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ALGORITHM-CITY – Total Collections of My Learning Experiences About Algorithm for City Planning — 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
lukashein.com ↗	68 match 1 shared topics	Lukas Hein \| Blog about data science, deep learning and NLP	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
swarmmind.app ↗	68 match 1 shared topics	SwarmMind ® \| AI Learning Infrastructure for Organizations	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
farsidenow.com ↗	67 match 1 shared topics	FarSideNow — AI Experiences	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
hcispace.com ↗	67 match 1 shared topics	HCI Space.com - About AI & Computer Learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
jonaslalin.com ↗	67 match 1 shared topics	I, Deep Learning \| Yet another blog about deep learning.	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
urbanplanadvisor.com ↗	66 match 1 shared topics	Urban Planning Made Simple: AI-Powered Solutions for Smarter Cities and Sustainable Development \| urbanplanadvisor.com	US~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
josephokonofua.com ↗	66 match 1 shared topics	Okonofua Joseph - Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nastavnyk.com ↗	66 match 1 shared topics	Nastavnyk: AI Personalized Learning Paths \| Nastavnyk - Personalized AI learning experience	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
joincmm.com ↗	66 match 1 shared topics	CMM Group \| Intelligent Platforms for Smarter Cities, Businesses & Experiences	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
sameetsonawane.com ↗	66 match 1 shared topics	Sameet Sonawane - Machine Learning Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
dignifiedpython.com ↗	66 match 1 shared topics	GitHub - dagster-io/skills: A collection of AI skills for working with Dagster · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
1cognition.com ↗	66 match 1 shared topics	1 Cognition - Deep Learning Visualized	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
usabilitycounts.com ↗	66 match 1 shared topics	Usability Counts - User Experience and Design	en	artificial-intelligence WordPress Gumroad	robotsllmsaihumans	emailphone	none
famelink.ai ↗	66 match 1 shared topics	FameLink \| Pioneering Conversational AI Experiences	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	email phone	none
pipeline-risk.com ↗	66 match 1 shared topics	PLR — Machine Learning for Asset Integrity	en	artificial-intelligence	robotsllmsaihumans	email phone	none
bemelproductions.com ↗	66 match 1 shared topics	BeMel Productions \| AI-Aligned Digital Planning for Women 40+	en	artificial-intelligence Shopify	robots llmsaihumans	emailphone	none
uselux.ai ↗	66 match 1 shared topics	Lux - Build Better Customer Experiences with AI	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
casabind.com ↗	66 match 1 shared topics	The Evolution of Algorithmic Compatibility in Digital Connections \| casabind.com	IT~ en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8

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.