It's a fascinating product. Having grown up professionally using news aggregation sites it's fun to bring this idea back with fully backed in AI to attempt to bring more transparency to the news.
Hardest part about using this app is training yourself to stop using social news feeds and go to a single app for news headlines!
Osaurus
Huge fan of Particle and love this use case. Really curious if Particle could spin up a podcast-focused competitor with Techmeme... WDYT @pandemona ?
Particle News
@chrismessina Super interesting convergence of podcasts and news.... I love it. We are building a Trends product on Radar that will start to get at some of this. Stay tuned!
Osaurus
@pandemona oo can't wait!
Curious how well it works when you only remember the rough idea, not the exact words.
Particle News
@dylan_friddle12 Great question! Currently, there's a semantic search option that matches the terminology used in the search. So rough ideas should yield some results. Soon, though, we'll have a smart search option that's closer to an LLM query (less exact should yield even more results).
Particle News
Hi I'm Sara, co-founder and CEO of Particle. I wanted to share a bit about the background of Radar, and the Podcast Intelligence API from Particle.
You may remember us from Particle News! Particle News is an iOS and Android app that summarizes and distills news from many sources, helping you get caught up quickly. Over a year ago, we started working on bringing podcast clips into news. We find the best, most relevant podcast clips and bring them into the Particle News stories, giving each story a very human layer of commentary and discussion. Our users love it, and they tell us. We realized that there is actually a ton of value that we could unlock from podcasts. But, in a world increasingly searched by agents, they can't "hear" podcast content unless someone has already transcribed it.
So we started building an API/MCP, the Podcast Intelligence API. We cover over 130,000 actively transcribed podcasts, with 20,000+ episodes added daily. Each episode is fully transcribed, speaker diarized and labeled, and rich entities (people, companies, etc.) are extracted so that you can use this data to establish trends, do deep research, discover podcasts and episodes or just catch up.
But our users also told us that sometimes, you just want to look at it or listen to it :) That's where Radar comes in. Radar is podcast search engine, powered by the Podcast Intelligence API.
With Radar, you can search podcast transcripts by entity, keyword/keyphrase, or semantic meaning, and soon we'll be introducing a Smart Search as well (which will put an LLM in front of your query to best route or answer it). You can also set up alerts for entities and guest appearances, which you can get delivered to you by Slack, email, or webhook, either as they happen or in daily/weekly digests.
Radar is free to try, and then it's $29 per month for Individuals and $399 for Businesses. Use promo code HUNT when you sign up for 50% off the first month. Each tier also gets you API/MCP access, so you can connect your agents to the same data. If you have any custom needs or want firehose access, let me know and we can discuss Enterprise options as well.
One of the most interesting things for me personally about Radar is the trends that are surfaced, and we're going to be releasing more in this space as well. It's amazing to see what podcasters are all talking about right now. Also, whenever there's relevant news about an entity, we surface it on Radar as well, so you can get caught up with what's happening as quickly and easily as possible.
I'd love to chat, so feel free to comment or message me here, or find me on X/Twitter: x.com/pandemona.
Huge thanks to @chrismessina for the hunt, and for all his continued support over the years!
I like that this goes beyond simply finding podcasts. Making all those conversations searchable could make podcast research much less time consuming.
@naomi_plasterer,I have so many half remembered podcast moments I gave up trying to find again. The idea of finally landing right on the exact one is oddly satisfying. This feels like it would change how I actually use everything sitting in my queue.