From: aidotengineer
Rust is proposed as a foundational language for artificial general intelligence (AGI) [00:00:03]. The focus is on creating Rust tools for AI coders, with human assistants playing a secondary role [00:00:10]. This perspective suggests a future where AI leads in code generation, making Rust a critical component due to its inherent properties [00:00:13].
Why Rust for AGI?
Rust, a programming language that recently celebrated its 10th anniversary, has consistently been ranked as the most beloved programming language in Stack Overflow’s developer surveys [00:52:00]. Despite its popularity, Rust is not as widely used as languages like Python or JavaScript for general programming [02:06:00]. This is primarily due to its steep learning curve for humans [03:34:00].
The challenge for humans stems from Rust’s powerful compiler, which enforces strict correctness and optimization from the outset [02:48:00]. Unlike languages such as C++, Python, or JavaScript, which may allow for bug-prone or unoptimized code to run, Rust demands rigorous adherence to its strong type system and structural orientation [03:01:00]. However, once the learning curve is overcome, writing correct and robust code in Rust becomes significantly easier [03:26:00].
Rust’s Suitability for Machines (AI)
While the learning curve is steep for humans, these very characteristics make Rust exceptionally well-suited for AI. Brett Taylor, Chairman of OpenAI, noted that while humans prefer Python for its ease of writing, Rust is better for machines due to its efficiency and structural rigor [04:05:00].
Key advantages for AI include:
- Efficiency and Rigor: Rust is structurally oriented for strong compiler checking and a robust type system [04:27:00].
- Tight Feedback Loop: The Rust compiler provides a very tight feedback loop [04:41:00]. A common experience for Rust developers is that once a project compiles, it has a high likelihood of running correctly as intended [04:52:00].
- Reward Function for AI: This property of the Rust compiler offers a clear “reward function” for AI in a reinforcement learning context [05:14:00]. When an AI generates Rust code, the compiler’s acceptance of the code serves as an immediate and strong feedback signal, allowing the AI to learn and improve rapidly [05:43:00].
In a future where AI generates most code, Rust is seen as a superior choice compared to human-comprehensible languages like Python or JavaScript. While these languages benefit from human readability, if code is primarily written by AI, a language that is difficult for humans but easy for AI, and whose correctness can be easily verified, becomes more desirable [06:06:00].
The Rust Coder Project
To leverage Rust’s potential for AI, the Rust Coder project was initiated with the specific goal of teaching Rust to AI and enabling AI to generate better Rust code [06:47:00]. This open-source project is sponsored by Linux Foundation internship grants and uses educational materials from the Rust Foundation [07:02:00].
The Rust Coder project has dual goals:
- For Humans: To make Rust easier to learn and work with, including facilitating AI assistance for learning Rust and integrating Rust into Integrated Development Environments (IDEs) [07:20:00].
- For Machines: To enable the generation of Rust code on the fly. This aligns with the belief that the path to AGI may involve code generators, where large language models plan and execute tasks by generating code [07:39:00]. The project aims to make it easier for machines to generate correct Rust code by working closely with the compiler for verification [08:02:02].
Demos of Rust Coder in Action
The project demonstrates its capabilities through two main applications:
1. Helping Humans Learn Rust
This demo showcases an AI agent’s ability to assist human learners with Rust programming tasks [08:23:00]. The process involves:
- Taking Rust educational materials and generating hundreds of common Rust development tasks [08:31:00].
- Building a knowledge base by creating embeddings and storing them in a vector database [08:46:00].
- Enabling a system where a user can ask programming questions or tasks, and the AI agent provides a Rust answer [08:52:00].
An example shown is a complex problem of converting numbers to different bases. The AI, running on a Gaia network using a ‘chancoder’ model, can immediately provide the correct Rust code and a detailed explanation of its structure and functions [09:08:00]. This system has been used by over a thousand developers in a university-based Rust boot camp, successfully solving exam questions in one shot and explaining the solutions to learners [11:08:00].
2. Helping Humans Code Rust in IDEs
This demonstration focuses on the integration of Rust Coder as an MCP (Machine Code Protocol) server within an IDE like Cursor [11:58:00]. The MCP server provides tools to:
- Generate new projects: Based on a description and requirements, it can generate entire Rust projects by drawing from a vector database of common algorithms and use cases [12:47:00].
- Compile and fix existing projects: This tool allows sending Rust project files to the MCP server. The server compiles them using its own Rust compiler. If errors occur, it uses a large language model to identify and attempt to fix the source code, repeating the compilation and fixing process until the code compiles correctly [13:28:00].
A simple example involves fixing a syntax error in a “hello world” Rust program. The compile and fix tool automatically identifies and corrects the error, demonstrating a tight feedback loop where the AI works with the Rust compiler to achieve correctness [14:32:00]. This integrated solution has its own knowledge base of Rust compiler error messages and how to fix them, and it self-improves as it is used, learning from new fixes or human corrections [17:02:02].
Underlying Technology Stack
The Rust Coder project is built on an open-source integrated stack:
- Coding Large Language Models: Configurable to use commercial or open-source models (e.g., ‘chancoder’ model optimized for specific prompts) [18:40:00].
- Self-Improving Knowledge Base: A core part of the system that contains Rust compiler error messages and examples of how to fix them, continually growing and becoming more intelligent with contributions [19:15:00].
- Llama Edge: A Linux Foundation project runtime for large language models and AI models, much smaller than PyTorch [19:50:00].
- Gaia Network: A product built on Llama Edge that integrates a knowledge base with various search capabilities (vortex search with ElasticSearch/TiDB, vector search with Qdrant, and various vector embedding models) [20:56:00].
- Open MCP Proxy: Used to turn the Gaia Network into an MCP server [21:05:00].
Rust and the Future of AI Agents
The vision extends beyond human-centric tools. While currently used to assist humans, MCP (Machine Code Protocol) is fundamentally designed for machines [21:39:00]. Just as APIs enabled computers to consume services deterministically, tool calls and MCPs allow large language models to consume services like humans, but as computers [23:01:00].
This means providing Rust compiler services and LLM-based bug-fixing services as tools for other LLMs to use [23:31:00]. The long-term vision for the Rust Coder project is to enable autonomous AI agents. For example, an AI controlling a drone could generate Rust code on the fly using an SDK from the MCP server’s knowledge base. This code would then be automatically compiled and debugged until it works, uploaded to the drone, and executed without human intervention, all with reasonable guarantees of correctness [23:46:00].
This project is part of a broader “Local Rust” initiative, providing Rust tools for computers via APIs for workflow engines and MCP services for large language models and autonomous agents [25:05:00]. The ultimate belief is that the road to AGI involves AI coders, and Rust is the optimal language for them, being far superior for AI than Python or JavaScript [28:46:00]. The goal is to build a larger, smarter knowledge base and create more functionalities for other agents to use [28:59:00].