I have been using GitHub Copilot and Copilot CLI for about one year. During that time, I also experimented with several developer tools and AI-assisted coding technologies. Recently I became interested in exploring Claude Code and related tools such as Claude Cowork. However, I did not want to immediately commit to a subscription before understanding whether Claude Code actually fits my workflow. So I spent some time experimenting with different ways to run Claude Code without paying for a subscription first.

Things are changing incredibly fast, and AI is undeniably the hottest topic right now. There’s no real option to stand still—the only sensible choice is to embrace the change and learn by doing. Recently, OpenClaw has stood out as one of the most exciting personal AI assistant projects. After following the project for several days and spending time reading through its documentation—especially around skills and agents—I decided to try it out myself.

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.