Li Jin avatar
Li Jin
@li
investing & writing about consumer tech since 2016. FID #30. hosting /web2web3 www.lisnewsletter.com
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An avenue for further improvement in AI models is to expand and enhance the data they can train on. We’re interested in exploring how decentralized methods could help generate new datasets and economically reward contributors and creators.
AI and the “Why Now” of Data DAOs
lisnewsletter.com
AI and the “Why Now” of Data DAOs
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I don't understand the reasoning behind "AI agents will need to transact via crypto" - why? I've seen AI agents where users just auth with their own traditional payment credentials to interact with websites. Why crypto? (trying to engage in a good faith discussion)
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pulling this out from the July 4th vortex: my recent essay about a framework for thinking about tokenized marketplaces:
The Marketplaces Edition
blog.variant.fund
The Marketplaces Edition
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I just minted a photo on Moshicam Northern California <3
Moshicam
moshi.cam
Moshicam
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this is cool!
Farcaster News - Amp
ampcast.xyz
Farcaster News - Amp
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AI and the "Why Now" of Data DAOs:
AI and the “Why Now” of Data DAOs
lisnewsletter.com
AI and the “Why Now” of Data DAOs
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Claiming my /scoop profile with address: let the scoop wars begin 🍦
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most interesting thing you've read lately?
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Over my years investing in marketplaces, I've realized there are marketplaces that should exist but that don’t, because of limitations in the systems they are built on. Crypto has potential answers to these problems, enabling new marketplaces to emerge.
Crypto and Net-New Marketplaces
variant.fund
Crypto and Net-New Marketplaces
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Next week @variant and @usv are cohosting a consumer crypto demo night We’re bringing together app builders that are focused on onchain social. Come share your ideas and learn from your peers and glean inspiration! Thurs. June 13th 6-8pm ET RSVP lu.ma/das9mdhs
Demo Night: Composability in Onchain Social · Luma
lu.ma
Demo Night: Composability in Onchain Social · Luma
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A fine-tuned LLM trained on Farcaster data with revenue flowing back to users/wallets whose data contributed to the training set
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