Crusoe on Dwarkesh Podcast
Adam Brown – Einstein's happiest thought: General Relativity from scratch
Estimated current placement value
Low confidenceWhat a comparable one-episode placement on this show might cost today—not reported advertiser spend or a historical invoice.
How this estimate works
A broad audience range for the top 2,000 popularity tier is multiplied by public CPM benchmarks and a 1× placement factor. Inputs for this read: 0:50 · Host-read (by Dwarkesh Patel) · mid-roll.
Transcript
Crusoe gave us early access to their serverless fine-tuning product, which lets you fine-tune open models without having to deal with infra or provisioning. I thought it'd be cool to try fine-tuning a question generator using the transcripts of my old interviews. The models have gotten so good that if they had all my research and prep and they could look at a conversation so far, they could ask a next question better than I would. Crusoe made the implementation super straightforward. I just uploaded the data, picked an open model, and started the run. I didn't have to touch any of the hyperparameters. Crusoe's Applied AI team maintains optimal recipes for each model, so I just set everything on auto. When the run finished, I deployed it as a self-server endpoint and built an eval for my team. I had them choose the best next question out of three anonymized choices, one that was produced by the base model, one that was produced by the fine-tuned model, and one that I actually asked. Fortunately, my team preferred my actual questions about two-thirds of the time. Hopefully, this benchmark doesn't saturate. And in the remaining cases, they almost always preferred the fine-tuned model over the base model. Serverless inference is live now, and serverless fine-tuning goes live next week. Learn more at crusoe.ai.com.
