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Read through hundreds of pages of developer documentation to get quick answers to technical questions. pic.twitter.com/xy9ya1omhg
— Anthropic (@AnthropicAI) 11 mai 2023
Read through hundreds of pages of developer documentation to get quick answers to technical questions.
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A bird or a drone?
A breakthrough in autonomous drone navigation employs brain-inspired liquid neural networks to explore and excel in unseen environments. Read more on how researchers developed this framework for imitation learning here: https://
bit.ly/3MeRH1I @MIT
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like you want to add memory to plan-and-execute agent?
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With @heypi_ai, we're creating a new kind of AI that can enhance our personal lives. “Your AI will ask questions, clarify, and sharpen your understanding,” says Inflection CEO @mustafasuleymn. Our Co-Founders @reidhoffman and Mustafa discuss Pi and the future of AI on
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Excited to announce our multi-modal agent API that can automatically chain 100,000+ of HF models (stable diffusion, whisper, OpenAssistant,…) for text, audio, image, video, time-series,… based on your commands. All open-source so it can work locally! https://
huggingface.co/docs/transform
ers/transformers_agents
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"This code is then executed with our small Python interpreter on the set of inputs passed along with your tools. We hear you screaming 'Arbitrary code execution!' in the back, but let us explain why that is not the case." https://
huggingface.co/docs/transform
ers/transformers_agents
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Tools are elementary: a name, a description, a function. Designing a tool and pushing it to the Hub can be done in a few lines of code. The toolkit of the agent serves as a base: extend it with your tools, or with other community-contributed tools: https://
huggingface.co/docs/transform
ers/custom_tools
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Please play with it, add your tools, and let's create *super-powerful agents* together. Here's a notebook to get started:
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How does it work in practice? It's straightforward prompt-building:
• Tell the agent what it aims to do
• Give it tools
• Show examples
• Give it a task The agent uses chain-of-thought reasoning to identify its task and outputs Python code using the tools.