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Why New Gemini 2.5 Computer Use dominates benchmarks [2025]

6.5K views· 28 likes· 12:20· Oct 14, 2025

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Meet Gemini 2.5 Computer Use—an API-powered UI agent that clicks, types, and navigates with benchmark-leading accuracy and lower latency. ☎️ Do you need any career or technical help? Book a call with me: https://calendly.com/mg_cafe Reference Code in Discord channel under reference section: https://discord.gg/2kcjQFMCr5 ****************** LET'S CONNECT! ******************* Join Discord Channel: https://discord.gg/2kcjQFMCr5 ✅ You can contact me at: LinkedIn: https://www.linkedin.com/in/mohammad-ghodratigohar/ Email: mo.ghodrati95@gmail.com Twitter: https://twitter.com/MG_cafe01 🔔 Subscribe for more cloud computing, data, and AI analytics videos by clicking on the subscribe button so you don't miss anything. #Gemini_Computer_Use #AI_agents #UI_automation

About This Video

Google just dropped a model that can literally control your computer, and in this video I break down Gemini 2.5 Computer Use—an API-powered UI agent that clicks, types, scrolls, and navigates your browser in an agentic loop. I explain how it works end-to-end: you capture the current browser state (screenshot + prior context + your new request), send it to the model, and the model returns the next UI actions (like click/type/scroll). You execute those actions, capture a new screenshot, and repeat until the task is done. I also walk through why this model matters: based on the benchmarks I show, Gemini 2.5 Computer Use is leading on accuracy while staying fast on latency, which is exactly what you need for real UI automation (nobody wants to wait forever for an agent to click around). Then I demo how to run it locally using Google’s Computer Use Preview GitHub repo with Playwright + Chrome, including the exact “gotchas” like CAPTCHA/“I’m not a robot” requiring manual intervention. Finally, I cover practical use cases—form filling, automated web app testing, and research across sites like Amazon or Google Maps—plus key implementation notes like the model name, multimodal input, and token limits.

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