Building AI applications in Rust

Name: Building AI applications in Rust
Uploaded: 2026-08-28T06:06:11.000Z
Duration: 1 h 46 min 48 s
Channel: JetBrains

JetBrainsAugust 28, 20261h 46m

Topics56

Introduction to the Rust and AI Series0:10 Guest Introduction: Stefan Cornfki0:30 Background in Rust2:30 Background in AI and Computational Linguistics5:03 Early AI Ecosystem8:32 Video Setup and Bevy Background10:30 Why Rust is Good for AI Development15:00 Borrow Checker and Productivity19:30 Favorite Rust Features22:03 Introduction to Rig23:03 Becoming Rig Maintainer25:32 Rat Code Demo Application27:00 Async Usage in Rig29:00 Completions API and Agents30:31 Tools in Rig32:00 Agent Tool Integration34:03 Tool Parameters and JSON Schema34:31 Tool Context System35:32 Basic Tool Implementation36:30 Minimal Coding Agent Design37:02 Rig Hook System37:32 Streaming API Implementation38:30 Max Turns Configuration39:32 Rust Traits and Type System42:02 Abstraction Philosophy44:32 Multi-Agent Architecture46:00 Database Integration and RAG47:35 RAG Applications50:00 Provider Endpoint Traits51:30 Testing Framework53:02 Rig Candle Integration56:03 Production Usage58:32 Rig Advantages1:00:32 Local Model Support1:02:02 Rust Limitations for AI1:03:03 AI-Assisted Development1:05:00 AI Impact on Code Quality1:07:01 AI as a Learning and Development Accelerator1:09:00 Context Switching with Multiple Agents1:09:31 Daily Workflow and Setup1:10:32 Code Review Process1:12:02 Satisfaction and Enjoyment of Programming1:14:04 Understanding AI-Generated Code1:17:32 Importance of Deep Technical Knowledge1:20:00 Reasons for Anti-AI Sentiment1:21:32 Recommendations for Trying AI Tools1:26:30 Cost and Dependency Concerns1:27:33 Future Tooling Evolution1:30:00 Short-term Excitement: Bevy Integration1:32:33 Rust's Growing Role in AI Development1:34:32 Bevy's AI Policy Change1:36:32 Open Source AI Challenges1:38:32 Avoiding Over-Engineering from AI Reviews1:41:00 Managing AI-Generated Changes in Pull Requests1:42:16 Session Conclusion and Resources1:43:31 Acknowledgments and Final Remarks1:45:02

In a Nutshell

Rust is the best language for AI development because its compiler, borrow checker, and static analysis provide clear guidance that benefits both humans and AI-generated code. Rig is a Rust library that abstracts LLM provider differences, enabling unified AI application development with support for agents, tools, RAG, and local models via Candle integration. AI coding tools have transformed development workflows, allowing developers to manage multiple projects simultaneously while maintaining code quality through rigorous review processes, though the Rust community faces ongoing challenges with AI-generated contributions and open source maintenance.

AI-Generated Notes

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Orhun, Rust developer advocate at JetBrains and lead maintainer of Ratatui, introduces a four-part series on Rust and AI collaboration with the Rust Foundation. The series explores multiple angles including using Rust to build AI-powered applications and using AI to build software in Rust. Multiple voices from the Rust community will examine both opportunities and challenges.

Stefan Cornfki, known online as Gold, Silver, Copper, joins as the lead maintainer of Rig, a Rust library for interacting with LLMs that supports major providers, AI agents, and various database integrations. He has been an open source developer for approximately 10 years.

Stefan initially heard about Rust at version 1.0 in 2015 but found it too new to adopt. He began programming in Rust full-time around 2023, motivated by his desire to consolidate C, Python, and JavaScript into a single language. He notes that Rust has evolved significantly since its early days, with the borrow checker becoming much more powerful and the compiler handling lifetime annotations automatically.

Stefan's background is in computational linguistics and natural language processing. He has been familiar with text generation techniques for over a decade, with early interests in text games like Zork from the 1970s-1980s written in Lisp. He began using machine learning around 2021 with the Spacy library for syntax tree generation, which impressed him with its accuracy compared to handrolled parsers. He was familiar with LLMs as early as 2019, including GPT-2 models and AI Dungeon, an early LLM-powered text game.

In the early days with GPT-2, AI was considered more of a toy than a serious tool. The ecosystem relied on a few libraries like Kold AI for inference, and developers often used Kaggle and Google Colab for better hardware access. Coding models existed but were largely unusable. AI-assisted programming has improved significantly in the last two years.

Stefan's video setup uses Bevy with Ratatui rendering to a texture on a TV model, with a machine learning model converting webcam input into 3D bones for tracking. His background with Ratatui stems from wanting to export TUI applications to the web and use them in GUI contexts, which was difficult in Python but possible in Rust due to Ratatui's abstractions allowing custom rendering backends.

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Building AI applications in Rust

Introduction to the Rust and AI Series00:10

Guest Introduction: Stefan Cornfki00:30

Background in Rust02:30

Background in AI and Computational Linguistics05:03

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