Rust Coder project and AI integration

From: aidotengineer

The Rust programming language is positioned as “the language of AGI” (Artificial General Intelligence) due to its unique characteristics that make it highly suitable for AI coders and their human assistants [00:00:03]. The focus is on an “AI first, human second” approach, reflecting the evolving landscape of code generation [00:00:13]. Michael Yu, the speaker, is involved in this initiative and showcases the Rust Coder project [00:00:21].

Why Rust for AI?

Rust has celebrated its 10-year anniversary and has consistently been the most beloved programming language in Stack Overflow’s developer surveys [00:00:53]. It is highly admired by developers, with an 82% admiration rate, significantly higher than other languages [00:01:44]. However, its “desired” usage (how much people want to use it) is lower than Python, although surprisingly higher than Go [00:02:01].

The main reason for this discrepancy is Rust’s steep learning curve for humans [00:02:34]. Its powerful compiler forces developers to write correct and optimized code from the outset, unlike languages like C++, Python, or JavaScript that are more forgiving of initial errors [00:02:48]. While challenging initially, mastering Rust leads to writing more correct code more easily [00:03:30].

Rust’s Suitability for AI

While humans prefer languages like Python for their ease of writing and quicker results, Rust is better suited for machines [00:04:20]. Brett Taylor, Chairman of OpenAI, noted Rust’s efficiency and structural orientation, which benefits from strong compiler checking and a robust type system [00:04:27].

A key advantage for AI is Rust’s tight feedback loop provided by its compiler [00:04:41]. Many Rust developers experience little debugging once a project compiles, indicating a high likelihood of correct execution [00:04:52]. This property allows the Rust compiler to provide a very good feedback loop for AI, creating an effective “reward function” for reinforcement learning models, similar to AlphaGo [00:05:14].

Therefore, Rust is a perfect fit for AI code generators [00:05:59]. In a future where most code is written by AI, generating human-incomprehensible Python or JavaScript may not be ideal. Instead, AI-generated Rust code, which is verifiable for correctness, becomes highly valuable [00:06:09].

The Rust Coder Project

The Rust Coder project aims to teach Rust to AI and enable AI to generate better Rust code [00:06:56]. It is sponsored by two internship grants from the Linux Foundation and utilizes educational materials from the Rust Foundation [00:07:03].

Goals

• For humans:
  • Make learning Rust easier through AI assistants [00:07:23].
  • Simplify writing code with Rust [00:07:28].
  • Improve Rust’s usability within IDEs [00:07:32].
• For machines:
  • Enable the generation of Rust code on the fly [00:07:39].
  • Support the belief that the path to AGI may come from code generators [00:07:45].
  • Facilitate machines generating correct Rust code by working with the compiler [00:08:02].

Demos

Demo 1: Helping Humans Learn Rust

This demo illustrates how Rust Coder assists students in learning Rust by solving complex programming problems [00:08:23].

• Process: Rust educational materials are used to generate hundreds of common Rust developer tasks [00:08:31]. These tasks are built into a knowledge base using embeddings in a vector database [00:08:48].
• Example: A student needs to write a Rust program to convert numbers to different bases [00:09:12].
  • Using Cursor’s AI assistant panel, the student provides the question along with input/output examples and specifies Rust as the language [00:09:27].
  • The CHV coder model, running on Gaia network, immediately generates the Rust code and an explanation of its structure and functions [00:09:55].
  • The generated code successfully compiles and runs, providing the correct result [00:10:50].
• Impact: This project is used by over a thousand developers in a university-based Rust camp [00:11:08]. Rust Coder can solve and explain exam questions in one shot [00:11:19].

Demo 2: Helping Humans Code Rust in the IDE

This demo focuses on the integration of AI into Development Environments and Editors to assist with Rust coding, particularly bug fixing [00:11:50].

