Developer Advocate
Cedric Clyburn
Cedric Clyburn (@cedricclyburn), Senior Developer Advocate at Red Hat, is an enthusiastic software technologist with a background in Kubernetes, DevOps, and container tools. He has experience speaking and organizing conferences including DevNexus, WeAreDevelopers, The Linux Foundation, KCD NYC, and more. Cedric loves all things open-source, and works to make developer's lives easier! Based out of New York.
Cedric Clyburn's contributions
Video
Computer Use: How AI Agents Can Automate Almost Anything
Agentic AI is cool, but how can we make it actually do things for us? Welcome to the world of Computer Use, where your model can take action for you on your desktop, and perform actions like typing, clicking, and more.
In this demo, we run an open source, natively multimodal model (Qwen3.6, 35B sparse MoE) locally on Mac using MLX, then connect it to Hermes, an AI agent with built-in guardrails for computer use. Watch it move a chess piece, apply a Photo Booth filter, and read a graph straight out of a research paper, all by seeing the screen and clicking, typing, and navigating like a human would. Everything runs on your own hardware through an OpenAI-compatible endpoint, so nothing you see or do ever leaves your machine.
#ComputerUse #AIAgents #RedHatAI #LocalLLM #PrivateAI #OpenSourceAI
Article
Computer use: How AI agents can automate almost anything
Learn how to set up local agentic AI computer use. Run quantized models like Qwen 3.6 with Hermes to automate desktop tasks on your own terms today.
Article
Run Claude Code locally with vLLM and OpenShift AI
Deploy a self-hosted AI coding assistant with vLLM and Red Hat OpenShift AI for privacy and operational independence.
Learning path
Article
Implement GPU-as-a-Service with Kueue and NVIDIA MIG
Learn how to implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.
Article
llama.cpp vs. vLLM: Choosing the right local LLM inference engine
Learn when to use llama.cpp and vLLM for local inference of large language models (LLMs). Discover the key differences, benchmarks, and use cases for each engine.
Article
Model-as-a-Service: How to run your own private AI API
Learn how Model-as-a-Service (MaaS) solves the problem of managing AI costs, security, and models for every developer in an organization.
Blog
Learn to optimize, deploy, and benchmark LLMs with vLLM: A New Free Course
Red Hat and DeepLearning.AI have released a free hands-on course on the full LLM
Computer Use: How AI Agents Can Automate Almost Anything
Agentic AI is cool, but how can we make it actually do things for us? Welcome to the world of Computer Use, where your model can take action for you on your desktop, and perform actions like typing, clicking, and more.
In this demo, we run an open source, natively multimodal model (Qwen3.6, 35B sparse MoE) locally on Mac using MLX, then connect it to Hermes, an AI agent with built-in guardrails for computer use. Watch it move a chess piece, apply a Photo Booth filter, and read a graph straight out of a research paper, all by seeing the screen and clicking, typing, and navigating like a human would. Everything runs on your own hardware through an OpenAI-compatible endpoint, so nothing you see or do ever leaves your machine.
#ComputerUse #AIAgents #RedHatAI #LocalLLM #PrivateAI #OpenSourceAI
Computer use: How AI agents can automate almost anything
Learn how to set up local agentic AI computer use. Run quantized models like Qwen 3.6 with Hermes to automate desktop tasks on your own terms today.
Run Claude Code locally with vLLM and OpenShift AI
Deploy a self-hosted AI coding assistant with vLLM and Red Hat OpenShift AI for privacy and operational independence.
Implement GPU-as-a-Service with Kueue and NVIDIA MIG
Learn how to implement GPU-as-a-Service on Red Hat OpenShift using Kueue, NVIDIA MIG, and a custom dashboard plug-in for self-service GPU resource booking.
llama.cpp vs. vLLM: Choosing the right local LLM inference engine
Learn when to use llama.cpp and vLLM for local inference of large language models (LLMs). Discover the key differences, benchmarks, and use cases for each engine.
Model-as-a-Service: How to run your own private AI API
Learn how Model-as-a-Service (MaaS) solves the problem of managing AI costs, security, and models for every developer in an organization.
Learn to optimize, deploy, and benchmark LLMs with vLLM: A New Free Course
Red Hat and DeepLearning.AI have released a free hands-on course on the full LLM
