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Content
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jacob pfp
jacob
@jacob
Imagine there is a memecoin for each of the ~600,000 words in the dictionary. What’s the market cap ordering of those words and why?
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LGHT pfp
LGHT
@lght.eth
god likely #1 hedge bet would be ‘death/die’
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horsefacts pfp
horsefacts
@horsefacts.eth
gigalong "butts"
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Gami ‪ ᡕᠵデ气亠 pfp
Gami ‪ ᡕᠵデ气亠
@gami
english dictionary? if so, i’d bet on the multilingual words that cross cultures larger tams
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borodutch pfp
borodutch
@warpcastadmin.eth
memecoins.science
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SeeingBlue pfp
SeeingBlue
@seeingblue
"OK" is one of the most frequently used words in the world.
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0xen 🎩 pfp
0xen 🎩
@0xen
i'd read this borges short story
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MJC pfp
MJC
@mjc716
https://warpcast.com/mjc716/0x704b778d
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depatchedmode pfp
depatchedmode
@depatchedmode
Why stop there? https://e.frames.unglish.co.uk
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Catabolismo pfp
Catabolismo
@catabolismo
Possible top 5: $usa $china $jesus $allah $messi
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tmo pfp
tmo
@tmo.eth
a 721 domain contract could prove this out
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Sound of Fractures   pfp
Sound of Fractures
@soundoffractures
# $cap
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optimist 🎩 pfp
optimist 🎩
@gigarahul.eth
the
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▫️Onyx 📚🎩 pfp
▫️Onyx 📚🎩
@cipherscript.eth
no, wouldn't it become too saturated? i think memecoins better serve the 100 most inappropriate words / communities degens are inappropriate, no? memecoins should be rare I think, no? thoughts ⬇️
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Symbiotech⌐◨-◨🟡🎩テ pfp
Symbiotech⌐◨-◨🟡🎩テ
@symbiotech.eth
Application in Natural Language Processing (NLP): In the context of NLP, algorithms could be developed to analyze and rank words based on various linguistic features, such as frequency, semantic similarity, and contextual relevance, to create a sort of "market cap" ordering for words.
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hoshino🎩 pfp
hoshino🎩
@hoshinoun.eth
! # $ % & ( ) * : ; ? @ [ ] ‘ ’ “ ” ′ ¥ 「 」 『 』 【 】 + < = > × ∩ ∪ ≡ ⊂ ⊃ ■ □ ▲ △ ▼ ▽ ◆ ◇ ○ ◎ ● ↑ → ← ↓ ・ ☆ ★ ※
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