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72match
manuillmusic.com
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5 co-citations5 shared tech2 shared categoryhip-hop-music
69match
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4 co-citations4 shared tech1 shared categorymusic
69match
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4 co-citations5 shared tech2 shared categorymusic
68match
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5 co-citations5 shared tech1 shared categorymusic
68match
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5 co-citations5 shared tech1 shared categoryhip-hop-music
66match
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6 co-citations2 shared tech2 shared categoryhip-hop-music
64match
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3 co-citations4 shared tech1 shared categorymusic
62match
booglarecords.com
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4 co-citations4 shared tech1 shared categorymusic
62match
genevievetohmusic.com
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5 co-citations4 shared tech1 shared categorymusic
62match
geromedurham.com
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4 co-citations5 shared tech0 shared categoryblues
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gridworkmusic.com
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3 co-citations4 shared tech1 shared categorymusic
62match
marcyeo.com
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3 co-citations4 shared tech1 shared categoryclassical-music
62match
gerardocontino.com
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3 co-citations5 shared tech1 shared categorymusic
61match
gretapope.com
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5 co-citations5 shared tech1 shared categorymusic
60match
jimmatus.com
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2 co-citations5 shared tech1 shared categoryjazz
60match
bonafidebeatz.com
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3 co-citations5 shared tech2 shared categoryhip-hop-music
60match
genewildest.com
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59match
jameswakelingmusic.com
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2 co-citations4 shared tech1 shared categorymusic

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.