This past weekend I stumbled on an App.net converastion about podcasts, what people listen to, how many subscriptions they have, etc.
I have gone back and forth, listening to more podcasts that I can handle and then parring way back. Having to comb through multiple hour-plus long podcasts doesn’t leave me much time to find great content. As an example, I love the 5by5 network, but the volume of their output is greater than the amount I can have input.
In the discussion, the service HuffDuffer was mentioned for saving “one off” episodes. I enjoy HuffDuffer, but ended up rolling my own. Mostly though, I was left wondering how do people find good “one off” podcasts? How do they know if they are listening to the best episode?
This left me thinking and formulating, and after a few hours I decided and setup This Episode. It is a blog. It is a podcast. It is a curated list of the best individual episodes of any podcast.
The slugline “Time is short, listen to the best episodes.” is the guiding focus of the site. In my many hours of listening, I would hear about once a week Dan Benjamin asking people who were listening at double speed to just stop listening. I used to be bothered by him saying this, but now it makes sense. Why rush through such delicious audio? Because your playlist is overrun with long episodes and you are worried that you might miss the best one?
The real slugline is a little longer and not quite as catchy: “Time is short, listen to the best episodes[ such great content doesn’t deserve to be listened to at 2x speed. Seriously].”
The idea behind This Episode is that listeners can have their standard fare of listening, but if they was excellent content without all of the listening and searching they can subscribe to one place and get a variety of fantastically produced shows. It is not a “best of” of a particular podcast, but the “best of” of all podcasting.
It is going to be a lot of listening to a lot of podcasts, but I am willing to listen to any suggestions. Send them to me at http://this-episode.net/suggest.