video1.0<iframe src="https://www.loom.com/embed/b28dd8b897a749b3a96dda8cf29cc33f" frameborder="0" width="1152" height="864" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8641152Loomhttps://www.loom.com8641152https://cdn.loom.com/sessions/thumbnails/b28dd8b897a749b3a96dda8cf29cc33f-6da23f56582526ea.gif306.993In this video, I explore the capabilities of large language models (LLMs) in handling arithmetic versus spatial reasoning tasks. I found that while LLMs excelled at arithmetic through visual activities and prompting, they struggled significantly with spatial tasks, even when provided with detailed examples and external libraries. My attempts to guide the model in creating geometric patterns revealed its limitations in understanding spatial relationships. Part I - https://www.loom.com/share/0c9639885c244e7889bb11843aa948d6 Part II - https://www.loom.com/share/b28dd8b897a749b3a96dda8cf29cc33f Part III - https://www.loom.com/share/3abd569af3b04335b8ad144b772e73e0