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Akeem

@akeem

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@akeem
are there good examples of media companies that have leaned into Farcaster?
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@akeem
hola
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@akeem
Earlier this week I articulated something that is probably worth sharing in writing. We have a situation where talented young people are entering a tight work force (for software engineers at least). They will probably end up creating a lot of new businesses for the next generation
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I had to split it into two listening sessions, but the one with Alex Danco is really really good!
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Ratifying @procoin governance
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happy to try out pro “for the culture”, I will say that it was kind of weird not to see a paymaster for the tx. I might be an abnormal user but it would be a ux win for everyone
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I’ve never had, but what makes a lemonade diet?
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I was 2 km away from today's mystery location 📍. Can you beat me?
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@akeem
I was 0 km away from today's mystery location 📍. Can you beat me?
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nice
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with hours and hours of audio to process, Meridian is on the back of a lot of LLM work. Initially I wanted a tool to provide trend data and the newsletter is a tangible way for me to justify the work. Particularly because it provides value to people without having to do the work.
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yesterday I simplified something that should make back search a lot easier: deepgram diarizied data -> split by speaker with metadata -> vectorized -> into pgvector. I should now be able to do give me references Cosmos within this date range, and who did the mentions etc
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One of my goals for Meridian is to ensure that it feels as native to the internet as possible. This will evolve as needed to resonate with the audience of today and tomorrow. With yesterday's issue, I also published a companion playlist that you should checkout https://blog.getmeridian.io/p/playlist-eth-scaling-solutions-by-2026-btc-edges-toward-83k
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I asked claude 3.7 Sonnet to write me a prompt for gpt-4o for the speaker assignment and now I have something I can pass a transcript to ,it samples, and provides predictions of which speakers are which
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with that said, feel free to sign up to https://www.getmeridian.io/
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We sort of do this heuristic in our head of eliminating who the speaker isn't each time we listen to a new podcast. Since we can give the model a fair bit of context it should be able to knock it all down
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The last issue of the newsletter covered 22 podcasts, that is a pretty heavy time investment for each issue. So I decided to see how far I could get with some of the easily accessible models
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The context is in the conversation, and is an easy task for a human. For examples sake, we can say that introductions occur within the first few minutes of a podcast and the enrichment can occur
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transcribing podcast in generally a starting point, base transcriptions can identify that several people are talking but not who they are. So enrichment is needed
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working on Meridian, my newsletter that aggregates insights from crypto podcast, I leverage LLMs regularly as leverage. Sometimes to eliminate things that would otherwise be a pain in the neck to write.
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