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Nates blog | Nathan Woloshyn is an undergraduate studying mathematics & computer science at the university of victoria, with a focus on machine learning. — 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
cova-strategies.com ↗	68 match 1 shared topics	Cova Strategies \| Data Science and Machine Learning \| Maryland, USA	en	artificial-intelligence Wix	robots llmsaihumans	emailphone	none
ikromshi.com ↗	67 match 1 shared topics	Ikrom Numonov’s Blog \| Notes on Machine Learning, Software Development, and other topics.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ijaimlds.com ↗	67 match 1 shared topics	Interdisciplinary Journal of AI, Machine Learning & Data Science	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
getclay.app ↗	67 match 1 shared topics	Clay - Craft Machine Learning Models	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
unsupervisedpleasures.com ↗	67 match 1 shared topics	Unsupervised Pleasures - DIY community practice space using queer methodologies to create datasets, machine learning models, bots, zines, and art with AI.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bridgingai.com ↗	67 match 1 shared topics	Bridging Academia and Industry – Thoughts on Big Data and Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
ijmla.net ↗	66 match 1 shared topics	International Journal of Machine Learning and Applications	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
synwave.site ↗	66 match 1 shared topics	StudioLogic — AI & Machine Learning Solutions	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
synflux.click ↗	66 match 1 shared topics	KOMP ART — AI & Machine Learning Solutions \| Steampunk Cyber-Tech Innovation	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ijml.org ↗	66 match 1 shared topics	International Journal of Machine Learning (IJML)	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
unlock-2020.com ↗	66 match 1 shared topics	Machine learning models in Online Casinos	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
ijaimla.com ↗	66 match 1 shared topics	IJAIMLA - International Journal of Artificial Intelligence and Machine Learning Applications	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
getai2easy.com ↗	66 match 1 shared topics	AI2Easy AI & Machine Learning Solutions for Business Efficiency	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
krishbhagirath.com ↗	66 match 1 shared topics	Krish Bhagirath - Computer Engineering Student \| Portfolio	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
synt.space ↗	66 match 1 shared topics	Synt World \| Enterprise Machine Learning as a Service	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
synt.world ↗	66 match 1 shared topics	Synt World \| Enterprise Machine Learning as a Service	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ksolovev.com ↗	66 match 1 shared topics	Kirill Solovev - Postdoctoral Researcher at the IDea_Lab	en	artificial-intelligence	robots llmsaihumans	emailphone	partial · 9
heinzerm.ch ↗	66 match 1 shared topics	Michael Heinzers blog	Switzerland 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.