https://learn.deeplearning.ai/courses/building-toward-computer-use-with-anthropic/lesson/a6k0z/introduction

https://youtu.be/Iabue7wtE4g?si=NlvvXGn80ZN80MxE In this video, I go through hands-on how to use the Anthropic computer use models and tools. Explain how they work and also show how you can get it started with Docker on your own computer. For more tutorials on using LLMs and building agents, check out my Patreon Patreon: / samwitteveen Twitter: / sam_witteveen Computer Use: https://www.anthropic.com/news/develo... Computer Use Docs: https://docs.anthropic.com/en/docs/bu... 👨‍💻Github: https://github.com/anthropics/anthrop...

https://astral.sh/ Useful Links: - https://docs.astral.sh/uv/getting-started/ - https://www.datacamp.com/tutorial/python-uv - https://www.mrcoder701.com/2024/08/04/how-to-create-python-virtual-environment-with-uv/ UV is a modern, high-performance Python package manager and installer written in Rust. It serves as a drop-in replacement for traditional Python package management tools like pip, offering significant improvements in speed, reliability, and dependency resolution. This tool represents a new generation of Python package managers, designed to address common pain points in the Python ecosystem such as slow installation times, dependency conflicts, and environment management complexity. UV achieves this through its innovative architecture and efficient implementation, making it 10-100 times faster than traditional package managers. Key features that make UV stand out: Lightning-fast package installation and dependency resolution Compatible with existing Python tools and workflows Built-in virtual environment management Support for modern packaging standards Reliable dependency locking and reproducible environments Memory-efficient operation, especially for large projects Whether working on small personal projects or managing large-scale Python applications, UV provides a robust and efficient solution for package management. In this tutorial, we will cover all essential aspects of UV so you can start using it immediately. #browser_use #computer_use #astral #uv #python_package_manager

https://x.com/AndrewYNg/status/1882125891821822398 Our first short course with @AnthropicAI! Building Towards Computer Use with Anthropic. This teaches you to build an LLM-based agent that uses a computer interface by generating mouse clicks and keystrokes. Computer Use is an important, emerging capability for LLMs that will let AI agents do many more tasks than were possible before, since it lets them interact with interfaces designed for humans to use, rather than only tools that provide explicit API access. I hope you will enjoy learning about it! This course is taught by Anthropic's Head of Curriculum, @Colt_Steele. You'll learn to apply image reasoning and tool use to "use" a computer as follows: a model processes an image of the screen, analyzes it to understand what's going on, and navigates the computer via mouse clicks and keystrokes. This course goes through the key building blocks, and culminates in a demo of an AI assistant that uses a web browser to search for a research paper, downloads the PDF, and finally summarizes the paper for you. In detail, you’ll: - Learn about Anthropic's family of models, when to use which one, and make API requests to Claude - Use multi-modal prompts that combine text and image content blocks, and also work with streaming responses - Improve your prompting by using prompt templates, using XML to structure prompts, and providing examples - Implement prompt caching to reduce cost and latency - Apply tool-use to build a chatbot that can call different tools to respond to queries - See all these building blocks come together in Computer Use demo Please sign up here: https://deeplearning.ai/short-courses/building-towards-computer-use-with-anthropic

https://www.anthropic.com/news/3-5-models-and-computer-use