{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Chain of Thought | AI Agents, Infrastructure & Engineering","title":"Switching Models Shouldn't Mean Starting Over | Walrus Protocol's Kimberly Logan","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/d893b635\"></iframe>","width":"100%","height":180,"duration":2259,"description":"If your agent's memory lives inside one model provider, switching models or harnesses means starting over. You need portable memory - yet when switching agent memory between models, you can swing accuracy by thirteen points or more, depending on which model reads them back. This episode is sponsored by Walrus.","thumbnail_url":"https://img.transistorcdn.com/KnEL-9mfZGF096BNQ33xaE_zVyhLvBNV8rHifHuvYgc/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hYWM2/NmYwMjc4MmJiYTYw/ZTI5NGZhNzRhNmZh/NTU0OC5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}