From: aidotengineer
This article explores the role of the Rust programming language and automation in the development of Artificial General Intelligence (AGI), focusing on how Rust’s unique properties make it suitable for AI-driven code generation and the tools being built to facilitate this.
Why Rust for AGI?
Rust is highlighted as a potential “language of AGI” [00:00:03]. While the speaker acknowledges the concept of “AI first, human second” in its evolution [00:00:13], the focus is on how Rust’s characteristics align with the needs of AI agents.
Rust’s Popularity and Challenges for Humans
Rust is consistently rated as the “most beloved programming language” in Stack Overflow’s developer surveys since its inception, celebrating its 10-year anniversary [00:01:01]. Despite its high admiration (82%), its “desired” usage is lower than Python, though higher than Go [00:02:01].
The primary reason for human hesitation is Rust’s steep learning curve [00:02:34]. Its powerful compiler forces developers to write correct and optimized code from the outset, unlike languages like C++ or Python/JavaScript, which have no strong type system or compiler [00:02:48]. This rigor makes initial learning difficult for humans, but once mastered, it simplifies writing correct code [00:03:30].
Rust’s Suitability for AI
Unlike humans who prioritize ease of writing (even with potential bugs), AI benefits from Rust’s rigor [00:03:41]. As Brett Taylor, Chairman of OpenAI, noted, Rust is better suited for machines because it’s more efficient and structurally oriented due to its strong compiler checking and type system [00:04:27].
A key advantage for AI is the compiler’s tight feedback loop [00:04:41]. Rust developers experience “little debugging” once a project compiles, indicating a high likelihood of correct execution [00:04:51]. For AI, this compiler feedback provides a “very good reward function” [00:05:23], similar to reinforcement learning models like AlphaGo or AlphaZero [00:05:26]. The compiler’s acceptance of code serves as a strong, immediate signal for the Large Language Model (LLM), enabling it to learn and improve efficiently [00:05:45].
This makes Rust a “perfect fit for AI code generators” [00:06:01]. If AI is to write most of the code in the future, Rust offers a path to verifiable correctness, even if the code is hard for humans to initially comprehend [00:06:06].
The Rust Coder Project
The Rust Coder project aims to teach AI Rust and enable AI to generate better Rust code [00:06:56]. Sponsored by Linux Foundation internship grants and collaborating with the Rust Foundation, its goals include:
- For humans: Making Rust easier to learn, write, and work with in Integrated Development Environments (IDEs) [00:07:23].
- For machines: Enabling on-the-fly Rust code generation. This aligns with the belief that the path to AGI may come from code generators, where an LLM can plan and execute tasks by generating verifiable Rust code [00:07:45].
Automation for Learning Rust
The Rust Coder project facilitates human learning through an AI assistant [00:08:26]. Educational materials from the Rust Foundation are used to generate hundreds of common Rust development tasks [00:08:31]. These tasks are built into a knowledge base using embeddings and a vector database, allowing an AI agent to answer programming questions and provide Rust code solutions [00:08:46].
A demonstration showed the AI model (CHV coder on Gaia network, accessed via Cursor IDE) solving a complex problem of converting numbers to different bases [00:09:12]. The AI provides code and an explanation of its structure and functions [00:10:19]. This tool is used by over a thousand developers in a university Rust bootcamp, successfully solving exam questions in one shot and explaining the answers to learners [00:11:08].
Automation for Developing and Optimizing AI Agents
For developing and optimizing AI agents, the Rust Coder project includes an MCP (Multi-Modal Compute Protocol) server for IDE integration (e.g., Cursor IDE) [00:11:58]. This server offers two main tools:
- Generate: Takes a description and requirements (in string format) to generate an entire Rust project. It uses a vector database of common algorithms and Rust use cases to find and modify templates [00:12:47].
- Compile and Fix: This MCP tool takes Rust project files, sends them to the MCP server’s Rust compiler. If errors are encountered, it uses its own LLM to understand and fix the source code, repeating the compilation until it passes [00:13:32].
A demonstration of “compile and fix” showed the tool automatically correcting a syntax error in a “hello world” Rust project [00:14:37]. The tool sends the project files, receives the fixed code, and the IDE (Cursor) applies the changes [00:15:41].
This Rust Coder MCP server provides a fully integrated solution with its own knowledge base of Rust compiler error messages and their fixes [00:17:05]. This knowledge base self-improves as it is used and contributes to fixing more complex issues [00:17:16]. This specialized approach is considered superior to generic coding LLMs (like those in Cursor for Python/JavaScript), which may only solve easy Rust problems [00:17:53].
The integrated stack combines:
- Coding Large Language Models (commercial or open-source like
chancoder) [00:18:40]. - Optimized prompts tailored to specific models [00:18:51].
- A self-improving knowledge base of Rust compiler error messages [00:19:15]. This database holds examples of common errors and their typical fixes, designed to grow with community contributions [00:19:20].
The underlying technology stack for “Local Rust” (which includes Rust Coder) is open source:
- Llama Edge Project: A Linux Foundation project that runs various AI models (LLMs, Yolo, Whisper, TTS, Stable Diffusion) across GPUs and MPUs. It is much smaller than PyTorch, measured in tens of megabytes [00:19:50].
- Integrated Knowledge Base: Uses vector search with Qdrant, and text search with Elastic Search and TiDB [00:20:36].
- Gaia Network: A product built on Llama Edge, which integrates the knowledge base and AI models [00:20:56].
- Open MCP Proxy: Used to turn the Gaia Network into an MCP server [00:21:05].
The Shift: From Human-Centric to Machine-Centric Interfaces
The speaker outlines an evolution in how software is consumed [00:21:50]:
- Early Days / 15 years ago: Human-centric UIs (web, desktop, mobile), geared towards human interaction [00:21:54].
- API Era: “API first” approach, where services are consumed by other computers or workflow engines (e.g., Stripe, Twilio) [00:22:19].
- Tool Call Era (Current): With the rise of LLMs, software is consumed by “tools” or “MCPS” [00:23:01]. The user is now a large language model that behaves like a human but is a computer [00:23:18].
The Rust Coder project offers Rust compiler services and LLM-based bug-fixing services as tools for other LLMs to use [00:23:29].
Long-Term Vision for Automation and AGI
The long-term vision for the Rust Coder project is to enable autonomous agents [00:23:46]. For example, an AI controlling a drone could generate Rust code (using an SDK in the MCP server’s knowledge base) to direct its flight and behavior [00:23:51]. This code would then be automatically compiled and debugged until it works, uploaded to the drone, and executed, all “entirely without human intervention, but with reasonable guarantees of the correctness of the code” [00:24:14]. This represents a significant step towards AGI through AI code generation.
How to Get Started with Local Rust
The “Local Rust” program provides APIs and MCP services for working with workflow engines or LLMs/autonomous agents [00:25:05].
- Installation: Clone the GitHub repository and run
docker compose upto spin up containers and connect to the specified LLM. It includes an embedded vector database [00:25:41]. - APIs:
generate: Accepts a JSON object withdescriptionandrequirementsto generate Rust project files [00:26:26].compile and fix errors: Takes a flattened text file of project files, fixes compiler errors using the Rust compiler and LLM, and returns the whole project in the same format [00:27:00].
- MCP Service: Offers the same functionality via command-line MCP clients or integration into modern agent frameworks [00:27:37].
The project is actively being developed, with ongoing Linux Foundation internships contributing to its progress [00:28:21]. The goal is to build a larger, smarter knowledge base and create more functionalities for other agents [00:28:59].