Not very interesting. These are all very popular, Americanocentric, and "mass appeal" shows, nothing that indicates specificity in recommendations. Which is in line with expectations for GPT-3.5/4.
The problem is ChatGPT doesn’t understand podcast popularity nor the journey heavy listeners tend to go on.
To end up with a list like OP’s, you typically have heard of popular podcasts, tried them and stopped listening, either unsubscribing or not adding them in a new podcast app.
To ChatGPT the OPML just looks like you overlooked some similar material and it happily fills in the gaps. Integrating with a listener stats data source and capping the max popularity of the recommendations to the minimum popularity in the OPML shows would make it way better.
Interesting. Maybe not Reply All but I would expect most Americans to know about Joe Rogan’s podcast if not what it’s called. He is absolutely massive in the States, and last time I checked his podcast was the biggest in the world.
OP’s topics lean more tech and programming related. Reply All is kind of a tech show but as the description implies it’s more of a society show centered around tech topics. It’s also, as grandparent implies, one of the biggest and more mainstream tech ancillary podcasts. It is quite good by the way, if you’ve never listened to it.
I would have wanted more technical and deep cuts based on my recommendations
Maybe most Americans do, sure. I don’t see how that matters, here in Ireland. :p
As for the recommendations, I find that surprisingly often it works to tell the bot that. Start by asking it what it’s getting from the list, then correct it before it creates the new one. And ask it to justify itself; if nothing else you’ll be able to skip a few.
To end up with a list like OP’s, you typically have heard of popular podcasts, tried them and stopped listening, either unsubscribing or not adding them in a new podcast app.
To ChatGPT the OPML just looks like you overlooked some similar material and it happily fills in the gaps. Integrating with a listener stats data source and capping the max popularity of the recommendations to the minimum popularity in the OPML shows would make it way better.