Snipd

“So it's actually not we're actually not doing exact matches, but we're doing fuzzy matches. To identify the moment. It's basically we basically built Shazam for podcasts. Just as a little side project to solve this issue.”

“In the future that would just be put everything into a big multimodal LLM. And it will output everything that you want.”

“Then we use perplexity API together with various other LLM orchestration to go out there on the internet, find everything that there is to know about the book.”

“For this specific feature, like, we actually also have, like, countless regexes. That, that, they're just there to correct certain things that the LLM is doing, because it doesn't always adhere to the format correctly, and then it looks super ugly on the front…”

“The big difference right now is still, like, the cost difference of doing speaker diarization this way, or doing transcription this way, is a huge difference to the pipeline that we've built up.”

“Like podcast apps today, they're still, they're basically repurposed music players, but we actually look at podcasts as one of the largest sources of knowledge in the world.”

“So we have more than a million podcasts that we've already processed.”

“Like for us, it's not just about taking the best model for every task, but it's really getting the best, like identifying what kind of intelligence level you need, and then getting the best price for that to be able to really scale this and provide us yeah, le…”

“We're just four people. We're just four people. Yeah. Like four, we all technical. Basically two on the backend side. So one of my co-founders is this person who got me into machine learning and startups, and we won the hackathon together. So we have two peopl…”

“Yeah, we have a search engine that is powered by ListenNotes, but I mean, in the meantime, we have a huge database of, like, 99% of all podcasts out there ourselves.”

“So the general tech stack is our entire back end is, or 90% of our back end is written in Python. Hosting everything on Google Cloud platform, and our front end is written with, well, we're using the Flutter framework.”

“Another thing is that we actually combine it with LLMs. So the transcripts, LLMs and the speaker diarization, like bringing all of these together to recalibrate some of the switching points.”