EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·20d ago·5 min read
Writing Rust code that's fast by asking agents to make the code faster
TL;DR
Modern agentic LLMs can generate Rust code that is significantly faster than existing methods, achieving speedups of 2x to 20x depending on the application. This advancement leverages the integration of Rust with Python through PyO3, enhancing performance while maintaining safety.
✦ Why It Matters
Start using agentic LLMs to generate optimized Rust code for your performance-critical applications today.
Key Takeaways
- Use iterative prompts to guide LLMs in code optimization.
- Set clear performance benchmarks to avoid ambiguity.
- Leverage Rust's features for safety and speed.
- Benchmark results should be independent and reliable.
How It Works
The optimization process involved using LLMs to iteratively refine Rust code by setting specific performance goals, such as achieving at least a 1.2x speed increase over a baseline. The LLMs utilized the criterion crate for benchmarking, allowing for systematic performance tracking and adjustments based on statistical significance.
By encouraging the LLMs to explore unconventional optimization techniques, significant speed improvements were achieved while maintaining code quality.