Join us

Modular: How Mojo gets a 35,000x speedup over Python

Modular: How Mojo gets a 35,000x speedup over Python

Mojo is a fast Python library that demonstrates the use of specific compute-bound problems to generate the Mandelbrot set with an impressive number of optimizations. The first part of the blog post series discusses the basic optimization techniques used by Mojo to achieve fast performance in Python code, comparing it to pure Python code that uses the numpy library.


Let's keep in touch!

Stay updated with my latest posts and news. I share insights, updates, and exclusive content.

Unsubscribe anytime. By subscribing, you share your email with @faun and accept our Terms & Privacy.

Give a Pawfive to this post!


Only registered users can post comments. Please, login or signup.

Start writing about what excites you in tech — connect with developers, grow your voice, and get rewarded.

Join other developers and claim your FAUN.dev() account now!

Avatar

The FAUN

FAUN.dev()

@faun
The FAUN watches over the forest of developers. It roams between Kubernetes clusters, code caves, AI trails, and cloud canopies, gathering the signals that matter and clearing out the noise.
Developer Influence
3k

Influence

302k

Total Hits

3712

Posts