DevOps

vLLM is a high-throughput, memory-efficient inference and serving engine for Large Language Models (LLMs) developed by UC Berkeley’s Sky Computing Lab.

You install KVM on Ubuntu 24.04 by checking CPU virtualization support, installing the KVM/libvirt packages, enabling the libvirtd service, and (optionally) installing virt‑manager for a GUI.

A well-configured bash prompt displaying git repository information can dramatically improve your development workflow.

Universal package managers have transformed Linux software distribution, making cross-distribution compatibility a reality. Snap and Flatpak emerged as the leading solutions, each bringing distinct philosophies to solving dependency hell and distribution fragmentation.

After automatically installing a new kernel, Ubuntu 24.04 has lost the ethernet network. This frustrating issue occurred for me a second time, so I’m documenting the solution here to help others facing the same problem.

Deploying a Hugo static site to AWS S3 using the AWS CLI provides a robust, scalable solution for hosting your website. This guide covers the complete deployment process, from initial setup to advanced automation and cache management strategies.

Table-driven tests are the idiomatic Go approach for testing multiple scenarios efficiently. When combined with parallel execution using t.Parallel(), you can dramatically reduce test suite runtime, especially for I/O-bound operations.

API documentation is crucial for any modern application, and for Go APIs Swagger (OpenAPI) has become the industry standard. For Go developers, swaggo provides an elegant solution to generate comprehensive API documentation directly from code annotations.

Local deployment of LLMs has become increasingly popular as developers and organizations seek enhanced privacy, reduced latency, and greater control over their AI infrastructure.

Modern Go development demands rigorous code quality standards. Linters for Go automate the detection of bugs, security vulnerabilities, and style inconsistencies before they reach production.

Python linters are essential tools that analyze your code for errors, style issues, and potential bugs without executing it. They enforce coding standards, improve readability, and help teams maintain high-quality codebases.

As AI and ML workloads become increasingly complex, the need for robust orchestration systems has become greater. Go’s simplicity, performance, and concurrency makes it an ideal choice for building the orchestration layer of ML pipelines, even when the models themselves are written in Python.

Python packaging has evolved significantly, with modern tools and standards making it easier than ever to distribute your code.

The democratization of AI is here. With open-source LLMs like Llama 3, Mixtral, and Qwen now rivaling proprietary models, teams can build powerful AI infrastructure using consumer hardware - slashing costs while maintaining complete control over data privacy and deployment.

Prometheus has become the de facto standard for monitoring cloud-native applications and infrastructure, offering metrics collection, querying, and integration with visualization tools.

Building high-performance REST APIs with Go has become a standard approach for powering systems at Google, Uber, Dropbox, and countless startups.