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Today, we're thrilled to announce the release of MAX 24.4, which introduces a powerful new quantization API for MAX Graphs and extends MAX’s reach to macOS. Together, these unlock a new industry standard paradigm where developers can leverage a single toolchain to build Generative AI pipelines locally and seamlessly deploy them to the cloud, all with industry-leading performance. Leveraging the Quantization API reduces the latency and memory cost of Generative AI pipelines by up to 8x on desktop architectures like macOS, and up to 7x on cloud CPU architectures like Intel and Graviton, without requiring developers to rewrite models or update any application code.

This post blog is the second part of the series of ownership in Mojo. Please make sure to check out the first part, What Ownership is Really About: A Mental Model Approach, as we will build on concepts developed there. This post serves as accompanying material for the deep dive on ownership by our CEO, Chris Lattner. Be sure to watch the video as well, which covers how ownership is implemented in Mojo's compiler, providing further insights and technical details.

Ownership is a well-known concept in modern programming languages such as Mojo that aims to provide a safe programming model for memory management while ensuring high performance. This allows programmers to build safe abstractions without the need to manually manage memory, making development more efficient and less error-prone. 

There are several clustering algorithms, but k-means — the algorithm we're going to implement from scratch in Python and Mojo🔥 in this blog post — is one of the most popular due to its simplicity and ease of implementation.

Last week, Chris Lattner sat down for an interview on the Developer Voices podcast with Kris Jenkins. It was a wide-ranging episode that explored a variety of topics, including the motivations behind creating Mojo, what it offers to both Python and non-Python programmers alike, how it is built for performance, and which performance features actually matter. This post recaps a number of highlights from the podcast, edited for clarity and brevity. You can find the full 90 minute interview on YouTube.

Mojo🔥 24.3 is now available for download and this is a very special release. This is the first major release since Mojo🔥 standard library was open sourced and it is packed with the wholesome goodness of community contributions! The enthusiasm from the Mojo community to enhance the standard library has been truly remarkable. And on behalf of the entire Mojo team, we’d like to thank you for all your feedback, discussion and, contributions to Mojo, helping shape it into a stronger and more inclusive platform for all. 

A matrix is a rectangular collection of row vectors and column vectors that defines linear transformation. A matrix however, is not implemented as a rectangular grid of numbers in computer memory, we store them as a large array of elements in contiguous memory.

This will be your example-driven guide to Mojo SDK 24.2, as part of the latest MAX release. If I had to pick a name for this release, I’d call it MAXimum⚡ Mojo🔥 Momentum 🚀 because there is so much much good stuff in this release, particularly for Python developers, adopting Mojo. 

Today, we’re excited to announce general availability of MAX 24.2, with several big features.

At Modular, open source is ingrained in our DNA. We firmly believe for Mojo to reach its full potential, it must be open source. We have been progressively open-sourcing more of Mojo and parts of the MAX platform, and today we’re thrilled to announce the release of the core modules from the Mojo standard library under the Apache 2 license!