The video reviews Google’s new Gemini CLI, an open-source AI agent for developers that offers promising features and faster performance than Cloud Code but suffers from stability issues, rate limits, and automatic model downgrades. Despite these challenges, the presenter successfully uses Gemini CLI to build and deploy a landing page with database integration, viewing it as a valuable tool for simpler projects while continuing to rely on Cloud Code for more complex tasks.
The video begins with an introduction to Google’s newly announced Gemini CLI, an open-source AI agent designed to compete with Cloud Code by bringing AI capabilities directly into the developer terminal. The presenter highlights the generous free tier pricing, which allows 60 model requests per minute and 1,000 requests per day when logged in with a Google account. After briefly reviewing the installation process from GitHub, the presenter logs in and explores the Gemini CLI interface, noting its strong resemblance to Cloud Code, including similar commands and tools such as file reading, writing, web fetching, and Google search.
Next, the presenter tests Gemini CLI by creating a landing page for a community project, using a prepared prompt to guide the AI in building the project step-by-step. The AI requests permission to edit files, installs necessary dependencies like Next.js, and organizes project files efficiently. However, the presenter encounters some challenges with model switching, as the system automatically downgrades from Gemini 2.5 Pro to the slower 2.5 Flash model due to rate limits, and there is no straightforward way to manually select the preferred model. Despite these hiccups, the AI successfully generates the landing page and pushes the code to GitHub for deployment on Vercel.
The deployment process goes smoothly with no build errors, and the presenter then focuses on integrating a Neon database to store form submissions from the landing page. Gemini CLI assists in creating the necessary database tables and updating the form to capture user messages. After some troubleshooting and adjustments, the form successfully submits data to the database, confirming that the integration works as intended. The presenter also adds a custom domain to the project, making the landing page live and inviting viewers to join the new community focused on AI and automation skills.
Throughout the testing, the presenter notes that Gemini CLI feels faster than Cloud Code at times but is less stable and polished. Frequent disconnections, rate limit errors, and automatic model downgrades detract from the user experience, making the tool feel somewhat “chunky” and less smooth compared to the more mature Cloud Code. Despite these issues, the presenter appreciates that Gemini CLI is free and open source, recognizing it as an early-stage product with potential. The competition it brings to the AI agent space is welcomed, and the presenter expresses interest in continuing to explore and use Gemini CLI for simpler projects.
In conclusion, the presenter plans to use Gemini CLI for smaller, straightforward tasks while continuing to rely on Cloud Code for more complex or established projects. They acknowledge the current limitations but remain optimistic about Gemini CLI’s future development. The video ends with an invitation to check out the new community at ninthbrain.com, where the presenter aims to foster collaboration among people serious about AI and automation. Overall, the video provides a balanced first impression of Gemini CLI as a promising but still maturing AI development tool.