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73match
adityachebrolu.com
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2 co-citations4 shared tech1 shared categoryweb-development
73match
anzook.dev
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73match
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2 co-citations4 shared tech1 shared categoryweb-development
73match
cesardev.com
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73match
geraosio.com
geraosio portfolio
2 co-citations4 shared tech1 shared categoryweb-development
73match
jaslynking.com
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3 co-citations3 shared tech1 shared categoryweb-development
69match
joelsantos.dev
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3 co-citations3 shared tech1 shared categoryweb-development
68match
manuhdez.com
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3 co-citations4 shared tech1 shared categoryweb-development
68match
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2 co-citations4 shared tech1 shared categorytechnology-and-computing
68match
heyazhar.dev
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2 co-citations4 shared tech1 shared categoryweb-development
68match
binovsky.com
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2 co-citations4 shared tech1 shared categorysoftware-and-applications
68match
cesarepolonara.com
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2 co-citations3 shared tech1 shared categoryweb-development
68match
geostacklabs.com
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2 co-citations4 shared tech1 shared categoryweb-development
67match
borabey.com
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2 co-citations3 shared tech1 shared categoryweb-development
67match
grimmle.com
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2 co-citations4 shared tech1 shared categoryweb-development
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ivanmiceta.dev
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2 co-citations3 shared tech1 shared categoryweb-development
66match
joelvargas.dev
Joel Vargas
2 co-citations3 shared tech1 shared categoryweb-development
66match
joemeus.dev
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2 co-citations3 shared tech1 shared categoryweb-development

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.