even if the code may be harder to maintain or bug-prone [03:57:00].
Once mastered, writing correct code in Rust becomes easier than in other languages, but the initial hurdle is significant [03:26:00]. A key benefit for Rust developers is that there is “little debugging once the project compiles” [04:49:00].
Suitability for AI
The challenges Rust poses for human developers become advantages for AI. Brett Taylor, Chairman of OpenAI, noted that while humans prefer Python, Rust is better suited for machines [04:07:00].
Reasons for Rust’s suitability for AI:
- Efficiency: Rust is more efficient [04:29:00].
- Structural Orientation: Its strong compiler checking and type system make it more rigorous [04:32:00].
- Tight Feedback Loop: The Rust compiler provides an excellent feedback loop for AI, akin to a strong reward function in reinforcement learning [04:41:00]. This property enables AI to generate correct code effectively [05:19:00].
- Ideal for AI Code Generators: Rust is described as a “perfect fit for AI code generators” [05:59:00]. In a future where most code is AI-written, Rust is preferable to “human incomprehensible Python or JavaScript,” whose primary benefit is human comprehensibility [06:09:00]. For AI, Rust allows for code that is hard for humans but easy for AI to verify for correctness [06:22:00].
The Rust Coder Project
The Rust Coder project aims to teach Rust to AI and improve AI-generated Rust code [06:56:00]. It’s sponsored by Linux Foundation internship grants and uses educational materials from the Rust Foundation [07:03:00].
The project’s goals are twofold:
- For Humans:
- Make AI assistants help humans learn Rust more easily [07:23:00].
- Assist in writing and debugging Rust code [07:28:00].
- Integrate with IDEs for a smoother Rust development experience [07:32:00].
- For Machines:
- Enable the generation of correct Rust code on the fly [07:40:00].
- The path to AGI may involve code generators, where large language models call APIs or generate code to perform tasks [07:45:00].
Demos and Functionality
The Rust Coder project offers capabilities for both human and AI assistance:
-
Learning Rust Assistance: The system builds a knowledge base from Rust educational materials and common tasks, allowing an AI agent to provide Rust code answers and explanations for programming questions [08:31:00]. This functionality has been used by over a thousand developers in a university-based Rust camp [11:08:00], capable of solving and explaining complex exam questions [11:15:00].
-
Coding Assistance in IDEs: The project includes an MCP (Machine Code Programming) server that integrates with IDEs like Cursor [11:58:00].
- Generate Tool: Allows users to describe a project and its requirements, prompting the system to generate an entire Rust project based on templates and common algorithms from its vector database [12:47:00].
- Compile and Fix Tool: This tool takes Rust project files, compiles them, and if errors are encountered, uses its own large language model and knowledge base of Rust compiler error messages to fix the source code [13:28:00]. This process repeats until compilation is successful [14:06:00]. This is presented as a more effective solution for Rust than generic coding large language models, which are often optimized for Python and JavaScript [17:53:00].
Technical Stack and Future Vision
The Rust Coder project is an integrated stack of tools, leveraging a self-improving knowledge base of Rust compiler error messages [19:15:00]. It uses open-source components:
- LlamaEdge Project: A Linux Foundation project for running large language models and other AI models (like YOLO, Whisper, TTS, Stable Diffusion) across various GPUs and MPUs [19:50:00]. It has a significantly smaller runtime footprint (tens of megabytes) compared to PyTorch (gigabytes) [20:25:00].
- Integrated Knowledge Base: Utilizes vortex search (Elastic Search, TiDB) and vector search (Qdrant) with various vector embedding models [20:36:00].
- Gai Network: A product built on LlamaEdge that packages these components [20:59:00].
- Open MCP Proxy: Used to turn the system into an MCP server [21:05:00].
The long-term vision for Rust Coder is to facilitate a future where machines, particularly autonomous agents, are the primary users of software [21:44:00]. This builds upon the evolution of computing interfaces from human-centric UIs to APIs for other computers, and now to “tool calls” for large language models [22:00:00]. Rust, with its correctness guarantees, is seen as crucial for scenarios like AI-controlled drones generating and debugging their own Rust code to perform tasks without human intervention [23:46:00].
The project is part of “Local Rust,” a set of Rust tools for computers, providing both APIs for workflow engines and MCP services for large language models to enable autonomous agents [25:05:00]. Installation is simplified via Docker Compose [25:47:00]. This ongoing work aims to build a larger and smarter knowledge base for Rust in AI development, creating more functionalities for AI agents [28:21:00].