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How AI Reasoning Models Are Changing the Way Machines Think — Depth Report — 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
pro-datascience.com ↗	67 match 2 shared topics	Zero Code AI Enabled Machine Learning Model Development tools	en	technology-and-computing WordPress WooCommerce	robots llmsaihumans	emailphone	none
hypogray.com ↗	66 match 2 shared topics	HypoGray — The first newsroom built for machines	en	technology-and-computing	robots llmsaihumans	emailphone	none
breakingmodel.com ↗	66 match 2 shared topics	Beyond Prompting: Building AI Tools and Workflows for Real Work - Breaking Model	en	artificial-intelligence	robots llmsaihumans	emailphone	partial · 8
accelerategpt.ai ↗	65 match 2 shared topics	AccelerateGPT — Accelerating Large Language Models	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
pretrained.dev ↗	65 match 2 shared topics	Pretrained Models	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nanomindsystems.com ↗	65 match 2 shared topics	NanoMind Systems – Unlocking the Power of Nano Language Models	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
leonardoburbano.com ↗	65 match 2 shared topics	Leonardo Burbano - Senior AI and Machine Learning Engineer	en	technology-and-computing	robotsllmsaihumans	emailphone	none
chasingthebear.net ↗	65 match 2 shared topics	Chasing the Bear	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nanolanguagemodel.com ↗	65 match 2 shared topics	Nano Language Model — Small Models. Big Results. – Nano Language Models	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
ata.network ↗	65 match 2 shared topics	Automata - Verifiable machines for Web3 and AI	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
cosine.sh ↗	65 match 2 shared topics	The Coding Model That Actually Gets It \| Cosine	en	artificial-intelligence Prismic	robotsllmsaihumans	emailphone	none
fluxreasoning.com ↗	65 match 2 shared topics	FluxReasoning — The Best AI Tools, Tested & Compared	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nadesri.com ↗	65 match 2 shared topics	Nade's Notes – Hello there! Here I talk about stuff I like, including Natural Language Processing, Machine Learning, & Software Engineering :)	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
narratime.ai ↗	65 match 2 shared topics	Narratime.ai - The AI-Native Reasoning Engine	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
naomilago.com ↗	65 match 2 shared topics	Naomi Lago — Data & AI Engineer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
predictablemachines.com ↗	65 match 2 shared topics	Predictable Machines \| Building Trust Through Verification	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
chayan.me ↗	65 match 2 shared topics	Al Amin Chayan — Principal Software Engineer & AI Architect	en	artificial-intelligence Spree Commerce	robotsllmsaihumans	emailphone	none
appcritica.com ↗	64 match 2 shared topics	AppCritica - Discover & Compare the Best AI Tools with Real Reviews	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.