Gemini CLI Guide: Setting Up via Vertex AI and How It Differs from Claude Code

⚡ The gist in 5 seconds - TheCode has published a hands-on breakdown of Gemini CLI — installation, setup, and how it differs from Claude Code. - For the local example, you need Google Cloud CLI installed, a project with the Vertex AI API enabled, and account permissions for that project. - Without a properly configured Google Cloud project and granted permissions, the local scenario won't run — that's the main barrier to entry. ### 🔍 What was found The TheCode.media resource published a piece on Gemini CLI (at the time of writing, it had been out for about 9 hours). The format is a tutorial: installation, setup, and a comparison with Anthropic's Claude Code. The description specifically covers the Google Cloud integration: for the local example, you'll need Google Cloud CLI installed and a project with the Vertex AI API enabled, and the account must be granted permissions for that project. This part is the key mechanics of the guide. The local example doesn't boil down to a single install command: first you set up Cloud CLI, then create or select a project with the Vertex AI API activated, and only then can an account with the right permissions access the model. The comparison with Claude Code is covered in a separate section — as a second terminal-based AI agent for console work. ### 💡 Why it matters CLI agents for developers are a competitive niche where Gemini CLI is directly compared to Claude Code, and breakdowns like this answer the main practical question: what exactly needs to be prepared in the cloud before the tool starts responding from the terminal. Setting up a Google Cloud project and access permissions is the most common stumbling point for beginners, so a step-by-step guide with explicitly stated requirements (Cloud CLI + Vertex AI API + permissions) saves time on the first run.