In the recent video by All About AI, titled ‘Gemini 2.5 Computer Use MCP | On The Edge #7,’ the host explores the capabilities and performance of Google’s newly released Gemini 2.5 computer-use model by building an MCP server. The server utilizes two stages: browser use and Mac control, showcasing how the computer-use model can operate within these environments. Particularly, the demonstration focuses on employing the model to control a Mac computer’s functionalities, starting with a MacOS application to simulate a user’s actions by finding and opening a video file. The process showed the model’s ability to successfully find and open the specified file using QuickTime Player, albeit at a slower speed compared to manual navigation. Despite the slow execution, the model demonstrated fairly reliable task execution.
Beyond local application control, the video ventures into browser automation tasks where the model fills out forms over a browser interface, role-playing as Neo from The Matrix, adding a lively flair to the demonstration. However, difficulties arose as the model hit limitations regarding processing turns, emphasizing the need for higher resource allocation for seamless operation. Lastly, an attempt to automate tasks via a terminal session highlighted the model’s limitations further. Despite successfully executing a Python script, the operation’s inefficiency revealed that perfecting the model is still underway. The creator noted the improvements from previous iterations and expressed optimistic expectations for the model’s continued evolution. While the model struggles with efficiency, it paves the way for further advancements in contextual engineering that hold potential benefits, especially when integrated with MCP tools. In sum, this presentation underscores Gemini 2.5’s emerging capabilities while acknowledging the necessity for continued development to enhance its application versatility.
Overall, Gemini 2.5 showcases the latest strides in computer-use models, offering examples of both successes and areas needing refinement. The video offers a promising glimpse into the future possibilities of AI-driven computer use, even as it navigates the complexities of achieving smooth, real-time control in complex digital environments.