Best of Azure — March 2025

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    Video
    Avatar of letsgetrustyLet's Get Rusty·1y

    Microsoft is using AI to convert code to Rust...

    Microsoft is increasingly adopting Rust across its products to enhance security by preventing memory safety issues. The company's AI tools, such as the C to Safe Rust transpiler and Python to Rust translator, are accelerating code migration to Rust. Notable applications of Rust at Microsoft include Windows components, Office algorithms, and Azure services. Feedback from developers highlights both the benefits and challenges of using Rust. Overall, Microsoft's efforts are significantly boosting the adoption of this memory-safe language in the industry.

  2. 2
    Video
    Avatar of programmersarealsohumanProgrammers are also human·1y

    Next-door 10x engineer // PART 2

    A tech-savvy individual, who has isolated himself at a data center for two months, engages in a humorous conversation about new projects, using different technologies like Google Cloud, AWS, Azure, Linux, and Rust. Various tech concepts, anime-inspired resumes, cross-compiling from punch cards, and AI awareness are discussed in lighthearted yet chaotic interactions.

  3. 3
    Article
    Avatar of milanjovanovicMilan Jovanović·1y

    Streamlining .NET 9 Deployment With GitHub Actions and Azure

    Milan Jovanović explains how to set up a CI/CD pipeline for .NET 9 applications using GitHub Actions and Azure App Service. The guide includes a detailed workflow that builds, tests, and deploys applications. It covers automation of tasks like database migrations and code coverage, offering practical tips from real-world deployments. The aim is to make deployment processes reliable and efficient.

  4. 4
    Article
    Avatar of freecodecampfreeCodeCamp·1y

    How to Host Local LLMs in a Docker Container on Azure

    Learn how to host local large language models (LLMs) in a Docker container on an Azure Virtual Machine. This guide demonstrates setting up a virtual machine, configuring network security, installing Docker, and running AI models within containers. The process involves creating scripts for automation and ensuring that AI models are easily manageable and can be run without overwhelming local resources.

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