Building with LLMs: How Spec-Kit Changed My Workflow Over the past three years, I’ve used LLMs—such as ChatGPT, Copilot, and Claude—primarily as assistants to generate code snippets for very specific requirements. They were useful, but always in a limited way: small pieces of logic, isolated helpers, or syntax scaffolding. What I didn’t do until recently was build an entire system around an LLM. The main reason was simple: most of my real work lives inside internal frameworks, legacy systems, and opinionated architectures.

Introduction Several days ago, I setup ollama on my MacBook M4, and it works pretty well. At that time, I tried to use it with copilt with local models codegemma:7b and qwen3:8b. My expectation was not so high as the hardware configuration of my macbook pro m4 is just a entry level, just want to see how it works. I also learned there are other options such as vllm. After comparing the two, I found vllm is more flexible, powerful, product-ready, used widely in enterprises.

After evaluating several local Kubernetes solutions I encountered repeated manual steps (downloading bootstrap images, pulling images from registry mirrors and retagging them, and loading images into clusters) that were time-consuming and error-prone. I decided to set up a properly air-gapped Kubernetes cluster using kubeadm and containerd, leveraging containerd’s registry mirror support. This post documents the steps I followed. Prerequisites This guide assumes a Debian/Ubuntu host. Installing the latest Docker Engine will also provide containerd as a dependency.