• MCP Server: Rust Coder includes an MCP (Machine Code Protocol) server, allowing it to integrate with IDEs like Cursor [00:11:58]. This server runs locally [00:12:39].
• Tools Provided:
  • generate: Takes a description and requirements to generate a complete Rust project. It uses a vector database of common algorithms and Rust use cases to find templates and modify them [00:12:47].
  • compile and fix: Takes Rust project files, compiles them using its own Rust compiler, and if errors are found, uses its coding large language model to identify and fix the source code [00:13:26]. This process repeats until the code compiles correctly [00:14:04].
• Example: Fixing a simple syntax error in a “hello world” Rust project [00:14:32].
  • The user requests the AI agent to compile and fix the Rust project [00:14:47].
  • The Cursor IDE calls the compile and fix MCP tool [00:15:09].
  • The tool identifies the missing parenthesis, suggests the fix, and the code becomes compilable [00:15:21].
• Advantage: Unlike generic coding large language models (like those in Cursor designed for Python/JavaScript), Rust Coder’s 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 user interactions and external contributions, allowing it to fix more complex issues [00:17:14].

How it Works: The Tech Stack

The Rust Coder project is built on an integrated stack of several open-source tools [00:18:25]:

• Coding Large Language Models (LLMs): Configurable to use either commercial or open-source models, such as the Chairman Coder model, optimized with prompts tailored to the specific model [00:18:40].
• Self-Improving Knowledge Base: A core part of the system is its knowledge base of Rust compiler error messages and their fixes [00:19:15]. This database is designed to grow and become more intelligent over time through contributions [00:19:32].
• Llama Edge Project: This Linux Foundation project runs LLMs and other AI models (e.g., YOLO, Whisper, TTS, Stable Diffusion) across various GPUs and MPUs [00:19:50]. It is much smaller than PyTorch, measured in tens of megabytes [00:20:25].
• Integrated Knowledge Base: Utilizes Vortex search with Elastic Search, TiDB, and Quadrant for vector search, along with various vector embedding models [00:20:36].
• Gaia Network: A product built on Llama Edge that packages these components [00:20:59].
• Open MCP Proxy: Used to turn the Gaia network into an MCP server, enabling tool calls for LLMs [00:21:05].

Vision for the Future

The MCP (Machine Code Protocol) is primarily designed for machines, not just humans [00:21:44]. In the progression of computer users from human-centric UIs (web, desktop, mobile) to API-first approaches for deterministic computer consumption, the era of “tools” for LLMs has emerged [00:21:50]. These tools allow LLMs, which behave like humans but are fundamentally computers, to consume software services [00:23:01].

Rust Coder provides services like Rust compiler service and LLM-based bug fixing as tools for other LLMs to use [00:23:29]. The long-term vision for Rust Coder is for AI to generate and verify Rust code for complex autonomous tasks, such as directing a drone, entirely without human intervention but with guarantees of correctness [00:23:46]. This aligns with the belief that AI coders are the road to AGI, and Rust is the best language for them [00:28:49].

Getting Started and Contribution

The Rust Coder project is part of a larger initiative called “Local Rust,” which aims to provide Rust tools for computers [00:25:05]. It offers both APIs (for workflow engines and deterministic software) and MCP services (for large language models and autonomous agents) [00:25:18].

Installation and Usage

• Installation: The project’s GitHub repository allows cloning, and it can be run using docker compose up with Docker Desktop [00:25:43]. It includes an embedded vector database [00:26:04].
• APIs:
  • Generate: A JSON object with description and requirements can be passed to generate Rust project files [00:26:26].
  • Compile and Fix Errors: Project files can be sent as a single flat text file to the API, which compiles, fixes errors using the LLM, and returns the corrected project [00:27:00].
• MCP Service: The MCP service provides similar functionality via command-line MCP clients or by integrating with modern agent frameworks [00:27:37].

The project is a work in progress, with ongoing Linux Foundation internships contributing to its development. Contributions are welcome to expand the knowledge base, enhance intelligence, and create more functionalities for other agents [00:28:21].