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other23h

A map of the Agentic Future

The current tech paradigm is shifting from direct interaction to a world of representation with agents, where everyone and everything will have an agent, including corporations, brands, places, and institutions.

Key takeaways
  • Shifting from direct interaction to a world of representation with agents.
  • Everyone and everything will have an agent.
  • Corporations, brands, places, institutions will have agents.
rr/artificial#future-of-work#representation#agents
otherJun 9

How an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces

An agent built a 3D Paris gallery by chaining two Hugging Face Spaces, demonstrating the potential of combining pre-trained models for complex tasks. The agent used two Spaces to generate and arrange 3D objects, showcasing the flexibility of the Hugging Face platform.

Key takeaways
  • Hugging Face Spaces can be chained to create complex agents.
  • Mishig built a 3D gallery by combining two Spaces.
  • Spaces can be used to build multi-step tasks.
HHugging Face Blog#hugging-face#agents#spaces
modelsApr 28

Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents

NVIDIA released Nemotron 3 Nano Omni, a multimodal model for processing documents, audio, and video. The model handles long-context inputs up to 128k tokens. It is designed for building agents that can understand and generate content across multiple modalities.

Key takeaways
  • Handles up to 128k tokens in a single input.
  • Supports multimodal processing of documents, audio, and video.
  • Available on Hugging Face for integration into applications.
HHugging Face Blog#multimodal#long-context#agents
toolsJan 24

Open-source LLMs as LangChain Agents

The Hugging Face Transformers library now supports using open-source LLMs as agents in LangChain applications. This integration enables developers to leverage open-source models like Llama, Mistral, and others within the LangChain framework. You can build custom agents that combine these models with other tools and services. The integration expands the range of available tools for building AI applications.

Key takeaways
  • Hugging Face Transformers supports open-source LLMs as LangChain agents.
  • Enables combining open-source models with other tools and services.
  • Expands options for building custom AI agents.
HHugging Face Blog#open-source#langchain#agents