The author suggests that Python, Go, and Rust each have a role in building AI systems: Python for developing models and prototyping, Go for production at scale, and Rust when speed is paramount. The author emphasizes the importance of choosing the right tool for the job and mentions ongoing research to make transitioning from Python to Go easier for AI applications. The aim is to build bridges between the Python and Go communities, enabling the creation of a new generation of production-grade AI-powered applications.
Key takeaways:
- The author believes Python, Go, and Rust each have a role to play in building AI-powered systems, with Python being great for prototyping, Go for production at scale, and Rust when speed is paramount.
- Python is the author's first love due to its simplicity and readability, while Go is appreciated for its performance, reliability, and scale.
- Despite Python's strengths, it doesn't scale well for large programs, teams, and systems, leading to a need for alternatives like Go in production environments.
- The author and his team are working on creating Go equivalents to Python libraries used in AI applications, aiming to complement Python and benefit both communities.