Yacine Mahdid shared a post about Mithil Vakde who trained a small transformer model. According to the summary the model reached 44 percent on the ARC-AGI-1 benchmark after training for 1.5 hours on one 5090 GPU. The total cost came to 67 cents. The approach combined compression with transformer architecture and data augmentation. The post includes an image and references another account. This achievement is presented as notable for its efficiency and low expense in the context of the benchmark. Further details on how the result matches previous efforts remain incomplete in the available information.