Cursor on Dwarkesh Podcast
Grant Sanderson – AI and the future of math
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
I don't think people appreciate the kinds of things that these models can just go handle for you when you equip them with a good harness like Cursor. For example, I started publishing my episodes on Bilibili for a burgeoning Chinese audience, but everything I upload there needs the sponsored segments cut out. Normally, that would have meant asking my editors to go back through all the old episodes, cut out the ads, and re-export everything. But in about just as much time as it would have taken me to send them that Slack message, I can just tell Cursor to do it instead and spare them. For research for the podcast, I have a whole repo where I've put every single book and paper relevant to prepping for any of the recent episodes. I've been able to hodgepodge everything because the Cursor harness is extremely good at helping the model figure out exactly what information to pull, whether that's from my repo or from the web, in order to answer the questions I have while doing research. So whatever you happen to be working on right now, just try pointing Cursor at it. Go to cursor.com/lorkash to get started.
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