Piperic Business Intelligence
similar sites
‹ profile

Sites similar to keithserver.net

My Resume · ranked by category · tech stack · backlink co-citation
65match
javiermino.com
My Resume
3 co-citations4 shared tech2 shared categorycareers
64match
holliezheng.com
My Resume
3 co-citations4 shared tech1 shared categoryresume-writing-and-advice
45match
jamestomasino.com
James Tomasino's Resume
2 co-citations3 shared tech2 shared categoryresume-writing-and-advice
42match
kkaushik.com
Karan Kaushik home
3 co-citations4 shared tech1 shared categorycareers
40match
bousmaha.com
CV - Fayçal Bousmaha
2 co-citations4 shared tech1 shared categoryresume-writing-and-advice
40match
dielekyhn.com
Diele Kyhn - Resume
2 co-citations1 shared tech2 shared categoryresume-writing-and-advice
38match
getmakedigital.com
Home - GetMakeDigital
2 co-citations3 shared tech2 shared categoryresume-writing-and-advice
38match
developwithjana.com
Jana Heinrichs - info sheet, resume, references - 2014
1 co-citations2 shared tech2 shared categoryresume-writing-and-advice
36match
hamzaahmed.dev
Resume
3 co-citations1 shared tech1 shared categorycareers
36match
maltson.com
Arthur Maltson's Resume
2 co-citations4 shared tech1 shared categorycareers
36match
jamessnell.com
James T Snell
2 co-citations4 shared tech2 shared categoryresume-writing-and-advice
35match
jhson.com
Jung Ho Son - Interactive Resume
2 co-citations4 shared tech1 shared categorycareers
35match
a-badr.com
Ahmed Badr | Resume
2 co-citations2 shared tech2 shared categoryresume-writing-and-advice
35match
hoanhnguyen.com
Hoanh Nguyen | Resume
2 co-citations0 shared tech2 shared categorycareers
35match
felipedepadua.com
Felipe De Padua
2 co-citations2 shared tech2 shared categorycareers
35match
marcosquezada.com
Professional Resume
1 co-citations1 shared tech2 shared categorycareers
34match
hanabiadgilgn.com
Hana Biadgilgn
2 co-citations3 shared tech1 shared categorycareers
34match
gerardocortes.com
Gerardo Cortés Resume / Blog / Portfolio
2 co-citations3 shared tech1 shared categorycareers

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.