Ian Fisher

Ian Fisher explains how Poetic uses a recursively self-improving meta-system to build or optimize reasoning harnesses on top of language models. These harnesses combine code, prompts, data, and model calls to improve performance on hard tasks, and can be adapted as newer models become available. He contrasts this approach with fine-tuning, which can be costly and quickly overtaken by newer models, and describes benchmark results and optimization costs as examples. He also discusses Poetic’s early-access program and advises engineers to experiment with AI and build things themselves.