Search Results for "setup"
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Showing 3 open source projects for "setup"
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Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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BILL eliminates tedious paperwork, automating data entry and approval routing.BILL is a leading financial operations platform for small and midsize businesses (SMBs). As a champion of SMBs, we are automating the future of finance so businesses can thrive. Our integrated platform helps businesses to more efficiently control their payables, receivables and spend and expense management. Hundreds of thousands of businesses rely on BILL’s proprietary member network of millions to pay or get paid faster. Headquartered in San Jose, California, BILL is a trusted partner of leading U.S. financial institutions, accounting firms, and accounting software providers. For more information, visit bill.com.
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c/ua
c/ua is the Docker Container for Computer-Use AI Agents
Cua is a Docker-based framework that facilitates the deployment and management of computer-use AI agents. It provides a sandboxed environment where agents can perform tasks on macOS and Linux virtual machines, supporting various AI models and ensuring safe execution. Cua is particularly useful for developers looking to test and run AI agents in controlled settings. -
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GELab-Zero
GUI Exploration Lab. One of the best GUI agent solutions
GELab-Zero is an open-source “GUI Agent” framework aiming to automate interactions with graphical user interfaces (GUIs), combining both the agent model and all supporting infrastructure — including inference, input orchestration, and GUI automation logic — in a plug-and-play package that runs locally, without cloud dependencies. The idea is to let developers or users harness an AI agent that can simulate clicking, typing, reading UI elements, and interacting with apps in a human-like way... -
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OWL
Optimized Workforce Learning for General Multi-Agent Assistance
Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation. OWL (Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation) is an advanced framework designed to enhance multi-agent collaboration, improving task automation across various domains. By utilizing dynamic agent interactions, OWL aims to streamline and optimize complex workflows, making AI collaboration more natural, efficient, and adaptable. It is built on...
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