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StupidJar — Narcan for AI — 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
agentlanguages.dev ↗	64 match 2 shared topics	agentlanguages.dev — Programming languages designed for AI agents to write	en	programming-languages	robots llmsaihumans	emailphone	none
dotllm.dev ↗	64 match 2 shared topics	dotLLM — Native .NET LLM Inference	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
yuler.dev ↗	62 match 2 shared topics	Yu Le — A programmer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
chess-programming.com ↗	62 match 2 shared topics	Chess Programming – Chess programming for absolute beginners!	en	artificial-intelligence WordPress WooCommerce	robotsllmsaihumans	emailphone	none
zcouncil.com ↗	62 match 2 shared topics	zcouncil — one chat, every AI	en	programming-languages	robots llmsaihumans	emailphone	none
packagejsai.com ↗	62 match 2 shared topics	PackageJS AI Program for Claude Code	en	artificial-intelligence Shopify	robotsllmsaihumans	emailphone	none
treenjohnportfolio.com ↗	62 match 2 shared topics	John Treen — Full-Stack & ML Engineer	en	programming-languages	robotsllmsaihumans	emailphone	none
rmoralesdelgado.com ↗	61 match 2 shared topics	Raul Morales Delgado — Reporting from another star	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
chatregex.com ↗	61 match 2 shared topics	Free AI Regex Generator & Tester - ChatRegex	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
developerpal.dev ↗	61 match 2 shared topics	Code Explainer, AI Code explainer, AI Code translator, code translator, python code explainer, java code explainer, Translation, The Ultimate Coding Companion - DeveloperPal	en	programming-languages	robotsllmsaihumans	emailphone	none
aidanholton.dev ↗	61 match 2 shared topics	Aidan Holton \| Portfolio	en	programming-languages	robotsllmsaihumans	emailphone	none
ai-context-engineering.com ↗	61 match 2 shared topics	Elite AI Assisted Coding \| Eleanor Berger \| Substack	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
robintegg.com ↗	61 match 2 shared topics	Robin Tegg \| Java Developer by trade, AI Explorer by choice, and a Spring Boot Wizard at heart. I specialize in building robust, scalable applications and systems while actively integrating the power of Generative AI into modern software workflows. Beyond the IDE, I'm a firm believer in sharing	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
agenticcodingnights.com ↗	61 match 2 shared topics	Agentic Coding Nights	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
helpmecoding.com ↗	61 match 2 shared topics	Help Me Coding - Your AI Pair Programmer	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
4gotn.net ↗	61 match 2 shared topics	Cover - Forgotten Bits: A Programmer's Guide to SE + AI Wisdom	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
ai-schema.org ↗	61 match 2 shared topics	JSON to OpenAI Schema Converter (Strict Mode Ready) - Free Online Tool	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
dotnetdeveloper.ai ↗	61 match 2 shared topics	Dotnet Developer AI – Learn how to build intelligent apps in .NET	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.