You may learn something from my experience building the same thing: https://www.joshbeckman.org/narro/

This is very cool, for sure. But what I'm looking for is something that is super optimized for threaded conversations, like this site. Imagine if each poster were a unique voice, and the format was basically listening to all of the top conversations for a given post, in a way that makes it easy to follow the conversation and back-and-forth from the threads. There would clearly need to be a little bit of design applied and some simplification, but I think that's where some GPT element could come into create a simplified conversation with a condensed amount of speakers.

What I've been dreaming of is a "podcast" generated for a day of HN, but focusing on the comments and conversations.

I come here for the convos. But I also commute in a car, and I exercise with headphones.

I want like a radio show made of segments. Each segment is a post with the best conversations between users. Use a generative Ai voice service (like Coqui), and have it speak the comments aloud.

Then I can listen to interesting chatter through my headphones.

Turn a YouTube channel into a personal podcast feed. https://gist.github.com/thebenlamm/9d862a3e6c9f481ab9d8a8afe...

More detailed instructions in the script but the general idea is: 1. When a channel publishes a new video IFTT puts a text file with youtube link in Dropbox 2. Script downloads audio from youtube 3. Justcast.com free tier to turn a Dropbox folder into a podcast feed

What is the developer experience of using OpenAI's Server-Sent Events endpoint from something else than their Python and Node.js libraries?

The SSE endpoint is required for use cases like chat so the end user doesn't have to wait until the whole reply has been generated.

I started implementing a simple SSE client on top of C#/.Net's HttpClient but it's harder than I first assumed.

We are working on an AI product in a highly regulated industry (Investing). Recently we have been experimenting using the GPT3 API as a Junior Equity Analyst. On an eyeball check the results of the technology are impressive.

The problem is that there is no way to validate the feedback on scale. I.e., we can't receive statistics about the feedback from the API.

In contrast, for our own Entity Recognition models we can (and do) calculate probabilities that explain why a certain entity is shown.

Hence, I think for API users of GPT3, OpenAI should return additional statistics why a certain result is returned the way it is to make it really useful and more importantly compliant.

"Is now open source!" is the new hot way to say "We quit." Open sourcing software you decided not to bother maintaining anymore is better than nothing, yes, but without a community devoted enough to it to keep it up to date, it's not much better than dead and gone.