fig.inc releases RealCUA dataset

The author argues that as frontier AI models rapidly increase their task horizons, humans will soon lack the ability to manually build verifiable, long-horizon environments to train and evaluate them. To solve this bottleneck, the team is turning real, open-ended desktop work into consumable learning signals by hierarchically decomposing sessions into actions, tasks, and objectives. Open article →