A journalist trained a custom paper-curation AI model in under 1 day using new self-improvement tools
A WIRED journalist has demonstrated that recursive self-improvement is no longer restricted to frontier AI labs. By combining off-the-shelf models with emerging training platforms, individuals can now build highly specialized systems to automate custom workflows. The experiment utilized AutoResearch, a tool created by prominent researcher Andrej Karpathy to help base models train smaller versions of themselves. Running Claude on an Nvidia DGX desktop system, the process adjusted training parameters autonomously over several hours. To expand the capability, the curator model was connected to Prime Intellect, a startup that recently secured $15 million in funding. The system gathered 100 research papers, generated synthetic data, and used reinforcement learning to optimize search and summarization. Vincent Weisser, CEO of Prime Intellect, stated that their goal is to democratize training infrastructure. He noted that they want a billion specialized intelligences filling various niches rather than a single, centralized authority controlling the technology.
I Built a Self-Improving AI, and So Can You
