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FDM-1 completes complex tasks and navigates interfaces well enough to use CAD applications. Forking VMs allow us to snapshot when a successful operation completes (extrusion, selection, etc.), letting us apply test-time compute to computer use.

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Standard Intelligence

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59,736 görüntüleme • 6 ay önce •via X (Twitter)

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Open Computer Agent - LLMs completing tasks using a VM. It's playground to test how well current LLM agents use a computer to solve everyday tasks. And this is just the start - very soon models will be 10x faster and 10x better at it! ❤️ built with e2b x qwen2.5-vl x smolagent
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Open Computer Agent - LLMs completing tasks using a VM. It's playground to test how well current LLM agents use a computer to solve everyday tasks. And this is just the start - very soon models will be 10x faster and 10x better at it! ❤️ built with e2b x qwen2.5-vl x smolagent

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1:56

Our first short course with Anthropic! 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:

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This might be the most advanced AI agent to date. It’s called Runner H by H, and it automates complex, multi-step tasks from just a prompt. You basically describe what you want and it takes over your computer to complete it. Some key features: - Outperforms competitors like Anthropic’s Computer Use on the WebVoyager benchmark. - Navigates web interfaces through pixel-level interpretation and semantic understanding. - Adapts to UI changes automatically. - Handles complex workflows like e-commerce and financial services onboarding. They have already raised $220M and looks like we are entering the agentic era! You can sign up to their waitlist now (link below).

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Internet Computer's Caffeine is Now Available in Claude Internet Computer (DFINITY Foundation)'s Caffeine launches a direct integration with Anthropic’s Claude, enabling the instantaneous generation of production-ready applications through natural language interfaces. This deployment enables "vibe coders" and enterprise teams to use caffeine’s modular frameworks to build, iterate on, and deploy complex software without leaving the LLM environment.

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4:23

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Local Autonomous Agent for All of Us Browser-Use Web UI is a gradio app that uses LLMs to automatically surf the web to achieve tasks. All you need to do is just tell it what to do. It works like magic. And now anyone can run it locally with 1 click.

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2:13

Zack Polanski, "Why does it seem that our government is incapable of standing up to a US president?" Jeremy Corbyn, "It's in the psychology of the Ministry of Defence, the Foreign Office, a pro-US position" "There's this assumption that we will always be close to the US, whatever it does" "So when the US comes along with something utterly absurd and bizarre, like the bombing of Iran. For what?" "They can't actually give a rational reason for bombing Iran" "On one side they say to get rid of Iran's nuclear weapons, when they knew full well that Iran doesn't have any nuclear weapons" "They then said we have to bomb Iran and the British government said they are not involved in this. Yet, at the same time allow US forces to use British bases" "More specifically, Trump goes onto Andy Burnham the day after he's become Prime Minister and says, can we use RAF bases for US planes to take off and bomb Iran, but don't worry its only defensive" "What was Burnham doing agreeing to that?"

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🚨BREAKING NEWS Iranian Parliament member Boroujerdi: "We are not pursuing a nuclear bomb. But when they come and bomb us and threaten to use nuclear bombs against us, we will not abide by the rules of the game. To summarize in one sentence, the time has come to withdraw from the Nuclear Non-Proliferation Treaty."

Aleksey The Great 🇷🇺🎖

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3:07

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,661 görüntüleme • 1 yıl önce