Challenges and benefits of using Rust

From: aidotengineer

Rust is a programming language that has been praised for its distinct qualities and is considered by some to be the “language of AGI” (Artificial General Intelligence) [00:00:03]. It recently celebrated its 10-year anniversary [00:00:53].

Human Perception and Challenges

Since Stack Overflow began its developer survey, Rust has consistently been voted the most beloved programming language every single year for the past decade [00:01:09]. In recent surveys, 82% of developers admire and love Rust, which is significantly higher than other languages on the list [00:01:50].

However, despite its popularity, Rust is not as widely desired for use by developers compared to languages like Python [00:02:06]. This stems from a significant challenge:

• Steep Learning Curve [00:03:34]: For humans, Rust has a very steep learning curve [00:03:34]. Its powerful compiler forces developers to write correct and optimized code from the outset, which can be difficult for beginners [00:03:17]. While this rigor ensures correctness once mastered, the initial hurdle can be challenging to overcome [00:03:35]. This contrasts with languages like C++, Python, or JavaScript, which have less stringent compilers or no compilers at all, allowing for more “anything goes” coding [00:03:03].

This human-centric view often prioritizes ease of writing and quicker results, even if it leads to bugs or difficult-to-maintain code, which is why Python and JavaScript are so popular [00:03:57].

Benefits for Machines and AI

Rust’s characteristics that make it challenging for humans paradoxically make it ideal for machines and AI. As Brett Taylor, Chairman of OpenAI, noted, while humans prefer Python, Rust is better suited for machines [00:04:20]:

• Efficiency [00:04:29]: Rust is more efficient in its execution [00:04:29].
• Structural Orientation and Rigor [00:04:32]: Its strong compiler checking and type system make it more rigorous and structurally oriented [00:04:32].
• Tight Feedback Loop for AI [00:04:41]: The Rust compiler provides a tight feedback loop for AI [00:04:41].
  • Once a Rust project compiles, there’s a high likelihood it will run correctly and as intended, requiring little debugging [00:04:52].
  • This compiler feedback creates a very good “reward function” for AI, especially in reinforcement learning contexts similar to AlphaGo or DeepSeek [00:05:23]. The compiler’s acceptance of code serves as a strong, correct answer, allowing large language models (LLMs) to learn and improve rapidly [00:05:43].
• Ideal for AI Code Generators [00:06:01]: Rust is a perfect fit for AI code generators [00:06:01]. In a future where AI writes most of the code, Rust allows AI to generate code that is hard for humans but easy for AI, and crucially, whose correctness can be fairly easily verified [00:06:06]. This is preferable to AI writing incomprehensible Python or JavaScript, whose primary benefit is human comprehensibility [00:06:12].

Rust’s Role in AGI Development

The path to AGI may significantly involve code generators [00:07:45]. If an LLM needs to execute a task, it could generate Rust code to perform it [00:07:53]. This includes complex scenarios like directing a drone’s flight or behavior using Rust SDKs, with the code automatically compiled and debugged until it works, all without human intervention, but with reasonable guarantees of correctness [00:23:51]. This vision positions Rust as central to the future of AI-driven code generation [00:29:07].

The Rust Coder Project

To leverage Rust’s strengths for AI, the Rust Coder project was initiated with the goal of teaching Rust to AI and enabling AI to generate better Rust code [00:06:56]. This project is sponsored by Linux Foundation internship grants and uses educational materials from the Rust Foundation [00:07:03].

The project aims to:

• For Humans: Make Rust easier to learn and work with, including in IDEs, through AI assistance [00:07:23].
• For Machines: Enable on-the-fly generation of correct Rust code [00:07:39].

Demos and Functionality

The project demonstrates two key functionalities:

1. AI Assistant for Learning Rust:

   • Rust education materials and hundreds of common Rust developer tasks are built into a knowledge base using embeddings in a vector database [00:08:31].
   • An AI agent can answer programming questions or fulfill programming tasks in Rust [00:08:55].
   • Example: An AI coder model (like the chancoder model running on the Gaia network, accessed via Cursor IDE) can solve complex problems like converting numbers to different bases, providing both the correct code and detailed explanations [00:09:12]. This system is used by over a thousand developers in a university-based Rust bootcamp [00:11:08].

2. AI-Powered Coding Large Language Models and Rust in IDEs:

   • The Rust Coder includes an MCP (Machine Code Protocol) server that integrates with IDEs like Cursor [00:11:58].
   • Generate Tool: Allows users to provide a description and requirements, and the tool generates an entire Rust project based on templates and a vector database of common algorithms and use cases [00:12:47].
   • Compile and Fix Tool: This tool takes Rust project files, sends them to the MCP server for compilation, and if errors are encountered, uses its own coding large language model to analyze compiler error messages and fix the source code [00:13:51]. It iteratively recompiles until the code is correct [00:14:04].
     • Example: A simple “hello world” Rust project with a syntax error (missing closing parenthesis) was automatically fixed by the compile and fix tool, which identified the error and provided the corrected code [00:14:32].
   • The Rust Coder MCP server is a fully integrated solution with its own knowledge base of Rust compiler error messages and how to fix them [00:17:05]. This knowledge base grows over time as it learns from fixes and user inputs, becoming more intelligent at Rust development than generic coding LLMs [00:17:16].

Technical Stack

The Rust Coder project is built on an open-source tech stack [00:19:48]:

• Llama Age: A Linux Foundation project that runs a variety of AI models (including LLMs, Yolo, Whisper, Stable Diffusion) across different GPUs and MPUs [00:19:50]. Unlike PyTorch, its runtime is measured in tens of megabytes, not gigabytes [00:20:25].
• Integrated Knowledge Base: A core part of the system, it uses Vortex search (with Elastic Search and TiDB) and vector search (with Qdrant), along with various vector embedding models [00:20:36]. This database contains examples of Rust compiler error messages and their typical fixes, designed to be community-contributed for continuous improvement [00:19:15].
• GIA Network: A product built on Llama Age that packages these components [00:20:59].
• Open MCP Proxy: Used to turn the system into an MCP server for integration with modern agent frameworks [00:21:05].

The project offers both API services (for workflow engines and deterministic software) and MCP services (for large language models and autonomous agents) [00:25:18]. It can be easily installed and run via Docker Compose [00:25:41].

The Rust Coder project is an ongoing work, with continuous progress tracked on GitHub, and welcomes contributions to expand its knowledge base and functionalities [00:28:21].