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RanDumSocks | Justin Chance Computer Science and Mathematics Web programmer Machine Learning Enthusiast George Fox University, Class of 2021 — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
markdeaconu.com ↗	70 match 1 shared topics	Mark Deaconu — Mathematics & Computer Science Researcher	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
jaspersands.com ↗	69 match 1 shared topics	Jasper Sands \| Quantum Algorithms & Machine Learning	en	artificial-intelligence Spree Commerce	robotsllmsaihumans	emailphone	none
cv-tricks.com ↗	69 match 1 shared topics	CV-Tricks.com – Learn Machine Learning, AI & Computer vision	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
oussamabouanani.com ↗	69 match 1 shared topics	Oussama Bouanani \| AI and Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
gaurangkakade.com ↗	68 match 1 shared topics	Gaurang Kakade \| Data Science & Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ahmedalttaee.com ↗	68 match 1 shared topics	Ahmed AL-TTAEE — Computer Science Researcher	en	artificial-intelligence	robotsllmsaihumans	email phone	none
dilekcelik.net ↗	68 match 1 shared topics	Dr Dilek Celik, Senior Data Scientist and Machine Learning Engineer	en	artificial-intelligence Wix	robots llmsaihumans	emailphone	none
adithyasm.dev ↗	68 match 1 shared topics	Adithya Sankar Menon - Computer Science Engineer \| AI & Robotics Portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
gareginmazmanyan.com ↗	68 match 1 shared topics	Garegin Mazmanyan - AI/Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
dharmhpatel.com ↗	67 match 1 shared topics	Dharm Patel \| Data Science-Machine Learning Portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
dendrite.ai ↗	67 match 1 shared topics	Dendrite \| Machine Learning and Business Intelligence	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
savancy.com ↗	67 match 1 shared topics	Savancy \| Data Science and Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	email phone	none
simonkutsche.com ↗	67 match 1 shared topics	Simon Kutsche - Consultant for AI and Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
sayyedasif.com ↗	67 match 1 shared topics	Asif Sayyed \| Data Scientist – Python, Machine Learning & AI Portfolio	India~ en	artificial-intelligence	robots llmsaihumans	emailphone	full · 16
thomaspaul.dev ↗	67 match 1 shared topics	Thomas Paul \| Techie, Husband, Deep learning Enthusiast, Traveler, and Lifelong learner.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
adityaagarwal.me ↗	67 match 1 shared topics	Aditya Agarwal \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
afmaml.net ↗	67 match 1 shared topics	Advances in Foundation Models and Applied Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ahmedabdulla.dev ↗	67 match 1 shared topics	Ahmed Abdulla \| Software Engineer \| Machine Learning Research	en	artificial-intelligence	robotsllmsaihumans	emailphone	none

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:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

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.