The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 12 resolved? a statement only gets an assessment when the public record can support or contradict it. opinions and what-ifs never can, and 1 checkable ones are still open, waiting for their date. predictions held up or didn't; assertions are supported or contradicted. on every card: ▮▮▮▮▮ certainty · ▮▮▮▮▮ debate potential. speakers are clickable
Ben-Smith: End-to-end multimodal LLMs will replace modular audio transcription pipelines
“In the future that would just be put everything into a big multimodal LLM. And it will output everything that you want.”
Ben-Smith: Podcast apps today are basically repurposed music players
“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.”
Ben-Smith: Snipd relies on regexes to format streaming LLM responses
“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…”
Ben-Smith: Multimodal LLM audio transcription remains vastly costlier than self-hosted pipelines
“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.”
Ben-Smith: Scaling AI products requires matching sub-tasks to the cheapest viable intelligence
“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…”
Ben-Smith: Snipd uses LLMs to recalibrate speaker diarization switching points
“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.”
Ben-Smith: Snipd Built Audio Fuzzy Matching to Re-Sync Dynamic Podcast Ads
“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.”
Ben-Smith: Snipd Uses Perplexity API to Fetch Book Metadata
“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.”
Snipd has processed more than 1 million podcasts
“So we have more than a million podcasts that we've already processed.”
Ben-Smith: Snipd indexes 99% of all podcasts in-house
“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.”
Ben-Smith: Snipd operates with a four-person, all-technical team
“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…”