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Ray Fix’s Blog | Welcome. This is Ray Fix’s personal blog. A stream of consciousness series of ramblings intended for a future self. — 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 Programming Languages →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
phrees.com ↗	67 match 1 shared topics	Phil Rees Personal Blog \| Personal blog for Phil Rees - writer of prose, poetry and code.	en	programming-languages	robotsllmsaihumans	emailphone	none
nwlr.net ↗	66 match 1 shared topics	NWLR \| This is my personal blog talking about various topics including programming, computing, music, and anything else that might interest me.	en	programming-languages	robotsllmsaihumans	emailphone	none
karolinakaszuba.com ↗	65 match 1 shared topics	All posts \| Karolina Kaszuba Personal Dev Blog	en	programming-languages	robotsllmsaihumans	emailphone	none
phoresl.com ↗	64 match 1 shared topics	Personal website of Orest Smertnyi	en	programming-languages	robotsllmsaihumans	emailphone	none
chrispenner.ca ↗	64 match 1 shared topics	Chris Penner	Canada en	programming-languages	robotsllmsaihumans	emailphone	none
mini-lang.org ↗	64 match 1 shared topics	mini lang’s homepage \| The official mini website. A programming language for exploring the concepts of compilers and interpreters in an easy fashion thanks to its simple syntax and semantics.	en	programming-languages	robotsllmsaihumans	emailphone	none
gulatimehak.com ↗	63 match 1 shared topics	Mehak Gulati's Personal Website	en	programming-languages Spree Commerce	robotsllmsaihumans	emailphone	none
qubitlang.net ↗	63 match 1 shared topics	QubitLang \| The Future of Quantum Programming	en	programming-languages	robots llmsaihumans	emailphone	none
qubitlang.org ↗	63 match 1 shared topics	QubitLang \| The Future of Quantum Programming	en	programming-languages	robots llmsaihumans	emailphone	none
mitchr.dev ↗	63 match 1 shared topics	Mitchell Riley’s Blog \| mitchr.github.io	en	programming-languages	robotsllmsaihumans	emailphone	none
turek.dev ↗	63 match 1 shared topics	Brian's Blog	en	programming-languages	robotsllmsaihumans	emailphone	none
grugsdatajourney.com ↗	63 match 1 shared topics	Grug's Data Journey \| Just another person's thoughts on all things data	en	programming-languages WordPress	robotsllmsaihumans	emailphone	none
seracoder.com ↗	63 match 1 shared topics	Hamim's Blog	en	programming-languages	robotsllmsaihumans	emailphone	none
nunofonseca.net ↗	63 match 1 shared topics	Home \| Nuno Fonseca’s Blog	en	programming-languages	robotsllmsaihumans	emailphone	none
petratrinke.com ↗	63 match 1 shared topics	Petra Trinke \|Petra Trinke is a Computer Science and Digital Technologies specialist teacher for the last seven years at Belridge Secondary College in Beldon WA. Petra is part of ECAWA’s committee and has held the position of conference conveyor. Petra volunteers at ECU’s Girls in Programming Ne	en	programming-languages WordPress	robotsllmsaihumans	emailphone	none
serialsignal.com ↗	63 match 1 shared topics	Serial Signal \| The personal programming, technology and computer science blog of Matthew Jump	en	programming-languages WordPress	robotsllmsaihumans	emailphone	none
sergeydmitriev.com ↗	63 match 1 shared topics	The personal site of Sergey Dmitriev	en	programming-languages	robotsllmsaihumans	emailphone	none
ashwinak.com ↗	63 match 1 shared topics	Ashwin's Blog – For Programming and maybe other things later?	en	programming-languages 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.