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What if you could interact with your documents and get real-time, accurate answers, directly from them? In this post, we’ll dig into how we built a RAG app backed by MAX, our framework for GenAI, with Streamlit for the UI.

Hands-on with Mojo 24.5 and learn how to apply new language features in your code

We’re excited to announce the release of MAX 24.5, which ships with significant improvements to Llama 3.1 CPU performance, new Python graph API bindings, our biggest update to Mojo ever, industry-standard packaging, and a clarified license.

We are pleased to announce the release of a new tool aimed at simplifying the management of stacked pull requests (PRs) on GitHub - stack-pr. This tool is still in its early development days, but we are excited to share it with the community and welcome your contributions.

Developer tooling is a big priority for Mojo and MAX, we want to vastly improve the debugging experience compared to the traditional Python, C++, and CUDA stack. Machine learning often requires inspecting the state of a program after a long running process, requiring more control than what "print debugging" gives you. Over time this tooling will extend to GPUs, allowing you to step through CPU code into GPU calls with the same developer experience.

The adoption of AI by enterprises has surged significantly over the last couple years, particularly with the advent of Generative AI (GenAI) and Large Language Models (LLMs). Most enterprises start by prototyping and building proof-of-concept products (POCs), using all-in-one API endpoints provided by big tech companies like OpenAI and Google, among others. However, as these companies transition to full-scale production, many are looking for ways to control their AI infrastructure. This requires the ability to effectively manage and deploy PyTorch.

In today’s rapidly evolving technology landscape, adopting and rolling out AI to enhance your enterprise is critical to improving your organization’s productivity and ensuring that you are delivering a world-class product and service experience to your customers. AI is without question, the single most important technological revolution of our time—representing a new technology super-cycle that your enterprise cannot be left behind on.

The recent surge in AI application development can be attributed to several factors: (1) advancements in machine learning algorithms that unlock previously intractable use cases, (2) the exponential growth in computational power enabling the training of ever-more complex models, and (3) the ubiquitous availability of vast datasets required to fuel these algorithms. However, as AI projects become increasingly pervasive, effective development paradigms, like those commonly found in traditional software development, remain elusive.