Matthew
@matthew
last chance to sign up for a @coffeebot chat this week! Drop your telegram at the cast below to join 😃☕️ farcaster://casts/0x9661add1165bd8a6978f01f48262db62ab0de82529939325cf306c8562c7b6bf/0x9661add1165bd8a6978f01f48262db62ab0de82529939325cf306c8562c7b6bf
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Daniel Svonava
@svonava
Hey @matthew, want to take @coffeebot up a notch and match people smartly based on what they do on the app? Ain’t nobody got time for random intros! (most churn after 2-3 random matches, this is what killed lunchclub.com - their matches were bad + the avg user quality went down).
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Matthew
@matthew
what would you want to see beyond random matches? @ishika and i have talked about making it more personalized! re lunchclub, i think what killed them is that people don’t want to pay for things they think they can get for free— e.g. intros to new friends. Also, the pandemic. And raising as an “AI” company.
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Daniel Svonava
@svonava
It’s very hard to scale a social experience without making the discovery actually smart - matching more senior people with other more senior people, matching people with a higher chance to collaborate or enjoy each other because of enough similarity in preferences, yet not totally the same.
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Matthew
@matthew
so…curate the matches?
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Daniel Svonava
@svonava
Yes, at scale and based on as many data points as possible. Doable semi-manually for 100s of matches per week if you have some basic tools built around it - breaks for 1000s+. 2 keys to scale: 1. Model that predicts relevance for all pairs of people. 2. Algo that pairs ppl up for that week - “max weight max matchin
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