This reminds me of the antics of streamer DougDoug, who often uses LLM APIs to live-summarize, analyze, or interact with his (often multi-thousand-strong) Twitch chat. Most recently I saw him do a GeoGuessr stream where he had ChatGPT assume the role of a detective who must comb through the thousands of chat messages for clues about where the chat thinks the location is, then synthesizes the clamor into a final guess. Aside from constantly being trolled by people spamming nothing but "Kyoto, Japan" in chat, it occasionaly demonstrated a pretty effective incarnation of "the wisdom of the crowd" and was strikingly accurate at times.

I love that there's not even a vague idea of the winner "metric" in your explanation. Like it's just, _the_ winner.

Are you raising a funding round? I'm bought in. This is hilarious.

This made me actually laugh out loud. Can you share more details on hardware and models used?

Heh, I made this comment and forgot to check back -- I'm always missing stuff on HN because of this!

If anyone is still paying attention, email me at hi@seikai.tv and I'll see if I can send you one.

Sounds cool! In fact, this can be applied to other areas such as "debate monitoring" for debate competitions

Yeah I'm still paying attention!

I'd love to hear more about the hardware behind this project. I've had concepts for tech requiring a mic on me at all times for various reasons. Always tricky to have enough power in a reasonable DIY form factor.

This is a product I want

What approach/stack would you recommend for listening to an ongoing conversation, transcribing it and passing through llm? I had some use cases in mind but I'm not very familiar with AI frameworks and tools

You can use the model to generate winning speeches also.

Tell me it also does sports style commentary on the ongoing debate. My mental image requires it.

wifey always wins. ;)

All computation on device?

what model do you use for speech to text?

Your SO must really love that lmao