The tech giant made thousands of engineers train their AI replacements--then fired them. When Meta engineer David Frenk posted an anti-AI farewell parody video in an internal message board, staff thought it perfectly captured shifts in company culture. Get your news from a source that's not owned and controlled by oligarchs. This week, Meta laid off 8,000 employees--10 percent of the company's staff--and reassigned another 7,000 to train AI models. Fear of the layoffs had been building around the company for weeks, compounded by the way that Meta has taken a sharp turn from a company built by coders to a company that has staked its future on AI.

Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on physical locations). Our aim is to provide a new framework for halo merger tree generation that takes advantage of the results of large volume simulations, with a modest computational cost. We treat halo merger tree construction as a matrix generation problem, and propose a Generative Adversarial Network that learns to generate realistic halo merger trees. We evaluate our proposal on merger trees from the EAGLE simulation suite, and show the quality of the generated trees.