Course collaboration

0:01 I think um you know I I was one of the first few to like sing with Chhatri's advanced voice mode and we actually didn't really know if that would ever come out as an API and now it's not as an API and since then it's just been a proliferation of voice products. I think the voice models keep improving. You've come out with Pipecat which started as kind of like an open- source orchestration framework that was from the daily folks but now is being adopted by a lot of others.

0:24 So what's your inspiration in in doing this voice AI course? I answer the same questions so many times and I love doing it because this stuff is so much fun and there's so much like interesting engineering stuff to rabbit hole on. But it seemed really clear that it would be awesome to get a bunch of people together, talk about all this stuff, use some of the materials we've been kind of building over the last few months and kind of see where it goes.

1:10 And the inspiration was Hammel and Dan's like LLM Woodstock course last year which you know I know you were part of. I got a lot out of it. It was super fun and lots of companies came and jumped in and helped everybody out like with seeing all of the tools that you should know about if you're building stuff. So I was like let's try to do that for Voice AI. Yeah, totally. So I think HML's course honestly to was the biggest thing for Maven because now Maven is like the default.

1:34 platform even though there are many many like it Maven seems to be popular and I mean okay so you have a lot of partners and maybe let's just go over the syllabus right like what do you think that people need to know in voice I think of it as three big things the first is like what's the landscape what models do you use why like what are the best practices for low latency real time conversational multimodality And it turns out that if you're building like agents that are multimodal, multi-turn, real time, it's just a totally different shape of