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agent-browser + Lightpanda + batch + multi-session is a dangerous combo Completed this benchmark task in 5.85s

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Chris Tate

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86,869 görüntüleme • 2 ay önce •via X (Twitter)

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Introducing agent-browser chat agent-browser is now a browser agent. → One-shot: agent-browser chat "open google, search for dogs" → Interactive: agent-browser chat → Built-in AI chat in the dashboard → Execute any agent-browser command → Use agent-browser as a sub-agent
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Introducing agent-browser chat agent-browser is now a browser agent. → One-shot: agent-browser chat "open google, search for dogs" → Interactive: agent-browser chat → Built-in AI chat in the dashboard → Execute any agent-browser command → Use agent-browser as a sub-agent

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🧵🚀 New WebAgent Benchmark Alert! 🚀 There is hope for human workers! We released WorkArena++, a new challenging benchmark for WebAgents. Our best agent achieves 0% accuracy on this benchmark, while human evaluators still obtain 94%! 🔗

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Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

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WTF is this? A startup announcing their benchmark in the sky! NEO ( Neo AI ) is shaking up the game and has stayed at #1 with 34.2% for 4 weeks. A multi-agent system with its own reasoning engine that not just plans and codes but executes the entire workflow, reducing the grunt work by 90% and saving ML teams thousands of hours.

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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

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Stop spending hours on manual work. You can now use a multi-agent AI workforce to get more work done in less time. Here's how 👇 --- Try Eigent AI - Lets you build and run a custom AI workforce on your desktop. - Automate complex workflows using multi-agent task execution. - Built on CAMEL-AI’s top open-source projects ( CAMEL-AI.org & OWL). - Boost productivity with deep customization and strong privacy --- Features: - Customize Your AI Workforce: Build task-specific agents with domain skills and tools. - Faster Execution: Eigent runs agents in parallel to automate complex workflows. - Human-in-the-loop: Automatically asks for help when tasks hit uncertainty. --- What sets Eigent apart? - 3–5× faster task execution using a parallel multi-agent workforce. - Modular design lets you add new capabilities without changing the core system. - Self-optimizing agents that replan and adapt during execution for higher success. - Deploy anywhere: cloud, local, or enterprise, with full open-source flexibility. --- Try building your multi-agent AI workforce here: Join their community to build your multi-agent workforce: Check their GitHub: ---

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Is this Terry best combo from a jump in now? #COTW_Terry

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Is this a combo video?

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