similar sites

Browse

Tools

🚀Submit Your Site to AIPush your URL to AI search engines 🤖AI Crawler CheckerSee which AI bots your site blocks 🎯AI Readiness CheckerScore your site 0–100 for AI 🔗Backlink CheckerWho links to any site — free All tools →

Sites similar to deeplie.com

Deeplie alternatives & similar sites

DEEP LIE – DEEP LIE is based on Deep 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
1cognition.com ↗	68 match 1 shared topics	1 Cognition - Deep Learning Visualized	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
etasnadi.com ↗	67 match 1 shared topics	Ervin Tasnadi's blog – GPU programming and deep learning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	partial · 8
mbmlbook.com ↗	66 match 1 shared topics	Model-Based Machine Learning: an online book	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
cong-ai.com ↗	66 match 1 shared topics	CONG - Artificial Intelligence and Machine Learning based app	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
morfelix.com ↗	66 match 1 shared topics	Sleep Learning Platform \| Sleepyrat.ai	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
morfelix.ai ↗	66 match 1 shared topics	Sleep Learning Platform \| Sleepyrat.ai	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
mbr026.com ↗	65 match 1 shared topics	MBR026_ROME – 10th International Conference on Model-Based Reasoning	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
configism.com ↗	65 match 1 shared topics	Configism – a Discipline of Thought Based on Configurational Logic	en	artificial-intelligence	robotsllmsaihumans	emailphone	full · 13
megamachinelearn.org ↗	65 match 1 shared topics	Modern Machine Learning – Mega Learning	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
heykarthik.com ↗	65 match 1 shared topics	Karthik Iyer \| Machine Learning Engineer & Researcher	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
polpuigdemont.com ↗	65 match 1 shared topics	Pol Puigdemont – Machine Learning PhD at EPFL	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
shuesila.com ↗	65 match 1 shared topics	Shuesila – Creative Learning for the AI Era	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
vmax-ai.com ↗	64 match 1 shared topics	Vmax — Reinforcement Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
conjectureai.com ↗	64 match 1 shared topics	ConjectureAI \| Machine Learning Solutions	en	artificial-intelligence Wix	robots llmsaihumans	emailphone	none
lampert.ai ↗	64 match 1 shared topics	Moritz Lampert \| Machine Learning on Graphs	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
polyml.com ↗	64 match 1 shared topics	Polyalgorithm Machine Learning – Authentic intelligence	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
aleenaimran.dev ↗	64 match 1 shared topics	Aleena Imran \| AI & Machine Learning	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
etsymba.com ↗	64 match 1 shared topics	etsymba❤️ personal blog – machine learning, research, and random stuff	en	artificial-intelligence WordPress	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.