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GitHub - lab-cosmo/flashmd: A universal ML model to predict molecular dynamics trajectories with long time steps · GitHub — 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
deepclaude.com ↗	65 match 2 shared topics	GitHub - winfunc/deepreasoning: A high-performance LLM inference API and Chat UI that integrates DeepSeek R1's CoT reasoning traces with Anthropic Claude models. · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
benedictwinery.com ↗	64 match 2 shared topics	bbenedict (Bob Benedict) · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
benedictwines.com ↗	64 match 2 shared topics	bbenedict (Bob Benedict) · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
sindycode.org ↗	64 match 2 shared topics	modular Sparse Identification of Nonlinear Dynamics \| mSINDy	en	technology-and-computing	robotsllmsaihumans	emailphone	none
dosh.do ↗	64 match 2 shared topics	GitHub - nige123/dosh.do: dosh (do shell) - LLM-powered shell commands · GitHub	en	technology-and-computing	robotsllmsaihumans	emailphone	none
dekterov.com ↗	64 match 2 shared topics	GitHub - dekterov/Leacme: Index for the Leacme projects. · GitHub	en	technology-and-computing	robotsllmsaihumans	emailphone	none
aitxt.ing ↗	64 match 2 shared topics	ai.txt — Universal Standard for AI Context	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
singulisp.org ↗	64 match 2 shared topics	GitHub - singulisp/singulisp: LLM friendly fastest lisp project. · GitHub	en	technology-and-computing	robotsllmsaihumans	emailphone	none
acez.re ↗	64 match 2 shared topics	GitHub - acama/acez.re: Archive for acez.re blog. · GitHub	en	technology-and-computing	robotsllmsaihumans	emailphone	none
ai2027-tracker.com ↗	64 match 2 shared topics	AI 2027 Tracker — Predictions vs Reality	en	artificial-intelligence	robots llmsaihumans	emailphone	none
mlscope.app ↗	64 match 2 shared topics	MLScope — AI-Powered ML Model Monitoring & Explainability	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
1chipml.org ↗	64 match 2 shared topics	GitHub - 1chipML/1chipML: a library for numerical crunching and machine learning on microcontrollers · GitHub	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nabeelkeblawi.com ↗	64 match 2 shared topics	Nabeel Keblawi Building Data Pipelines, ML Models, and AI Agents With Governance	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
moderntimeseriesforecastingbook.com ↗	63 match 2 shared topics	Mastering Modern Time Series Forecasting \| Valeriy Manokhin, PhD	en	technology-and-computing Gumroad	robotsllmsaihumans	emailphone	partial · 6
githuw.com ↗	63 match 2 shared topics	Github [W]rite - Chat with any GitHub repo using LLMs	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
singularity-countdown.com ↗	63 match 2 shared topics	Singularity Countdown - Expert Predictions Leading to the Singularity	en	technology-and-computing	robotsllmsaihumans	emailphone	none
daergoth.net ↗	63 match 2 shared topics	daergoth (István Lakatos) · GitHub	en	technology-and-computing	robotsllmsaihumans	emailphone	none
365-thoughts.com ↗	63 match 2 shared topics	Scott Roller – This is my home base to discuss all things Microsoft Dynamics 365 and Power Platform.	en	technology-and-computing 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.