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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.

Joe Pamer recently joined Modular to lead the Mojo engineering team. Joe is a veteran of Meta, Apple, and Microsoft and has led large scale infrastructure projects but can never seem to escape programming languages. We thought it would be fun to dive into Joe’s background and perspective and how he sees the opportunity ahead of Mojo and MAX and the industry at large.

MAX Engine is a next-generation compiler and runtime library for running AI inference. With support for PyTorch (TorchScript), ONNX, and native Mojo models, it delivers low-latency, high-throughput inference on a wide range of hardware to accelerate your entire AI workload.

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. 

Today, we're exploring how a multimodal approach can further enhance semantic search by combining textual and visual data and we will discuss how MAX Engine can optimize multiple models for inference.

Modern software and delivery mechanisms are more vulnerable to security attacks than ever before. It is estimated that in 2024, 96% of total codebases contained open-source code with an average of 500+ components per app.

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.