Ben Burtenshaw from Hugging Face demonstrates how coding agents are tackling complex AI systems engineering tasks. He outlines a three-tiered approach: interactively writing CUDA kernels, autonomously fine-tuning LLMs, and deploying a multi-agent research lab (AutoLab) to parallelize experiments, all powered by file-based "skills" and open primitives on the Hugging Face Hub.
Merve Noyan from Hugging Face discusses how open-source models have achieved parity with closed-source counterparts, highlighting the Hugging Face ecosystem built to support this shift. She covers tools for model selection, local agent deployment, and the transformative "Hugging Face Skills" that allow agents to automate complex ML engineering tasks like fine-tuning models with a single prompt.
Experts discuss OpenAI's new ad model for ChatGPT, the breakout moment for agentic coding with Claude Code, IBM's "Enterprise in 2030" report on the shift from AI efficiency to innovation, and Hugging Face's new "Open Responses" standard for agent APIs.
Thomas Wolf, co-founder of Hugging Face, details the LeRobot project, aiming to replicate the success of Transformers in the robotics domain. He discusses the vision of creating a massive open-source community, tackling data scarcity, and the future of physical AI hardware, arguing that we are at a key inflection point for robotics similar to where LLMs were years ago.