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January 2, 2025
The world of artificial intelligence is evolving rapidly. Every day there are new advancements in large language models (LLMs), Retrieval Augmented Generation (RAG), and especially in AI agents. These advancements enable developers and even non-programmers to automate increasingly complex tasks and create innovative applications.
AI agents are programs that can independently perform tasks by collecting, processing, and reacting to information from various sources. They can be used in a variety of areas, from organizing email inboxes to creating financial analyses. One example is the development of "Smolagents" by Hugging Face. This library provides developers with tools for creating agents that can interact with different environments.
Another interesting aspect is the increasing multimodality of AI agents. They are no longer limited to text but can also process images, audio, and video. An example of this is an agent developed by Shubham Saboo (@Saboo_Shubham_ on X) that uses Google Gemini 2.0 to process various media types simultaneously and perform web searches in real-time. This ability allows the agent to provide more comprehensive and context-aware responses.
The development of low-code and no-code platforms like Praison significantly simplifies the creation and management of AI agents. Through simple YAML configurations and pre-built workflows, even users without programming skills can create and deploy their own agents. This democratizes access to AI technology and allows a wider audience to benefit from the advantages of automation.
In addition to the development of AI agents, there are also continuous advancements in the underlying technologies like LLMs and RAG. New models like DeepSeek V3 Base from Hugging Face offer improved performance and scalability. RAG techniques allow LLMs to retrieve information from external sources and integrate it into their responses, leading to more accurate and informative results.
The development and optimization of AI models is supported by new tools and frameworks. Microsoft's PromptWizard, for example, automates the optimization of prompts by using feedback from language models. Tools like the Memory Visualizer from PyTorch help developers analyze and optimize memory consumption during training.
The rapid advancements in the field of AI agents and LLMs open up numerous possibilities for the future. From automating everyday tasks to developing complex applications, AI technology will increasingly shape our daily lives and our working world. The development of low-code and no-code platforms will further reinforce this trend and enable access to AI technology for a wider audience.
Bibliography: - https://x.com/Saboo_Shubham_/status/1872328345695990163 - https://huggingface.co/blog/smolagents - https://github.com/huggingface/smolagents - https://www.linkedin.com/posts/shubhamsaboo_i-just-built-an-ai-agent-that-can-see-hear-activity-7274263855977951232-6G1W - https://www.reddit.com/r/LocalLLaMA/comments/1hqgz3s/smolagents_new_agent_library_by_hugging_face/ - https://www.linkedin.com/posts/shubhamsaboo_%3F%3F%3F%3F%3F-%3F-%3F%3F%3F%3F%3F-%3F%3F%3F%3F%3F-%3F%3F-activity-7242006959619555330-sJ84 - https://x.com/Saboo_Shubham_/status/1872327267336294549 - https://twitter.com/Saboo_Shubham_/status/1798179722410520601