Welcome to this course on Gemini CLI, built in partnership with Google. Experienced developers are using agentic coding assistants that dramatically speed up our work. In this course, you'll learn about many of Google's internal best practices for agentic coding, including how to apply it to common use cases like building web front ends or automating code reviews using GitHub actions, and even non-coding tasks like creating multimedia content for social media. I really enjoy using Gemini CLI myself, and hope you also get a lot of value out of it. I'm delighted that the instructor for this course is Google's Jack Wotherspoon, who is developer advocate working on Gemini CLI. Thanks Andrew. Gemini CLI is one of a number of agentic coding assistants you may have used or heard of. This course will take you from installing Gemini CLI to organizing and coordinating multiple tools and MCP servers in an automated workflow. You'll learn how to coordinate between local files and cloud services to develop features and analyze data. You'll engineer Gemini CLI's context and memory to get the best possible outputs. In the later lessons, you'll use sophisticated automations to take care of routine tasks like code review. Throughout this course, you'll explore Gemini CLI for software development and beyond, using the example of planning an AI conference, like the next AI Dev conference, for example. You'll build key features for a conference website using Gemini CLI's native Google Workspace extension to work with docs and calendar. You'll also use the Canva MCP server to create conference marketing materials. You'll build a data dashboard that combines attendee data with an existing company database. and use Gemini CLI to work with multimedia, turning a conference podcast into social media clips and posts. As a bonus, you'll see how Gemini CLI can help organize and search messy course materials to make studying easier, which will hopefully help in your next DeepLearning.AI course. Many people are using agentic coding systems like Gemini CLI, Claude Code, or OpenAI Codex. These agentic tools are valuable because they have access to your local machine and so can be given permission to run commands on your machine like pip install or playwright or npx or git to get work done for you. In fact, with just a small set of tools, these agentic coding assistants can set up the development environment needed and build entire apps or feature sets quickly. So, especially when it comes to prototyping, I now almost never write code by hand anymore, but instead get AI coding assistants to write it for me. And this is dramatically speeding up me and many other developers. Prototyping has never been faster. What's also exciting about Gemini CLI is that it's fully open source. You can see exactly how it works. Every line of code is public on our GitHub. right down to the actual instructions and system prompt. We've merged thousands of pull requests from members of the community, including popular features like session management. And we're welcoming your contributions too. Open source software is very important for innovation, especially in the age of AI. If you feel so moved, go to the GitHub repo and check out the prompts. And after you've completed this course, you might also consider contributing to Gemini CLI or to other open source projects. These contributions create value for everyone. Speaking of contributions, many people have worked to create this course. I'd like to thank from Google, Denise Kwan, and from DeepLearning.AI, Isabel Zaro and Esmaeil Gargari. In the first lessons, you'll see how to get Gemini CLI up and running and get a taste of some of the most common tasks, built-in tools and usage patterns we're seeing in the community. I think you have a lot of fun using Gemini CLI. Let's go on to the next video and get started.