Updates and recent posts about vLLM..

Posts
Description

Activity

@work4bots started using tool Spring , 2 weeks, 3 days ago.

Activity

@work4bots started using tool Helm , 2 weeks, 3 days ago.

Activity

@work4bots started using tool Azure Pipelines , 2 weeks, 3 days ago.

Activity

@work4bots started using tool Azure Kubernetes Service (AKS) , 2 weeks, 3 days ago.

Activity

@work4bots started using tool Azure , 2 weeks, 3 days ago.

Activity

@work4bots added a new tool Bicep , 2 weeks, 3 days ago.

Story FAUN.dev() Team

@eon01 shared a post, 2 weeks, 3 days ago

Founder, FAUN.dev

AWX in Action is out, and there's a course

#red hat... #ansible #Tower #AWX

"AWX in Action: Ansible Orchestration at Scale" is now available in print and ebook. It covers running AWX on Kubernetes for real, not a sandbox demo that falls over the moment you add a second execution node.

Link

@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

GitHub breach: The development ecosystem is in the hot seat

GitHub is reeling from an infrastructure breach by TeamPCP, highlighting the vulnerability of developer environments. Privileged access was achieved not through traditional perimeter exploitation, but by targeting trusted developer tools like IDE extensions. This incident serves as a stark reminder .. read more

Link

@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

When Code Becomes Cheap, What's Left?

Teams that use Claude Opus 4.6 for spec-driven development generate code at low cost, so they spend scarce developer time on review and QA. Developers create more value by judging code than by typing it... read more

Link

@varbear shared a link, 2 weeks, 3 days ago

FAUN.dev()

Design Patterns Are Dead. Long Live Design Patterns.

Design patterns were created for human comprehension, not machines, serving as a shared vocabulary to communicate complex ideas quickly, manage working memory, and standardize solutions. Even in the era of AI-generated code, design patterns are crucial for containing the limitations of AI models and.. read more

vLLM is an advanced open-source framework for serving and running large language models efficiently at scale. Developed by researchers and engineers from UC Berkeley and adopted widely across the AI industry, vLLM focuses on optimizing inference performance through its innovative PagedAttention mechanism — a memory management system that enables near-zero waste in GPU memory utilization. It supports model parallelism, continuous batching, tensor parallelism, and dynamic batching across GPUs, making it ideal for real-world deployment of foundation models. vLLM integrates seamlessly with Hugging Face Transformers, OpenAI-compatible APIs, and popular orchestration tools like Ray Serve and Kubernetes. Its design allows developers and enterprises to host LLMs with reduced latency, lower hardware costs, and increased throughput, powering everything from chatbots to enterprise-scale AI services.