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Neil Simon's Thoughts – A standard nerd's thoughts on machine learning and more — 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
pgasanova.com ↗	68 match 2 shared topics	My Personal Blog – Machine Learning and beyond	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
hydrosphere.io ↗	68 match 2 shared topics	Hydrosphere - Serving and Monitoring Machine Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
odsy.io ↗	67 match 2 shared topics	Adventures of a Software Engineer – Machine learning and software engineering	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
twimlai.com ↗	67 match 2 shared topics	TWIML \| The Voice of Machine Learning and Artificial Intelligence	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
twimlcon.com ↗	67 match 2 shared topics	TWIML \| The Voice of Machine Learning and Artificial Intelligence	en	artificial-intelligence	robots llms ai humans	emailphone	none
twimlfest.com ↗	67 match 2 shared topics	TWIML \| The Voice of Machine Learning and Artificial Intelligence	en	artificial-intelligence	robots llms ai humans	emailphone	none
electricweegie.com ↗	67 match 2 shared topics	Andy McMahon – Machine Learning Engineer and Data Scientist	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
quantml.org ↗	67 match 2 shared topics	QuantML \| See Machine Learning Happen	en	artificial-intelligence	robots llmsaihumans	emailphone	none
efimov-ml.com ↗	67 match 2 shared topics	Dmitry Efimov Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
hackernoon.ai ↗	67 match 2 shared topics	HackerNoon AI: Blogging, AI CMS, and Machine Learning Tools	en	technology-and-computing	robotsllmsaihumans	emailphone	none
karimali.dev ↗	67 match 2 shared topics	Karim Ali \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
trainingdatabook.com ↗	66 match 2 shared topics	Training Data for Machine Learning [Book]	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ashutoshtripathi.com ↗	66 match 2 shared topics	Data Science Duniya – Learn Data Science, Machine Learning and Artificial Intelligence	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
milesmurdocca.com ↗	66 match 2 shared topics	Miles Murdocca, PhD – Machine Learning Engineer, Technologist and Author	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
guidetomlandai.com ↗	66 match 2 shared topics	Projects \| Guide to machine learning and artificial intelligence by Manuel Cuevas	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
guillaumeetchepare.com ↗	66 match 2 shared topics	Guillaume Etchepare \| Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
officehogaryestudiantes.com ↗	66 match 2 shared topics	Home - MLTut - Machine Learning Blogs for Everyone	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
dataroboticsreply.eu ↗	66 match 2 shared topics	Artificial Intelligence & Machine Learning \| Reply	European Union 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.